BU

Res

earc

h C

om

pen

diu

m-2

020

LEARNING RESOURCE CENTRE

BENNETT UNIVERSITY

GREATER NOIDA

Edited and Compiled by

Dr. Sanjay Kataria

Editorial Assistance Dr. Jamil Ahmed

Dr. Shiv Singh

Ms. Tulika Dey

Mr. Tarun Kumar Singh

IIIIIIII

II

II

Table of Contents

Foreword III

Message 1V

Preface V

Summary of BU Research Publications VI-X

School-wise Publications VI

Department-wise Publications VI

Type of Publications VII

Publications Indexed VIII

Prolific Authors VIII

Top-Cited Papers IX

Top Impact Factor Papers X

List of Abbreviations XI

School of Engineering & Applied Sciences 1-76

Department of Biotechnology 1-5

Department of Civil Engineering 6-7

Department of Computer Science Engineering 8-49

Department of Electronics & Communication Engineering 50-61

Department of Mathematics 62-64

Department of Mechanical and Aerospace Engineering 65-70

Department of Physics 71-76

School of Law 77-82

School of Management 83-95

Times School of Media 96-98

Learning Resource Centre 99-100

Author Index 101-103

IIIIIIIIIIII

III

I am happy to learn that the Learning Resource Centre (Library) has compiled Volume 2nd of the BU

Research Compendium which contains BU publications published during 2020. It’s a matter of great

satisfaction to know the research progress of the University. In 2020 itself, a total of 216 publications

had been published in reputed Journals, Conferences and Book Chapters, etc. Out of the 216

publications, 169 publications are indexed in the SCOPUS and 87 indexed in the Web of Science

databases. It’s a matter of pride to see that these publications received 296 citations which reflects the

quality of research produced by the faculty members and research scholars.

The first volume of BU Research Compendium was compiled and published in 2020 with a

collection of 373 publications published during 2016 to 2019. The indexed publications received a

total of 1590 citations.

During the period (2016-2020), BU published a total of 589 papers and received 1,886 citations. Also,

the H-index of the University as per SCOPUS is 19, which is quite commendable for a young

establishment like Bennett.

I must congratulate all faculty members, research scholars and staff members who have contributed

their best in terms of publications during the last five years.

BU Research Compendium aims at inspiring, motivating and supporting the Research Fraternity of

the University. It will also help at promoting research and scholarly activities, and remain as a ready

reference of research contribution and the work published by researchers across schools and

disciplines. The Compendium aims to inspire young professionals with a stimulus to continue in this

direction.

I congratulate Dr. Sanjay Kataria and his team for this excellent work.

Dr. Prabhu Kumar Aggarwal

Vice-Chancellor

III

FOREWORD

IVIVIVIV

IV

I am glad to see that the 2nd Volume of Bennett University Research Compendium, a compilation of

Publications with University affiliation, has been compiled and published by Learning Resource Centre

(Library). The Compendium aims at sharing the innovative and practical achievements/experiences of all

academicians and researchers from diverse domains of the University. Being a 5-year-old establishment,

faculty and Ph.D. scholars have shown strong research acumen by publishing 589 papers with a total of 1,886

citations, which is a matter of great pride and satisfaction for the entire University fraternity.

During 2020 itself, BU published around 216 publications that received overall 296 citations, demonstrating

the tenacity of researchers in pursuing their passions even during difficulty times of COVID crisis. Of the total

216 publications, 169 publications are indexed in the SCOPUS and 87 indexed in the Web of Science

databases.

The 1st volume of BU Research Compendium compiled publications from 2016 to 2019, comprising 373

publications that received a total of 1590 citations.

In addition to complementing the research productivity of University researchers, I also would like to caution

the readers that the differential proportion of school-wise and Department-wise distribution of publications

presented in the compendium shouldn’t be taken as the yardstick of productivity in producing higher numbers

of publications by a particular School/Department because the School of Engineering & Applied Sciences

enrolled Ph.D. programme in 2016 with substantial growth in numbers of research-driven faculty and scholars

in subsequent years compared to other schools which launched Ph.D. programmes much later.

I would like to put on record the support of University management and administration in establishing a culture

of research that has not only resulted in publications but also enabled implementing experiential project-based

learning among all undergraduate programmes.

I complement the strenuous efforts and initiative by Dr. Sanjay Kataria and his team in bringing out research

compendium and wish that they continue this activity at regular intervals.

Prof. Rajinder Singh Chauhan

Dean (Research & Consultancy)

IV

MESSAGE

VVVV

V

V

Bennett University lays a strong emphasis not only in teaching but also creating new knowledge. New

knowledge or the intellectual asset of the establishment is curated and compiled by the Learning

Resource Centre and published annually as BU Research Compendium. Research work not only helps in

building global visibility of the institute but also finding collaborators and grants.

The BU Research Compendium is a compilation of research publications published by the faculty,

scholars, and staff members of the University.

It’s a matter of great pride and satisfaction that BU published 589 publications during the last 5 years

(2016-2020) that received a reasonably good number of 1886 citations and H-index of the University

as per Scopus is 19, which is also another quality indicator of the research.

In this 2nd volume of BU Research compendium, 216 publications published in the year of 2020 has

been compiled and these publications received 296 citations, which is again a strong reflection of the

quality research work produced by faculty, research scholars and staff members. Out of 216

publications, 169 publications are indexed in the SCOPUS and 87 indexed in the Web of Science

databases.

The first volume of BU Research Compendium was compiled and published in 2020 and a total of 373

publications were published during 2016 to 2019, in reputed Journals, Conferences, Book Chapters and

conference proceedings. These publications have received a total of 1590 citations.

BU Research Compendium is broadly divided into two parts. The first part contains statistical

representation of research publications with research metrics including prolific authors, highly cited

papers, impact factor of the journals, etc., and second part contains abstract of publications arranged

school wise.

Dr. Suneet Kumar Gupta from the Department of Computer Science Engineering remains top

prolific authors for 2020 with 11 papers.

Dr. Hiren Kumar Thakkar’s paper from the Department of Computer Science Engineering

“MUVINE: Multi-Stage Virtual Network Embedding in Cloud Data Centers using Reinforcement

Learning Based Predictions” remained the top-most paper with an impact factor of 11.42.

Dr. Suvel Namasudra’s paper “Towards DNA based data security in the cloud computing

environment,” received highest of 26 citations during 2020 itself.

Contribution of publication is high in SEAS & SOM as these Schools were started with the inception of

the University along with the Ph.D. program in 2016.

I must appreciate the hard work and efforts put in by the faculty, research scholars and staff members for

research writing and publishing quality research. I am sure that our collaborative efforts will take BU to

new heights in global higher education.

Looking forward for your suggestions and feedback for further improvement of the Compendium.

Dr. Sanjay Kataria

Editor

PREFACE

VIVIVIVI

VI

VI

Summary of BU Research Publications

VIIVIIVIIVII

VII

VII

VIIIVIIIVIIIVIII

VIII

VIII

School Document Type No. of Publications Scopus Indexed SCIE Indexed

SEAS

Book Chapters 6 6

Conference Papers 56 50

Journal Papers 101 90 82

Article Review 3 3

Papers in arXiv Repository 1

SOL

Edited Book 1

Book Chapters 5

Journal Papers 5 1

SOM

Book Chapters 2

Conference Papers 2

Journal Papers 16 14 5

Magazine Article 5

Working Paper 2

TSOM Book Chapters 4

Journal Papers 3 1

Library Journal Papers 3 3

Conference Papers 1 1

216 169 87

Publications Indexed

S.No Name of Author No. of Paper Designation Department

1 Suneet Kumar Gupta 11 Assistant Professor CSE

2 Shivani Goel 10 Professor CSE

3 Arjun Kumar 10 Assistant Professor ECE

4 Palakh Jain 9 Assistant Professor SOM

5 Arpit Bhardwaj 8 Assistant Professor CSE

6 Rohit Kumar Kaliyar 8 Research Scholar CSE

7 Ashok Kumar 7 Research Scholar ECE

8 Mohit Agarwal 7 Assistant Professor CSE

9 Vipul Kumar Mishra 7 Assistant Professor CSE

Prolific Authors

IXIXIXIX

IX

*As per Scopus data 21 April 2021

IX

Top Cited Papers

S.No

Faculty Name

Depart.

Title of Article

Name of Journal

Year

Cited

1 Suyel Namasudra CSE Towards DNA based data security in the cloud computing environment

Computer Communications

2020

26

2 Balmukund Mishra, Deepak Garg, Vipul

Kumar Mishra

CSE Drone-surveillance for search and rescue in natural disaster

Computer Communications 2020

18

3 Amit Singhal ECE An efficient removal of power-line interference and baseline wander from ECG signals by em-ploying Fourier decomposition technique

Biomedical Signal Processing and Control 2020

17

4 Tejalal Choudhary CSE A comprehensive survey on model compression and acceleration

Artificial Intelligence Review

2020

15

5 Arpit Bhardwaj CSE Unbalanced breast cancer data classification using novel fitness functions in genetic programming

Expert Systems with Applications 2020

15

6 Rohit Kumar Kaliyar, Anurag Goswami

CSE FNDNet – A deep convolutional neural network for fake news de-tection

Cognitive Systems Research 2020

12

7 Rishav Singh CSE Multi-Fault Bearing Classification Using Sensors and ConvNet-Based Transfer Learning Ap-proach

IEEE Sensors Journal 2020

10

8 Deepali Atheaya Mech Engg.

Thermodynamic analysis of Or-ganic Rankine cycle driven by reversed absorber hybrid photo-voltaic thermal compound para-bolic concentrator system

Renewable Energy 2020

10

9 Amit Singhal ECE Modeling and prediction of COVID-19 pandemic using Gaussian mixture model

Chaos Solitons & Fractals 2020

9

10 Amit Singhal ECE Detection of apnea events from ECG segments using Fourier de-composition method

Biomedical Signal Processing and Con-trol

2020

7

11 Divya Acharya, Shivani Goel, Arpit

Bhardwaj

CSE A novel fitness function in genetic programming to handle unbal-anced emotion recognition data

Pattern Recognition Letters 2020

7

12 Gaurav Singal, Tapas Badal

CSE Coinnet: platform independent application to recognize Indian currency notes using deep learning techniques

Multimedia Tools and Applications

2020

6

13 Sanjeet Kumar Nayak

CSE IECA: an efficient IoT friendly image encryption technique using programmable cellular automata

Journal of Ambient Intelligence and Hu-manized Computing

2020

6

14 Divya Acharya, Anosh Billimoria,

Neishka Srivastava, Shivani Goel, Arpit

Bhardwaj

CSE Emotion recognition using fourier transform and genetic program-ming

Applied Acoustics

2020

6

15 Suneet Kumar Gupta CSE 3-D optimized classification and characterization artificial intelli-gence paradigm for cardiovascu-lar/stroke risk stratification using carotid ultrasound-based delineat-ed plaque: Atheromatic™ 2.0

Computers in Biology and Medicine

2020

6

XXXX

X

* As per JCR 2019

X

Top Impact Factor Papers

S.No Faculty Name Depart. Title of Paper/Book Title Journal/

IF Year

1

Hiren Kumar Thakkar

CSE MUVINE: Multi-Stage Virtual Net-work Embedding in Cloud Data Cen-ters using Reinforcement Learning Based Predictions

IEEE Journal on Selected Areas in Communications

11.42 2020

2

Deepali Atheaya

MAE Thermodynamic analysis of Organic Rankine cycle driven by reversed ab-sorber hybrid photovoltaic thermal compound parabolic concentrator sys-tem

Renewable Ener-gy

6.274 2020

3

Gaurav Singal CSE A texture feature based approach for person verification using footprint bio-metric

Artificial Intelli-gence Review

5.747 2020

4 Vipul Kumar Mishra,Tejalal Choudhary, Anurag Goswami

CSE A comprehensive survey on model compression and acceleration

Artificial Intelli-gence Review

5.747 2020

5 Arpit Bhardwaj, Divyaansh Devarriya, Cairo Gulati, Vidhi Mansharamani

CSE

Unbalanced breast cancer data classifi-cation using novel fitness functions in genetic programming

Expert Systems with Applications

5.452 2020

6 Deepak Garg, Balmukund Mishra, Vipul Kumar Mishra

CSE A hybrid approach for search and res-cue using 3DCNN and PSO

Neural Compu-ting & Applica-tions

4.774 2020

7 Shivani Goel

CSE SP-J48: a novel optimization and ma-chine-learning-based approach for solving complex problems: special application in software engineering for detecting code smells

Neural Compu-ting & Applica-tions

4.774 2020

8 Dilbag Singh, Manjit Kaur

CSE Rapid COVID-19 diagnosis using en-semble deep transfer learning models from chest radiographic images

Journal of Ambi-ent Intelligence and Humanized Computing

4.594 2020

9 Manjit Kaur, Dilbag Singh

CSE Multi-modality medical image fusion technique using multi-objective differ-ential evolution based deep neural net-works

Journal of Ambi-ent Intelligence and Humanized Computing

4.594 2020

10 Sanjeet Kumar Nayak

CSE IECA: an efficient IoT friendly image encryption technique using program-mable cellular automata

Journal of Ambi-ent Intelligence and Humanized Computing

4.594 2020

11 Tanveer Ahmed, Rishav Singh

CSE Identifying tiny faces in thermal imag-es using transfer learning

Journal of Ambi-ent Intelligence and Humanized Computing

4.594 2020

12 Sridhar Swaminathan

CSE Sparse low rank factorization for deep neural network compression

Neurocomputing 4.438 2020

13 Deepa Khare

BT Non-intrinsic ATP-binding cassette proteins ABCI19, ABCI20 and ABCI21 modulate cytokinin response at the endoplasmic reticulum in Ara-bidopsis thaliana

Plant Cell Reports 3.825 2020

XIXIXIXI

XI

List of Abbreviations

Short Form Full Form

BT Biotechnology

BU Bennett University

CIE Centre for Innovation & Entrepreneurship

Civil Civil Engineering

CSE Computer Science and Engineering

DRS@BU Digital Repository Service @Bennett University

ECE Electronics & Communication Engineering

IF Impact Factor

JCR Journal Citation Reports

LRC Learning Resources Centre

Maths Mathematics

ME Mechanical Engineering

MGMT Management

Phy Physics

SEAS School of Engineering & Applied Science

SOL School of Law

SOM School of Management

TSOM Times School of Media

XI

1

Department of Biotechnology

Journal Papers

Dr. Deepa Khare, Assistant Professor, Department of Biotechnology

Khare, Deepa (2020). Non-intrinsic ATP-binding cassette proteins ABCI19, ABCI20 and

ABCI21 modulate cytokinin response at the endoplasmic reticulum in Arabidopsis thaliana.

Plant Cell Reports, 1-15.doi:10.1007/s00299-019-02503-0

Abstract: The plant ATP-binding cassette (ABC) I subfamily (ABCIs) comprises

heterogeneous proteins containing any of the domains found in other ABC proteins. Some

ABCIs are known to function in basic metabolism and stress responses, but many remain

functionally uncharacterized. ABCI19, ABCI20, and ABCI21 of Arabidopsis thaliana cluster

together in a phylogenetic tree and are suggested to be targets of the transcription factor

ELONGATED HYPOCOTYL 5 (HY5). Here, we reveal that these three ABCIs are involved

in modulating cytokinin responses during early seedling development. The ABCI19, ABCI20

and ABCI21 promoters harbor HY5-binding motifs, and ABCI20 and ABCI21 expression was

induced by light in a HY5-dependent manner. abci19 abci20 abci21 triple and abci20 abci21

double knockout mutants were hypersensitive to cytokinin in seedling growth retardation

assays, but did not show phenotypic differences from the wild type in either control medium

or auxin-, ABA-, GA-, ACC- or BR-containing media. ABCI19, ABCI20, and ABCI21 were

expressed in young seedlings and the three proteins interacted with each other, forming a large

protein complex at the endoplasmic reticulum (ER) membrane. These results suggest that

ABCI19, ABCI20, and ABCI21 fine-tune the cytokinin response at the ER under the control

of HY5 at the young seedling stage.

Keywords: ABC transporter; Cytokinin; Endoplasmic reticulum; Seedling growth; HY5

Dr. Rajinder Singh Chauhan, Professor & HOD, Department of Biotechnology

Chauhan, Rajinder Singh (2020). Mean corpuscular volume/mean corpuscular hemoglobin

values are not reliable predictors of the β-thalassemia carrier status among healthy diverse

populations of Himachal Pradesh, India. Asian Journal of Transfusion Science, 14172-

178.10.4103/ajts.AJTS_109_18

Abstract: Background: Himachal Pradesh is a hill state in North India in the Western

Himalayas. β-thalassemia is a genetic disorder of hemoglobin inherited in an autosomal

recessive manner that results in defective globin production leading to the early destruction of

red blood cells. β-thalassemia has long been neglected in Himachal Pradesh due to popular

belief that it runs along 'Lahore-Gujarat-Punjab' belt in India. Therefore, there is no β-

thalassemia testing facility currently in the state. Methods: To estimate the prevalence of β-

thalassemia carriers, we calculated the sample size based on probability proportional to size

self-weighing design. In each of 20 selected colleges, 111 students having an age of 18-25 were

tested for high-performance liquid chromatography (HPLC) and complete blood count. Some

were further tested for the mutations. We computed sensitivity, specificity, positive predictive

value (PPV) and negative predictive value, and receiver operating characteristic curve for mean

corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) red cell parameters.

Results: Of the 2220 students, 57 were found to be β-thalassemia carrier by HPLC. The overall

prevalence rate was 2.6% which translates to probable 180,000 β-thalassemia carriers in

Himachal Pradesh. Six districts bordering highly endemic Punjab had a higher prevalence.

Hemoglobin D-Punjab, Heterozygous-Iran Trait, and raised fetal hemoglobin were found.

Dep

art

men

t of

Bio

tech

nolo

gy

2

Thalassemia major and sickle cell disease were not found. Anemic status or MCV/MCH

parameters were not found to be reliable predictors of thalassemia carrier status among the

healthy populations of HP. The predominant mutation found was IVS 1-5 G > C. Conclusion:

Popular ongoing strategy for screening with MCV and MCH has low-PPV and can miss upto

37% of true thalassemia carriers. HPLC is better strategy for screening carriers and reduces

further spread of thalassemia.

Keywords: Complete blood count; Hematological parameters; High-performance liquid

chromatography; Prevalence; β-thalassemia carriers

Dr. Saurabh Jyoti Sarma, Assistant Professor, Department of Biotechnology

Sarma, Saurabh Jyoti (2020). Fermentation of primary sludge at multimillion litres scale and

volatile fatty acid production for biological nutrient removal. International Journal of

Environment and Health Sciences, 2(1), 14-35.

Abstract: Biological nutrient removal is a multimillion liters scale biotechnological process

operated across the world. Volatile fatty acids (VFAs) produced by fermentation of primary

sludge are important part of the modern day biological nutrient removal processes. Anoxic

heterotrophic denitrification can be promoted by using these VFAs as external electron donors.

Similarly, polyhydroxyalkanoates (PHAs) synthesis can be enhanced by introducing VFAs as

external carbon source. Later in the aerobic phase of biological phosphorus removal process,

these PHAs are utilized by phosphate accumulating organisms (PAO) for intracellular

phosphorus accumulation and removal. This article critically reviews two years’ (2014 & 2015)

weekly collected data of a full scale (1.745 million liters) fermenter being used for VFAs

production by fermentation of primary sludge. Average VFAs production was around 440.7 ±

208.5 mg/L, out of which 44.21 ± 4.02% were acetic acid and 41.15 ± 5.66% were propionic

acid. It has been concluded that there is a liner correlation (R2=0.93) between acetic and

propionic acid concentration in the fermenter supernatant and a static fermenter is more suitable

for propionic acid rich VFAs production. Oxidation of propionic acid has been proposed as the

dominant mechanism of acetic acid production. Co-digestion of primary sludge with crude

glycerol, hydrolysis of nonliving fraction of the primary sludge, combined hydrogen and VFAs

production and pH, DO and hydrogen partial pressure based metabolic shifts are identified as

potential strategies for improved VFAs production.

Keywords: Acetic acid; Crude glycerol; Denitrification; Hydrogen production; Phosphorus

removal; Primary sludge; Propionic acid; Volatile fatty acid

Sarma, Saurabh Jyoti & Sharma, Sumit (2020). Bio-Succinic Acid: an Environment

friendly Platform Chemical. International Journal of Environment and Health Sciences

(IJEHS), 2(2), 69-80. https://www.stenvironment.org/images/artical/Article-2-2-1.pdf

Abstract: Global research for biomass-based products is swelling gradually due to depletion

of fossil-based raw material as well as their negative effects on the environment. The hazardous

chemical processes used for production of valuable industrial chemicals are directly

responsible for environmental pollution. Therefore, use of alternative renewable resources as

feedstock to produce such chemicals is gaining popularity. Succinic acid (SA) is platform

chemical which can be used to produce bulk industrial chemicals with huge global demand

such as adipic acid, 1,4-butandiol, maleic anhydride. Non-hazardous biological fermentation

process can be used to produce succinic acid, which can be further converted to these

chemicals. The bio-based approach uses CO2 as supplement during the process and it replaces

Dep

art

men

t of

Bio

tech

nolo

gy

3

fossil-based raw materials; therefore, it is environment friendly. Mostly, pure sugars or sugars

derived from crop residues or lignocellulosic materials are used to produce succinic acid.

However, utilization of agricultural waste aromatic compounds to produce succinic acid has

not been investigated in detail and not being implemented anywhere. Lignin waste management

is a problem for the cellulosic bio-refineries and finding the way or its biological utilization is

still in the stage of research and development. Unlike sugar metabolism, bioconversion of

aromatic compounds is relatively complicated. Interestingly, bioconversion of agricultural

waste aromatic compounds could be targeted towards production of succinic acid. The purpose

of the present review is to highlight this possibility.

Keywords: Succinic acid; Platform chemical; Market potential; Lignin; Aromatic substrates.

Sarma, Saurabh Jyoti & Sharma, Sumit (2020). Isolation of Fungus from Roots of Water

Hyacinth and Cellulase Production by The Microorganism from Cardboard Waste.

International Journal of Environment and Health Sciences (IJEHS), 2(2), 87-96.

https://www.stenvironment.org/images/artical/Article-2-2-3.pdf

Abstract: Bioethanol production from agro-industrial waste is becoming a major research area

for alternative fuel. The cellulase enzyme is the key ingredient used for the cellulosic

bioethanol fermentation process. India is abundant in cellulosic resources that come from paper

and pulp industries. Single-use newspaper and packaging cardboards constitute a significant

portion of cellulosic wastes. Here in this study, an industrial enzyme cellulase was produced

from single-use newspaper and cardboard wastes. A fungus was isolated (THS8) from the root

of the water hyacinth collected from an abundant pond located near Badoli village, Faridabad

(India). The fungus was used to do a comparative study for cellulase production by using

different substrates such as pure carboxymethyl cellulose (CMC), cardboard (CB), coated

cardboard (CBC) and newspaper (NP). The maximum cellulase activity 0.149 FPU (Filter

paper unit)/ml was estimated in cardboard (20g/l) grown in MW (Mandel Weber) medium.

Likewise, a total of 60% degradation of the cardboard was observed after 7 days of incubation

at 30°C with 0.27 FPU/ml optimized at CB 20g/l. This demonstrates the feasibility of using

cardboard as a less expensive feedstock, which is usually dumped after unpacking items in

households and industries, to produce cellulase enzyme by the isolated microorganism.

Keywords: Cardboard; Water hyacinth; Trichoderma; Cellulase; FPU.

Sarma, Saurabh Jyoti & Sharma, Sumit (2020). Genomics and proteomics of

Saccharomyces cerevisiae used for biofuel production. International Journal of Environment

and Health Sciences, 2(1), 36-50.

Abstract: Saccharomyces cerevisiae is the first eukaryotic organism whose genome was

sequenced with genomic information of a total of 16 chromosomes. It is being used as a model

organism to study the molecular structure and functional characterization of higher eukaryotes.

The genomic, proteomic, transcript and metabolic resources are the best platforms to provide

information regarding genome, proteins, transcripts and metabolic pathways. The evolution of

simple genetics of yeast to genome editing and utilization of genomic resources are

summarized in this review. It will give a general overview of various database resources and

tools for gathering and generating computational information of yeast. SGD (Saccharomyces

Genome Database) is an open resource for comprehensive biological integrated information

for Saccharomyces cerevisiae and helps in finding the target gene such as ADH (alcohol

dehydrogenase) for improving biofuel production enhancement. Genome-wide homology

annotation also helps in identifying genes and ORFs and also in assigning functions. The

Dep

art

men

t of

Bio

tech

no

logy

4

potential utilization of these resources may overcome the findings of a target for gene discovery

and their use in industrially relevant gene modifications.

Keywords: Saccharomyces cerevisiae; Genetics; Genomics; Proteomics; Transcriptomics;

Metabolomics

Sarma, Saurabh Jyoti., Sharma, Sumit, Singh, Shikha., Sarma, Nilakshi & Sharma,

Chitra (2020). Algae Derived High-Value Green Bioproducts. International Journal of

Environment and Health Sciences, 2(1), 51 -63.

Abstract: Microalgae are a renewable potential source of high-value products. The superficial

activity of CO2 sequestration and accumulation of lipids inside make it useful for large scale

cultivation. Constant high energy demands and depletion of fossil fuel resources have focused

on the technologies to improve biofuel yield through micro-algae development. The ability of

microalgae to produce high-value products, such as DHA (docosahexaenoic acid) and EPA

(eicosapentaenoic acid) helps in increasing their global demand. This review focused on

production, purification of DHA, EPA and carotenoids, their operational conditions, bio-

synthesis pathways, and applications.

Keywords: DHA; EPA; Carotenoids; Downstream Processing; Applications

Sarma, Saurabh Jyoti & Sharma, Sumit (2020). Role of acid hydrolysis behavior on

structure and depolymerization of lignin extracted from rice straw. International Journal of

Environment and Health Sciences (IJEHS), 2(3), 9-14.

Abstract: Chemical reactions mostly have a dependency on catalyst concentration. Sometimes,

the action of higher concentration enhances the rate very high that disrupts the product

appearance and structural integrity. It is also applicable for biological material degradation.

Here in this study, the degradation of rice straw biomass was executed for lignin extraction

using the acid hydrolysis treatment method. This technique helps in the removal of maximum

cellulosic parts like cellulose and hemicellulose by converting them into soluble sugars and

remains left out lignin. It was investigated that the higher concentration of sulfuric acid as 72%

v/v (80.77% w/w) caused some structural changes in chemical bonds and formed highly

condensed lignin (L-72). While mild concentration of sulfuric acid 63% v/v (72.26% w/w)

does not have any adverse effect on lignin structural integrity and was found as free-form lignin

(L-63). The impact of condensation was observed during the depolymerization of L-72 and

L63. The depolymerization efficiency of L-72 and L-63 in alkaline medium (NaOH 1.5% and

Na2S 0.5%) comparatively lesser for L-72 (34%) than L-63 (98.3%) using lignin 2 g/l.FT-IR

analysis also showed the presence of CO-O-CO (anhydride) and C=C (alkenes) in condensed

lignin but not found in free-form lignin. This means the structural condensation decreasing the

depolymerization efficiency of lignin. Hence, it is concluded that free-form, light- brown lignin

should be used for depolymerization and monolignolextraction.

Keywords: Acid hydrolysis; Condensed Lignin; Free-form lignin; Depolymerization

Dep

art

men

t of

Bio

tech

nolo

gy

5

Review Article

Dr. Sarika Chaudhary, Assistant Professor, Department of Biotechnology

Chaudhary, Sarika., Tandon, Siddhi., Aggarwal, Anchal, Jain, Shubhra & Shukla,

Sanjay (2020). Perspective on the Role of Antibodies and Potential Therapeutic Drugs to

Combat COVID-19. Protein Journal, 39(6), 631-643.10.1007/s10930-020-09921-0

Abstract: The sudden emergence of the novel severe acute respiratory syndrome coronavirus

2 (SARS-CoV-2) causing the coronavirus disease of 2019 (COVID-19) has brought the world

to a standstill. Thousands of people across the globe are biting the dust with every passing day

and yet more are being tested positive for the SARS-CoV-2 infection. In order to dispense this

current crisis, numerous treatment options have been tried and tested and many more are still

under scrutiny. The development of vaccines may help in the prevention of the global

pandemic, however, there is still a need for the development of alternate approaches to combat

the disease. In this review we highlight the new discoveries and furtherance in the antibody

based therapeutic options and the potent drugs, with special emphasis on the development of

the monoclonal and polyclonal antibodies and the repurposed drugs, which may prove to be of

significant importance for the treatment of COVID-19, in the days to come. It is an attempt to

evaluate the currently presented challenges so as to provide a scope for the ongoing research

and assistance in the development of the effective therapeutic options against SARS-CoV-2.

Keywords: Antibody; Convalescent plasma; COVID-19; Cytokine; SARS-CoV-2;

Therapeutic drug

Dep

art

men

t of

Bio

tech

nolo

gy

6

Department of Civil Engineering

Book Chapter

Dr. Visalakshi Talakokula, Professor, Department of Civil Engineering

Talakokula, Visalakshi & Bansal, Tushar (2020). Monitoring Strength Development of

Cement Substituted by Limestone Calcined Clay Using Different Piezo Configurations In S.

Bishnoi (ed.), Calcined Clays for Sustainable Concrete, RILEM Book series 25 (pp. 555-562).

Springer.doi:10.1007/978-981-15-2806-4_62

Abstract: Recently developed limestone calcined clay cement (LC3) with low clinker factor

is growing rapidly in the construction industry because of its several benefits over ordinary

Portland cement (OPC) and Portland pozzolana cement (PPC). In this paper, monitoring

strength development of cement substituted by (LC3) using different piezo configurations was

studied. The experimental study was carried out on concrete cube specimens of OPC and LC3,

in which different piezo configurations such as embedded piezo sensor (EPS), surface-bonded

piezo sensor (SBPS), jacketed piezo sensor (JPS) and non-bonded piezo sensor (NBPS) are

installed to the specimen to acquire data in the form of conductance and susceptance signatures

via electro-mechanical impedance (EMI) technique. The sensitivity of the different piezo

configurations was calibrated with the compressive strength, based on the results, it can be

concluded that the compressive strength of OPC and LC3 is comparable to each other. All the

piezo configurations were effective in monitoring the compressive strength; however, the

embedded sensors, EPS and JPS, perform the best in LC3 and OPC, respectively.

Keywords: Limestone calcined clay cement (LC3); Piezo sensor; Compressive strength;

Structural parameters

Talakokula, Visalakshi & Bansal, Tushar (2020). Study of Durability Aspects of Limestone

Calcined Clay Cement Using Different Piezo Configurations In S. Bishnoi (ed.), Calcined

Clays for Sustainable Concrete, RILEM Bookseries 25 (pp. 723-730). Springer.

doi:10.1007/978-981-15-2806-4_80

Abstract: The present research focuses on durability aspects of limestone calcined clay cement

(LC3) using different piezo configurations specifically aimed at acid attack. The experimental

study was carried out on concrete cylindrical specimens of ordinary Portland cement (OPC)

and LC3, which were immersed into the sulphuric acid (H2SO4) and hydrochloric acid (HCl)

solution. Different piezo configurations were installed to acquire the data in the form of

conductance and susceptance signatures via electro-mechanical impedance (EMI) technique.

From the acquired data, the equivalent structural mass parameter was extracted which was then

validated with actual mass obtained by physical measurement. Based on the results, it can be

concluded that the equivalent mass parameter is able to identify the mass loss in the specimens

subjected to acidic environment non-destructively.

Keywords: Limestone calcined clay cement (LC3); Piezo sensor; Acid attack

Dep

art

men

t of

Civ

il E

ngin

eeri

ng

7

Journal Papers

Talakokula, Visalakshi (2020). Pipeline corrosion assessment using piezo-sensors in reusable

non-bonded configuration. NDT and E International, 111102220. doi:

10.1016/j.ndteint.2020.102220

Abstract: In this paper, a piezoelectric sensor is used in its non-bonded configuration for non-

destructive evaluation of a pipeline segment subjected to progressive corrosion damage. A new

non-bonded sensor configuration, specially adapted for small diameter pipelines, known as

modified non-bonded sensor (MNS), is proposed and evaluated. The new sensor configuration

is evaluated for sensitivity in monitoring corrosion induced damage in pipeline segments in

controlled laboratory setup through accelerated corrosion tests. The conventional surface-

bonded piezo-sensor (SBPS) configuration is used as standard for comparison. Signature

analysis using different methods is carried out in the study. Using the principles of the electro-

mechanical impedance (EMI) technique, equivalent stiffness, damping and mass parameters

are computed from the admittance signatures of both the configurations (MNS & SBPS) and

compared. Experimental results show that piezo based a non-dimensional mass parameter from

the MNS measurements are very effective in damage detection and quantification. The results

are correlated with actual mass loss measurements and consequent corrosion rates are

calibrated. The results clearly make case for practical usage of MNS configuration in corrosion

detection and health monitoring of pipeline systems. The new MNS configuration proves to be

a successful alternative to the conventional bonded piezo configuration.

Keywords: Condition assessment; Pipelines; Accelerated corrosion; Lead zirconate titanate

(PZT) patch; Electro-mechanical impedance technique

Dep

art

men

t of

Civ

il E

ngin

eeri

ng

8

Department of CSE

Book Chapter

Arvind Kumar, Research Scholar, Department of CSE

Kumar, Arvind (2020). Classification of Forest Cover Type Using Random Forests

Algorithm. In Kolhe M., Tiwari S., Trivedi M., Mishra K. (Eds), Advances in Data and

Information Sciences. Lecture Notes in Networks and Systems, vol 94 (pp. 395-402). Springer.

doi:10.1007/978-981-15-0694-9_37

Abstract: Natural resource planning is an important aspect for any society. Knowing forest

cover type is one of them. Multiple statistical and machine learning approaches are already

proposed in past for classification. In the current work, publicly available dataset of Forest

Cover type (FC) from UCI repository was taken and classified for the forest cover type, using

random forests machine learning algorithm. On ten-fold cross validation, we got accuracy of

94.6% over 70.8% accuracy of original work presented at UCI repository. The result is also

compared with existing work done in past and it have been shown that random forests algorithm

performed better than most of existing works.

Keywords: Classification; Random forests; Machine learning; Unbalanced data; Forest cover

type

Dr. Kuldeep Chaurasia, Assistant Professor, Department of CSE

Chaurasia, Kuldeep (2020). Integration of lidar data in topographical feature extraction from

very high-resolution aerial imagery. Lecture Notes in Civil Engineering, 5139-44.

10.1007/978-3-030-37393-1_5

Abstract: Geospatial technology has been demonstrated as a reliable and efficient tool for

monitoring of the land cover pattern for vast geographical areas. Although, the demand for the

various thematic layers including landcover maps at finer scale has got increased for various

applications such as urban studies, forestry and disaster management. In this paper, the

utilization of LiDAR data for urban land cover classification of aerial imagery has been

discussed. The study area has been classified into seven land-use/cover classes based on the

textural, and spectral features using object-oriented classification approach. The applicability

of various texture measures based on the gray level co-occurrence matrix along with the effect

of varying pixel window has also been discussed. The classification results indicate that

homogeneity texture image generated using 3 * 3 window size is best suitable for extraction of

various topographical objects. The suitability of the various textural features has also been

investigated. The LiDAR data has been found best suitable for identification of small objects

such as buildings, trees and vehicles over aerial imagery. The overall accuracy of the

classification has been obtained as 87.21% with the kappa coefficient of 0.84. The outcome of

the study can be effectively utilized for disaster management applications such as evacuation

planning, damage assessment, and post-flood recovery effort.

Keywords: Feature extraction; Image classification; LiDAR; Remote sensing; Textural feature

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ng

inee

rin

g

9

Conference Papers

Dr. Anurag Goswami, Assistant Professor, Department of CSE

Goswami, Anurag & Kaliyar, Rohit Kumar (2020). MCNNet: Generalizing Fake News

Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19

Dataset. ACM International Conference Proceeding Series, 437-.10.1145/3430984.3431064

Abstract: During the pandemic of COVID-19, the propagation of fake news is spreading like

wildfire on social media. Such fake news articles have created confusion among people and

serious social disruptions as well. To detect such news articles effectively, we propose a

generalized classification model (MCNNet) having the power of learning across different

kernel-sized convolutional layers in different parallel channel network. The capability of

MCNNet is lucrative towards any real-world fake news dataset. Experimental results have

demonstrated the performance of our model with different real-world fake news datasets.

Keywords: MCNNet; COVID-19; Fake News

Apar Garg, UG Student, Department of CSE

Garg, Apar & Kaliyar, Rohit Kumar (2020). PSent20: An Effective Political Sentiment

Analysis with Deep Learning Using Real-Time Social Media Tweets. 2020 5th IEEE

International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020

- Proceeding, 10.1109/ICRAIE51050.2020.9358379

Abstract: In the current era of computing, the use of social networking sites like Twitter and

Facebook, is growing significantly over time. People from different cultures and backgrounds

share vast volumes of textual comments that show their viewpoints on several aspects of life

and make them available to all for commenting. Monitoring real social media activities has

now become a prime concern for politicians in understanding their social image. In this paper,

we are going to analyse the tweets of various social media platforms regarding two prominent

political leaders and classify them as positive, negative or neutral using Machine Learning and

Deep Learning methods. We have proposed a Deep Learning approach for a better solution.

Our proposed model has provided state-of-the-art results using Deep Learning models.

Keywords: Neural Network; Sentiment Analysis; Social Media; Text Classification

Dr. Arpit Bhardwaj Assistant Professor, Department of CSE

Bhardwaj, Arpit & Acharya, Divya (2020). Multi-class Emotion Classification Using EEG

Signals. In D. Garg et.al (Eds) 10th IACC 2020 Conference (pp. 474-491).

Abstract: Recently, the availability of large EEG datasets, advancements in Brain-Computer

interface (BCI) systems and Machine Learning have led to the implementation of deep learning

architectures, especially in the analysis of emotions using EEG signals. These signals can be

generated by the user while performing various mental, emotional and physical tasks thus,

reflecting the brain functionality. Extracting the important feature values from these

unprocessed signals remain a vital step in the deployment. Fast Fourier Transformation proves

to be better than the traditional feature extraction techniques. In this paper we have compared

the deep learning models namely Long Short-term Memory (LSTM) and Convolutional Neural

Network (CNN) on 80-20 and 75-25 Train-Test splits.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

10

The best result was obtained from LSTM classifier with an accuracy of 88.6% on the liking

emotion. CNN also gave a good accuracy of 87.72% due to its capability to extract spatial

feature from the input signals. Thus, both these models are quite beneficial in this context.

Keywords: Emotion Recognition; EEG; Convolutional Neural Network; Long Short-term

Memory Networks; Deep Learning.

Bhardwaj, Arpit & Acharya, Divya (2020). Epileptic Seizure Detection using CNN. In D.

Garg et.al (Eds) 10th IACC 2020 Conference. (pp.3-16)

Abstract: Epilepsy is one of the most devastating diseases in the history of mankind. A neuro-

logical disorder in which irregular transmission of brain waves result in seizures and physical

and emotional imbalance. This paper presents the study of effective use of Convolutional

Neural Network (CNN) a deep learning algorithm for epileptic seizure detection. This

innovative technology will help the real world to make diagnosis of the disease faster with

greater accuracy. A binary class epilepsy dataset is considered with 179 feature extracted as

the attributes. Binary and multiclass classification is performed by considering the class with

epileptic activity against all non-epileptic class. For classification CNN model is proposed

which achieved an accuracy of 98.32%. Then by applying a different multiclass data set having

4 classes, the degree of generalization of our model is also checked as the accuracy of end

results was high. For 10-fold cross validation and 70-30 data splitting method our models for

performance is evaluated using various performance metrics.

Keywords: Epilepsy; EEG; Deep learning; CNN; Machine Learning

Arvind Kumar, Research Scholar, Department of CSE

Kumar, Arvind (2020). Test Suite Optimization Using Lion Search Algorithm. In Hu YC.,

Tiwari S., Trivedi M., Mishra K. (eds), Ambient Communications and Computer Systems.

Advances in Intelligent Systems and Computing, 1097 (pp. 77-90). doi:10.1007/978-981-15-

1518-7_7

Abstract: Testing is a continuous activity since visualization of the product. Regression testing

is a type of testing which is performed to make sure that change in code has not impacted any

already working functionality of the software. This is an unavoidable and expensive activity.

Running all the test cases of regression test suite takes a lot of time and is expensive too. At

the same time with the evolution of software, the software test suite size also increases, which

increases the cost of test case execution. It is not feasible to rerun each test case. One of the

most efficient ways to improve regression testing and reduce the cost is test case prioritization

for regression test suite. This is a technique to prioritized regression test suite according to

some specific criteria and execute the test cases according to the prioritized list, i.e. higher

priority test case first and then the lower priority test cases. But the challenge is how to optimize

the test cases order according to criterion. To optimize test suite, in this paper Lion optimization

algorithm (LOA) has been proposed. LOA is a population-based metaheuristic algorithm. This

approach utilized the historical execution data of the regression cycles, which will generate the

list of prioritized test cases. At last, the optimized outcome has been compared by fault

detection matrix.

Keywords: Lion optimization algorithm; Metaheuristic algorithm

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

11

Dr. Deepak Garg. Professor & HoD, Department of CSE

Garg, Deepak (2020). A Capacitated Facility Allocation Approach Based on Residue for

Constrained Regions. In Khanna A., Gupta D., Bhattacharyya S., Snasel V., Platos J.,

Hassanien A. (Eds), International Conference on Innovative Computing and Communications.

Advances in Intelligent Systems and Computing, vol 1059 (pp. 473-488). doi:10.1007/978-

981-15-0324-5_41

Abstract: Allocation of services has observed widespread applications in real life. Therefore,

it has gained comprehensive interest of researchers in location modelling. In this paper, the

authors aim to allocate p capacitated facilities to n demand nodes in constrained demand plane.

The allocation is aimed to minimize the total transportation cost. The authors consider

continuous demand plane, which is constrained by the presence of barriers. Here, the authors

present a residue-based capacitated facilities allocation (RBCFA) approach for allocation of

capacitated facilities. Finally, an illustration of RBCFA is presented in order to demonstrate its

execution. Authors also perform tests to validate the solution, and the tests yield that suggested

approach outperforms traditional approach of allocation. It is observed that although the

achievement by RBCFA is not significant for few resources, achievement is significant as the

number of resources rises.

Keywords: Facility location Barriers; Visibility graph; Constrained demand plane; Convex

hull

Dr. Gaurav Singal, Assistant Professor, Department of CSE

Singal, Gaurav (2020). A Novel Approach for Detection of Counterfeit Indian Currency Notes

Using Deep Convolutional Neural Network. IOP Conference Series: Materials Science and

Engineering, 981 .10.1088/1757-899X/981/2/022018

Abstract: In recent years, the Indian economy has shown rapid growth among all other major

economies. India has been tragically reviled with issues like corruption and black currency,

fake money notes is additionally major issues to it, in spite of a strong security feature are

endorsed by RBI to print original currency. The advancement of color printing technology

helped local racketeers and foreign racketeers to print a large amount of counterfeit Indian

currency notes in the market. Albeit counterfeit money is being printed with accuracy, it likely

is distinguished with some effort. In this paper, the proposed model efficiently detect the

counterfeit Indian currency notes by adapting three-layered Deep Convolutional Neural

Network (Deep ConvNet), and achieved an accuracy of 96.6%.

Keywords: Counterfeit detection; Currency note; Deep Convolutional Neural Network (Deep

CNN); Image processing

Gaurav Singal, Jain, Shreyansh Sharad., Garg, Deepak & Gupta, Suneet Kumar (2020).

SecureDorm: Sensor-Based Girls Hostel Surveillance System. In Somani A., Shekhawat R.,

Mundra A., Srivastava S., Verma V. (Eds), Smart Systems and IoT: Innovations in Computing.

Smart Innovation, Systems and Technologies, vol 141 (pp. 327-337).

Abstract: The intent is of developing a technology-driven security system for hostels to

provide higher security to hostel inmates, which ultimately will help to generate the trust in the

parents and guardians so that they will be motivated and encouraged to send their wards to

hostels for education. The system is based on Wireless Sensor Network (WSN), which helps

to keep the track of system components and their status.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

12

And these components are majorly sensors which keep track of breach made. We have

proposed a communication system between various sensors to the server for identifying any

breaches in the hostel. We have used ultrasonic sensor and GSM module for the

implementation. Since the application requires us to install the components on the parameters

of the hostel, it should be available.

Keywords: Sukanya Rakshak; Girls hostel; Security surveillance; WSN; Intrusion detection;

NodeMCU; Raspberry Pi; HC-SR04.

Dr. Hiren Kumar Thakkar, Assistant Professor, Department of CSE

Thakkar, Hiren Kumar (2020). A Comparative Analysis of Machine Learning Classifiers for

Robust Heart Disease Prediction. 2020 IEEE 17th India Council International Conference,

INDICON 2020, 10.1109/INDICON49873.2020.9342444

Abstract: In the past decade, Cardiovascular Diseases (CVD) have become a significant public

health concern across the nations due to the treatment cost. Therefore, the low-cost cardiac

health monitoring system is highly essential, which analyzes the collected data and makes

robust predictions. The statistical data analysis has limitations to infer the knowledge from

several parameters. On the contrary, Machine Learning (ML) models deal with numerous

parameters and can successfully extract the patterns. In this paper, ML classifiers are designed,

and comparative analysis is carried out for the robust heart disease prediction. Five ML

classifiers such as Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support

Vector Machine (SVM), and K-Nearest Neighbor (KNN) are implemented, and their

performance is extensively evaluated on the Cleveland Heart Disease Data set. The

comparative analysis across five performance metrics confirms the applicability of ML

classifiers for heart disease predictions.

Keywords: comparative analysis; Heart disease prediction; Machine learning

Thakkar, Hiren Kumar & Rai, Deepak (2020). Performance Characterization of Binary

Classifiers for Automatic Annotation of Aortic Valve Opening in Seismocardiogram Signals.

ACM International Conference Proceeding Series, 77-82. 10.1145/3431943.3431956

Abstract: In the recent past seismocardiogram (SCG) has emerged as one of the potential non-

invasive modalities to estimate cardiac health parameters. Each SCG cycle contains specific

SCG peaks that help identify specific cardiac mechanical events. The accurate and automatic

annotation of SCG peaks has potential daily-life applications such as continuous cardiac health

monitoring. However, it is challenging to automate the SCG annotation due to the

morphological variations of SCG signals. In this paper, automatic annotation of the most

important SCG peak called Aortic valve Opening (AO) is explored by formulating the AO

annotation as a binary classification problem. Four binary classifiers such as Logistic

Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Gaussian Naïve

Bayes (GNB) are used for AO annotation and supported by empirical features such as

"Amplitude" and "Time of Appearance". The performance comparison of these binary

classifiers is carried out using 759 SCG signals acquired from the Physionet public repository

"cebsdb". The classifiers are trained and comprehensively tested followed by 5-fold cross-

validation. The experimental results show that GNB and DT consistently perform well on

established seven performance metrics.

Keywords: Aortic Valve Opening (AO) peak; Binary classifiers; Cardiac Health; Machine

learning; Seismocardiogram (SCG)

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

13

Thakkar, Hiren Kumar., Rai, Deepak & Singh, Deepak (2020). Machine Learning Assisted

Automatic Annotation of Isovolumic Movement and Aortic Valve Closure using

Seismocardiogram Signals. 2020 IEEE 17th India Council International Conference,

INDICON 2020 10.1109/INDICON49873.2020.9342412

Abstract: Recently, Seismocardiogram (SCG) is used to monitoring vital cardiac health

parameters through relevant SCG peaks annotation. However, accurate annotation of the SCG

peaks is a challenging task. In this paper, we design binary classifiers to annotate the

Isovolumic movement and the Aortic valve closure events of SCG. Five binary classifiers, such

as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Gaussian

Nave Bayes, are implemented atop customized features Amplitude and Time of Appearance.

The experiments are carried out on the cebs database available at Physionet public repository.

In particular, seven rigorous performance metrics are employed to evaluate the classifiers'

annotation ability, followed by the 5-fold cross-validation to ensure the classifiers' performance

reliability. Experiment results show that RF consistently performs better for both IM and AC

annotation. On the contrary, SVM and LR perform poorly for IM and AC, respectively.

Keywords: Annotation; Aortic Valve Closure; Isovolumic Movement; Machine Learning;

Seismocardiogram

Dr. Jagendra Singh, Assistant Professor, Department of CSE

Singh, Jagendra (2020). Collaborative filtering based hybrid music recommendation system.

Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS

2020, 186-190.10.1109/ICISS49785.2020.9315913

Abstract: Even though people are nowadays listening all types of songs, still algorithms are

struggling in many fields. With less historic figures, how does the system know the listeners

like a new artist or a new song? And, how do you know which songs to suggest to new users?

In this perspective, the proposed research work aims to assess the likelihood that a user will

listen to the song repeatedly after the first apparent listening experience begins in the time

frame. If the user has a recurring auditory occurrence within a month following the first

apparent listening event, its goal is 1 and otherwise 0 identified. The approaches like

collaborative filtering, content-based filtering, singular value decomposition (SVD) and

factorization machines (FM) are also used. At last, the proposed system is hybridized by using

SVD and FM.

Keywords: Collaborative Filtering; Content Based Filtering; Factorization Machine;

Hybridization; Singular Value Decomposition.

Singh, Jagendra (2020). Learning based driver drowsiness detection model. Proceedings of

the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 698-701.

10.1109/ICISS49785.2020.9316131

Abstract: Drivers drive for long hours which deteriorates their health. Due to this, the drivers

are often tired and often on the verge of fatigue. This leads them to have bursts of microsleep

i.e. a interim episode of sleepiness which may last for a fraction of a second or up to 30 seconds,

where the victim fails to react to some stimulus from the environment and becomes

unconscious. As a result of this, road accidents have become a common occurrence globally.

The purpose of this paper is to devise a way to alert drowsy drivers while driving. This study

attempted to address the issue by creating a module i.e. drowsy eyes detection. Using 68 key

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

14

points, we devised a new detection approach for facial regions. After that we use these facial

regions as a key point to evaluate the driver's state. The accuracy was up to 92.5%.

Keywords: Deep learning; Eye detection; Face detection; Image processing; Object detection;

Video processing

Dr. Kuldeep Chaurasia, Assistant Professor, Department of CSE

Chaurasia, Kuldeep (2020). AI based prediction of daily rainfall from satellite observation

for disaster management. Proceedings of SPIE - The International Society for Optical

Engineering, 11525. 10.1117/12.2580628

Abstract: We are all well aware of the fact that heavy rainfall is one of the major sources of

causing disasters. Be it flood or drought, landslide or erosion rainfall is the prime factor to be

causing these calamities. Timely prediction of rainfall is important to be ready for facing

upcoming disasters. In this research work, an attempt has been made to predict daily rainfall

from satellite observation for disaster management using Artificial Intelligence (AI). A multi-

stacked LSTM based model has been used for prediction of daily rainfall. The model uses 40

years of dataset provided by the National Aeronautics and Space Administration (NASA) /

Goddard Space Flight Center through MERRA-2 portal. The dataset belongs to Yamuna Nagar

district in Haryana, India during the period 1980 - 2020 for the training purpose. The outcome

of the research proves that LSTM based neural networks are better alternative to forecast

general weather conditions when compared with traditional methods. The outcome of the

model can further be improved by including more parameters and better hyper parameter

tuning.

Keywords: Artificial Intelligence; CNN; LSTM; Rainfall Prediction; Satellite Observation

Chaurasia, Kuldeep., Neeraj, Baipureddy, Burle, Dattu & Mishra, Vipul Kumar (2020).

Topographical Feature Extraction Using Machine Learning Techniques from Sentinel-2A

Imagery. International Geoscience and Remote Sensing Symposium (IGARSS), 1659-1662.

10.1109/IGARSS39084.2020.9324713

Abstract: The advancement in the satellite technology has made it possible to easily and

frequently obtain the satellite images of most of the regions on the Earth. The satellite data

contains the abundant amount of information which can be very useful for variety of societal

applications. However, manual identification of the land cover in a particular area is a very

challenging and time-consuming task. In this manuscript, an attempt has been made to better

extract the landcover types from Sentinel-2A imagery using the popular classifiers such as

random forest, SVM, Naive Bayes, Decision Tree (CART). The manuscript also validates the

results obtained by the used models by computing the performance metrics. The analysis

reveals that random forest classifier outperforms the rest of the classification methods in terms

of better accuracy of 95.67%. This automated approach can be applied to large sets of data,

reducing the need for manual labeling.

Keywords: Feature extraction; Landcover mapping; Machine Learning; Remote sensing;

Satellite Image

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

15

Chaurasia, Kuldeep & Dixit, Mayank (2020). Predicting H1N1 and Seasonal Flu : Vaccine

Cases using Ensemble Learning approach. Proceedings - IEEE 2020 2nd International

Conference on Advances in Computing, Communication Control and Networking, ICACCCN

2020, 172-176. 10.1109/ICACCCN51052.2020.9362909.

Abstract: We propose a data-driven machine learning model to predict the likelihood of a

person being vaccinated against H1N1 and seasonal flu. In 2009, a pandemic that was caused

by the H1N1 influenza virus, named "swine flu", spread like wildfire across the world.

Researchers and Healthcare specialists estimated that in the initial year, it was responsible for

between \underline150, 000\ \textto\ 600, 000\ \textdeaths in the whole world. A

vaccine against the H1N1 (swine flu) virus was made available in October 2009. We have

implemented 9 machine learning models. The models include MlBox (model1), TPOT (model

2), Random forest (model 3), MLP (model 4), Linear regression, (model 5), Decision trees

(model 6), polynomial feature (model 7), XgBoost (model 8) and CatBoost (model 9). Out of

these 9 models, CatBoost gave the best performance with an accuracy of 0.8617 followed by

the XgBoost and MlBox. Hence, this study showed that cast boost gave the best performance

of all the models and can be used for further prediction models in this category.

Keywords: AutoML algorithms; Ensemble learning; Gradient boosting algorithms; H1N1

vaccine; Machine learning; Seasonal flu vaccine

Dr. Madhushi Verma, Assistant Professor, Department of CSE

Verma, Madhushi (2020). Flux Optimization of DTC Based Induction Motor Drive Using

Recurrent Neural Network. In Singh P., Panigrahi B., Suryadevara N., Sharma S., Singh A.

(Eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, 605 (pp. 330-

338). Springer. https://doi-org-443. webvpn.jnu.edu.cn/10.1007/978-3-030-30577-2_28

Abstract: Due to high energy conservation concern, an energy efficient electric drive becomes

a challenge for researchers. Therefore, to meet this challenge, it is proposed that efficiency

improvement of Direct Torque Control (DTC) based induction motor drives can be achieved

by optimizing adaptive reference flux. The optimized value of flux from supply system can be

obtained by using recurrent neural network (RNN). This paper focuses on optimizing the value

of flux using recurrent neural network with input power as objective function. The performance

of DTC based induction motor has been presented, analyzed and realized thoroughly with

different training algorithms using MATLAB/Simulink®.

Keywords: Direct torque control; Recurrent neural network (RNN); Flux weakening; Speed

control; Three phase induction motor.

Verma, Madhushi (2020). Automated Motion of a Robot based on Emotion Analysis. Journal

of Physics: Conference Series, 1438, (pp. 12013).

https://iopscience.iop.org/article/10.1088/1742-6596/1438/1/012013.

Abstract: Several people in this world are suffering from neuro-motor disability which makes

their life difficult and such people become dependent on others. In some cases, such people

can speak clearly whereas in other cases their speech may not be very clear. Here, we present

a solution to control the movement of such people who are not able to walk because of their

disability but are able to speak clearly.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

16

Their speech is analysed, and a mapping of movement has been defined for an emotion. Further,

a prototype has been built in the form of an arduino robot to demonstrate that the mapping is

able to control the movement which can further be refined in future to make it more accurate

and mapped with relevant movements according to the need in real-life. Other practical

applications of this work include covert operation, feedback industry etc.

Keywords: Neuro-motor disability; Operation feedback industry.

Dr. Mohit Sajwan, Assistant Professor, Department of CSE

Sajwan, Mohit., Singh, Deepak., Rathor, Vijaypal Singh & Singh, Simranjit (2020).

Tolerance Satisfiability Sequences for Image Similarity. Proceedings - IEEE 2020 2nd

International Conference on Advances in Computing, Communication Control and

Networking, ICACCCN 2020, 1002-1007.10.1109/ICACCCN51052.2020.9362789

Abstract: We propose a solution to a fundamental problem of content based image retrieval

(CBIR), "How to calculate the degree of similarity among a pair of images". This article

introduces a novel technique called Tolerance Satisfiablity Sequence (TSS) which is based on

the concept of tolerance space and near set theory. In this work, images are divided into

different visual element (objects) and affinity is calculated between the corresponding object

of the two images. Each object is further divided into sub-objects that are mapped to each other

under the constraints of tolerance satisfiability. The maximal satisfying set of sub objects

(between the two parent objects) is found. This maximal set of sub-object is (termed as TSS)

compared with the parent object i.e. The ratio of the cardinality of maximum satisfying set to

the cardinality of the parent object is calculated and this gives the quantified similarity index

termed as Tolerant Satisfiable Similarity Metric (TSSM). Initially, we verified that proposed

approach is metric as well as pseudo-metric and later it is compared with popular image

nearness metrics, viz., Hausdorff and Structural Similarity Index (SSIM). It is experimentally

proved that the TSSM outperformed them in visual acuity.

Keywords: Euclidean colour Space; Hausdorff Distance; Near Sets; SSIM; Tolerance Relation

Nisha Ahuja, Ph.D. Scholar, Department of CSE

Ahuja, Nisha (2020). Online product recommendation engine. 2020 IEEE International

Conference on Computing, Power and Communication Technologies, GUCON 2020, 257-260.

10.1109/GUCON48875.2020.9231069

Abstract: In this modern world, most of the products are brought through internet. So by using

the recommendation technique we can improve our selling of the product. We have been

provided with two csv files. The train.csv dataset contains the User ID, Invoice Number, Date

Time, Quantity, Country, Unit Price and Stock Code. By using these available parameters

recommend the products to the customers. Previous work has used SVD algorithm or Matrix

factorization for doing the recommendations. In this paper we have used Restricted Boltzmann

Machine (RBM) to decode the text into a latent vector sequence, especially for end-to-end

collaborative filtering on the required task. For the task of recommending a stock id for each

customer with the RBM model helps in yielding a model with significantly higher accuracy.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

17

Multi-task Learning is just used to improve the efficiency of learning through RBM which will

give better predictions based on user and items it had purchased. This helps our model to

recommend a stock code for the individual users based on the product they have already

purchased. We have achieved a recommendation score of 94.4% to the customers with RBM

method.

Keywords: Deep Learning; Machine Learning; Multi-task Learning; Recommender Systems;

Restricted Boltzmann Machine

Rohit Kumar Kaliyar, Ph.D. Scholar, Department of CSE

Kaliyar, Rohit Kumar (2020). DeepNet: An efficient neural network for fake news detection

using news-user engagements. Proceedings of the 2020 International Conference on

Computing, Communication and Security, ICCCS 2020. 10.1109/ICCCS49678.2020.9277353

Abstract: The rise of social media allows every user to share and immediately publish their

views. Today, the problem of fake news has obtained significant consideration among

researchers due to its harmful nature to deceive the people of the society. It has created an

alarming situation in the world. News ecosystem evolved from a small set of trusted and

regulated sources to numerous online news sources. Fake news has an adverse impact on

society as it may manipulate public opinions. Thus, it is essential to investigate the credibility

of news articles shared on social media outlets. In this paper, we have designed an effective

deep neural network that is capable of handling not only the content of the news article but also

the user-relationships in the social network. We have designed our proposed approach using

tensor factorization method. A tensor expresses the social context of news articles formed by a

combination of the news, user, and user-group information. Our proposed method (DeepNet)

has validated on a real-world fake news dataset: BuzzFeed and Fakeddit. DeepNet outperforms

from existing fake news detection methods by employing deep architecture with different

kernelsizes convolutional layers combining news content and social context-based features.

Keywords: Factorization Method; Fake News; Social Media; Tensor; User-relationships

Kaliyar, Rohit Kumar (2020). RUEvaL20: Improving Rumour Detection on Social Media

using a Deep Convolutional Neural Network. ACM International Conference Proceeding

Series, 439. 10.1145/3430984.3431070

Abstract: Due to the tremendous increase in the activity of social media, there has been a

considerable drift in the propagation of rumours causing damage to several organizations or

personal. Since last few years, a significant exploration has been done due to hazardous effects

of rumours on society. In this paper, we propose a deep neural network (RUEvaL20) for

effective rumour detection. Experiments have been conducted with real-world dataset:

PHEME. We have achieved remarkable results, and our proposed model outperformed with

most of the existing state-of-the-art methods.

Keywords: RUEvaL20; Social Media; PHEME

Kaliyar, Rohit Kumar (2020). A multi-layer bidirectional transformer encoder for pre-trained

word embedding: A survey of BERT. Proceedings of the Confluence 2020 - 10th International

Conference on Cloud Computing, Data Science and Engineering, 336-340.

10.1109/Confluence47617.2020.9058044

Abstract: Language modeling is the task of assigning a probability distribution over sequences

of words that matches the distribution of a language. A language model is required to represent

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

18

the text to a form understandable from the machine point of view. A language model is capable

to predict the probability of a word occurring in the context-related text. Although it sounds

formidable, in the existing research, most of the language models are based on unidirectional

training. In this paper, we have investigated a bi-directional training model-BERT

(Bidirectional Encoder Representations from Transformers). BERT builds on top of the

bidirectional idea as compared to other word embedding models (like Elmo). It practices the

comparatively new transformer encoder-based architecture to compute word embedding. In

this paper, it has been described that how this model is to be producing or achieving state-of-

the-art results on various NLP tasks. BERT has the capability to train the model in bi-

directional over a large corpus. All the existing methods are based on unidirectional training

(either the left or the right). This bi-directionality of the language model helps to obtain better

results in the context-related classification tasks in which the word(s) was used as input vectors.

Additionally, BERT is outlined to do multi-task learning using context-related datasets. It can

perform different NLP tasks simultaneously. This survey focuses on the detailed representation

of the BERT-based technique for word embedding, its architecture, and the importance of this

model for pre-training purposes using a large corpus.

Keywords: BERT; Bidirectional Encoder; Language modeling; NLP

Kaliyar, Rohit Kumar., Tiwari, Smita, Ahuja, Nisha & Agrawal, Mohit (2020). Affects in

tweets with real time emotions using deep learning techniques: A novel approach. Proceedings

of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science

and Engineering, 17-21.10.1109/Confluence47617.2020.9057913

Abstract: Twitter is an online microblogging tool that has 400 million messages per day.

SemEval-2018 Tasks have already been presented and explored in the previous years by the

name of 'Affect in Tweets' but the scope for improvement never ends. So, in this research paper,

we come up with deep learning architecture which is extremely coherent for the given task of

extracting emotion intensity and classes from tweets (description of the task is given on

www.codalab.com for details). Deep learning models are productive due to their automatic

learning capability and automatic feature extraction. This research paper highlights the

implementation of deep learning-based models such as convolutional neural networks and

LSTM for classifications. The implemented tasks are-:1. emotion intensity regression 2.

Emotion intensity ordinal classification, 3. Multilabel emotion classification. We have

expressed that the fine-grained intensity scores that we have obtained are reliable. Our dataset

is beneficial for testing supervised machine learning algorithms for multi-label classification,

intensity regression, sleuthing ordinal category of intensity of feeling (low, moderate, etc.). We

have implemented various machine learning and deep learning-based models and achieved an

accuracy of 77.64% in E-oc (Emotion ordinal classification) task, which is the highest among

all competitors.

Keywords: Classification methods; Deep Learning; Machine Learning; Twitter

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

19

Dr. Shakti Sharma, Assistant Professor, Department of CSE

Sharma, Shakti (2020). Penalty Driven Training Sample Refinement Technique for

Hyperspectral Images Classification Using Ant Colony Optimization. International Geoscience

and Remote Sensing Symposium (IGARSS), 6953-6956.

10.1109/IGARSS39084.2020.9324307

Abstract: Training samples play an important role in learning of a classifier. In order to achieve

better spectral information hyperspectral imaging sensors capture reflectance in smaller

bandwidth than multispectral sensors. Smaller bandwidth causes lower Signal to Noise Ratio

(SNR). Therefore, spatial resolution is kept low in hyperspectral images to improve SNR, but

some of the pixels can still be erroneous. If selected in training data, these pixels can cause

faulty training which leads to the misclassification. Ant Colony Optimization (ACO) is used to

remove erroneous training samples to achieve better accuracy of classification. Available

literature suggest reward based methods for selecting more accurate pixels. These methods are

not useful if training sample size is already small, as very limited pixels remain available for

validation which reduces the efficiency of reward based techniques. In this article, cases with

limited number of training samples have been considered for experiments. Results show an

improvement of 5-7% in accuracy when reducing the training sample size to 10-30%.

Keywords: ACO; Classification; Hyperspectral; Training

Dr. Shivani Goel, Professor, Department of CSE

Goel, Shivani., Acharya, Divya & Bhardwaj, Arpit (2020). A Long Short Term Memory

Deep Learning Network for the Classification of Negative Emotions Using EEG Signals. 2020

International Joint Conference on Neural Networks (IJCNN), (pp. 1-8).

10.1109/IJCNN48605.2020.9207280.

Abstract: In cognitive science and human-computer interaction, automatic human emotion

recognition using physiological stimuli is a key technology. This research considers

classification of negative emotions using EEG signals in response to emotional clips. This

paper introduces a long short term memory deep learning (LSTM) network to recognize

emotions using EEG signals. The primary goal of this approach is to assess the classification

performance of the LSTM model for classifying emotions. The secondary goal is to assess the

human behavior of different age groups and genders. We have compared the performance of

Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM),

Deep Belief Network based SVM (DBN-SVM), and LSTM based deep learning model for

classification of negative emotions using brain signals. The analysis shows that for four class

of negative emotion recognition LSTM based deep learning model provides classification

accuracy as 81.63%, 84.64%, 89.73%, and 92.84% for 50-50, 60-40, 70-30, and 10-fold cross

validation. Generalizability and reliability of this approach is evaluated by applying our

approach to publicly available EEG dataset DEAP and SEED. In compliance with the self

reported feelings, brain signals of 26-35 years of age group provided the highest emotional

identification. Among genders, females are more emotionally active as compared to males.

Keywords: Emotion Recognition; Deep learning; EEG; FastFourier Transformation; LSTM

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

20

Goel, Shivani., Bhardwaj, Manika & Sharma, Pankaj (2020). Web Based Classification for

safer browsing. International Conference on Computational Methods and Data Engineering,

2020 (pp. 105-115).

Abstract: In the cyber world, attack (phishing) is a big problem and it is clear from the reports

produced by the Anti-Phishing Working Group (APWG) every year. Phishing is a crime that

can be committed over the internet by the attacker to fetches the personal credentials of a user

by asking their details like login/credit or debit card credentials, etc. for financial gains. Since

1996 phishing is a well-known threat and internet crime. Research community is still working

on phishing detection and prevention. There is no such model/solution existed that can prevent

this threat completely. One useful practice is to make the users aware of possible phishing

attacking sites. The other is to detect the phishing site. The objective of this paper is to analyze

and study existing solutions for phishing detection. The proposed technique uses logistic

regression to correctly classify whether the given site is malicious or not.

Keywords: Phishing; Cybercrime; Logistic regression; TF-IDF.

Goel, Shivani., Tiwari, Smita & Bhardwaj, Arpit (2020). Machine Learning approach for

the classification of EEG signals of multiple imagery tasks. 2020 11th International Conference

on Computing, Communication and Networking Technologies, ICCCNT 2020.

10.1109/ICCCNT49239.2020.9225291

Abstract: Electroencephalogram (EEG) signals can be used to capture the electrical pattern

generated on the surface of the human brain. The electrical activity in terms of EEG signals

occurs in the brain as a result of any type of emotions, behavior, thoughts that occur in the

human brain. These EEG signals can be utilized as an input to the Brain-Computer Interface

(BCI) application. The proper feature extraction and classification of the extracted features are

a very important step for BCI applications. In this paper, the collection and recording of EEG

signals are done corresponding to eight imagery tasks. The extraction of the features is done in

the form of absolute band powers, based on the logarithm of the Power Spectral Density (PSD)

of the EEG data for each channel. The selected features are used for the classification and

analysis of the captured EEG signals corresponding to the eight cognitive imagery tasks such

as 'forward', 'backward', 'left', 'right', 'hungry', 'food', 'water', and 'sleep'. The selected features

are then used to train the Machine Learning (ML) models of Logistic Regression (LR), and

Quadratic Discriminant Analysis (QDA). The classifiers are analyzed and investigated on

several evaluation metrics such as the average accuracy of the classifiers, accuracy of each

imagery tasks, precision, and recall. The average accuracy achieved by the classifier is 72.5%,

and 74.7% for LR, and QDA respectively.

Keywords: Artificial Intelligence; Classification; EEG Device; EEG Signals; Imagery Tasks;

Machine Learning

Dr. Sridhar Swaminathan, Assistant Professor, Department of CSE

Swaminathan, Sridhar (2020). Multimodal Emotion Recognition in Polish (Student

Consortium). Proceedings - 2020 IEEE 6th International Conference on Multimedia Big Data,

BigMM 2020, 307-311. 10.1109/BigMM50055.2020.00054

Abstract: Multimodal emotion recognition is a challenging task because emotions can be

expressed through various forms and modalities. It can be applied in various fields, for

example, human-computer interaction, crime, healthcare, multimedia retrieval, etc. In recent

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

21

times, neural networks have achieved overwhelming success in determining emotional states.

Motivated by these advancements, we present a multimodal emotion recognition system that

uses the following modalities: facial expression, speech, and body language. This paper

specifies techniques used for the Emotion Recognition on Polish problem based Polish

Emotion dataset. To recognise the current state of emotion in the various given videos,

preprocessing of data and extraction of robust features is employed. For the given task, we have

made use of facial landmark detection, and Mel-frequency cepstral coefficients (MFCCs) for

speech audio. The presented data was in the format of variable length videos, subsequently,

which led to underperformance by traditional algorithms for classification. Thus, they could

not be used. Therefore, we implemented several long short-term memory (LSTM) networks.

Each particular modality was trained for its specific LSTM model, in order to return the

emotion having the highest probability at a given instance. Finally, each individual model is

fused together using a weight average approach, where in context to all the modalities, the

emotion having highest probability is the desirable emotion.

Keywords: Artificial neural networks; Facial landmark detection; Kinect body capture; Long

short-term memory networks; MFCC; Recurrent neural networks

Swaminathan, Sridhar (2020). HRS-TECHIE@Dravidian-CodeMix and HASOC-

FIRE2020: Sentiment analysis and hate speech identification using machine learning, deep

learning and ensemble models. CEUR Workshop Proceedings, 2826, 241-252.

Abstract: In this paper, we (HRS-TECHIE) present our submissions to challenges Dravidian-

CodeMix and HASOC at FIRE 2020. Classification of sentiments from social media posts and

comments is essential in this modern digital era. Dravidian-CodeMix (Sentiment analysis for

Dravidian Languages in Code-Mixed Text) at FIRE 2020 is a challenge for classification of

sentiments of YouTube comments posted in mix of Tamil-English (Task 1) and Malayalam-

English (Task 2) languages. Our chosen task is to classify YouTube comments written in

Tamil-English into one of five types of sentiment classes. Identification of hate speech,

offensive and profane contents from social media posts and comments is essential in this

modern digital era for preventing individuals in the digital media from the cyber harassment.

HASOC 2020 (Hate Speech and Offensive Content Identification in Multiple Languages) at

FIRE 2020 is a challenge of identifying the bullying content from Twitter comments posted in

English, German and Hindi (subtask A) languages and further classifying the type of bullying

present in that comment for each language (subtask B). We worked on both subtasks A and B

for the English language to identify the bullying comment and type of bullying from Twitter

comments. As part of these two challenges, we submitted different state-of-the-art machine

learning and deep learning models for text classification. The models trained for sentiment

classification task in Dravidian-CodeMix are Naïve Bayes, Decision tree, Random Forest,

AdaBoost and Long Short Term Memory (LSTM). The models trained for hate speech and

offensive content identification are Naïve Bayes, SVM, Decision tree, Random Forest, Long

Short Term Memory (LSTM) and Gated Recurrent Unit (GRU).

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

22

We have also developed an ensemble of Machine Learning classifiers for both challenges. In

Dravidian-CodeMix, we have achieved the best weighted F1-score 61% for both Naïve Bayes

and LSTM models where weighted average F1-score of 60% was achieved for ensemble

approach. In HASOC, we have achieved the best Macro average F1-score of 50.02% from

LSTM model for subtask A and Macro average F1-score of 24.26% from ensemble approach

for subtask B on the private test data.

Keywords: AdaBoost; Cyber Bullying; Decision tree; Ensemble Learning; GRU; LSTM;

Naïve Bayes; Random forest; Sentiment Analysis; TF-IDF; Word Embedding.

Dr. Suneet Kumar Gupta, Assistant Professor, Department of CSE

Gupta, Suneet Kumar (2020). EEPMS: Energy Efficient Path planning for Mobile Sink in

Wireless Sensor Networks: A Genetic Algorithm based approach. The 3rd International

Conference on Electrical Communication and Information Technologies (ICECIT) (pp. 1-8).

doi:10.1007/978-981-15-1275-9_9

Abstract: The area of wireless sensor networks (WSNs) has been highly explored due to its

vast application in various domains. The main constraint of WSN is the energy of the sensor

nodes. The use of mobile sink (MS) is one of the prominent methods to preserve the energy of

sensor nodes. Moreover, use of mobile sink is also solving the hot-spot problem of wireless

sensor network. In the paper, we propose a genetic algorithm-based approach to plan the path

for mobile sink. All the basic intermediate operations of genetic algorithms, i.e., chromosome

representation, crossover and mutation are well explained with suitable examples. The

proposed algorithm is shown its efficacy over the randomly generated path.

Keywords: Mobile sink; Energy efficacy; Wireless sensor network; Path planning; Data

gathering.

Gupta, Suneet Kumar (2020). A Robust Copy Move Forgery Classification Using End to End

Convolution Neural Network. ICRITO 2020 - IEEE 8th International Conference on

Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 253-258.

10.1109/ICRITO48877.2020.9197955

Abstract: In this digital world it is not surprising to do manipulation with digital images. With

advantage of such technologies it has become very easy to misguide the observer about the

reality appearing in the images. The objective behind such manipulation for fun and

entertainment is acceptable but when such things are applied on sensitive information such as

evidences used in judiciary system, to prove certain claim, using such manipulated images on

social media becomes dangerous. Although many types of forgeries that could be performed

with images such as copying certain part of image then pasting it in same image somewhere

else in document with such precision that it appears normal to observer called copy move

forgery. Other forgeries include splicing of images, image morphing, retouching etc. Two

different categories of approaches are being used till recently for this problem of copy-move

forgery detection. These are block based and Keypoint based approach wherein block based is

computationally intensive and suffers from many disadvantages and other is based interest

points or high textured areas whose features vectors are formed for comparison to find the

duplicated regions. In this paper, a deep neural network based approach has been proposed

with promising results that can classify images based whether any copy move forgery has been

there in the images. The proposed work aims to classify all the images having copy move

forgery with presence of scaling, rotation, different compression level. A new CNN model has

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

23

been researched for this problem to obtain the accuracy of around 93-95 percent for different

datasets alone as well on the combination of two or more datasets.

Keywords: Blockbased; CMFD; Convolutional neural network; Forgery detection; Keypoint

based; Tempering

Gupta, Suneet Kumar & Agarwal, Mohit (2020). Potato Crop Disease Classification Using

Convolutional Neural Network. In: Somani A., Shekhawat R., Mundra A., Srivastava S.,

Verma V. (Eds), Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems

and Technologies, vol 141. (pp. 391-400). doi.org/10.1007/978-981-13-8406-6_37

Abstract: Potato is one of the most cultivated and in-demand crops after rice and wheat. Potato

farming dominates as an occupation in the agriculture domain in more than 125 countries.

However, even these crops are, subjected to infections and diseases, mostly categorized into

two grades: (i) Early blight and (ii) Late blight. Moreover, these diseases lead to damage the

crop and decreases its production. In this paper, we propose a deep learning-based approach to

detect the early and late blight diseases in potato by analyzing the visual interpretation of the

leaf of several potato crops. The experimental results demonstrate the efficiency of the

proposed model even under adverse situations such as variable backgrounds, varying image

sizes, spatial differentiation, a high-frequency variation of grades of illumination, and real

scene images. In the proposed Convolution Neural Network Architecture (CNN), there are four

convolution layers with 32, 16, and 8 filters in each respective layer. The training accuracy of

the proposed model is obtained to be 99.47% and testing accuracy is 99.8%.

Keywords: Convolution; Deep neural network; Leaf disease; Potato leaf.

Gupta, Suneet Kumar & Agarwal, Mohit (2020). Convoluted Cosmos: Classifying Galaxy

Images Using Deep Learning. In: Sharma N., Chakrabarti A., Balas V. (Eds), Data

Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, 1042

(pp. 569-579). 10.1007/978-981-32-9949-8_40.

Abstract: In this paper, a deep learning-based approach has been developed to classify the

images of galaxies into three major categories, namely, elliptical, spiral, and irregular. The

classifier successfully classified the images with an accuracy of 97.3958%, which

outperformed conventional classifiers like Support Vector Machine and Naive Bayes. The

convolutional neural network architecture involves one input convolution layer having 16

filters, followed by 4 hidden layers, 1 penultimate dense layer, and an output Softmax layer.

The model was trained on 4614 images for 200 epochs using NVIDIA-DGX-1 Tesla-V100

Supercomputer machine and was subsequently tested on new images to evaluate its robustness

and accuracy.

Keywords: Convolution neural network (CNN); Softmax; Dropout; Galaxy type.

Dr. Tanmay Bhowmik, Assistant Professor, Department of CSE

Bhowmik, Tanmay (2020). Efficient Human Feature Recognition Process Using Sclera.

Communications in Computer and Information Science, 1241 CCIS, 168-181. 10.1007/978-

981-15-6318-8_15

Abstract: This paper proposes a biometric system that works on a new promising idea of sclera

recognition. Sclera is the white part which is evenly present in the human eye with a unique

blood vessel pattern. Therefore, it can be used as a new biometric feature or human ID. In this

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

24

paper different methods are proposed which will help in making a sclera-based biometric

system. Firstly, the captured image is undergone image segmentation for further analysis.

Secondly, these images are converted into grayscale for complexity reduction and then

Gaussian blur and median blur will be applied to reduce the high-frequency components of the

image. Thirdly, Otsu’s method and adaptive thresholding are applied to the grayscale images

for thresholding. The images obtained after applying thresholding methods are superimposed

for better image. After this, CNN is used for classification of the dataset into sclera or not. The

proposed model achieved 86.84% training accuracy as well as 85.27% testing accuracy.

Keywords: Biometric; Blood vessels; Sclera; Segmentation

Dr. Vipul Kumar Mishra, Assistant Professor, Department of CSE

Mishra, Vipul Kumar (2020). Securing IP Cores in CE Systems using Key-driven Hash-

chaining based Steganography. IEEE International Conference on Consumer Electronics -

Berlin, ICCE-Berlin, 10.1109/ICCE-Berlin50680.2020.9352192

Abstract: Digital signal processor (DSP) intellectual property (IP) cores are the underlying

hardware responsible for high performance data intensive applications. However an

unauthorized IP vendor may counterfeit the DSP IPs and infuse them into the design-chain.

Thus fake IPs or integrated circuits (ICs) are unknowingly integrated into consumer electronics

(CE) systems, leading to reliability and safety issues for users. The latent solution to this threat

is hardware steganography wherein vendor's secret information is covertly inserted into the

design to enable detection of counterfeiting. A key-regulated hash-modules chaining based IP

steganography is presented in our paper to secure against counterfeiting threat. The proposed

approach yielded a robust steganography achieving very high security with regard to stego-key

length than previous approaches.

Keywords: Counterfeiting; DSP; Hardware steganography

Mishra, Vipul Kumar., Choudhary, Tejalal & Goswami, Anurag (2020). DualPrune: A

dual purpose pruning of convolutional neural networks for resource-constrained devices. 10th

IACC 2020 Conference (pp. 395-406).

Abstract: Many successful applications of deep learning have been witnessed in various

domains. However, the use of deep learning models in edge-devices is still limited. Deploying

a large model onto small devices for real-time inference requires an adequate amount of

resources. In the last couple of years, pruning has evolved as an important and widely used

technique to reduce the inference cost and compress the storage-intensive deep learning models

for small devices. In this paper, we proposed a novel dual-purpose pruning approach to

accelerate the model performance and reduce the storage requirement of the model. The

experiments on the CIFAR10 dataset with AlexNet and VGG16 models show that our proposed

approach is effective and can be used to make the deployment of the trained model easier for

edge-devices with marginal loss of accuracy. For the VGG16 experiment, our approach reduces

parameters from 14.98M to 3.7M resulting in a 74.73% reduction in floating-point operations

with only 0.8% loss in the accuracy.

Keywords: Neural Networks; Deep Learning; AlexNet and VGG16.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

25

Journal Papers

Dr. Anurag Goswami Assistant Professor, Department of CSE

Goswami, Anurag & Kaliyar, Rohit Kumar (2020). DeepFakE- Improving Fake News

Detection using Tensor Decomposition-based Deep Neural Network. The Journal of

Supercomputing, 77, 1015-1037. doi:10.1007/s11227-020-03294-y

Abstract: Social media platforms have simplified the sharing of information, which includes

news as well, as compared to traditional ways. The ease of access and sharing the data with the

revolution in mobile technology has led to the proliferation of fake news. Fake news has the

potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to

examine the credibility and authenticity of the news articles being shared on social media.

Nowadays, the problem of fake news has gained massive attention from research communities

and needed an optimal solution with high efficiency and low efficacy. Existing detection

methods are based on either news-content or social-context using user-based features as an

individual. In this paper, the content of the news article and the existence of echo chambers

(community of social media-based users sharing the same opinions) in the social network are

taken into account for fake news detection. A tensor representing social context (correlation

between user profiles on social media and news articles) is formed by combining the news,

user and community information. The news content is fused with the tensor and coupled

matrix-tensor factorization is employed to get a representation of both news content and social

context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors

obtained after decomposition have been used as features for news classification. An ensemble

machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are

employed for the task of classification. Our proposed model (DeepFakE) outperforms with the

existing fake news detection methods by applying deep learning on combined news content

and social context-based features as an echo-chamber.

Keywords: Social media; Fake news; Deep learning; Echo chamber; Tensor factorization.

Goswami, Anurag & Kaliyar, Rohit Kumar (2020). FNDNet – A deep convolutional neural

network for fake news detection. Cognitive Systems Research, 61, 32-44.

10.1016/j.cogsys.2019.12.005

Abstract: With the increasing popularity of social media and web-based forums, the

distribution of fake news has become a major threat to various sectors and agencies. This has

abated trust in the media, leaving readers in a state of perplexity. There exists an enormous

assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news

detection. In the past, much of the focus has been given on classifying online reviews and freely

accessible online social networking-based posts. In this work, we propose a deep convolutional

neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features,

our model (FNDNet) is designed to automatically learn the discriminatory features for fake

news classification through multiple hidden layers built in the deep neural network. We create

a deep Convolutional Neural Network (CNN) to extract several features at each layer. We

compare the performance of the proposed approach with several baseline models.

Benchmarked datasets were used to train and test the model, and the proposed model achieved

state-of-the-art results with an accuracy of 98.36% on the test data. Various performance

evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

26

accuracy, etc. were used to validate the results. These results demonstrate significant

improvements in the area of fake news detection as compared to existing state-of-the-art results

and affirm the potential of our approach for classifying fake news on social media. This

research will assist researchers in broadening the understanding of the applicability of CNN-

based deep models for fake news detection.

Keywords: Fake news; Social media; Machine learning; Deep learning; Neural network.

Dr. Arpit Bhardwaj, Assistant Professor, Department of CSE

Bhardwaj, Arpit., Kumar, Arvind (2020). A novel fitness function in genetic programming

for medical data classification. Journal of Biomedical Informatics, 112. (pp. 103623).

10.1016/j.jbi.2020.103623.

Abstract: In the last decade, machine learning (ML) techniques have been widely applied to

identify different diseases. This facilitates an early diagnosis and increases the chance of

survival. The majority of medical data-sets are unbalanced. Due to this, ML classification

techniques give biased classification over the majority class. In this paper, a novel fitness

function in Genetic Programming, for medical data classification has been proposed that

handles the problem of unbalanced data. Four benchmark medical data-sets named chronic

kidney disease(CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer

(WDBC) have been taken from the University of California (UCI) machine learning repository.

Classification is done using the proposed technique. The proposed technique achieved the best

accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%,99.12%, 85.0%, and 75.36%

respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes

show an improvement over other GP and SVM methods that confirm the efficiency of our

proposed algorithm.

Keywords: Medical data classification; Genetic Programming; Fitness function; Unbalanced

data classification.

Bhardwaj, Arpit., Devarriya, Divyaansh., Gulati, Cairo., & Mansharamani, Vidhi (2020).

Unbalanced breast cancer data classification using novel fitness functions in genetic

programming. Expert Systems with Applications, 140, 112866.

doi:10.1016/j.eswa.2019.112866

Abstract: Breast Cancer is a common disease and to prevent it, the disease must be identified

at earlier stages. Available breast cancer datasets are unbalanced in nature, i.e. there are more

instances of benign (non-cancerous) cases then malignant (cancerous) ones. Therefore, it is a

challenging task for most machine learning (ML) models to classify between benign and

malignant cases properly, even though they have high accuracy. Accuracy is not a good metric

to assess the results of ML models on breast cancer dataset because of biased results. To address

this issue, we use Genetic Programming (GP) and propose two fitness functions. First one is

F2 score which focuses on learning more about the minority class, which contains more

relevant information, the second one is a novel fitness function known as Distance score (D

score) which learns about both the classes by giving them equal importance and being unbiased.

The GP framework in which we implemented D score is named as D-score GP (DGP) and the

framework implemented with F2 score is named as F2GP. The proposed F2GP achieved a

maximum accuracy of 99.63%, 99.51% and 100% for 60-40, 70-30 partition schemes and 10

fold cross validation scheme respectively and DGP achieves a maximum accuracy of 99.63%,

98.5% and 100% in 60-40, 70-30 partition schemes and 10 fold cross validation scheme

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

27

respectively. The proposed models also achieves a recall of 100% for all the test cases. This

shows that using a new fitness function for unbalanced data classification improves the

performance of a classifier.

Keywords: Breast cancer; Unbalanced data; Genetic programming; Fitness function

Dr. Deepak Garg, Professor & HoD, Department of CSE

Garg, Deepak., Mishra, Balmukund & Mishra, Vipul Kumar (2020). A hybrid approach

for search and rescue using 3DCNN and PSO. Neural Computing & Applications, 1-15.

10.1007/s00521-020-05001-7

Abstract: Search and rescue are essential applications of disaster management in which people

are evacuated from the disaster-prone area to a safer place. This overall process of search and

rescue can be more efficient if an automated system can quickly locate the human or area where

rescue is required. To provide a faster and accurate search of those places, this paper proposes

a novel approach to search and rescue using automated drone surveillance. In this paper, a

complex scene classification problem is solved using the proposed 3DCNN model. The

proposed model uses spatial as well as temporal features of the video for the classification of

the scene as help or non-help in the natural disaster. Due to the unavailability of such kind of

dataset, it is impossible to train the model. Therefore, it is essential to develop a dataset for

search and rescue. The proposed dataset is a first and unique dataset for scene classification

using drone surveillance. The major contribution of this paper is (1) a novel 3DCNN powered

model for scene classification in drone surveillance, (2) to develop the required dataset for the

training of scene classification model, and (3) particular swarm optimization (PSO)-based

hyper-parameter tuning for getting the best value of multiple parameters used for training the

model. Our hybridization of parameter tuning with PSO helps for the convergence of parameter

values of proposed 3DCNN model, and the proposed scene classification model

(3DCNN+PSO) is applied to the dataset. The proposed model gives an impressive performance

to help situation identification with 98% training and 99% validation accuracy.

Keywords: Search and rescue; Unmanned aerial vehicle; Drone-based surveillance; Particle

swarm optimization (PSO); Action recognition; Convolution neural network.

Garg, Deepak., Mishra, Balmukund & Mishra, Vipul Kumar (2020). Drone-surveillance

for search and rescue in natural disaster. Computer Communications, 156, 1-10.

10.1016/j.comcom.2020.03.012

Abstract: Due to the increasing capability of drones and requirements to monitor remote areas,

drone surveillance is becoming popular. In case of natural disaster, it can scan the wide

affected-area quickly and make the search and rescue (SAR) faster to save more human lives.

However, using autonomous drone for search and rescue is least explored and require attention

of researchers to develop efficient algorithms in autonomous drone surveillance. To develop

an automated application using recent advancement of deep-learning, dataset is the key. For

this, a substantial amount of human detection and action detection dataset is required to train

the deep-learning models. As dataset of drone surveillance in SAR is not available in literature,

this paper proposes an image dataset for human action detection for SAR. Proposed dataset

contains 2000 unique images filtered from 75,000 images. It contains 30000 human instances

of different actions. Also, in this paper various experiments are conducted with proposed

dataset, publicly available dataset, and stat-of-the art detection method. Our experiments shows

that existing models are not adequate for critical applications such as SAR, and that motivates

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

28

us to propose a model which is inspired by the pyramidal feature extraction of SSD for human

detection and action recognition Proposed model achieves 0.98mAP when applied on proposed

dataset which is a significant contribution. In addition, proposed model achieve 7% higher

mAP value when applied to standard Okutama dataset in comparison with the state-of-the-art

detection models in literature.

Keywords: Drone surveillance; Convolution neural network (CNN); Object detection (OD);

Action recognition; Aerial action dataset.

Dr. Deepak Singh, Assistant Professor, Department of CSE

Singh, Deepak (2020). An evolutionary based technique to Characterize anamoly in Internet

of Things Networks. International Journal of Internet Technology and Secure Transactions, 1-

18.

Abstract: Internet of Things (IoT) can connect devices embedded in various systems to the

internet. Distributed denial-of-service (DDoS) attacks on the Internet of Things are position a

serious threat to any networks. A distributed denial-of-service attack is a malicious attempt to

interrupt normal traffic of a victim system, service or enterprises by formidable the victim or

its nearby infrastructure with Internet traffic overflow. Towards defending DoS attacks the

significant number of mechanisms in recent years have introduced for building intrusion

detection system (IDS) such as evolutionary algorithm and artificial intelligence.

Unfortunately, the modern well-known DDoS attack detection strategies have been failed to

rationalize the objective as fast and early detection of DDoS attack. In order to understand

different DDoS attacks activities, in this paper, Teaching Learning-based Optimization

(TLBO) with learning algorithm is integrated to mitigate denial of service attacks. The

approach is based on building an intrusion detection system to the requirements of the

monitored environment, called TLBOIDS. Furthermore, TLBOIDS selects the most relevant

features from original IDS dataset which can help to distinguish typical low-rate DDoS attacks

with the use classifiers namely support vector machine, decision tree, na

Keywords: Evolutionary Algorithm; Intrusion Detection; Denial-of-Service; IoT.

Singh, Deepak (2020). Realising transfer learning through convolutional neural network and

support vector machine for mental task classification. Electronics Letters, 56, 1375-1378.

10.1049/el.2020.2632

Abstract: Electroencephalogram (EEG) measures brainwaves that have been the widely used

modality for brain-computer interface (BCI) applications. EEG signal with machine learning

has gained substantial success in the BCI. However, the availability of limited training data,

appropriate model selection and high false-positive rates are the challenges that need

immediate attention. Therefore, in this Letter, the authors present a mental task classification

model based on the notion of transfer learning that addresses the issue of data scarcity, model

selection and misclassification ratio. In the framework, the proposed model uses pre-trained

network for the extraction of diverse feature and classify using support vector machine. The

authors employed four pre-trained networks to identify the optimal network for the proposed

framework: Vgg16, Vgg19, Resnet18 and Resnet50. The highest classification accuracy of

86.85% (using Resnet50) was achieved using transfer learning. Comparison results showed

that convolutional neural network-based approach outperformed conventional machine

learning approaches and hence it can be concluded that the EEG-based classification of the

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

29

mental task using transfer learning model could be used in developing a superior model despite

the limited data availability.

Keywords: EEG; Brain Computer Interface; Data Security

Dr. Dilbag Singh, Assistant Professor, Department of CSE

Singh, Dilbag (2020). Deep Q learning-based mitigation of man in the middle attack over

secure sockets layer websites. Modern Physics Letters B, 34-.10.1142/S0217984920503662

Abstract: To ensure the security of web applications and to reduce the constant risk of

increasing cybercrime, basic security principles like integrity, confidentiality and availability

should not be omitted. Even though Transport Layer Security/Secure Socket Layer (TLS/SSL)

authentication protocols are developed to shield websites from intruders, these protocols also

have their fair share of problems. Incorrect authentication process of websites can give birth to

notorious attack like Man in The Middle attack, which is widespread in HTTPS websites. In

MITM attack, the violator basically positions himself in a communication channel between

user and website either to eavesdrop or impersonate the communicating party to achieve

malicious goals. Initially, the MITM attack is defined as a binary machine learning problem.

Deep Q learning is utilized to build the MITM attack classification model. Thereafter, training

process is applied on 60% of the obtained dataset. Remaining 40% dataset is used for testing

purpose. The experimental results indicate that the proposed technique performs significantly

better than the existing machine learning technique-based MITM prediction techniques for

SSL/TLS-based websites.

Keywords: Attacks; Deep learning; Man in the middle; Reinforcement learning; Security;

SSL/TLS

Singh, Dilbag & Kaur, Manjit (2020). Rapid COVID-19 diagnosis using ensemble deep

transfer learning models from chest radiographic images. Journal of Ambient Intelligence and

Humanized Computing. 10.1007/s12652-020-02669-6

Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel

coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The

early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of

coronavirus infection from radiography images is the fastest method. In this paper, two

different ensemble deep transfer learning models have been designed for COVID-19 diagnosis

utilizing the chest X-rays. Both models have utilized pre-trained models for better performance.

They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both

models have been developed to improve the generalization capability of the classifier for binary

and multi-class problems. The proposed models have been tested on two well-known datasets.

Experimental results reveal that the proposed framework outperforms the existing techniques

in terms of sensitivity, specificity, and accuracy.

Keywords: Chest X-ray; COVID-19; Ensemble models; SARS-CoV-2; Transfer learning

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

30

Dr. Gaurav Singal, Assistant Professor, Department of CSE

Singal, Gaurav (2020). A texture feature based approach for person verification using

footprint bio-metric. Artificial Intelligence Review, 54, 1581–1611. 10.1007/s10462-020-

09887-6.

Abstract: Biometrics is the study of unique characteristics present in the human body such as

fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely,

only a few people have been considered the foot-palm region, despite having unique properties.

Prior work has explored the foot shape features using length, width, major axis, minor axis,

centroid, etc. but they are not reliable for personal verification due to similarity in the physical

composition of two persons. It increases the demand for more unique features based on the

footprint. Footprint texture features coming from creases of foot palm are unique and

permanent like palmprint texture features. Hence the main objective of the paper is to

investigate various kinds of texture feature techniques. These techniques will be further used

in correct extraction of footprint features. After extraction of footprint features a detailed

experimental analysis is performed to discover the uniqueness in foot texture. It is further

utilized to test its viability as a human recognition trait. We describe a detailed feature

extraction and classification technique applied to a collected footprint data-set. For feature

extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram

Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is

performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector

Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD).

GLCM provides less accuracy, while HOG generates a big feature vector which takes more

execution time. LBP provides a trade-off between the accuracy and the execution time.

Detailed quantitative experiments show: GLCM with LDA provides an accuracy of 88.5%,

HOG with Fine-KNN achieves 86.5% accuracy and LBP with LDA achieves the accuracy of

97.9%.

Keywords: Footprint biometrics; Texture feature; Newborn identification; Personal

recognition; Ensemble subspace discriminant.

Singal, Gaurav., Gupta, Surbhi., Badal, Tapas & Garg, Deepak (2020). Corridor

segmentation for automatic robot navigation in indoor environment using edge devices.

Computer Networks, 178. 10.1016/j.comnet.2020.107374

Abstract: Corridor segmentation is an essential component of the Indoor Navigation System

(INS) for robotic activity. It is required for navigation from one place to another in the indoor

environment for different applications such as autonomous corridor cleaning, robot-based

customer service in hotels, malls, and hospitals. Global Positioning System (GPS) does not

work in indoor as it is applicable only in the outdoor environment for navigation and existing

beacon-based systems require infrastructure support for mapping the floor plan. The robots

with edge devices are resource-constrained. Hence there is a need for memory,

computationally, and communicationally efficient solutions. The applications of a robot for

these specific task encourages us to solve the navigation problem by visual perception. Image

processing based solution cuts the communication cost however it is challenging to stack the

right set of operations in proper order for the segmentation. To address such a challenge, this

paper builds a right recipe that detects and segments the corridor by real-time image processing

and tackles the side obstacles using sensor input. The proposed region-based method using

Gray conversion, Canny edge detection, Morphological operations, can process frames at the

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

31

speed of 6 fps. Its effectiveness is verified by testing on varied real-world illumination scene

videos and reported significant precision. With this hardware cum software prototype, an

average precision score of 0.82 has been achieved.

Keywords: Computer vision; Freespace detection; Indoor navigation; Lane detection; Path;

Robot

Singal, Gaurav & Badal, Tapas (2020). Coinnet: platform independent application to

recognize Indian currency notes using deep learning techniques. Multimedia Tools and

Applications, 79(31-32), 22569-22594. 10.1007/s11042-020-09031-0

Abstract: In India, nearly 12 million visually impaired people had difficulty in identifying the

currency notes. There is a need to develop an application that can recognize the currency note

and provide a vocal message. In this paper, a novel lightweight Convolutional Neural Network

(CNN) model is developed for efficient web and mobile applications to recognize the Indian

currency notes. A new dataset for Indian currency notes has been created to train, validate, and

test the CNN model. This CNN based web and mobile applications will provide a text and

audio output based on the recognized currency note. The proposed model is developed using

TensorFlow and improved by selection of optimal hyperparameter value and compared with

existing well known CNN architectures using transfer learning. Based on the results it has been

observed that proposed model perform well over six widely used existing architectures in terms

of training and testing accuracy.

Keywords: Deep learning; Convolutional neural network; Indian currency note; Web

application; Mobile application.

Dr. Hiren Kumar Thakkar, Assistant Professor, Department of CSE

Thakkar, Hiren Kumar (2020). MUVINE: Multi-Stage Virtual Network Embedding in

Cloud Data Centers using Reinforcement Learning Based Predictions. IEEE Journal on

Selected Areas in Communications, 38(6), 1058-1074. 10.1109/JSAC.2020.2986663.

Abstract: The recent advances in virtualization technology have enabled the sharing of

computing and networking resources of cloud data centers among multiple users. Virtual

Network Embedding (VNE) is highly important and is an integral part of the cloud resource

management. The lack of historical knowledge on cloud functioning and inability to foresee

the future resource demand are two fundamental shortcomings of the traditional VNE

approaches. The consequence of those shortcomings is the inefficient embedding of virtual

resources on Substrate Nodes (SNs). On the contrary, application of Artificial Intelligence (AI)

in VNE is still in the premature stage and needs further investigation. Considering the

underlying complexity of VNE that includes numerous parameters, intelligent solutions are

required to utilize the cloud resources efficiently via careful selection of appropriate SNs for

the VNE. In this paper, Reinforcement Learning based prediction model is designed for the

efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers.

The proposed MUVINE scheme is extensively simulated and evaluated against the recent state-

of-the-art schemes. The simulation outcomes show that the proposed MUVINE scheme

consistently outperforms over the existing schemes and provides the promising results.

Keywords: Virtual network embedding; Cloud computing; Artificial intelligence;

Reinforcement learning; Virtual resource allocation.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

32

Thakkar, Hiren Kumar (2020). Towards Automatic and Fast Annotation of

Seismocardiogram Signals Using Machine Learning. IEEE Sensors Journal, 20(5), 2578-2589.

10.1109/JSEN.2019.2951068.

Abstract: The automatic annotation of Seismocardiogram (SCG) potentially aid to estimate

various cardiac health parameters continuously. However, the inter-subject variability of SCG

poses great difficulties to automate its accurate annotation. The objective of the research is to

design SCG peak retrieval methods on the top of the ensemble features extracted from the SCG

morphology for the automatic annotation of SCG signals. The annotation scheme is formulated

as a binary classification problem. Three binary classifiers such as Naïve Bayes (NB), Support

Vector Machine (SVM), and Logistic Regression (LR) are employed for the annotation and the

results are compared with the recent state-of-the-art schemes. The performance evaluation is

carried out using 9000 SCG signals of 20 presumably healthy volunteers with no known serious

cardiac abnormalities. The SCG signals are acquired from the Physionet public repository

“cebsdb”. The models are rigorously validated using metrics “Precision”, “Recall”, and “F-

measure” followed by 5-fold cross-validation. The experimental validation with recent state-

of-the-art solutions establishes the robustness of the proposed NB, SVM and LR with average

annotation accuracy of 0.86, 0.925 and 0.935, respectively. The mean response time of

proposed models is in the fraction of 1/10 sec, which establishes its application for the real-

time annotation.

Keywords: Seismocardiogram (SCG); Automatic annotation; Cardiac health parameters

(CHPs); Non-invasive; Machine learning.

Thakkar, Hiren Kumar (2020). Predicting clinically significant motor function improvement

after contemporary task-oriented interventions using machine learning approaches. Journal of

NeuroEngineering and Rehabilitation, 17. 10.1186/s12984-020-00758-3

Abstract: Background: Accurate prediction of motor recovery after stroke is critical for

treatment decisions and planning. Machine learning has been proposed to be a promising

technique for outcome prediction because of its high accuracy and ability to process large

volumes of data. It has been used to predict acute stroke recovery; however, whether machine

learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for

common contemporary task-oriented interventions remains largely unexplored. This study

aimed to determine the accuracy and performance of machine learning to predict clinically

significant motor function improvements after contemporary task-oriented intervention in

chronic stroke patients and identify important predictors for building machine learning

prediction models. Methods: This study was a secondary analysis of data using two common

machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural

network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training

including the constraint-induced movement therapy, bilateral arm training, robot-assisted

therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the

main outcome. Potential predictors include age, gender, side of lesion, time since stroke,

baseline functional status, motor function and quality of life. We divided the data set into a

training set and a test set and used the cross-validation procedure to construct machine learning

models based on the training set. After the models were built, we used the test data set to

evaluate the accuracy and prediction performance of the models. Results: Three important

predictors were identified, which were time since stroke, baseline functional independence

measure (FIM) and baseline FMA scores. Models for predicting motor function improvements

were accurate. The prediction accuracy of the KNN model was 85.42% and area under the

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

33

receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the

ANN model was 81.25% and the AUC-ROC was 0.77. Conclusions: Incorporating machine

learning into clinical outcome prediction using three key predictors including time since stroke,

baseline functional and motor ability may help clinicians/therapists to identify patients that are

most likely to benefit from contemporary task-oriented interventions. The KNN and ANN

models may be potentially useful for predicting clinically significant motor recovery in chronic

stroke.

Keywords: Machine learning; Motor function; Prognosis; Rehabilitation; Stroke

Dr. Indrajeet Gupta, Assistant Professor, Department of CSE

Gupta, Indrajeet (2020). An Energy Efficient Algorithm for Workflow Scheduling in IaaS

Cloud. Journal of Grid Computing, 18, 357-376. 10.1007/s10723-019-09490-2

Abstract: Energy efficient workflow scheduling is the demand of the present time’s computing

platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy

can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is

important to decrease makespan of a schedule as well, so that it may not extend beyond the

deadline specified by the cloud user. In this paper, we propose a workflow scheduling

algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm.

The proposed scheme is shown to be energy efficient. Apart from this, it is also shown to

minimize makespan. We refer the proposed approach as energy efficient workflow scheduling

(EEWS) algorithm. The EEWS is introduced with a novel measure to determine the amount of

energy which can be conserved by considering a DVS-enabled environment. Through

simulations on a variety of scientific workflow applications, we demonstrate that the proposed

scheme performs better than the existing algorithms such as HCRO and multiple priority

queues genetic algorithm (MPQGA) in terms of various performance metrics including

makespan and the amount of energy conserved. The significance of the proposed algorithm is

also judged through the analysis of variance (ANOVA) test and its subsequent LSD analysis.

Keywords: Chemical reaction optimization; Cloud; Energy conservation; Makespan;

Workflow scheduling

Dr. Madhushi Verma, Assistant Professor, Department of CSE

Verma, Madhushi, Manjari, Kanak & Singal, Gaurav (2020). A survey on Assistive

Technology for visually impaired. Internet of Things, 11, 1-20. 10.1016/j.iot.2020.100188.

Abstract: One-Sixth of the world population is suffering from vision impairment, as per

WHO’s report. In the past decades, many efforts have been done in developing several devices

to provide support to visually blind and enhance the quality of their lives by making them

capable. Many of those devices are either heavy or costly for general purposes. In this paper, a

detailed comparative study of all the relevant devices developed for them has been presented

which are wearable and handheld. The focus was on the prominent features of those devices

and analysis has also been performed based on few factors like power consumption, weight,

economic and user-friendliness. The idea was to build a path for the researchers who are trying

to work in this field by either developing a portable device or through some efficient algorithm

to ensure independence, mobility, and safety for visually impaired persons.

Keywords: Visually blind; Blind person; Assistive technology.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

34

Dr. Manjit Kaur, Assistant Professor, Department of CSE

Kaur, Manjit & Singh, Dilbag (2020). Multi objective evolutionary optimization techniques

based hyperchaotic map and their applications in image encryption. Multidimensional Systems

and Signal Processing, 281-301. 10.1007/s11045-020-00739-8

Abstract: Chaotic-based image encryption approaches have attracted great attention in the

field of information security. The properties of chaotic maps such as randomness and

sensitivity have given new ways to develop efficient encryption approaches. But chaotic maps

require initial parameters to develop random sequences. The selection of these parameters is a

tedious task. To obtain the optimal initial parameters, evolutionary optimization approaches

have been utilized in image encryption. Therefore, in this paper, a hyper-chaotic map is

optimized using a multi objective evolutionary optimization approach. A dual local search

based multi objective optimization (DLS-MO) is used to obtain the optimal parameters of a

hyper-chaotic map and encryption factors. Then, using optimal parameters, a hyper-chaotic

map develops the secret keys. These secret keys are then used to perform permutation and

diffusion on a plain image to develop the encrypted image. To perform encryption,

permutation–permutation–diffusion–diffusion architecture is adopted for better confusion and

diffusion. Experimental results show that the proposed approach provides better performance

in comparison to existing competitive approaches.

Keywords: Image encryption; DLS-MO; Hyper-chaotic map; Parameter tuning.

Kaur, Manjit & Singh, Dilbag (2020). Multi-modality medical image fusion technique using

multi-objective differential evolution based deep neural networks. Journal of Ambient

Intelligence and Humanized Computing, 2483-2493. 10.1007/s12652-020-02386-0

Abstract: The advancements in automated diagnostic tools allow researchers to obtain more

and more information from medical images. Recently, to obtain more informative medical

images, multi-modality images have been used. These images have significantly more

information as compared to traditional medical images. However, the construction of multi-

modality images is not an easy task. The proposed approach, initially, decomposes the image

into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an

extreme version of the Inception (Xception) is used for feature extraction of the source images.

The multi-objective differential evolution is used to select the optimal features. Thereafter, the

coefficient of determination and the energy loss based fusion functions are used to obtain the

fused coefficients. Finally, the fused image is computed by applying the inverse NSCT.

Extensive experimental results show that the proposed approach outperforms the competitive

multi-modality image fusion approaches.

Keywords: Fusion Diagnosis; CNN; Multi-modality; Differential evolution.

Kaur, Manjit & Singh, Dilbag (2020). Color image encryption using minimax differential

evolution-based 7D hyper-chaotic map. Applied Physics B: Lasers and Optics, 126(9).

10.1007/s00340-020-07480-x

Abstract: Hyperchaotic maps are generally used in the encryption to generate the secret keys.

The number of hyperchaotic maps has been implemented so far. These maps involve a large

number of state and control parameters. The major concern is the estimation of these

parameters. Because the estimation requires extensive computational search. In this paper, a

7D hyperchaotic map is used to produce the secret keys for image encryption. As this

hyperchaotic map require a large number of initial parameters, the manual estimation is

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

35

computationally extensive. Therefore, minimax differential evolution is utilized to provide the

optimal parameters to the hyperchaotic map. The fitness of the parameters is evaluated using

correlation coefficient and entropy. The secrets keys are then produced by the proposed

hyperchaotic map. These keys are further used to perform the diffusion operation on the input

image to generate the encrypted images. Extensive experiments are conducted to investigate

the performance of the proposed approach considering the well-known measures. The

comparative results show that the proposed approach performs significantly better as compared

to the competitive approaches.

Keywords: 7D hyperchaotic map; Color image encryption

Dr. R Shashidhara, Assistant Professor, Department of CSE

Shashidhara, R. (2020). A novel DNA based password authentication system for global

roaming in resource-limited mobile environments. Multimedia Tools and Applications, 79,

2185-2212. 10.1007/s11042-019-08349-8.

Abstract: Mobile environments are highly vulnerable to security threats and pose a great

challenge for the wireless and mobile networks being used today. Because the mode of a

wireless channel is open, these networks do not carry any inherent security and hence are more

prone to attacks. Therefore, designing a secure and robust protocol for authentication in a

global mobile network is always a challenging. In these networks, it is crucial to provide

authentication to establish a secure communication between the Mobile User (MU), Foreign

Agent (FA) and Home Agent (HA). In order to secure communication among these entities, a

number of authentication protocols have been proposed. The main security flaw of the existing

authentication protocols is that attackers have the ability to impersonate a legal user at any

time. Moreover, the existing authentication protocols in the literature are exposed to various

kind of cryptographic attacks. Besides, the authentication protocols require larger key length

and more computation overhead. To remedy these weaknesses in mobility networks, DNA

(Deoxyribo Nucleic Acid) based authentication scheme using Hyper Elliptic Curve

Cryptosystem (HECC) is introduced. It offers greater security and allows an MU, FA and HA

to establish a secure communication channel, in order to exchange the sensitive information

over the radio link. The proposed system derive benefit from HECC, which is smaller in terms

of key size, more computational efficiency. In addition, the security strength of this

authentication system is validated through widely accepted security verification tool called

ProVerif. Further, the performance analysis shows that the DNA based authentication system

using HECC is secure and practically implementable in the resource-constrained mobility

nodes.

Keywords: Authentication; DNA cryptography; Global roaming; Security; Smart-card; User

anonymity

Shashidhara, R. (2020). A Robust user authentication protocol with privacy-preserving for

roaming service in mobility environments. Peer-To-Peer Networking and Applications, 13,

1943-1966. 10.1007/s12083-020-00929-y.

Abstract: The authentication system plays a crucial role in the context of GLObal MObility

NETwork (GLOMONET) where Mobile User (MU) often need to seamless and secure

roaming service over multiple Foreign Agents (FA). However, designing a robust and

anonymous authentication protocol along with a user privacy is essential and challenging task.

Due to the resource constrained property of mobile terminals, the broadcast nature of a wireless

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

36

channel, mobility environments are frequently exposed to several attacks. Many researchers

focus their interests on designing an efficient and secure mobile user authentication protocol

for mobility networks. Very recently (in 2018), Xu et al presented the novel anonymous

authentication system for roaming in GLOMONET, and insisted that their protocol is more

secure than existing authentication protocols. The security strength of Xu et al.’s authentication

protocol is analysed and identified that the protocol is vulnerable to stolen verifier attack,

privileged insider attack, impersonation attack and denial of service attack. In-fact, the protocol

suffers from clock synchronization problem and cannot afford local password-verification to

detect wrong passwords quickly. As a remedy, we proposed an efficient and robust anonymous

authentication protocol for mobility networks. The proposed mobile user authentication

protocol achieves the provable security and has the ability to resist against numerous network

attacks. Besides, the correctness of the novel authentication protocol is validated using formal

security tool called AVISPA (Automated Validation of Internet Security Protocols &

Applications). Finally, the performance analysis and simulation results reveals that the

proposed authentication protocol is computationally efficient and practically implementable in

resource limited mobility environments.

Keywords: Authentication; Global roaming; Mobile networks; Privacy; User anonymity;

Computationally efficient.

Dr. Raghunath Reddy Madireddy, Assistant Professor, Department of CSE

Madireddy, Raghunath Reddy (2020). Maximum independent and disjoint coverage. Journal

of Combinatorial Optimization, 39, 1017-1037. 10.1007/s10878-020-00536-w

Abstract: Set cover is one of the most studied optimization problems in Computer Science. In

this paper, we target two interesting variations of this problem in a geometric setting: (i)

maximum disjoint coverage (MDC), and (ii) maximum independent coverage (MIC) problems.

In both problems, the input consists of a set P of points and a set O of geometric objects in the

plane. The objective is to maximize the number of points covered by a set O′ of selected objects

from O. In the MDC problem we restrict the objects in O′ are pairwise disjoint (non-

intersecting). Whereas, in the MIC problem any pair of objects in O′ should not share a point

from P (however, they may intersect each other). We consider various geometric objects as

covering objects such as axis-parallel infinite lines, axis-parallel line segments, unit disks, axis-

parallel unit squares, and intervals on a real line. For the covering objects axis-parallel infinite

lines, we show that both MDC and MIC problems admit polynomial time algorithms. In

addition to that, we give polynomial time algorithms for both MDC and MIC problems with

intervals on the real line. On the other hand, we prove that the MIC problem is NP-complete

when the objects are horizontal infinite lines and vertical segments. We also prove that both

MDC and MIC problems are NP-complete for axis-parallel unit segments in the plane. For unit

disks and axis-parallel unit squares, we prove that both these problems are NP-complete.

Further, we present PTAS es for the MDC problem for unit disks as well as unit squares using

Hochbaum and Maass’s “shifting strategy”. For unit squares, we design a PTAS for the MIC

problem using Chan and Hu’s “mod-one transformation” technique.

Keywords: Set cover; Maximum coverage; Independent set; Disjoint coverage; NP-hard;

PTAS; Line; Segment; Disk, Square.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

37

Dr. Rishav Singh, Assistant Professor, Department of CSE

Singh, Rishav (2020). Multi-Fault Bearing Classification Using Sensors and ConvNet-Based

Transfer Learning Approach. IEEE Sensors Journal, 20(3), 1433-1444.

10.1109/JSEN.2019.2947026

Abstract: The development of sensor technology and modern computing allows the fault

diagnosis of rotating machinery to exploit under different working conditions. As an effect,

digital health monitoring of machine is performed based on the actual data values obtained

from the multi-sensor, and also, it supports to design the data-rich datasets using multi-sensor.

Thus, it motivates to develop the data-driven approaches for fault diagnosis based on multi-

sensor data. However, they require a large amount of historical evidence for the development,

and in a real-world situation, usually sufficient data is not available for building the data-driven

model. As a result, they become less competent to perform the task of fault diagnosis.

Therefore, in this paper, the transfer of knowledge from data-rich datasets is proposed to

identifying the fault in new working conditions of the related domain. To transfer the

experience, firstly the data has transformed to kurtogram, which is the fusion of multilevel

kurtosis values and then convolutional neural network-based transfer learning approach has

presented for fault diagnosis under various operating conditions. Also, the simultaneous change

in speed and load in source and target domain has analyzed to explore the effect of the proposed

method on performance of fault diagnosis. Further, the issue of negative transfer of knowledge

has investigated by determining the association of speed as well as a load between the source

and target domain. The proposed method is tested using two vibration datasets, and results

demonstrate the decent performance of fault diagnosis using transfer learning approach.

Keywords: Convolutional neural network; Fault diagnosis; Kurtogram; Transfer learning.

Dr. Rupak Chakraborty, Assistant Professor, Department of CSE

Chakraborty, Rupak (2020). Mutual-inclusive learning-based multi-swarm PSO algorithm

for image segmentation using an innovative objective function. International Journal of

Computational Science and Engineering, 21(4), 483-494. 10.1504/IJCSE.2020.106864

Abstract: This paper presents a novel image segmentation algorithm formed by the normalised

index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational

complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive

multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a

high dimensional problem of particle swarm optimisation (PSO) is divided into several one-

dimensional problems to get rid of the 'high dimensionality' problem whereas premature

convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based

technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images

which produce better optimal thresholds at a faster convergence rate with high functional

values as compared to the considered optimisation techniques like PSO, genetic algorithm

(GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity

parameters of the proposed algorithm is carried out over the other stated global optimisers.

Keywords: Multilevel thresholding; Normalised index value; Probability; Multi-swarm PSO.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

38

Dr. Samya Muhuri, Assistant Professor, Department of CSE

Muhuri, Samya (2020). Differentiate the Game Maker in Any Soccer Match Based on Social

Network Approach. IEEE Transactions on Computational Social Systems, 7(6), 1399-1408.

10.1109/TCSS.2020.3036546

Abstract: A game maker controls the game by passing the ball among the teammates and is

considered the most influential player in any ball-passing multiplayer team game. In the current

approach, we have represented the players and the ball passing among themselves using a

dynamic social network structure. Now, the challenging task is to differentiate the game maker

from any match where the performance of any individual player is not directly involved with

the cumulative effect of the scoring mechanism. Different temporal graph centrality metrics,

such as degree, closeness, betweenness, and clustering coefficient, have differentiated the game

maker in the ball-passing network. We have analyzed some random games of FIFA men's

world cup 2018, to distinguish the game makers from the matches as a case study. Another

challenging problem is to locate the tactical position of the influential passes. We have

investigated the games and identified those areas in different time frames. The accuracy of our

method is tested over the real-life benchmark data set. The analytical results exhibit notable

improvement over the existing methods. Our study opens up a new framework for the strategic

analysis not only for soccer but also for similar ball-passing multiplayer team games, such as

basketball and hockey.

Keywords: Ball passing; Game maker; Network centrality; Soccer; Social network.

Dr. Sanjeet Kumar Nayak, Assistant Professor, Department of CSE

Nayak, Sanjeet Kumar (2020). IECA: an efficient IoT friendly image encryption technique

using programmable cellular automata. Journal of Ambient Intelligence and Humanized

Computing, 11, 5083–5102. 10.1007/s12652-020-01813-6

Abstract: Digital images play a vital role in multimedia communications in the modern era.

With the advent of internet-of-things (IoT) applications, multimedia transfers are happening at

a rapid pace. However, providing security to these data is essential if the data is sensitive. It is

a challenging task in case of IoT applications due to the limitations of the sensors in terms of

memory and computational efficiency. Therefore, conventional ciphers cannot be applied in

the IoT devices. However, cellular automata (CA) can be used in this resource-constrained

environment for providing security to the IoT devices, as it is inherently capable of creating

complex patterns and pseudo-random sequences. It is also easy to implement in hardware. In

this work, an IoT friendly programmable cellular automata (PCA) based block cipher called

IECA is proposed. Further, randomness in the generated cipher-image has been tested using

various statistical testings present in NIST test suite and DIEHARD test suite. The test results

show that IECA generates high degree of randomness in the produced cipher-image. In addition

to this, correlation, entropy and differential analysis of the proposed scheme justifies the

robustness against different types of attacks. Experimental results show the efficiency of IECA

as compared to the existing block ciphers.

Keywords: Image cipher; IoT; Programmable cellular automata; Lightweight; Block cipher.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

39

Nayak, Sanjeet Kumar (2020). IEVCA: An efficient image encryption technique for IoT

applications using 2-D Von-Neumann cellular automata. Multimedia Tools and Applications.

doi.org/10.1007/s11042-020-09880-9

Abstract: Present era is marked by exponential growth in transfer of multimedia data through

internet. Most of the Internet of Things(IoT) applications send images to cloud storages through

internet. However, in sensitive applications such as healthcare, defense, etc., these images

should be encrypted before transmission through insecure public channels to gateway fog

nodes. Conventional encryption algorithms cannot be used there due to the resource constraint

characters of IoT devices. Here, Cellular Automata (CA) based encryption algorithms can be

used because of their inherent simplicity in implementation in hardware, without affecting the

capability of generating highly random sequences. In this paper, a lightweight, robust and

secure image encryption technique has been proposed using 2-D Von-Neumann Cellular

Automata (VCA), called IEVCA, which is lossless, correlation immune and has all the essential

properties of a good image cipher. Additionally, the proposed technique passes all the

randomness tests of DIEHARD and NIST statistical test suites. Moreover, several security and

performance analyses of the IEVCA proved its efficiency and resistance against security

attacks. Experimental results of the IEVCA show its better performance when compared to the

existing encryption techniques.

Keywords: Lightweight; IoT; Image encryption; 2-D Cellular automata; Block cipher.

Dr. Shivani Goel, Professor, Department of CSE

Goel, Shivani (2020). A survey on brain tumor detection techniques for MR images.

Multimedia Tools and Applications, 79(29-30), 21771-21814. doi:10.1007/s11042-020-

08898-3

Abstract: One of the most crucial tasks in any brain tumor detection system is the isolation of

abnormal tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has

effectively utilized the concepts of medical image processing, particularly on MR images, to

automate the core steps, i.e. extraction, segmentation, classification for proximate detection of

tumor. Research is more inclined towards MR for its non-invasive imaging properties.

Computer aided diagnosis or detection systems are becoming challenging and are still an open

problem due to variability in shapes, areas, and sizes of tumor. The past works of many

researchers under medical image processing and soft computing have made noteworthy review

analysis on automatic brain tumor detection techniques focusing segmentation as well as

classification and their combinations. In the manuscript, various brain tumor detection

techniques for MR images are reviewed along with the strengths and difficulties encountered

in each to detect various brain tumor types. The current segmentation, classification and

detection techniques are also conferred emphasizing on the pros and cons of the medical

imaging approaches in each modality. The survey presented here aims to help the researchers

to derive the essential characteristics of brain tumor types and identifies various

segmentation/classification techniques which are successful for detection of a range of brain

diseases. The manuscript covers most relevant strategies, methods, their working rules,

preferences, constraints, and their future snags on MR image brain tumor detection. An attempt

to summarize the current state-of-art with respect to different tumor types would help

researchers in exploring future directions.

Keywords: Brain tumor detection systems; Computer-aided diagnosis; Medical imaging;

Magnetic resonance images; Segmentation Classification

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

40

Goel, Shivani (2020). Generative adversarial network-convolution neural network-based

breast cancer classification using optical coherence tomographic images. Laser Physics, 30.

10.1088/1555-6611/abb596

Abstract: Currently, breast tissue images are primarily classified by pathologists, which is

time-consuming and subjective. Deep learning, however, can perform this task with the utmost

precision. In order to achieve an improved performance, a large number of annotated datasets

are required to train the network, which is a challenging task in the medical field. In this paper,

we propose an intelligent system, based on generative adversarial networks (GANs) and a

convolution neural network (CNN) for the automatic classification of breast cancer, using

optical coherence tomography (OCT) images. In this network, the GAN is used to generate

synthetic datasets and to further utilize these synthetic datasets to increase the quantity of

information, so as to improve the classification performance of the CNN. Our method is

demonstrated by means of a limited set of OCT images of breast tissue. The classification

performance of our method, using only the classic data increase, yielded a sensitivity level of

93.6%, with 90.8% specificity and 91.7% accuracy, based on the test datasets. By adding the

synthetic data increase, the accuracy of the training datasets increased to 93.7% from 92.0%.

We believe that this approach will help radiologists and pathologists to improve their diagnostic

capability.

Keywords: Breast tissue; Deep learning; Generative adversarial networks; Optical coherence

tomography

Goel, Shivani (2020). SP-J48: a novel optimization and machine-learning-based approach for

solving complex problems: special application in software engineering for detecting code

smells. Neural Computing and Applications, 32, 7009-7027. 10.1007/s00521-019-04175-z

Abstract: This paper presents a novel hybrid algorithm based on optimization and machine-

learning approaches for solving real-life complex problems. The optimization algorithm is

inspired from the searching and attacking behaviors of sandpipers, called as Sandpiper

Optimization Algorithm (SPOA). These two behaviors are modeled and implemented

computationally to emphasize intensification and diversification in the search space. A

comparison of the proposed SPOA algorithm is performed with nine competing optimization

algorithms over 23 benchmark test functions. The proposed SPOA is further hybridized with

B-J48 pruned machine-learning approach for efficiently detecting the code smells from the data

set. The results reveal that the proposed technique is able to solve challenging problems and

outperforms the other well-known approaches.

Keywords: Bio-inspired metaheuristic techniques; Code smells; Machine-learning;

Optimization

Goel, Shivani (2020). Sandpiper optimization algorithm: a novel approach for solving real-life

engineering problems. Applied Intelligence, 50, 582-619. 10.1007/s10489-019-01507-3

Abstract: This paper presents a novel bio-inspired algorithm called Sandpiper Optimization

Algorithm (SOA) and applies it to solve challenging real-life problems. The main inspiration

behind this algorithm is the migration and attacking behaviour of sandpipers. These two steps

are modeled and implemented computationally to emphasize intensification and diversification

in the search space. The comparison of proposed SOA algorithm is performed with nine

competing optimization algorithms over 44 benchmark functions. The analysis of

computational complexity and convergence behaviors of the proposed algorithm have been

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

41

evaluated. Further, SOA algorithm is hybridized with decision tree machine-learning algorithm

to solve real-life applications. The experimental results demonstrated that the proposed

algorithm is able to solve challenging constrained optimization problems and outperforms the

other state-of-the-art optimization algorithms.

Keywords: Benchmark test problems; Bio-inspired metaheuristic techniques; Machine-

learning; Optimization

Goel, Shivani, Acharya, Divya, Billimoria, Anosh, Srivastava, Neishka & Bhardwaj,

Arpit (2020). Emotion recognition using fourier transform and genetic programming. Applied

Acoustics, 16, 4107260. 10.1016/j.apacoust.2020.10726

Abstract: In cognitive science, the real-time recognition of human’s emotional state is

pertinent for machine emotional intelligence and human-machine interaction. Conventional

emotion recognition systems use subjective feedback questionnaires, analysis of facial features

from videos, and online sentiment analysis. This research proposes a system for real-time

detection of emotions in response to emotional movie clips. These movie clips elicitate

emotions in humans, and during that time, we have recorded their brain signals using

Electroencephalogram (EEG) device and analyze their emotional state. This research work

considered four class of emotions (happy, calm, fear, and sadness). This method leverages Fast

Fourier Transform (FFT) for feature extraction and Genetic Programming (GP) for

classification of EEG data. Experiments were conducted on EEG data acquired with a single

dry electrode device NeuroSky Mind Wave 2. To collect data, a standardized database of 23

emotional Hindi film clips were used. All clips individually induce different emotions, and data

collection was done based on these emotions elicited as the clips contain emotionally inductive

scenes. Twenty participants took part in this study and volunteered for data collection. This

system classifies four discrete emotions which are: happy, calm, fear, and sadness with an

average of 89.14% accuracy. These results demonstrated improvements in state-of-the-art

methods and affirmed the potential use of our method for recognizing these emotions.

Keywords: Genetic programming; Electroencephalogram; Fast Fourier Transform; Emotion,

recognition; Movie clips.

Goel, Shivani., Acharya, Divya & Bhardwaj, Arpit (2020). A novel fitness function in

genetic programming to handle unbalanced emotion recognition data. Pattern Recognition

Letters, 133, 272-279. 10.1016/j.patrec.2020.03.005

Abstract: In the area of behavioral psychology, real-time emotion recognition by using

physiological stimuli is an active topic of interest. This research considers the recognition of

two class of emotions i.e., positive and negative emotions using EEG signals in response to

happy, horror, sad, and neutral genres. In a noise-free framework for data acquisition of 50

participants, Neuro Sky Mind Wave 2 is used. The dataset collected is unbalanced i.e., there

are more instances of positive classes than negative ones. Therefore, accuracy is not a useful

metric to assess the results of the unbalanced dataset because of biased results. So, the primary

goal of this research is to address the issue of unbalanced emotion recognition dataset

classification, for which we are proposing a novel fitness function known as Gap score (G

score), which learns about both the classes by giving them equal importance and being

unbiased. The genetic programming (GP) framework in which we implemented G score is

named as G-score GP (GGP). The second goal is to assess how distinct genres affect human

emotion recognition process and to identify an age group that is more active emotionally when

their emotions are elicited. Experiments were conducted on EEG data acquired with a single-

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

42

channel EEG device. We have compared the performance of GGP for the classification of

emotions with state-of-the-art methods. The analysis shows that GGP provides 87.61%

classification accuracy by using EEG. In compliance with the self-reported feelings, brain

signals of 26 to 35 years of age group provided the highest emotion recognition rate.

Keywords: Emotion recognition; Fitness function; Genetic programming; EEG; Fast Fourier

transformation

Dr. Simranjit Singh, Assistant Professor, Department of CSE

Singh, Simranjit (2020). A Pre-processing framework for spectral classification of

hyperspectral images. Multimedia Tools and Applications, 243-261. 10.1007/s11042-020-

09180-2

Abstract: Classification of Hyperspectral images is mostly based on the spectral-spatial

features in existing classification techniques. The captured Hyperspectral images from

satellites may contain some noisy bands due to water absorption. The process of radiometric

and atmospheric corrections leads to the removal of useful bands present in the acquired HSI.

In this paper, a novel framework is proposed in which interpolation is used to accommodate

the loss of noisy bands. Further, the extraction of hybrid features is performed using PCA and

LPP to preserve spatial information, and these features are passed as input to the machine

learning models. The proposed framework is compared with the existing spectral-spatial and

spectral based frameworks by using the standard datasets-Indian Pines, Salinas, Pavia

University, and Kennedy Space Centre. The accuracy of the classification is increased

significantly when the proposed framework is blended with state-of-art classifiers.

Keywords: Hyperspectral images; PCA; LPP; SVM; Classification; LSTM

Dr. Sridhar Swaminathan, Assistant Professor, Department of CSE

Swaminathan, Sridhar (2020). Sparse low rank factorization for deep neural network

compression. Neurocomputing, Elsevier, 185-196. 10.1016/j.neucom.2020.02.035

Abstract: Storing and processing millions of parameters in deep neural networks is highly

challenging during the deployment of model in real-time application on resource constrained

devices. Popular low-rank approximation approach singular value decomposition (SVD) is

generally applied to the weights of fully connected layers where compact storage is achieved

by keeping only the most prominent components of the decomposed matrices. Years of

research on pruning-based neural network model compression revealed that the relative

importance or contribution of each neuron in a layer highly vary among each other. Recently,

synapses pruning has also demonstrated that having sparse matrices in network architecture

achieve lower space and faster computation during inference time. We extend these arguments

by proposing that the low-rank decomposition of weight matrices should also consider

significance of both input as well as output neurons of a layer. Combining the ideas of sparsity

and existence of unequal contributions of neurons towards achieving the target, we propose

sparse low rank (SLR) method which sparsifies SVD matrices to achieve better compression

rate by keeping lower rank for unimportant neurons. We demonstrate the effectiveness of our

method in compressing famous convolutional neural networks based image recognition

frameworks which are trained on popular datasets.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

43

Experimental results show that the proposed approach SLR outperforms vanilla truncated

SVD and a pruning baseline, achieving better compression rates with minimal or no loss in the

accuracy. Code of the proposed approach is available at https://github.com/sridarah/slr.

Keywords: Low-rank approximation; Singular value decomposition; Sparse matrix; Deep

neural networks; Convolutional neural networks.

Dr. Suneet Kumar Gupta, Assistant Professor, Department of CSE

Gupta, Suneet Kumar (2020). 3-D optimized classification and characterization artificial

intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-

based delineated plaque: Atheromatic™ 2.0. Computers in Biology and Medicine, 125.

10.1016/j.compbiomed.2020.103958

Abstract: Background and Purpose: Atherosclerotic plaque tissue rupture is one of the leading

causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity

and mortality. Manual ultrasound plaque classification and characterization methods are time-

consuming and can be imprecise due to significant variations in tissue characteristics. We

report a novel artificial intelligence (AI)-based plaque tissue classification and characterization

system. Methods: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid

core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic

plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often

calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds

of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN),

Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D

optimized by varying the number of CNN layers and data augmentation frameworks. The DL

systems were benchmarked against four types of machine learning (ML) classification systems,

and the CADx system was characterized using two novel strategies consisting of DL mean

feature strength (MFS) and a bispectrum model using higher-order spectra. Results: After

balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process

was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to

test the ratio of 90%–10%), tCNN and DCNN yielded accuracy (area under the curve (AUC))

pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was

superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic

plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001).

A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by

5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer.

Conclusions: The performance order of the three AI systems was DCNN > tCNN > ML.

Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue

characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue

classification and characterization.

Keywords: Accuracy; And speed; Artificial intelligence; Asymptomatic; Atherosclerosis;

Carotid plaque; deep learning; Machine learning; Performance; Supercomputer; symptomatic;

ultrasound

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

44

Gupta, Suneet Kumar & Agarwal, Mohit (2020). Brain MRI-based Wilson disease tissue

classification: An optimised deep transfer learning approach. Electronics Letters, 56(25), 1395-

1398. 10.1049/el.2020.2102

Abstract: Wilson's disease (WD) is caused by the excessive accumulation of copper in the

brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white

matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD

against controls when comparing visually, primarily due to subtle differences in WMH. This

Letter presents a computer-aided design-based automated classification strategy that uses

optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii)

the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against

a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to

MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%,

0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet

and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked

against the ML-based soft classifier - Random Forest.

Keywords: MobileNet; Wilson disease; MRI; WMH

Dr. Suneet Kumar Gupta, Assistant Professor, Department of CSE

Gupta, Suneet Kumar., Agarwal, Mohit (2020). ToLeD: Tomato Leaf Disease Detection

using Convolution Neural Network. Procedia Computer Science, 167, 293-301.

10.1016/j.procs.2020.03.225.

Abstract: Tomato is the most popular crop in the world and in every kitchen, it is found in

different forms irrespective of the cuisine. After potato and sweet potato, it is the crop which

is cultivated worldwide. India ranked 2 in the production of tomato. However, the quality and

quantity of tomato crop goes down due to the various kinds of diseases. So, to detect the disease

a deep learning-based approach is discussed in the article. For the disease detection and

classification, a Convolution Neural Network based approach is applied. In this model, there

are 3 convolution and 3 max pooling layers followed by 2 fully connected layer. The

experimental results shows the efficacy of the proposed model over pre-trained model i.e.

VGG16, InceptionV3 and Mobile Net. The classification accuracy varies from 76% to 100%

with respect to classes and average accuracy of the proposed model is 91.2% for the 9 disease

and 1 healthy class.

Keywords: Convolution Neural Network (CNN); Supervised learning; Hyper parameters;

Plant leaves dataset.

Gupta, Suneet Kumar, Agarwal, Mohit & Biswas, Kanad Kishore (2020). Development of

Efficient CNN model for Tomato crop disease identification. Sustainable Computing:

Informatics and Systems, 28, 10.1016/j.suscom.2020.100407

Abstract: Tomato is an important vegetable crop cultivated worldwide coming next only to

potato. However, the crop can be damaged due to various diseases. It is important for the farmer

to know the type of disease for timely treatment of the crop. It has been observed that leaves

are clear indicator of specific diseases. A number of Machine Learning (ML) algorithms and

Convolution Neural Network (CNN) models have been proposed in literature for identification

of tomato crop diseases. CNN models are based on Deep Learning Neural Networks and differ

inherently from traditional Machine Learning algorithms like k-NN, Decision-Trees etc. While

pretrained CNN models perform fairly well, they tend to be computationally heavy due to large

Dep

art

men

t of

Co

mp

ute

r S

cien

ce E

ngin

eeri

ng

45

number of parameters involved. In this paper a simplified CNN model is proposed comprising

of 8 hidden layers. Using the publicly available dataset PlantVillage, proposed light weight

model performs better than the traditional machine learning approaches as well as pretrained

models and achieves an accuracy of 98.4%. PlantVillage dataset comprises of 39 classes of

different crops like apple, potato, corn, grapes etc. of which 10 classes are of tomato diseases.

While traditional ML methods gives best accuracy of 94.9% with k-NN, best accuracy of

93.5% is obtained with VGG16 in pretrained models. To increase performance of proposed

CNN, image pre-processing has been used by changing image brightness by a random value of

a random width of image after image augmentation. The proposed model also performs

extremely well on dataset other than PlantVillage with accuracy of 98.7%.

Keywords: Augmentation; Convolution Neural Network (CNN); Image pre-processing;

Machine Learning; Max Pooling

Gupta, Suneet Kumar, Agarwal, Mohit & Biswas, Kanad Kishore (2020). A new Conv2D

model with modified ReLU activation function for identification of disease type and severity

in cucumber plant. Sustainable Computing: Informatics and Systems.

10.1016/j.suscom.2020.100473

Abstract: Cucumber is one of the important crop and farmers of most of the counties are

cultivating the cucumber crop. Generally, this crop is infected with Angular Spot, Anthracnose,

etc. In past research community has developed various learning models to identify the disease

in cucumber crop in early-stage and reported maximum accuracy of 85.7%. In proposed work,

a convolution neural network based approach has been discussed and disease identification is

improved by 8.05% by achieving the accuracy of 93.75%. The proposed model has been trained

on a different combination of hyperparameters and activation function. However, the best

accuracy has been achieved by introducing a modified ReLU activation function. A

segmentation algorithm has also been proposed to estimate the severity of the disease. To

establish the efficacy of the proposed model, its performance has been compared with other

CNN models as well as traditional machine learning methods.

Keywords: Classification; Deep learning; Disease extent; Image processing; Modified ReLU

Dr. Suyel Namasudra, Assistant Professor, Department of CSE

Namasudra, Suyel (2020). Towards DNA based data security in the cloud computing

environment. Computer Communications, 151(1), 539-547. 10.1016/j.comcom.2019.12.041.

Abstract: Nowadays, data size is increasing day by day from gigabytes to terabytes or even

petabytes, mainly because of the evolution of a large amount of real-time data. Most of the big

data is transmitted through the internet and they are stored on the cloud computing

environment. As cloud computing provides internet-based services, there are many attackers

and malicious users. They always try to access user’s confidential big data without having the

access right. Sometimes, they replace the original data by any fake data. Therefore, big data

security has become a significant concern recently. Deoxyribonucleic Acid (DNA) computing

is an advanced emerged field for improving data security, which is based on the biological

concept of DNA. A novel DNA based data encryption scheme has been proposed in this paper

for the cloud computing environment. Here, a 1024-bit secret key is generated based on DNA

computing, user’s attributes and Media Access Control (MAC) address of the user, and decimal

encoding rule, American Standard Code for Information Interchange (ASCII) value, DNA

bases and complementary rule are used to generate the secret key that enables the system to

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

46

protect against many security attacks. Experimental results, as well as theoretical analyses,

show the efficiency and effectivity of the proposed scheme over some well-known existing

schemes.

Keywords: Cloud computing; DNA computing; Big data security; MAC address;

Complementary rule; Cloud Sim.

Dr. Tanmay Bhowmik, Assistant Professor, Department of CSE

Bhowmik, Tanmay (2020). Prosodic word boundary detection from Bengali continuous

speech. Language Resources and Evaluation, 54, 747-765. 10.1007/s10579-019-09478-0

Abstract: Detection of word boundaries in continuous speech is a tedious process due to the

absence of a definite pause or silence in the word boundary position. Thus, continuous speech

recognition is a very challenging task. However, the prosodic word boundaries, unlike the

written word boundaries, can be predicted using the prosodic parameters of continuous speech.

This paper proposes a method for detecting such prosodic word boundaries from Bengali

continuous speech. Bengali is a bound-stress language, where stress is observed on the first

syllable of a prosodic word. Empirical Mode Decomposition is applied to the logarithm of

fundamental frequency (F0) contour of continuous speech to detect prosodic word boundaries.

200 Bengali readout sentences, read by ten speakers, are analyzed for the present work. An

overall prosodic boundary detection accuracy of 88.05% is achieved, whereas precision and

recall values are 90.73% and 88.31%, respectively, with f-score as 89.5. A prosodic word

dictionary comprising 5031 prosodic words has been developed by analyzing 1526 Bengali

sentences with the proposed prosodic word boundary detection method.

Keywords: Accent command; F0 contour; Fundamental frequency; Offset; Onset; Prosodic

word boundaries

Dr. Tanveer Ahmed, Assistant Professor, Department of CSE

Ahmed, Tanveer (2020). Imbalanced Breast Cancer Classification Using Transfer Learning.

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-11.

10.1109/TCBB.2020.2980831

Abstract: Accurate breast cancer detection using automated algorithms remains a problem

within the literature. Although a plethora of work has tried to address this issue, an exact

solution is yet to be found. This problem is further exacerbated by the fact that most of the

existing datasets are imbalanced, i.e. the number of instances of a particular class far exceeds

that of the others. In this paper, we propose a framework based on the notion of transfer learning

to address this issue and focus our efforts on histopathological and imbalanced image

classification. We use the popular VGG-19 as the base model and complement it with several

state-of-the-art techniques to improve the overall performance of the system. With the

ImageNet dataset taken as the source domain, we apply the learned knowledge in the target

domain consisting of histopathological images. With experimentation performed on a large-

scale dataset consisting of 277,524 images, we show that the framework proposed in this paper

gives superior performance than those available in the existing literature. Through numerical

simulations conducted on a supercomputer, we also present guidelines for work in transfer

learning and imbalanced image classification.

Keywords: Transfer Learning; Imbalanced Class; Classification; Deep Learning.

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

47

Ahmed, Tanveer (2020). A Cognitive Model to predict human Internest in smart

environments. Computer Communications, 161, 1-9. 10.1016/j.comcom.2020.07.007.

Abstract: Recently, the idea of smart cities has made several strides forward in literature. Work

has hypothesize that the combination of Artificial Intelligence, Cloud Computing, and High

powered computers will make technology more human-centric, even, the idea that smart cities

will be able to understand the thought process of a human being seems very much likely today.

This paper is along this line of thought. In particular, we try to present a method to model the

cognitive state of human interest. This is done to take one more step towards the realization of

a smart cognitive city. An approach which is Subjective–Objective in nature is presented to

model the computation of activity inspired by interest. Based on activity, human latent state

values are indirectly deduced. Inspiration is drawn from Physics and interest is modeled upon

the Ornstein–Uhlenbeck (OU) process. Concepts of Adaptive filtering are used to formulate an

evolving transformation function that automatically and adaptively models the conversion of

interest into activity. Particle filter is employed to provide an elucidation which is

computationally feasible. To validate the viability of the method, experimentation is performed

with real datasets.

Keywords: Cognition; Interest; Machine learning; Man Machine systems

Ahmed, Tanveer & Singh, Rishav (2020). Identifying tiny faces in thermal images using

transfer learning. Journal of Ambient Intelligence and Humanized Computing, 11(5), 1957-

1966. 10.1007/s12652-019-01470-4

Abstract: This article focuses on identifying tiny faces in thermal images using transfer

learning. Although the issue of identifying faces in images is not new, the problem of tiny face

identification is a recently identified research area. Indeed challenging, however, in this paper,

we take the problem one step ahead and focus on recognizing tiny faces in thermal images. To

do that, we use the paradigm of transfer learning. We use the famous residual network to extract

the features in the target domain. Subsequently, with this model as a reference point, we then

retrain it in the target domain of thermal images. Through testing performed in Terravic

datasets, we have found that the method outperforms existing methods in literature to identify

tiny faces in thermal images.

Keywords: Biometrics; Machine learning; Thermal images; Tiny faces

Dr. Vijaypal Singh Rathor, Assistant Professor, Department of CSE

Rathor, Vijaypal Singh (2020). New lightweight Anti-SAT block design and obfuscation

technique to thwart removal attack. Integration, 75, 178-188. 10.1016/j.vlsi.2020.05.001.

Abstract: Logic locking has emerged as a prominent technique to protect an integrated circuit

from piracy, overbuilding, and hardware Trojans. Most of the well-known logic locking

techniques are vulnerable to satisfiability (SAT) based attack. Though several SAT-resistant

logic locking techniques such as Anti-SAT block (ASB) are reported that increase the time to

decipher the secret key, the existing techniques are either vulnerable to signal probability skew

(SPS) based removal attack or require significant design overhead. Therefore, a new

lightweight ASB design and obfuscation technique is proposed that effectively integrate and

obfuscates the ASB in the design to thwart removal attack. We first propose a new ASB

design/integration approach that effectively thwarts the structural/functional analysis based

removal attack with minimum overhead. Further, we also propose an ASB obfuscation

approach that shifts the inverter deep in the circuit using De Morgan's law and replaces an ASB

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

48

gate with a key-gate to thwart SPS based removal attack. Moreover, a new algorithm is

proposed that inserts the ASB in the locked design to achieve desired output corruptibility.

Finally, a new INV/BUFF key-gate is proposed that constructs the ASB with reduced overhead

over the XOR/XNOR. Experimental evaluation on ISCAS-85 benchmarks shows that our ASB

design and obfuscation approaches, on an average, reduce area overhead by 25.5% and 22%

respectively, and effectively prevent removal attack without reducing any security.

Keywords: Anti-SAT Block; Logic locking; Obfuscation; Removal attack; SAT Attack;

Signal probability skew

Dr. Vipul Kumar Mishra, Assistant Professor, Department of CSE

Mishra, Vipul Kumar (2020). GDSE: An Automated Gravitational Design Space Exploration

of Data Path during Power-Performance Trade Off in Architectural Synthesis. IEEE

Transactions on Very Large Scale Integration (VLSI) Systems, 6(1), 1-7.

Abstract: This paper presents a novel automated gravitational design space exploration

(GDSE) framework for power execution time trade off during architectural synthesis (AS) of

data intensive applications and application specific processor (ASP). The proposed GDSE in

this paper is based on the law of gravity and mass interactions that guides in convergence to a

high-quality solution with the higher exploration speed. The major contribution of the paper is

a novel gravitational force based design space exploration algorithm during AS. The proposed

approach achieved reduction in exploration time (more than 89%) without compromising

quality of results as compared to recent approaches.

Keywords: VLSI; Architectural Synthesis; GDSE Algorithm.

Mishra, Vipul Kumar., Choudhary, Tejalal & Goswami, Anurag (2020). A comprehensive

survey on model compression and acceleration. Artificial Intelligence Review, 53, 5113-5155.

10.1007/s10462-020-09816-7

Abstract: In recent years, machine learning (ML) and deep learning (DL) have shown

remarkable improvement in computer vision, natural language processing, stock prediction,

forecasting, and audio processing to name a few. The size of the trained DL model is large for

these complex tasks, which makes it difficult to deploy on resource-constrained devices. For

instance, size of the pre-trained VGG16 model trained on the ImageNet dataset is more than

500 MB. Resource-constrained devices such as mobile phones and internet of things devices

have limited memory and less computation power. For real-time applications, the trained

models should be deployed on resource-constrained devices. Popular convolutional neural

network models have millions of parameters that leads to increase in the size of the trained

model. Hence, it becomes essential to compress and accelerate these models before deploying

on resource-constrained devices while making the least compromise with the model accuracy.

It is a challenging task to retain the same accuracy after compressing the model. To address

this challenge, in the last couple of years many researchers have suggested different techniques

for model compression and acceleration. In this paper, we have presented a survey of various

techniques suggested for compressing and accelerating the ML and DL models. We have also

discussed the challenges of the existing techniques and have provided future research directions

in the field.

Keywords: Model compression and acceleration; Machine learning; Deep learning; CNN;

RNN; Resource-constrained devices; Efficient neural networks

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

49

Review Article

Dr. Shivani Goel, Professor, Department of CSE

Goel, Shivani (2020). A classification and systematic review of product line feature model

defects. Software Quality Journal, 28, 1507–1550. 10.1007/s11219-020-09522-1

Abstract: Product line (PL)-based development is a thriving research area to develop software-

intensive systems. Feature models (FMs) facilitate derivation of valid products from a PL by

managing commonalities and variabilities among software products. However, the researchers

in academia as well as in the industries experience difficulties in quality assessment of FMs.

The increasing complexity and size of FMs may lead to defects, which outweigh the benefits

of PL. This paper provides a systematic literature review and key research issues related to the

FM defects in PL. We derive a typology of FM defects according to their level of importance.

The information on defects’ identification and explanations are provided with formalization.

Further, corrective explanations are presented which incorporates various techniques used to

fix defects with their implementation. This information would help software engineering

community by enabling developers or modelers to find the types of defects and their causes

and to choose an appropriate technique to fix defects in order to produce defect-free products

from FMs, thereby enhancing the overall quality of PL-based development.

Keywords: Feature model, Software product line, Defect, Product line model, Quality

Dr. Suneet Kumar Gupta, Assistant Professor, Department of CSE

Gupta, Suneet Kumar (2020). A comprehensive analysis of image forensics techniques:

Challenges and future direction. Recent Patents on Engineering, 14, 458-467.

10.2174/1872212113666190722143334.

Abstract: Background: Image forensics deal with the problem of authentication of pictures or

their origins. There are two types of forensics techniques namely active and passive. Passive

forgery is also known as blind forensics technique. In passive forgery, copy-move (cloning)

image forensics is most common forgery technique. In this approach, an object or region of a

picture is copied and positioned somewhere else in the same image. The active method used

watermarking to solve picture genuineness problem. It has limitations like human involvement

or particularly equipped cameras. To overwhelm these limitations, numerous passive

authentication approaches have been developed. Moreover, both approaches do not require any

prior information about the picture. Objective: The prime objective of this survey is to provide

an inclusive summary as well as recent advancement, challenges and future direction in image

forensics. In today’s digital era, digital pictures and videos are having a great impact on our

life as well as society, as they became an important source of information. Though earlier it

was very difficult to doctor the picture, nowadays digital pictures can be doctored easily with

the help of editing tools and the internet. These practices make pictures as well as videos

genuineness deceptive. Conclusion: This paper presents the current state-of-the-art of passive

(cloning) image forensics techniques, challenges and future direction of this research domain.

Furthermore, the major open issues in developing a robust cloning image forensics detector

with their performance are discussed. Lastly, the available benchmark datasets are also

discussed.

Keywords: Blind forgery; Cloning; Copy-move; Image alteration; Image forensics;

Tampering detection

Dep

art

men

t of

Com

pu

ter

Sci

ence

En

gin

eeri

ng

50

Department of ECE

Book Chapter:

Dr. Amit Singhal, Assistant Professor, Department of ECE

Singhal, Amit, Mehla, Virender Kumar & Kumar, Ashish (2020). Noise removal and

classification of EEG signals using the fourier decomposition method. Modelling and Analysis

of Active Biopotential Signals in Healthcare, Volume 1,

Abstract: Through rapid innovation in the health care sector, several types of biomedical

signals, including the electrocardiogram (ECG), electroencephalogram (EEG), positron

emission tomography (PET), electromyogram (EMG) and electrogastrogram (EGG), now play

an important role in the accurate analysis and detection of many health problems, such as heart

related problems, sleep disorders, brain abnormalities etc. In the current chapter, an efficient

approach based on the Fourier decomposition method (FDM) is proposed for noise removal

and classification of epileptic and healthy EEG signals. This work is composed of three main

steps. In the first, preprocessing of the EEG signal is performed to remove noise or artifacts

present in EEG recordings. In the second step, the FDM is utilized to fragment non-stationary

data into a set of orthogonal basis functions known as Fourier intrinsic band functions (FIBFs).

In the third step, FIBF based features are extracted and fed to various models for the accurate

classification of EEG signals. The simulation results reveal that the current study has achieved

superior performance compared to the existing techniques discussed in the literature.

Keywords: EEG: Positron emission tomography; Fourier decomposition method

Dr. Manjeet Kumar, Assistant Professor, Department of ECE

Kumar, Manjeet (2020). A Novel Approach for Optimal Digital FIR Filter Design Using

Hybrid Grey Wolf and Cuckoo Search Optimization. Lecture Notes in Networks and Systems,

121, 329-343. 10.1007/978-981-15-3369-3_26

Abstract: This paper represents designing of one-dimensional finite impulse filter (FIR) by

computing the optimal filter coefficients in a way that the frequency response of the designed

filter approximates ideal frequency response using proposed optimization algorithms. In this

work, hybrid optimization of grey wolf and cuckoo search algorithm is considered to design a

linear phase FIR low-pass, high-pass, band-pass and band-stop filters of 20th order. Analysis

of simulation results of the designed filter is done, and various parameters are calculated such

as maximum stopband ripple, maximum passband ripple and maximum attenuation in stop

band. Results have been compared to recently published algorithms for optimization such as

cat swarm optimization (CSO), particle swarm optimization (PSO), real-coded genetic

algorithm (RCGA) and differential evolution (DE) to prove the superiority of the proposed

algorithm.

Keywords: Bio-inspired algorithms; Cat swarm optimization algorithm; Cuckoo search

algorithm; Digital filter design; Filter response; Grey wolf algorithm

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

51

Conference Papers:

Dr. Ajay Yadav, Assistant Professor, Department of ECE

Yadav, Ajay (2020). Performance Analysis of Underwater 2D OCDMA System. Lecture

Notes in Electrical Engineering, 648, 477-484. 10.1007/978-981-15-2926-9_53

Abstract: The performance of two-dimensional optical code-division multiple access system

is analyzed in the presence of weak turbulence and different water types. For weak turbulence,

lognormal probability density function is used in the performance analysis. The different water

types are pure sea, clear ocean, and coastal water. The error probability of 10−9 is achievable

for all the water types with four-user OCDMA system. Also, error probability is very high

(&amp;gt;10−3) in the presence of coastal water as compared to pure sea and clear ocean.

Keywords: 2D; HL; OCDMA; OCFHC/QCC; UWOC

Dr. Amit Singhal, Assistant Professor, Department of ECE

Singhal, Amit., Mehla, Virender Kumar & Singh, Pushpendra (2020). EMD-based

discrimination of mental arithmetic tasks from EEG signals. 17th IEEE INDICON 2020

Conference held between 11th-13th December 2020 (pp. 1-4).

Abstract: Recognition of mental arithmetic activities from electroencephalogram (EEG)

signals forms an important constituent in developing brain-computer interface (BCI) systems.

In this paper, we explore an approach based on empirical mode decomposition (EMD) to

identify different arithmetic activities from EEG signals. The EMD is a technique proficient at

processing non-stationary signals, such as EEG signals, by decomposing into different

frequency components referred as intrinsic mode functions (IMFs). Eight attributes are

computed from each IMF to obtain the feature vector for a signal. These features are then

passed to support vector machine (SVM) classifier for the recognition of normal EEG signal

and the signal recorded for mental arithmetic tasks. Using 10-fold cross-validation technique,

the proposed method accomplished an average classification accuracy of 95%, which is better

than the current approaches explored in the literature.

Keywords: Brain computer interface; Empirical mode decomposition; Intrinsic mode

functions; Support vector machine

Dr. Arjun Kumar, Assistant Professor, Department of ECE

Kumar, Arjun (2020). Asymmetric CPW-Fed Multistubs Loaded Compact Printed Multiband

Antenna for Wireless Applications. Lecture Notes in Electrical Engineering, 648, 317-324.

10.1007/978-981-15-2926-9_35

Abstract: An asymmetric coplanar waveguide-fed branched multistubs resonators loaded

printed antenna is presented for multiband operation. By incorporating an asymmetrical long

and short L-shaped branched stubs, the lower resonances for 2.4 and 3.5 GHz bands are

achieved while the higher resonance for 5.5 GHz band is achieved by embedding horizontal

stub on asymmetric CPW-fed printed antenna. By placing multistubs resonators at suitable

location on asymmetric CPW-fed printed antenna, it is likely to excite the triple resonances at

2.44/3.66/5.25 GHz frequencies and antenna exhibits the bandwidths of 5.73% (140 MHz,

2.36–2.50 GHz), 16.98% (630 MHz, 3.40–4.03 GHz), and 14.65% (800 MHz, 5.06–5.86 GHz),

respectively.

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

52

The design procedure and antenna characteristics of the proposed multistubs loaded multiband

antenna are presented.

Keywords: Multiband antenna; Multistubs resonators; Printed antenna

Kumar, Arjun., Dixit, Amrita & Kumar, Ashok (2020). Investigation of Substrate

Integrated Waveguide (SIW) Filter Using Defected Ground Structure (DGS). Lecture Notes in

Electrical Engineering, 648, 449-456. 10.1007/978-981-15-2926-9_50

Abstract: In this paper, the behavior of substrate integrated waveguide (SIW) with

conventional defected ground structures has been studied and compared their performance in

terms of size reduction, quality factor, and filter selectivity. Various dumbbell-shaped defected

ground structures (DB-DGS) have been investigated with SIW having filtering characteristics

at 3-dB cutoff at 2 GHz by keeping the same dimension for all structures. This article holds

good studies for beginners in filter designing. For comparative analysis, all the simulations

have been carried out using HFSS 19.1.

Keywords: DB-DGS; High-frequency structure simulator (HFSS); Q-factor; Substrate

integrated waveguide (SIW) filter

Kumar, Arjun., Dixit, Amrita & Kumar, Ashok (2020). Wideband Bandpass Substrate

Integrated Waveguide (SIW) Filter For C Band Application. 4th International Conference on

Optical and Wireless Technologies 2020 (OWT-2020), Jaipur. October 03-04, 2020,

Abstract: This paper presents a Substrate Integrated Waveguide (SIW) filter using

Electromagnetic Band Gap (EBG) structure. The periodic EBG structure are etched on the top

metal surface of SIW cavity. These periodic structures create a slow wave effect on the filter

performance to achieve wide pass band at lower frequency in a small compact size. In the

proposed design Rogers 4350 is used as a dielectric material with the permittivity(εr) of 3.48

and thickness 1.524 mm. The simulated results obtained by HFSS 19.1 has a broadband from

3.25 GHz to 6.94 GHz with the bandwidth of 3.38 GHz in C band used for satellite

communication. The insertion loss is less than 0.5dB and return loss is better than 18 dB. The

size of filter is 48x10 mm2. The fractional bandwidth (FBW) of proposed filter is 68%.

Keywords: Substrate Integrated Waveguide (SIW) filter; Wideband Bandpass (WB-BPF)

Filter; Electromagnetic Band Gap (EBG).

Kumar, Arjun., Kumar, Ashok & (2020). Design and Investigation of Octagonal Patch

Antenna Using Artificial Magnetic Conductor for 5G Applications. Lecture Notes in Electrical

Engineering, 648, 393-400. 10.1007/978-981-15-2926-9_44

Abstract: In this paper, the impact of artificial magnetic conductor (AMC) enabled octagonal

patch antenna is presented for 5G applications. The different configurations of three AMC

square cells and modified square unit cells supported octagonal patch antenna are analyzed. By

using microstrip inset feed and quarter-wave transformer, it is feasible to provide better

impedance matching. It is designed on Rogers RT/duroid 5880 substrate with εr = 2.2 and size

of 10 × 10 mm2. The simulated impedance bandwidth is 17.45 GHz, which is 57% more than

the basic octagonal patch antenna.

Keywords: 5G frequencies; Artificial magnetic conductor (AMC); Metamaterials; Octagonal

patch; Patch antenna

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

53

Kumar, Arjun., Kumar, Ashok & (2020). Design and studies of bandstop filters using

modified CSRR DGS for WLAN applications. Lecture Notes in Electrical Engineering, 546,

467-475. 10.1007/978-981-13-6159-3_49

Abstract: In this paper, various dumbbell-shaped defected ground structure (DB-DGS) are

compared with complementary split ring resonator DB-DGS which has sharp roll-off factor; as

compared to other DB-DGS and a one pole bandstop filter with modified complementary split

ring resonator (CSRR), DB-DGS has been proposed with compact size 0.12λ × 0.28λ and low

return loss in passband at 2.4 GHz. The proposed filter is fabricated and measured with

insertion loss of −28 dB in stopband and −0.8 dB return loss.

Keywords: Band stop; DGS (Defected ground structure); HFSS (High-frequency structure

simulator); Modified CSRR

Kumar, Arjun., Kumar, Ashok & Dixit, Amrita (2020). Studies of Various Artificial

Magnetic Conductor for 5G Applications. Lecture Notes in Electrical Engineering, 648, 523-

530. 10.1007/978-981-15-2926-9_57

Abstract: In this paper, the characteristics of numerous types of artificial magnetic conductor

(AMC) surface structures are investigated on high-frequency structure simulator (HFSS) and

presented. These AMC surface structures are presented with zero-degree reflection phase

property for a plane wave at 28 GHz frequency band. The ±90° in-phase reflection bandwidth,

magnitude, and surface impedance of all AMC structures are simulated and compared with

others. The simulated results show that all AMC surfaces act as a high impedance surface and

good reflector. The simulated fractional bandwidth of proposed AMC type I, II, III, and IV

structures are 4.68%, 7.12%, 9.03%, and 20.64%, respectively. The better performance of the

proposed AMC type IV makes it as useful to improve the antenna’s characteristics.

Keywords: Artificial magnetic conductor (AMC); Bandwidth; HFSS; Metamaterials;

Reflection phase

Kumar, Arjun., Jain, Utkarsh., Warke, Aakash., Chauhan, Aditi., Gupta, Manan &

Kumar, Ashok., Dixit, Amrita (2020). Wide-Band Microstrip Patch Antenna for Millimetre

Wave-band Applications∗. Proceedings of 2020 IEEE-HYDCON International Conference on

Engineering in the 4th Industrial Revolution, HYDCON 2020,

10.1109/HYDCON48903.2020.9242748

Abstract: In this paper, a wide-band micro-strip patch antenna is designed for Ka-band

applications. A50 \Omega microstrip feed line is used for exciting the antenna. By using partial

ground below the substrate large bandwidth from 26.1-40.0GHz is achieved. This proposed

antenna is simulated using Ansys HFSS 19.1 simulation tool. The proposed antenna can

provide broadband characteristics and peak gain radiation performance for Ka-band

applications. The dielectric substrate Rogers RO4003 with permittivity 3.55 and loss tangent

of 0.0027 is used to design this antenna with dimensions 13 x 13 x 1.52 mm3. The simulated

antenna has a peak gain of 7.0 dBi over the operating frequency band.

Keywords: 5G; Ka band; Millimetre wave (mm Wave); Partial grounds; Wide band

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

54

Kumar, Arjun., Kumar, Ashok & Dixit, Amrita (2020). AMC Loaded CPW-fed Monopole

Antenna for Ka-band Applications. 4th International Conference on Optical and Wireless

Technologies 2020 (OWT-2020), Jaipur. October 03-04, 2020,

Abstract: A compact CPW-fed monopole antenna over a periodic arrangement of artificial

magnetic conductor (AMC) has been proposed for Ka-band application. The 50Ω coplanar

waveguide fed is used to excite the antenna. 0. The full wave simulation tool Ansys HFSS 19.1

is used to simulate and design the proposed antenna. The radiation characteristics of monopole

antenna is improved by using AMC reflector surface. The square AMC with four stub and

circular slot is designed for 28 GHz. Both the proposed monopole and AMC reflector surface

are designed on same dielectric substrate by using AMC reflector, it is demonstrated that the

gain and bandwidth is increased upto 8.76dBi and 25.76GHz-35.86 GHz (32.7%).

Keywords: Monopole Antenna; Artificial magnetic conductor (AMC); Ka-band; HFSS (High

frequency structure simulator).

Dr. Manoj Sharma, Assistant Professor, Department of ECE

Sharma, Manoj., Kumar, Kalagara Chaitanya., Lala, Aryan & Vyas, Ritesh (2020).

Periocular recognition via effective textural descriptor. 7th IEEE Uttar Pradesh Section

International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020.

10.1109/UPCON50219.2020.9376575

Abstract: The region surrounding human eye, termed as periocular region, can act as a

potential biometric trait when it comes to secure authentication of human subjects. Periocular

region, being larger than the iris region and less vulnerable than the entire face region, has been

an emerging biometric trait since last decade. In view of this, the present work is an attempt to

develop more secure periocular recognition through the application of wavelets and a texture

pattern with more robust thresholding mechanism. The wavelets are helping in the

multiresolution analysis of periocular images. Whereas the texture pattern facilitates the

representation of periocular region in form of histogram feature vectors. Experiments

performed with a challenging publicly available database corroborate the premise of the

suggested methodology. The performance measured using popular metrics like equal error rate,

genuine acceptance rate and decidability index, validates the hypothesis of the suggested

method. Comparison with existing approaches establishes the outperforming nature of the

proposed approach.

Keywords: Biometrics; Local pattern; Multiresolution analysis; Periocular recognition

Dr. Rama S Komaragiri, Professor & HOD, Department of ECE

Komaragiri, Rama S. (2020). Reconfigurable circuits based on Single Gate Reconfigurable

Field-Effect Transistors. Proceedings of CONECCT 2020 - 6th IEEE International Conference

on Electronics, Computing and Communication Technologies.

10.1109/CONECCT50063.2020.9198322.

Abstract: Design and simulation of reconfigurable circuits using a single gate reconfigurable

field-effect transistor (SG-RFET) are presented. The device characteristics of SG-RFET is

benchmarked against the silicon gate-all-around FET (GAAFET) with same gate length.

Reconfigurable circuits- inverter, current mirror, ring oscillator using SG-RFET device are

validated with simulation results in Sentaurus technology computer-aided design (TCAD) tool.

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

55

Simple design, low path delay and reduced routing complexity make SG-RFET a potential

candidate compared to the existing transistors.

Keywords: Current mirror; GAAFET; Reconfigurable circuits; Ring oscillator; SG-RFET

Komaragiri, Rama S. (2020). Performance analysis of non-spiral planar microcoils for

biomedical electromagnetic microactuators. AIP Conference Proceedings, 2222.

10.1063/5.0003966

Abstract: This paper presents mathematical modelling and electrical characterization of non-

spiral planar microcoils of circular and square geometries which find use in electromagnetic

microactuators for biomedical devices. Non-spiral planar microcoils are advantageous over

conventional spiral coils regarding fabrication easiness and heating losses. Circular and square

coil geometries are compared through experimental analysis of coils' electrical parameters such

as inductance, series resistance and parasitic capacitance. Comprehensive finite element

analysis of the electromagnetic microactuator based on non-spiral planar microcoils is

performed. The force outputs from the actuator are compared for circular and square coil

geometries to reach an optimum actuator geometry which can generate sufficient force for

microfluidic applications in biomedical engineering.

Keywords: Microactuators; Non-spiral planar microcoils; Finite element analysis

Komaragiri, Rama S. (2020). Performance Evaluation of Via-free Non-spiral Planar

Microcoils. 15th IEEE International Conference on Nano/Micro Engineered and Molecular

System, NEMS 2020, 247-250.10.1109/NEMS50311.2020.9265635

Abstract: Planar microcoils are essential components in magnetic biosensors for the detection

of magnetic micro or nanoparticles. The particles are surface-functionalized to recognize

specific molecular targets. Various nonspiral planar coil geometries are analyzed in this work

to select the most suitable geometry for biosensing applications. Nonspiral planar coils have

the advantage of the easier fabrication process and lower coil parasitics than that of its spiral

counterparts. The microcoils are fabricated using a single mask level lithography and

characterized to extract the electrical parameters of the microcoil. The results suggest that the

circular geometry possesses the lowest series coil resistance and a uniform magnetic field

distribution along the coil turns suitable for biosensing applications among the various

nonspiral planar microcoils under consideration.

Keywords: biosensor; coil parasitics; magnetic particle; molecular targets; single mask level

lithography

Dr. Ritesh Vyas, Assistant Professor, Department of ECE

Vyas, Ritesh (2020). Towards Ocular Recognition Through Local Image Descriptors. In: Nain

N., Vipparthi S., Raman B. (eds) Computer Vision and Image Processing. CVIP 2019.

Communications in Computer and Information Science, 1147 CCIS, 3-12. 10.1007/978-981-

15-4015-8_1

Abstract: Iris and periocular (collectively termed as ocular) biometric modalities have been

the most sought for modalities, due to their increased discrimination ability. Moreover, both

these modalities can be captured through a single acquisition setup, leading to ease in user

interaction. Owing to these advantages, this paper presents two local descriptors, namely

statistical and transform-based descriptors, to investigate the worthiness of ocular recognition.

The first descriptor uses mean and variance formulae after two levels of partitioning to extract

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

56

the distinctive features. Whereas, second descriptor comprises of implementation of curvelet

transform, followed by polynomial fitting, to extract the features. Individual iris and periocular

features are combined through simple concatenation operation. The experiments performed on

the challenging Cross-Eyed database, vindicate the efficacy of both the employed descriptors

in same-spectral as well as cross-spectral matching scenarios.

Keywords: Ocular recognition ·Iris recognition ·Periocular recognition ·Feature extraction

Vyas, Ritesh (2020). Towards adept hand-crafted features for ocular biometrics. 2020 8th

International Workshop on Biometrics and Forensics, IWBF 2020 - Proceedings.

10.1109/IWBF49977.2020.9107952

Abstract: This article presents a hand-crafted feature descriptor for ocular recognition, which

as opposed to the deep-learning based approaches, is free from any kind of learning. The

proposed approach is able to mitigate the limitations of iris recognition, such as poor iris

segmentation, partial or covered iris. The proposed approach leverages by the unique texture

present in the periocular region, which can provide complementary details along with the iris

modality or can act as a potential standalone trait. The proposed descriptor is evaluated on three

benchmark databases, namely VISOB, Cross Eyed and MICHE. Two of these databases

(VISOB and MICHE) provide eye images captured through the smartphones, whereas the third

database provides standard eye images registered in visible as well as near-infrared

wavelengths. Hence, the evaluation reported in this article becomes a comprehensive one. The

experimental results exhibit that the proposed approach proves to be suitable in challenging

evaluation frameworks.

Keywords: Cross-spectral; Fusion; Ocular recognition; Smartphone biometrics

Journal Papers

Dr. Amit Singhal, Assistant Professor, Department of ECE

Singhal, Amit (2020). An efficient removal of power-line interference and baseline wander

from ECG signals by employing Fourier decomposition technique. Biomedical Signal

Processing and Control, 57, 101741. 10.1016/j.bspc.2019.101741.

Abstract: Baseline wander (BW) and power-line interference (PLI) tend to occur in every

recorded electrocardiogram (ECG) signal and can significantly deteriorate the quality of the

signal. They need to be separated from the ECG signal to facilitate an accurate diagnosis of the

patient. In this paper, we propose a new methodology based on the Fourier decomposition

method (FDM) to separate both BW and PLI simultaneously from the recorded ECG signal

and obtain clean ECG data. The proposed method employs either of discrete Fourier transform

(DFT) or discrete cosine transform (DCT) in order to process the signal. Key DFT/DCT

coefficients relating to BW and PLI are identified and then suppressed using optimally

designed FDM based on a zero-phase filtering approach. The effectiveness of our method is

validated on the MIT-BIH Arrhythmia database. Simulation results clearly demonstrate that

the proposed method performs superior in comparison to the existing state-of-the-art

techniques at different levels of signal to noise ratio power (SNR). Moreover, this method has

low computational complexity which makes it suitable for real-time pre-processing of ECG

signals.

Keywords: Baseline wander; ECG signal; Noise removal; Power-line interference; Fourier

decomposition method.

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

57

Singhal, Amit (2020). Detection of apnea events from ECG segments using Fourier

decomposition method. Biomedical Signal Processing and Control, 61, 102005.

10.1016/j.bspc.2020.102005.

Abstract: Absence of airflow in breathing during sleep for more than 10 s is known as sleep

apnea. It causes low oxygen levels in the blood which may lead to many cardiovascular

problems. Current methods of detection are rather time-consuming and expensive. Automated

detection using electrocardiogram (ECG) signal is seen as a promising and efficient method

for the identification of sleep apnea events. In this paper, the single-lead ECG signal is divided

into 1-min segments, and separated into frequency bands using Fourier decomposition method.

From these signal components, features like mean absolute deviation and entropy are computed

to classify the ECG segments using machine learning algorithms. The proposed method yields

an accuracy of 92.59%, sensitivity of 89.70%, specificity of 94.67% and precision of 91.27%

on MIT Physio Net Apnea-ECG dataset, using a support vector machine (SVM) classifier with

the Gaussian kernel. The strength of the proposed method has been verified on two more

datasets, namely MIT-BIH polysomnography and University College Dublin sleep apnea

database (UCDDB). The classification results are compared with the existing state-of-the-art

techniques to demonstrate the superior performance of the proposed method. Proposed

methodology is implemented using the fast Fourier transform (FFT) which makes it

computationally efficient and can be used for real-time sleep apnea detection.

Keywords: Electrocardiogram (ECG) signal; Entropy; Fourier decomposition method (FDM);

Sleep apnea.

Singhal, Amit (2020). Modeling and prediction of COVID-19 pandemic using Gaussian

mixture model. Chaos Solitons & Fractals, 138, 110023. 10.1016/j.chaos.2020.110023

Abstract: COVID-19 is caused by a novel coronavirus and has played havoc on many

countries across the globe. A majority of the world population is now living in a restricted

environment for more than a month with minimal economic activities, to prevent exposure to

this highly infectious disease. Medical professionals are going through a stressful period while

trying to save the larger population. In this paper, we develop two different models to capture

the trend of a number of cases and also predict the cases in the days to come, so that appropriate

preparations can be made to fight this disease. The first one is a mathematical model accounting

for various parameters relating to the spread of the virus, while the second one is a non-

parametric model based on the Fourier decomposition method (FDM), fitted on the available

data. The study is performed for various countries, but detailed results are provided for the

India, Italy, and United States of America (USA). The turnaround dates for the trend of infected

cases are estimated. The end-dates are also predicted and are found to agree well with a very

popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the

total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively,

predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study

produces promising results with the potential to serve as a good complement to existing

methods for continuous predictive monitoring of the COVID-19 pandemic.

Keywords: COVID-19; Discrete cosine transform (DCT); Fourier decomposition method

(FDM); Gaussian mixture model (GMM); Mathematical model; Susceptible-infected-

recovered (SIR) model.

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

58

Singhal, Amit & Mehla, Virender Kumar (2020). A novel approach for automated

alcoholism detection using Fourier decomposition method. Journal of Neuroscience Methods,

346, 108945. doi.org/10.1016/j.jneumeth.2020.108945

Abstract: Background The identification of alcoholism is of prime importance because of its

adverse effects on the central nervous system. Moreover, people suffering from alcoholism are

susceptible to various health problems such as cardiomyopathy, immune system disorder, high

blood pressure, cirrhosis, brain anomalies, and heart problems. New method This study

presents a novel approach, based on Fourier theory, known as Fourier decomposition method

(FDM) for automatic identification of alcoholism using electroencephalogram (EEG) signals.

The FDM approach is employed to decompose EEG signals into a set of desired orthogonal

components, commonly referred as Fourier intrinsic band functions (FIBFs), obtained by

dividing the complete bandwidth of EEG signal under analysis into equal frequency bands.

Time-domain features such as Hjorth parameters, kurtosis, inter-quartile range, and median

frequency are extracted from FIBFs. To reduce the complexity, Kruskal–Wallis (KW)

statistical test, is performed to adopt the most significant features. Results Simulation results

are obtained using different classification methods, namely, k-nearest neighbor (kNN), support

vector machine (SVM), and linear discriminant analysis (LDA). The proposed approach with

the SVM classifier using radial basis function provides average accuracy of 99.98%, sensitivity

of 99.99% and specificity of 99.97%. Performance is also tested in the presence of noise.

Comparison with existing method(s) Classification results highlight the superior performance

of our method in comparison to existing works. Conclusions The proposed scheme provides

an efficient approach and can be employed in real-time alcoholism detection.

Keywords: Alcoholism; EEG signal; Fourier decomposition method; Kruskal–Wallis test;

Support vector machine.

Dr. Ankit Kumar Pandey, Assistant Professor, Department of ECE

Pandey, Ankit Kumar (2020). Schottky barrier photodetector utilizing tungsten grating

nanostructure. Journal of Nanophotonics, 14(4), 046002-8. 10.1117/1.JNP.14.046002

Abstract: Schottky barrier photodetector (PD) (tungsten/nickel/aluminum nitride) based on

stacked tungsten gratings is reported in our work. The absorbance of the grating-based structure

is obtained using rigorous coupled-wave analysis method. In addition, a responsivity value of

6.164 mA∕W is calculated at λ ¼ 559.38 nm based on Fowler’s model. More than three times

enhancement in responsivity is achieved as compared with that of bulk structure (without

grating) based Schottky barrier PD. In addition, a detectivity value of 33.11 × 1011 cm Hz1∕2∕W

is obtained. Furthermore, the effect of grating variables and incident angle on responsivity are

also demonstrated. In future, the proposed structure can also be used as a plasmonic sensor,

absorber, or spectral filter.

Keywords: Schottky barrier; Tungsten; Plasmonic; Grating; Photodetection; Grating;

Rigorous; Coupled wave analysis.

Pandey, Ankit Kumar (2020). Advancements in grating nanostructure based plasmonic

sensors in last two decades: A Review. IEEE Sensors Journal, 1-12.

10.1109/JSEN.2020.3045292

Abstract: Grating nanostructures play a crucial role for their application in plasmonic sensors.

A comprehensive review on grating nanostructure based plasmonic sensor is presented in this

article. The structural developments and sensing performance enhancements in last two

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

59

decades are focused upon. Surface plasmon resonance (SPR) and localized SPR (LSPR)

sensors have gained much popularity and are still flourishing due to advancements in

nanofabrication technology. Refractive index change and field enhancement are the two

phenomena that are responsible for qualitative and quantitative detection and analysis of a

variety of samples. The future scenario and possibilities regarding the advancements in grating

plasmonic sensors have also been discussed.

Keywords: Plasmon, Sensor, Grating, Nanostructure, Sensitivity

Pandey, Ankit Kumar (2020). Plasmonic sensor utilizing Ti3C2Tx MXene layer and fluoride

glass substrate for bio- and gas-sensing applications: Performance evaluation. Photonics and

Nanostructures - Fundamentals and Applications, 42, 100863.

Abstract: The two-dimensional (2D) heterostructure of black phosphorus (BP)–MXene shows

great stability in liquid and gaseous environments. Sensitivity enhancement of a surface

plasmon resonance (SPR) sensor utilizing BP and Ti3C2Tx MXene layer is proposed in this

work. A CaF2 glass prism is considered as the coupling substrate and 2D Ti3C2Tx MXene as

analyte interacting layer. The sensor’s performance is demonstrated in terms of sensitivity and

figure-of-merit (FOM) values at an operating wavelength of 633 nm. An extremely high

angular sensitivity of 322.46/RIU is obtained, with a significant FOM value of 55.596/RIU

(analyte refractive index ∼ 1.33) for three-layer BP–Ti3C2Tx MXene monolayer based SPR

sensor. Further, the sensing of gaseous analyte using the proposed sensor structure is also

explored. This work will open new avenues for application of BP–MXene heterostructure in

various optoelectronic devices.

Keywords: Mxene; Surface plasmon resonance; Sensitivity; Black phosphorus; FOM.

Dr. Arjun Kumar, Assistant Professor, Department of ECE

Kumar, Arjun (2020). Wideband circularly polarized parasitic patches loaded coplanar

waveguide-fed square slot antenna with grounded strips and slots for wireless communication

systems. AEU - International Journal of Electronics and Communications, 114, 153011.

10.1016/j.aeue.2019.153011

Abstract: In this paper, the design, implementation, and experimental validation of a wideband

circularly polarized square slot antenna (CPSSA) is presented. The proposed antenna

comprises of an irregular square patch, asymmetrical grounded-L strips, an inverted-L

grounded slot, a parasitic asymmetrical rectangular split ring resonator (RSRR) and inverted-

L strip patches with the overall size of 0.305λL × 0.305λL × 0.007λL. By appropriately

embedding strips, slots and parasitic patches on the antenna structure, the measured wideband

3-dB axial ratio bandwidth (ARBW) of 83.2% (2.33–5.65 GHz) is obtained, while the

measured impedance bandwidth (IBW) of 86.72% (2.165–5.48 GHz) is achieved. Overall, CP

bandwidth is 80.66%. A parametric study of different design parameters is investigated, and

the overall performance of the antenna is presented.

Keywords: Circular polarization; Grounded-L strip; Square slot antenna; Split-ring resonator.

Kumar, Arjun (2020). Design of dual mode wideband SIW slot antenna for 5G applications.

International Journal of RF and Microwave Computer-Aided Engineering, 30.

10.1002/mmce.22449

Abstract: A compact dual mode wideband antenna is presented in this paper which operates

from 24.8 to 31.6 GHz (suitable for 5G applications). The antenna is based on substrate

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

60

integrated waveguide (SIW) and consists of a two-element array of half-bow-tie slots. Each

slot excites two modes (slot mode and half patch mode) in the antenna due to its geometry. The

resonant frequencies in these modes are optimized to achieve wide impedance bandwidth using

Ansys HFSS. A prototype is fabricated and its measured impedance bandwidth is 6.8 GHz,

which is in close agreement with the simulated one. It also has a peak gain of 8.5 dBi at 30.4

GHz and its physical size is 2.4λg × λg, where λg is the guide wavelength at center frequency.

Keywords: 5G; Antenna; Dual mode; Substrate integrated waveguide (SIW)

Dr. Manjeet Kumar, Assistant Professor, Department of ECE

Kumar, Manjeet (2020). Design of optimal two-dimensional FIR filters with quadrantally

symmetric properties using vortex search algorithm. Journal of Circuits, Systems and

Computers, 29. 10.1142/S0218126620501558

Abstract: This research paper presents a new evolutionary technique named vortex search

optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved

performance both in pass-band and stop-band regions. Optimum filter coefficients are

calculated by minimizing the deviation of actual frequency response from specified or desired

response. Efficiency of the designed filter is measured by several parameters, such as maximum

pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to

execute the code. Analysis of the performance of designed filter is correlated with various

different algorithms like real coded genetic algorithm, particle swarm optimization, genetic

search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative

study shows significant reduction in pass-band error, stop-band error and execution time.

Keywords: 2D finite impulse response filter; Digital filter; Optimization algorithms;

Quadrantally symmetric impulse response; Vortex search algorithm

Dr. Ritesh Vyas, Assistant Professor, Department of ECE

Vyas, Ritesh (2020). Smartphone based iris recognition through optimized textural

representation. Multimedia Tools and Applications,79, 14127–14146. 10.1007/s11042-019-

08598-7.

Abstract: Mobile devices have become ubiquitous nowadays and so is the need of secure

access to these devices. Iris being the most reliable and hard-to-tamper biometric trait, can

serve the aforementioned purpose. Iris recognition on mobile phones has become a significant

and challenging task for the research community. With advancement in technology, it has now

become feasible to use mobile devices’ in-built cameras to unlock the device through the user’s

iris. This paper presents a convenient and efficient approach: optimal bit-transition codes

(OBTC), for representing mobile iris images in a more distinctive manner. The approach is

derived from the texture analysis property of 2D Gabor filters. Optimization of Gabor

parameters is performed for iris images from two challenging mobile iris databases: MICHE I

(which comprises of eye images acquired from three different smartphones: iPhone5, Galaxy

S4 and Galaxy Tab2) and VISOB (which contains eye images acquired from iPhone5S,

Samsung Note 4 and Oppo N1). After filtering, the image responses are converted to binary

numbers and stored in concatenated vectors. Later, the concatenated vectors produce binary

strings across the direction of concatenation and number of bit-transitions in these binary

strings are encoded to form the complete feature vectors. A capacious experimentation is

performed on the challenging MICHE I and VISOB iris databases. Comparison of the proposed

approach with several state-of-the-art approaches clearly shows its expediency. More

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

61

importantly, the proposed iris recognition approach performs at par with a commercial iris

matcher, named VeriEye, which proves its usefulness.

Keywords: Iris biometrics; Mobile devices; Bit-transition codes; Optimization; Gabor filtering

Dr. Sudhir Chandra, Professor, Department of ECE

Chandra, Sudhir (2020). Self-encapsulated DC MEMS switch using recessed cantilever beam

and anodic bonding between silicon and glass. Microsystem Technologies-Micro-and

Nanosystems-Information Storage and Processing Systems, 863-869. 10.1007/s00542-020-

04993-5

Abstract: In the present work, we report design, fabrication and testing of a novel DC MEMS

switch incorporating a self-encapsulated and recessed micro-cantilever beam. Wafer level

anodic bonding with press-on contacts between silicon and glass is used innovatively to secure

the recessed cantilever beam from one side. The cantilever is made of single crystal silicon in

a recessed cavity whereas actuating electrode and signal lines are formed on corning glass.

Anodic bonding provides “press on contacts” between silicon and glass plate and also secures

the cantilever beam in the recessed cavity in silicon. The signal lines and pull-in electrode are

formed on the glass plate using aluminium metallization while the cantilever has a gold pad at

its tip. The anodic bonding provides three major advantages (i) it encapsulates the fragile beam

and thus protects it from damage during dicing and packaging process (ii) it provides press-on

contact between signal lines on glass plate and bonding pads on silicon (iii) it makes the beam

optically visible. The devices were simulated using COMSOL multiphysics software and the

results were compared with experimentally measured values. The cantilever based switch

operates at low actuation voltage (average ~ 12 V) indicating that it can be used for power

electronic circuits and various other applications.

Keywords: Microsystem Technology; DC MEMS switch; Electronic circuits.

Dep

art

men

t of

Ele

ctro

nic

s an

d C

om

mu

nic

ati

on

En

gin

eeri

ng

62

Department of Mathematics

Journal Papers

Dr. Anirudha Poria, Assistant Professor, Department of Mathematics

Poria, Anirudha (2020). Positive Weight Function and Classification of g-Frames. Numerical

Functional Analysis and Optimization, 41(8), 950-968. 10.1080/01630563.2020.1728771

Abstract: Given a positive weight function and an isometry map on a Hilbert spaces H,H, we

study a class of linear maps which is a g-frame, g-Riesz basis, and a g-orthonormal basis for

HH with respect to CC in terms of the weight function. We apply our results to study the frame

for shift-invariant subspaces on the Heisenberg group.

Keywords: g-frames; g-Riesz bases; Heisenberg group; Shift-invariant subspaces; Weight

function.

Poria, Anirudha (2020). Semi-Continuous G-Frames in Hilbert Spaces. Methods of

Functional Analysis and Topology, 26, 249-261. 10.31392/MFAT-npu263.2020.06.

Abstract: In this paper, we introduce the concept of semi-continuous g-frames in Hilbert

spaces. We first construct an example of semi-continuous g-frames using the Fourier transform

of the Heisenberg group and study the structure of such frames. Then, as an application we

provide some fundamental identities and inequalities for semi-continuous g-frames. Finally,

we present a classical perturbation result and prove that semi-continuous g-frames are stable

under small perturbations.

Keywords: Continuous g-frames; Frame identity; g-frames; Perturbation; Semi-continuous g-

frames; Stability

Dr. Neelam Choudhary, Assistant Professor, Department of Mathematics

Choudhary, Neelam (2020). On approximating the nearest Ω-stable matrix. Numerical Linear

Algebra with Applications, 27(3), e2282. 10.1002/nla.2282.

Abstract: In this paper, we consider the problem of approximating a given matrix with a matrix

whose eigenvalues lie in some specific region Ω of the complex plane. More precisely, we

consider three types of regions and their intersections: conic sectors, vertical strips, and disks.

We refer to this problem as the nearest Ω‐stable matrix problem. This includes as special cases

the stable matrices for continuous and discrete time linear time‐invariant systems. In order to

achieve this goal, we parameterize this problem using dissipative Hamiltonian matrices and

linear matrix inequalities. This leads to a reformulation of the problem with a convex feasible

set. By applying a block coordinate descent method on this reformulation, we are able to

compute solutions to the approximation problem, which is illustrated on some examples.

Keywords: Ω‐stability; Convex optimization; Linear time‐invariant systems; Stability radius.

Choudhary, Neelam (2020). Liquid vibrations in circular cylindrical tanks with and without

baffles under horizontal and vertical excitations. Engineering Analysis with Boundary

Elements, 12013-27. 10.1016/j.enganabound.2020.07.024

Abstract: The paper is devoted to issues of numerical simulation of free surface elevations in

rigid circular cylindrical fluid-filled tanks under external vertical and horizontal loadings with

usage boundary element methods. The liquid in reservoirs is assumed to be ideal and

incompressible, and its flow is irrotational. The influence of horizontal and vertical baffle

Dep

art

men

t of

Math

emati

cs

63

installation is considered and damping effects due baffles are estimated. The influence of

elasticity effects is considered. Free, forced and parametrical vibrations are examined. Modes

of the free liquid vibrations present basic functions for analysis of forced and parametric

vibrations. The modes and frequencies of the free liquid vibrations in baffled and un-baffled

circular cylindrical tanks are received by using single- and multi-domain boundary element

methods. The new effective procedure is developed for numerical solution of resolving system

of singular integral equations. The problems of forced vibrations are reduced to solving the

systems of second order ordinary differential equations. For parametric vibrations the system

of Mathieu equations with non-zero right-hand parts is obtained. The numerical simulation of

different kinds of loadings and baffle configurations is accomplished. The instability conditions

of the fluid-filled tank subjected to both horizontal and vertical excitations are considered.

Keywords: Boundary element method; Circular cylindrical tank; Free and forced vibrations;

Ince-Strutt diagram; Parametric resonance; Sloshing; Vertical and horizontal baffles

Dr. Sarika Goyal, Assistant Professor, Department of Mathematics

Sarika Goyal (2020). Nonresonance and Resonance Problems for Nonlocal Elliptic Equations

with Respect to the Fucik Spectrum. Taiwanese Journal of Mathematics, 24(5), 1153-1177.

Abstract: In this article, we consider the following problem

where Ω⊂Rn is a bounded domain with Lipschitz boundary, n>2s, 0<s<1, (α,β)∈R2, f:R→R

is a bounded and continuous function and h∈L2(Ω). We prove the existence results in two

cases: first, the nonresonance case where (α,β) is not an element of the Fučik spectrum. Second,

the resonance case where (α,β) is an element of the Fučik spectrum. Our existence results

follows as an application of the saddle point theorem. It extends some results, well known for

Laplace operator, to the nonlocal operator.

Keywords: Nonlocal problem; Fučik spectrum; Resonance; Nonresonance; Saddle point

theorem

Sarika Goyal (2020). Polyharmonic Systems Involving Critical Nonlinearities with Sign-

Changing Weight Functions. Electronic Journal of Differential Equations, 119, 1-25.

Abstract: This article concerns the existence of multiple solutions of the polyharmonic system

involving critical nonlinearities with sign-changing weight functions

where (−∆)m denotes the polyharmonic operators, Ω is a bounded domain in RN with smooth

boundary ∂Ω, m ∈ N, N ≥ 2m + 1, 1 < r < 2 and β > 1, γ > 1 satisfying 2 < β + γ ≤ 2 ∗m with

2∗m = 2N N−2m as a critical Sobolev exponent and λ, µ > 0. The functions f, g and h : Ω →

R are sign-changing weight functions satisfying f, g ∈ Lα(Ω) and h ∈ L∞(Ω) respectively.

Using the variational methods and Nehari manifold, we prove that the system admits at least

two nontrivial solutions with respect to parameter (λ, µ) ∈ R2 + \ (0, 0).

Keywords: Polyharmonic operator system; Sign-changing weight functions; Critical

exponent; Nehari manifold; Concave-convex nonlinearities.

Dep

art

men

t of

Math

emati

cs

64

Goyal, Sarika (2020). Fractional Hardy-Sobolev operator with sign-changing and singular

nonlinearity. Applicable Analysis, 992892-2916.10.1080/00036811.2019.1585535

Abstract: We study the following fractional equation with Hardy potential and singular

nonlinearity (Formula presented.) where (Formula presented.) is a bounded domain in

(Formula presented.) with smooth boundary (Formula presented.), n>2s, (Formula presented.),

(Formula presented.) the sharp constant of the fractional Hardy Sobolev inequality, (Formula

presented.), (Formula presented.) with (Formula presented.), (Formula presented.) such that

(Formula presented.), and (Formula presented.) is a sign-changing function such that (Formula

presented.). Using variational methods, we show existence, non-existence and multiplicity of

positive solutions of (Formula presented.) with respect to the parameter λ.

Keywords: Alexander Pankov; Hardy term; Nehari manifold; Non-local operator; sign-

changing weight function; singular nonlinearity

Dep

art

men

t of

Math

ema

tics

65

Department of Mechanical and Aerospace Engineering (MAE)

Conference Papers

Dr. Deepali Atheaya, Assistant Professor, Department of MAE

Atheaya, Deepali & Saurabh, Ashish (2020). Study of hybrid photovoltaic thermal systems.

IOP Conference Series: Materials Science and Engineering, 748(1), 12016. 10.1088/1757-

899X/748/1/012016

Abstract: Sun is the primary source of renewable energy. It is abundant, inexhaustible and

clean. It plays a very important role in the present energy crisis. Solar energy can be harnessed

by hybrid photovoltaic thermal system to generate power and heat. These devices generate

thermal and electrical energy simultaneously. Hybrid photovoltaic thermal systems have high

efficiency. There is ample scope in this area as much work remains to be done. The hybrid

system has huge potential in India where the availability of solar energy is spread throughout

the country. The paper focusses on the study of hybrid photovoltaic thermal systems.

Keywords: Photovoltaic thermal; Hybrid solar; PVT performance.

Dr. Vinayak Ranjan, Professor & HOD, Department of Mechanical and Aerospace

Engineering (MAE)

Ranjan, Vinayak & Deshpande, Sahil Sujit (2020). Kinematics Evaluation of Gait

Spatiotemporal Parameters of Transfemoral Amputees While Walking with Jaipur Knee and

3R15 Knee Joints: A Case Study with Indian Population. Smart Innovation, Systems and

Technologies, 174, 449-460. 10.1007/978-981-15-2647-3_40.

Abstract: After amputation surgery, patients use prosthesis to compensate missing

functionality. The performance of the above-knee amputee is significantly reduced than normal

subjects. The knee joint plays an important role in overall performance of the prosthesis. In

India, a cost-effective solution like a Jaipur knee joint has been widely used. Many researchers

have studied knee joints like 3R15 and 3R20; however, the performance of Jaipur knee joint

during gait has not been reported with the 3R15 joint yet. Therefore, the objective of this

research was to evaluate the comparative variations in spatiotemporal gait parameters of the

transfemoral patients with 3R15 and Jaipur knee joints. Gait analysis was performed on 11

unilateral transfemoral amputees. Subjects were longtime users of the Jaipur knee joint.

Subjects walked with their comfortable speed to evaluate the gait parameters with six VICON

cameras and KINEMATRIX SYSTEM-a 3D motion analysis system. The difference between

the performances of the subjects with Jaipur knee and 3R15 joints was associated with the

walking speed, which enhanced gait performance with Jaipur joint. The spatiotemporal

parameters were compared using the paired t-test method. After the study, it was found that the

spatiotemporal parameters of patients were enhanced with the Jaipur knee joint. Subjects

reported walking speed of 61.5 m/min with Jaipur knee joint, while the reported speed with

3R15 was 29.8 m/min (p-value = 0.05). Based on the increase in few parameters (p-value &lt;

0.055), the gait symmetry was reported enhanced than that of 3R15 knee joint.

Keywords: Amputation; Gait; Prosthetic knee; Spatiotemporal; Transfemoral

Dep

art

men

t of

Mec

han

ical

an

d A

ero

spa

ce E

ng

inee

rin

g

66

Dr. Deepali Atheaya, Assistant Professor, Department of MAE

Atheaya, Deepali (2020). Thermodynamic analysis of Organic Rankine cycle driven by

reversed absorber hybrid photovoltaic thermal compound parabolic concentrator system.

Renewable Energy, 147(1), 2118-2127.

Abstract: In current study, an effort is made to improve the overall performance of the solar

powered organic Rankine system to meet increasing energy demand and mitigate the resulting

menace caused by environmental imbalance. Hourly performance evaluation of Solar powered

Organic Rankine Cycle (ORC) system is done for varying concentration ratio in terms of

collector output temperature, heat gain in evaporator, combined thermal and exergetic

efficiency. The Heptane/R245fa is used as working fluid at fixed (0.1/0.9) fractional mass

component. Organic Rankine system is coupled with reversed absorber hybrid photovoltaic

thermal compound parabolic concentrator (PVTCPC). The highest collector outlet temperature

125.45 °C, heat gain 3.21 × 105 W, combined thermal efficiency 7.8%, and exergetic efficiency

14.38% is observed at 13:00 h for 6 concentration ratio of the concentrator. This study

establishes the utility of low-grade renewable heat source conversion into solar electrification,

solar irrigation, solar cooling and solar water purification.

Keywords: Organic Rankine cycle; Reversed absorber; Concentration ratio; Heptane /R245fa;

Zeotropic mixture.

Dr. Neelanchali Asija Bhalla Assistant Professor, Department of MAE

Bhalla, Neelanchali Asija., Chauhan, Vimal & Danish, Mohammad (2020). Study of shear

thickening behavior using experimental, mathematical modeling and numerical simulation

studies. Materials Today: Proceedings. 10.1016/j.matpr.2020.10.638.

Abstract: In this paper, the effect of increasing shear rate was studied on the viscosity of shear

thickening fluid (STF). It was observed that the viscosity as well as the severity of shear

thickening increased with the shear rate. The recovery rate ratio for STF at 25°C was measured

as 88.54 % after 30 seconds, in three interval thixotropy test. Mathematical modeling was also

done with the existing viscosity function of STF at 25°C. It was observed that the viscosity

obtained from mathematical model was in good match with the experimental data. For the

validation of numerical simulation results, a 3D model of cone and plate was generated in

ANSYS FLUENT. A user defined function (UDF) with all the chemical and physical properties

of STF was used in 3D model at 25°C. It was found that STF with UDF depicted similar results

as obtained from experimental results, showing shear thinning at low shear rates and shear

thickening beyond the critical shear rate. Critical viscosity obtained from experimental,

mathematical model and numerical simulation was 0.653 Pa.s at critical shear rate of 10 s-1.

Post shear thickening viscosity of 30.017 Pa.s was achieved for experimental and mathematical

model and 30 Pa.s for numerical simulation.

Keywords: Shear thickening fluid; Viscosity; Shear rate; Rheometer; Fumed silica.

Dr. Prabhakar Sathujoda, Professor, Department of MAE

Sathujoda, Prabhakar (2020). Free Vibration Analysis of a Thermally Loaded Porous

Functionally Graded Rotor–Bearing System. Applied Sciences journal, 10(22), 8197.

10.3390/app10228197.

Abstract: The present work deals with natural and whirl frequency analysis of a porous

functionally graded (FG) rotor–bearing system using the finite element method (FEM).

Dep

art

men

t of

Mec

han

ical

an

d A

erosp

ace

En

gin

eeri

ng

Journal Papers

67

Stiffness, mass and gyroscopic matrices are derived for porous and non-porous FG shafts by

developing a novel two-noded porous FG shaft element using Timoshenko beam theory (TBT),

considering the effects of translational inertia, rotatory inertia, gyroscopic moments and shear

deformation. A functionally graded shaft whose inner core is comprised of stainless steel (SS)

and an outer layer made of ceramic (ZrO2) is considered. The effects of porosity on the volume

fractions and the material properties are modelled using a porosity index. The non-linear

temperature distribution (NLTD) method based on the Fourier law of heat conduction is used

for the temperature distribution in the radial direction. The natural and whirl frequencies of the

porous and non-porous FG rotor systems have been computed for different power law indices,

volume fractions of porosity and thermal gradients to investigate the influence of porosity on

fundamental frequencies. It has been found that the power law index, volume fraction of

porosity and thermal gradient have a significant influence on the natural and whirl frequencies

of the FG rotor–bearing system.

Keywords: Porosity; Functionally graded rotor; Finite element method; Non-linear

temperature distribution; Power law; Whirl frequency.

Sathujoda, Prabhakar (2020). Detection of a slant crack in a rotor bearing system during

shut-down. Mechanics Based Design of Structures and Machines, 48(2), 266-276.

10.1080/15397734.2019.1707686.

Abstract: The transient response of a slant-cracked rotor system during shut-down has been

analyzed while it is decelerating through the critical speed. Vibration response has been

simulated by using finite element method (FEM) for flexural vibrations. An unbalance force

and a harmonically-varying torque is applied on the rotor system. Subharmonic frequency

components at an interval frequency corresponding to torsional frequency were found in the

frequency domain (Fast Fourier Transform (FFT) plot). These peaks are centered on the critical

speed of the rotor system when a slant crack is present and can be used to detect the crack. The

fundamental mode shapes of the rotor bearing systems are wavelet transformed to identify the

crack location. A peak is observed at the crack location in the spatial variation of the wavelet-

transformed fundamental mode shape. A few suggestions for the detection of slant cracks in

the rotor during shut-down are described.

Keywords: Rotor bearing system; Slant crack; Continuous wavelet transform critical speed

mode shape.

Sathujoda, Prabhakar (2020). Detection of Coupling Misalignment in a Rotor System Using

Wavelet Transforms. World Academy of Science, Engineering and Technology International

Journal of Aerospace and Mechanical Engineering, 14(4), 152-157.

Abstract: Vibration analysis of a misaligned rotor coupling bearing system has been carried

out while decelerating through its critical speed. The finite element method (FEM) is used to

model the rotor system and simulate flexural vibrations. A flexible coupling with a frictionless

joint is considered in the present work. The continuous wavelet transform is used to extract the

misalignment features from the simulated time response. Subcritical speeds at one-half, one-

third, and one-fourth the critical speed have appeared in the wavelet transformed vibration

response of a misaligned rotor coupling bearing system. These features are also verified

through a parametric study.

Keywords: Continuous wavelet transform; Flexible coupling; Rotor system; Sub critical

speed.

Dep

art

men

t of

Mec

han

ical

an

d A

ero

spa

ce E

ng

inee

rin

g

68

Sathujoda, Prabhakar & Bose, Arnab (2020). Natural Frequency Analysis of a Porous

Functionally Graded Shaft System. World Academy of Science, Engineering and Technology

International Journal of Aerospace and Mechanical Engineering, 14(3), 123-131.

Abstract: The vibration characteristics of a functionally graded (FG) rotor model having

porosities and micro-voids is investigated using three-dimensional finite element analysis. The

FG shaft is mounted with a steel disc located at the midspan. The shaft ends are supported on

isotropic bearings. The FG material is composed of a metallic (stainless-steel) and ceramic

phase (zirconium oxide) as its constituent phases. The layer wise material property variation is

governed by power law. Material property equations are developed for the porosity modelling.

Python code is developed to assign the material properties to each layer including the effect of

porosities. ANSYS commercial software is used to extract the natural frequencies and whirl

frequencies for the FG shaft system. The obtained results show the influence of porosity

volume fraction and power-law index, on the vibration characteristics of the ceramic-based FG

shaft system.

Keywords: Finite element method, Functionally graded material; Porosity volume fraction;

Power law.

Sathujoda, Prabhakar & Bose, Arnab (2020). Effect of thermal gradient on vibration

characteristics of a functionally graded shaft system. Mathematical Modelling of Engineering

Problems, 7(2), 212-222. 10.18280/mmep.070207.

Abstract: The study aims to investigate the vibration characteristics of ceramic-based

functionally graded materials (FGM) when subjected to dynamic conditions. Effect of thermal

gradients on natural frequencies of rotating FG shafts has been investigated using three

dimensional (3D) solid elements which is rarely found in literature. The FG material used in

the work has metal and ceramic as its constituents. Three-dimensional finite element modelling

and analysis of the FG shaft system has been carried out using ANSYS software to determine

the natural frequencies with proper validations. The study involves modelling of the shaft using

two different material laws; power-law and exponential law. Continuous variation of material

properties is achieved using a Python code that discretizes the shaft radially and applies the

position-dependent material properties. The shaft has been subjected to different thermal

environments by applying temperature gradients accordingly. Thermal gradation is achieved

using two different methods, nonlinear temperature distribution (NLTD) and exponential

temperature distribution (ETD). Effects of material gradation on the fundamental frequencies

of the shaft is presented. The variations due to different material laws and thermal gradients

are also identified. The whirl frequencies have been determined for the rotating shaft system.

The effect of material gradation laws and temperature gradients on separation of the

frequencies at rotating speeds is determined. Attempts have been made to obtain suitable

reasons for the behaviours based on the material properties. The obtained results show the

influence of material gradation methods, power-law index, and temperature gradients on

vibration characteristics of the ceramic-based FG shaft system. Furthermore, the reasons of the

variations are also discussed in detail.

Keywords: Exponential temperature distribution; Finite element method; Functionally graded

material; Non-linear temperature distribution; Whirl frequencies

Dep

art

men

t of

Mec

han

ical

an

d A

ero

spa

ce E

ng

inee

rin

g

69

Sathujoda, Prabhakar., Obalareddy, Bharath & Batchu, Aneesh (2020). Effect of

Corrosion on the Natural and Whirl Frequencies of a Functionally Graded Rotor-Bearing

System Subjected to Thermal Gradients. Materials, 13(20), 4546. 10.3390/ma13204546

Abstract: Corrosion causes a loss of material resulting in the reduction of mass and stiffness

of a component, which consequently affects the dynamic characteristics of any system.

Fundamental frequency analysis of a corroded functionally graded (FG) rotor system, using

the finite element method based on the Timoshenko beam theory, was investigated in the

present paper. The functionally graded shaft consisting of an inner metallic core and an outer

ceramic layer was considered with the radial gradation of material properties based on the

power law. Nonlinear temperature distribution (NLTD) based on the Fourier law of heat

conduction was used to simulate the thermal gradient through the cross-section of the FG rotor.

The finite element formulation for a functionally graded shaft with a corrosion defect was

developed and the dynamic characteristics were investigated, which is the novelty of the

present work. The corrosion parameters such as length, depth and position of the corrosion

defect in the shaft were varied and a parametric study was performed to investigate changes in

the natural and whirl frequencies. An analysis was carried out for different power indexes and

temperature gradients of the functionally graded shaft. The effects of corrosion were analysed

and important conclusions are drawn from the investigations

Keywords: Functionally graded materials; Corrosion; Timoshenko beam theory; Finite

element method; Rotor-bearing systems.

Dr. Vinayak Ranjan, Professor & HoD, Department of MAE

Ranjan, Vinayak (2020). Effect of Shape, Density, and an Array of Dimples on the Friction

and Wear Performance of Laser Textured Bearing Steel under Dry Sliding. Journal of Materials

Engineering and Performance, 29, 2827–2838. 10.1007/s11665-020-04816-8

Abstract: The present study is directed to reveal the effect of density and shape of the dimples

on the friction and wear characteristics of textured bearing steel (100Cr6) under dry sliding

conditions. One untextured and four laser-textured disks of 100Cr6 steel with circular and bi-

triangular dimples having 7 and 20% density, respectively, in the spiral layout were tested using

a pin-on-disk unidirectional tribometer against a 100Cr6 pin having a flat surface with rounded

corners under flat-on-flat configuration. The tests were carried at different speeds of 0.2, 0.6,

and 1 m/s at a constant load of 15 N. The results indicate that a higher density of bi-triangular

dimples had less wear and a lower coefficient of friction compared to untextured disk or disk

with circular dimples, particularly at a relatively higher speed. This has been attributed to a

relatively higher number of dimples in the contact zone in case of a spiral array, which increases

the entrapment of more wear particles leading to a reduction in wear. The friction coefficient

had a relatively higher value at a relatively lower speed but decreased at the highest speed of

1.0 m/s used in the present study. The results suggest that dimples may not be too effective

under dry contact because they lose their effectiveness to trap wear debris after getting filled.

Keywords: Friction; Laser texturing; Wear; Wear of steels.

Ranjan, Vinayak (2020). Determination of static transmission error of helical gears using

finite element analysis. Journal of Measurements in Engineering, 8(4), 167-181.

10.21595/jme.2020.21825.

Abstract: At present, increasing emphasis is being placed on low noise levels, especially in

the automotive industry. One of the dominant sources of noise (and vibration) in vehicles (with

Dep

art

men

t of

Mec

han

ical

an

d A

erosp

ace

En

gin

eeri

ng

70

an electric motor as well as an internal combustion engine) is the transmission system. In order

to effectively reduce vibration and noise of gears and transmission systems, some important

gear parameters should be determined / measured. One of these parameters is the static

transmission error, which is addressed in this article. The evaluated parameter is the peak-to-

peak value of the transmission error, which appears to be closely related to NVH (Noise

Vibration and Harshness). The transmission error depends, inter alia, on the tooth

macrogeometry (expressed by the contact ratio) and the tooth microgeometry (intentional tooth

modifications), the influence of both has been analyzed and is presented in this article. The

transmission error can be determined by virtual measurement (computationally) in software

enabling finite element analysis or by technical experiments on a test rig. The results in this

paper are based on the outputs of numerical simulations using the finite element method. This

approach can be used at developing phase to find the optimal solution and to save a lot of time.

In addition, no physical components are required and a wide range of arbitrary gear

configurations can be analyzed. Nevertheless, the technical experiment is still necessary, thus

the test rig will be designed/constructed in the near future, including the possibility of

lubrication. The results will then be compared with numerical simulations.

Keywords: Static transmission error; Helical gears; Gear mesh; Contact ratio; Gear teeth

modifications; Tip relief; Root relief; FEA; FEM; Numerical simulations.

Ranjan, Vinayak (2020). Acoustic Response of Thin Plate with Discrete Patches with

Variable Thickness in Water Medium. Journal of Vibrational Engineering and Technologies,

8, 27-34. 10.1007/s42417-018-0046-z

Abstract: Background: This paper analyses acoustic response of thin plate with discrete

patches with variable thickness in water medium. Method: Finite-element method and Rayleigh

integral method are used for sound power calculation in water medium. Results: For low-

frequency range up to 50 Hz, the peak sound power level (SPL) is achieved at higher taper ratio

of 0.9 with parabolically increasing–decreasing thickness variation with one patch. In the

frequency range of 70–180 Hz and 260–290 Hz, a taper ratio of 0.6 with four patches on the

plate with parabolically increasing–decreasing variation is preferred for minimum SPL.

Conclusions: It is observed that SPL is influenced by redistribution of patches, thickness

variations, and taper ratios.

Keywords: Isotropic rectangular plate; Patch; Sound power level (SPL); Taper parameter

Ranjan, Vinayak & Sharma, Himank (2020). Measurement of spine parameters and possible

scoliosis cases with surface topography Techniques: A case study with young Indian males.

Measurement, 161, 107872 '1-5. 10.1016/j.measurement.2020.107872.

Abstract: In India, the measurement of Idiopathic Scoliosis (IS) is assessed by multiple

radiographs. The long-term exposure of x-ray radiation ill effects. The Statico 3D system based

on surface topography is compared the first time with radiography for the assessment of

twenty-two subjects identified with IS. In the case of surface topography, a standard deviation

of ±3.4 degrees and the reliability coefficient were found 0.996. At 95% confidence interval, a

strong correlation (Pearson coefficient > 0.75) was observed in thoracic kyphosis, lumbar

lordosis angle and left vertebral rotation while a moderate correlation (Pearson coefficient >

0.65) was measured in right vertebral rotation using radiograph and the Statico 3D system.

ANOVA indicated a significant relation (p-value < 0.0001 and Alpha 0.05) for lumbar lordosis,

thoracic kyphosis, vertebral rotation (right & left) between radiographs and surface topography

using Statico 3D system.

Keywords: Idiopathic Scoliosis (IS); Radiograph; Surface topography.

Dep

art

men

t of

Mec

han

ical

an

d A

erosp

ace

En

gin

eeri

ng

71

Department of Physics

Conference Papers

Aakash Warke, Student, Department of Physics

Warke, Aakash & Jain, Valay Kumar (2020). Quantum Machine Learning: A Review and

Current Status. Advances in Intelligent Systems and Computing, 1175 (pp. 101-145).

10.1007/978-981-15-5619-7_8.

Abstract: Quantum machine learning is at the intersection of two of the most sought after

research areas-quantum computing and classical machine learning. Quantum machine train a

classical computation model is ever growing and reaching the limits which normal computing

devices can handle. In such a scenario, quantum computation can aid in continuing training

with huge data. Quantum machine learning looks to devise learning algorithms faster than their

classical counterparts. Classical machine learning is about trying to find patterns in data and

using those patterns to predict further events. Quantum systems, on the other hand, produce

atypical patterns which are not producible by classical systems, thereby postulating that

quantum computers may overtake classical computers on machine learning tasks. Here, we

review the previous literature on quantum machine learning and provide the current status of

it. learning investigates how results from the quantum world can be used to solve problems

from machine learning. The amount of data needed to reliably.

Keywords: Quantum machine learning; Quantum renormalization procedure; Quantum hhl

algorithm; Quantum support vector machine; Quantum classifier; Quantum artificial

intelligence; Quantum entanglement; Quantum neural network; Quantum computer.

Journal Papers

Aakash Warke, Student, Department of Physics.

Warke, Aakash (2020). Experimental realization of three quantum key distribution protocols.

Quantum Information Processing, 19(407), 1-15. 10.1007/s11128-020-02914-z.

Abstract: Classical computers allow security of cryptographic protocols based on the

mathematical complexity of encoding functions and the shared key. This implies that high

computational power can have a positive outcome in breaking cryptographic protocols that

employ classical computers. Quantum machines claim to possess such power. Two parties

interested in communicating with each other take up the process of measuring entangled states

in order to construct a secret key which is safeguarded against an eavesdropper capable of

performing quantum operations. At first, experimental verification of the BB84 protocol using

three bases has been performed in this paper, out of which sub-cases have been considered

based on whether or not Eve has attempted an attack. The following part includes experimental

realization of the B92 protocol which was introduced by Charles Bennett in the year 1992.

Possibility of an Eve’s attack is considered and implemented. Succeeding part relies on

experimental implementation of the protocol that was introduced by Acin, Massar and Pironio

in the year 2006 (New J Phys 8:126, 2006). All the implementations have been done using the

IBM Quantum Experience platform.

Keywords: Quantum cryptography; IBM quantum experience; Quantum information theory.

Dep

art

men

t o

f P

hy

sics

72

Warke, Aakash (2020). Deterministic hierarchical remote state preparation of a two-qubit

entangled state using Brown et al. state in a noisy environment. IET Quantum Communication,

1(2), 49-54. doi:10.1049/iet-qtc.2020.0005 www.ietdl.org

Abstract: Quantum communication is one of the cutting-edge research areas today, where the

scheme of remote state preparation (RSP) has drawn significant attention of researchers. The

authors propose here a hierarchical RSP protocol for sending a two-qubit entangled state using

a seven-qubit highly entangled state derived from Brown et al. state. They have also studied

here the effects of two well-known noise models namely amplitude damping (AD) and phase

damping (PD). An investigation on the variation of the fidelity of the state with respect to the

noise operator and receiver is made. PD noise is found to affect the fidelity more than the AD

noise. Furthermore, the higher power receiver obtains the state with higher fidelity than the

lower power receiver under the effect of noise. To the best of their knowledge, they believe

that they have achieved the highest fidelity for the higher power receiver, 0.89 in the presence

of maximum AD noise and 0.72 in the presence of maximum PD noise, compared to all the

previously proposed RSP protocols in noisy environments. The study of noise is described in

a very pedagogical manner for a better understanding of the application of noise models to a

communication protocol.

Keywords: Quantum entanglement; Quantum communication; Noise operator; RSP protocols;

Hierarchical RSP protocol

Dr. Ayan Khan, Assistant Professor, Department of Physics

Khan, Ayan & Debnath, Argha (2020). On solving cubic-quartic nonlinear Schrodinger

equation in a cnoidal trap. The European Physical Journal D, 74(184), 1-6.

doi:10.1140/epjd/e2020-10364-4

Abstract: The recent observations of quantum droplet in ultra-cold atomic gases have opened

up new avenues of fundamental research. The competition between mean-field and beyond

mean-field interactions, in ultra-cold dilute alkali gases, is believed to be instrumental in

stabilizing the droplets. This new understanding has motivated us to investigate the analytical

solutions of a trapped cubic-quartic nonlinear Schrodinger equation (CQNLSE). The quartic

contribution in the NLSE is derived from the beyond mean-field formalism of Bose–Einstein

condensate (BEC). To the best of our knowledge, a comprehensive analytical description of

CQNLSE is non-existent. Here, we study the existence of the analytical solutions which are of

the cnoidal type for CQNLSE. The external trapping plays a significant role in the stabilization

of the system. In the limiting case, the cnoidal wave solutions lead to the localized solution of

bright solution and delocalized kink-antikink pair. The nonexistence of the sinusoidal mode in

the current scheme is also revealed in our analysis.

Keywords: Cold Matter; Quantum Gases

Khan, Ayan & Das, Priyam (2020). Formation of solitonic bound state via light-matter

interaction. The European Physical Journal D, 74(213), 1-8. 10.1140/epjd/e2020-10251-0.

Abstract: Exchange of energy by means of light-matter interaction provides a new dimension

to various nonlinear dynamical systems. Here, the effects of light-matter interaction are

investigated for a situation, where two counter-propagating, orthogonally polarized laser pulses

are incident on the atomic condensate. It is observed that a localized laser pulse profile can

induce localized modes in Bose-Einstein condensate. A stability analysis performed using

Vakhitov-Kolokolov-like criterion has established that these localized modes are stable, when

Dep

art

men

t o

f P

hy

sics

73

the atom-atom interaction is repulsive. The cooperative effects of light-matter interactions and

atom-atom interactions on the Lieb-mode have been studied in the stable region through atomic

dispersion, revealing the signature of bound state formation when the optical potential is Posch

Teller type. The energy diagram also indicates a continuous transfer of energy from the laser

pulses to the atoms as the light-matter interaction changes its sign.

Keywords: Cold Matter; Quantum Gases

Dr. K. Rama Koteswara Rao, Assistant Professor, Department of Physics

Rao, K. Rama Koteswara (2020). Optimal photon energies for initialization of hybrid spin

quantum registers of nitrogen-vacancy centers in diamond. Physical Review A, 101, (1), 3835,

1-7. 10.1103/PhysRevA.101.013835

Abstract: Initializing quantum registers with high fidelity is a fundamental precondition for

many applications like quantum information processing and sensing. The electronic and

nuclear spins of a nitrogen-vacancy (NV) center in diamond form an interesting hybrid

quantum register that can be initialized by a combination of laser, microwave, and radio-

frequency pulses. However, the laser illumination, which is necessary for achieving electron

spin polarization, also has the unwanted side effect of depolarizing the nuclear spin. Here we

study how the depolarization dynamics of the 14 N nuclear spin depends on the laser

wavelength. We show experimentally that excitation with an orange laser (594 nm) causes

significantly less nuclear spin depolarization compared to the green laser (532 nm) typically

used for excitation and hence leads to higher nuclear spin polarization. This could be because

orange light excitation inhibits ionization of NV 0 into NV−and therefore suppresses one

source of noise acting on the nuclear spin.

Keywords: Quantum information processing and sensing; Microwave

Dr. Krishna Thyagarajan, Professor & HoD, Department of Physics

Thyagarajan, Krishna & (2020). Wideband two-process frequency conversion under

stimulated Raman adiabatic passage via a continuum of dark intermediate states. Journal of the

Optical Society of America B, 37(11), 3370-3378.

Abstract: In this paper, we propose a novel hybrid planar-channel waveguide configuration in

which the channel waveguide is submerged within the planar region, with an aim to study two

simultaneous three-wave mixing processes under stimulated Raman adiabatic passage. In our

study, an input frequency is converted to an output frequency lying very close to or very far

from the input frequency via an intermediate frequency. This frequency is in the form of a

continuum of modes in the planar region, while all other frequencies propagate as guided

modes of the channel waveguide. The continuum of modes at the intermediate frequency

allows the simultaneous phase matching to be satisfied over a wide range of wavelengths. As

a consequence, this leads to a wideband and efficient conversion from the input to the output

frequency without any significant power accumulation at the intermediate stage, which is

ensured through the counterintuitive and adiabatic variation in the nonlinear coupling strengths.

Keywords: Channeled waveguides; Light sources; Optical systems; Phase matching; Planar

waveguides; Waveguide modes.

Dep

art

men

t o

f P

hysi

cs

74

Dr. Rama Koteswara Rao Kamineni, Assistant Professor, Department of Physics

Kamineni, Rama Koteswara Rao (2020). Level anti-crossings of a nitrogen-vacancy center

in diamond: Decoherence-free subspaces and 3D sensors of microwave magnetic fields. New

Journal of Physics, 22. 10.1088/1367-2630/abc083

Abstract: Nitrogen-vacancy (NV) centers in diamond have become an important tool for

quantum technologies. All of these applications rely on long coherence times of electron and

nuclear spins associated with these centers. Here, we study the energy level anti-crossings of

an NV center in diamond coupled to a first-shell 13C nuclear spin in a small static magnetic

field. These level anti-crossings (LACs) occur for specific orientations of the static magnetic

field due to the strong non-secular components of the Hamiltonian. At these orientations we

observe decoherence-free subspaces, where the electron spin coherence times (T2∗) are 5-7

times longer than those at other orientations. Another interesting property at these LACs is that

individual transition amplitudes are dominated by a single component of the magnetic dipole

moment. Accordingly, this can be used for vector detection of microwave magnetic fields with

a single NV center. This is particularly important to precisely control the center using numerical

optimal control techniques.

Keywords: Coherence times; Decoherence-free subspaces; Level anti-crossings; NV center;

ZEFOZ shift

Dr. Soumyendu Roy, Assistant Professor, Department of Physics

Roy, Soumyendu (2020). Fast growth of nanodiamond in a microwave oven under

atmospheric conditions. Journal of Materials Science, 55(2), 535-544. 10.1007/s10853-019-

03936-4

Abstract: Nanodiamonds (NDs) were synthesized under atmospheric conditions by heating a

precursor powder mixture consisting of naphthalene and a microwave (MW) absorbing

material inside an ordinary MW oven for 10 min. Pyrolysis of naphthalene led to the formation

of onion-like carbon particles which then converted to NDs after prolonged MW irradiation.

Different carbon-based materials like graphite, carbon black, graphene, and carbon nanotubes

were used as microwave radiation absorbers that assisted in the dissociation of naphthalene and

formation of NDs. ND particles were formed in both isolated as well as aggregated forms. Size

of the particles ranged from 2 to 700 nm. Scanning electron microscopy, transmission electron

microscopy, electron energy loss spectroscopy, Raman spectroscopy and thermogravimetric

analysis were used to characterize the NDs present in the MW-synthesized product. The

proposed MW-based ND synthesis technique is simple, fast, inexpensive, energy efficient and

could be suitable for industrial scale production.

Keywords: Nanodiamonds; SEM image

Dr. Swarup K Panda, Assistant Professor, Department of Physics

Panda, Swarup K. (2020). 4f spin driven ferroelectric-ferromagnetic multiferroicity in

PrMn2O5 under a magnetic field. Physical Review B, 102(9), 94408.

10.1103/PhysRevB.102.094408.

Abstract: In contrast to all other members of the R Mn 2 O 5 family with nonzero 4 f electrons

(R = Nd to Lu), PrMn 2 O 5 does not show any spin driven ferroelectricity in the magnetically

ordered phase. By means of high-field electric polarization measurements up to 45 T, we have

found that this exceptional candidate undergoes a spin driven multiferroic phase under

Dep

art

men

t o

f P

hy

sics

75

magnetic field. X-ray magnetic circular dichroism studies up to 30 T at the Pr L 2 edge show

that this ferroelectricity originates from and directly couples to the ferromagnetic component

of the Pr3+ spins. Experimental observations along with our generalized gradient-

approximation +U calculations reveal that this exotic ferroelectric-ferromagnetic combination

stabilizes through the exchange-striction mechanism solely driven by a 3 d − 4 f -type coupling,

as opposed to the other R Mn 2 O 5 members with 3d-3d driven ferroelectric-

antiferromagnetic-type conventional type-II multiferroicity.

Keywords: 4 f electrons; Magnetoelectric multiferroics; X-ray magnetic circular dichroism;

X-ray absorption spectrum.

Panda, Swarup K. (2020). Origin of itinerant carriers in antiferromagnetic LaF e1-x M ox O3

studied by X-ray spectroscopies. Physical Review Materials, 4, 34405-9.

10.1103/PhysRevMaterials.4.034405.

Abstract: We report on the electronic structure of doped LaFe O3 at the crossover from an

insulating-to-metallic phase transition. Comprehensive x-ray spectroscopic methodologies are

used to understand core and valence electronic structure as well as crystal structure distortions

associated with the electronic transition. Despite the antiferromagnetic (AFM) ordering at room

temperature, we show direct evidence of itinerant carriers at the Fermi level revealed by

resonant photoemission spectroscopy (RPES) at the Mo L 3 edge. RPES data taken at the Fe

L3 edge show spectral weight near the valence band edge and significant hybridization with O

2p states required for AFM ordering. Resonant inelastic x-ray scattering spectra taken across

Fe L2,3 edges show electron correlation effects (U) driven by Coulomb interactions of d

electrons as well as broad charge-transfer excitations for x≥0.2 where the compound crosses

over to a metallic state. Site substitution of Fe by Mo ions in the Fe-O6 octahedra enhances the

separation f the two Fe-O bonds and Fe-O-Fe bonding angles relative to the orthorhombic

LaFeO3, but no considerable distortions are present to the overall structure. Mo ions appear to

e homogeneously doped, with average valency of both metal sites monotonically decreasing

with increasing Mo concentration. This insulator-to-metal phase transition with FM stability is

primarily understood through intermediate interaction strengths between correlation (U) and

bandwidth (W) at the Fe site, where an estimation of this ratio is given. These results highlight

the important role of extrinsic carriers in stabilizing a unique phase transition that can guide

future efforts in antiferromagnetic-metal spintronics.

Keywords: Antiferromagnetism, Electronic structure, Magnetism, X-ray spectroscopy.

Dr. Thounaojam Umeshkanta Singh, Assistant Professor, Department of Physics

Singh, Thounaojam Umeshkanta (2020). Teaching the ‘principles of science’ within a liberal

arts curriculum. Current Science, 119, 1252-1259. 10.18520/cs/v119/i8/1252-1259

Abstract: The question of developing and teaching a foundation course in science within a

liberal arts curriculum is examined through lived experience. I discuss and analyse the

formulation of the course and its structure, contents and functions. This is based on my

experience of teaching the course for two semesters to liberal arts undergraduates. This article

aims to invoke inquiry to understand the basis of science and scientific methods, the nature of

science, the criteria of scientificity and the dynamics of scientific rationality at various stages

of human history. Science is examined from different perspectives in the humanities and social

sciences to find their inter-relatedness. The humanities, in particular history and philosophy,

play an important role in examining science and scientific methods in order to understand the

Dep

art

men

t o

f P

hy

sics

76

limitations of science. Finally, the need for integrating science and humanities in contemporary

education is discussed.

Keywords: Contemporary education; foundation course; liberal arts; principles of science

Papers in arXiv Repository

Dr. Ayan Khan Assistant Professor, Department of Physics

Khan, Ayan & Debnath, Argha (2020). Analytical solutions of a CQNLSE with a source.

arXiv:2002.04001v1, 1-8.

Abstract: Motivated by the recent observation of quantum droplet and its beyond mean-field

description, we investigate the existence of cnoidal waves in a driven cubic-quartic nonlinear

Schrödinger equation (CQNLSE) as the most simplistic modeling of beyond mean-field

formalism of Bose-Einstein condensate (BEC) can be traced in the framework of CQNLSE.

To the best of our knowledge, a comprehensive analytical description of CQNLSE is

nonexistent. We check for the existence of the analytical solutions of forced CQNLSE. The

stabilization of the system necessitates the inclusion of the source term. We derive analytical

cnoidal wave solutions which are both localized as well as nonlocalized type. Further we reveal

the existence of bright soliton like structure, a flat valley like structure and $w$-type solitons.

We are also able to see the non-existence of sinusoidal modes.

Keywords: CQNLSE; Bose-Einstein condensate (BEC)

Dep

art

men

t o

f P

hysi

cs

77

School of Law

Edited Book

Dr. Debabrata Baral, Assistant Professor, School of Law

Baral, Debabrata (2020). Development, Enviornment, Migration: Lessons for Sustainability.

Routledge India, 254.

Abstract: This book brings the discourses around social justice and sustainable development

back into focus by looking at India’s mining sector and the state’s frameworks for economic

development. The chapters in this volume analyse mining practices in the mineral-rich areas of

eastern India through various case studies and highlight their immense human and

environmental costs.

This volume critically analyses selected mining projects in India that have resulted in large-

scale displacements, impoverishment and environmental degradation. It identifies the gaps in

policy, its implementation, and the lack of safeguards which threaten the socio-economic and

ecological ways of life and the livelihoods of the local communities. Based on documents,

reports, interviews and field observations, this book engages with the issues surrounding the

mining sector, e.g., land acquisition, land use and degradation, the politics of compensation,

policies, agitation and social mobilisation, health and agriculture, livelihood and gender. It

further provides an assessment of local political economies and offers suggestive frameworks

for inclusive growth in this sector.

This book will be of interest to students and researchers exploring the disciplines of

development studies, sociology, law and governance, human ecology and economics.

Book Chapter

Dr. Debabrata Baral, Assistant Professor, School of Law

Baral, Debabrata (2020). The contours of religiosity in hinduism: locating religious doctrines,

interrogating communal behaviour. In: Demmrich S., Riegel U. (eds) Religiosity in East and

West. Veröffentlichungen der Sektion Religionssoziologie der Deutschen Gesellschaft für

Soziologie. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-31035-6_10, 183-

204.

Abstract: What accounts for the rise of aggressive communal violence in India? Does religious

doctrines govern communal behaviors? What is the link between communal act with the

spiritual principles, embedded within Hinduism? This chapter inquires into the contexts that

govern religiosity in Hinduism. The first section, conceptualized religious fundamentalism as

a form of religiosity. The second section, outlines the core principles from the sacred scriptures,

which govern religiosity within Hinduism. The third section, describes the impact of the

constitution of India on the religiosity in India. The fourth section, maps the contexts around

which the changing forms of religiosity emerges. The fifth section. interrogates communal

behaviors by taking into account religious doctrines. By reference to the theoretical framework

of Allport and Ross (1967). This paper argues that, first, religiosity within Hinduism has both

intrinsic and extrinsic character. Religious scriptures constitute the inherent dimensions of

religiosity. Second, the Indian Constitution, judicial activism, the partition of India,

commercialization of religion, and symbolism have impacted the religiosity within Hinduism

and its religious-spiritual code. While the performative aspects of religiosity superseded

religious knowledge. Third, communal behavior stands contrary to the philosophies of the

Sch

ool

of

Law

(S

OL

)

78

sacred text. Hence, communal action or religious bigotry should be looked at as the extrinsic

dimension of religiosity as religion is ‘used’ for individual gains.

Keywords: Religious scriptures; Religiosity; Communal violence; Hinduism Symbolism

Baral, Debabrata (2020). Gender and the Mining Sector: Mapping the Praxis of Women

Workforce in the Mining Sector. . In S. Irudaya Rajan, Debabrata Baral (Eds) Development,

Environment and Migration: Lessons for Sustainability, Routledge India, .

Abstract:The mining sector is dominantly identified as a key project for growth and

development. But unfortunately there has been a rising concern over the gendered nature of the

mining sector. It is in this context this chapter will explore, first, the status of women in the

mining sector in India and at the global level; second, the socio-cultural contexts around which

women are discriminated and exclusionary practices are operating in the mining sector; and,

third, to outline ways through which the exclusionary practices and gender gap in terms of

participation of the women in the large-scale mining projects can be addressed. This chapter

suggests that a redistribution of power and resources is extremely important in order to achieve

gender equality and promote diversity. The chapter is based on both primary and secondary

literature. Government policy documents and reports of international organisations were

reviewed as part of secondary literature while interviews with women working and associated

with the mining sector in Odisha were interviewed using semi-structured questionnaire.

Random, purposive and snowball sampling methods were used in identification of respondents

Baral, Debabrata (2020). In Transition: Mapping the economic and socio-cultural challenges

of people in Posco-India Transit Camp. . In S. Irudaya Rajan, Debabrata Baral (Eds)

Development, Environment and Migration: Lessons for Sustainability, Routledge India, .

Abstract: This chapter is the study of the POSCO-India transit camp located in eastern coastal

part of India, in the district of Badagabapur. This camp was established by POSCO-India as a

temporary measure to rehabilitate the people displaced by violent conflicts in its proposed

project site. This transit camp was initially started to house people supporting the stablishment

of the development project. In general, the impacts of development projects should be

evaluated in two phases, i.e., (i) during and (ii) after the establishment of the development

project. Hence, a review of the rehabilitation and resettlement provisions and through an

assessment of the socio-economic-ecological conditions, the impact of the development project

as accessed. But what about the people who are in transit? How does one classify those sections,

who in principle, agreed with the establishment of the development project, vacated the land

and are left dispossessed and neglected? This chapter outlines the economic and other socio-

cultural challenges faced by the people in the transit camp. Contrary to the dominant literature

that suggests that development projects bring growth and welfare, this chapter highlights that

the people who have agreed to give land and cooperate with the development project failed to

get adequate support either from the government or from the development project. They were

gradually sidelined and subjected to a condition of impoverishment. This chapter also maps the

shift in the temperament of the people residing in the transit camp, e.g., from acceptance to

being sceptical of the development project. Data for this chapter was collected through field

visits. Semi-structured interviews were used and random and purposive sampling methods

were used for data collection.

Sch

ool

of

Law

(S

OL

)

79

Baral, Debabrata (2020). Mining sector and the Idea of Development: Challenges, Policies

and Possibilities.. In S. Irudaya Rajan, Debabrata Baral (Eds) Development, Environment and

Migration: Lessons for Sustainability, Routledge India, .

Abstract: The mining sector has become a priority in this paradigm of development. But at the

same time, mining projects popularly known as development projects have been aggressively

criticized for not being inclusive for the immediate local community or being harmful to the

environment. From the policy perspective, there are safeguards guaranteed to the people

impacted by these mining projects. But still the rise of aggressive social movements puts a

question mark on the inclusiveness and the effectiveness of these safeguards. It is in this context

that this chapter will first outline and review various programs, policies and promises that

facilitate the mining projects and even assure safeguards to the impacted people. Second, it will

also outline the resistance that has emerged, challenging various mining projects in India.

Third, this volume reviews selected development projects, from both the macro perspective as

well as through selected case studies. It aims to outline the various ‘human’ and ‘environment’

cost as well as in identifying the ‘governance gaps’ which challenge the inclusiveness of these

development projects. Chapters selected in the volume are engages from the perspective of the

local political economy and political ecology. This volume aims at providing suggestive

frameworks for inclusive growth.

Dr. Suman Luhach, Assistant Professor, School of Law

Luhach, Suman (2020). Online Group Argument Writing and Online Discussion Forums.

Technology-Enhanced Language Teaching in Action by the Asia-Pacific Association for

Computer-Assisted Language Learning (pp. 27-30).

Abstract: This activity uses online discussion forums on a learning management system

(LMS) for writing and discussing argument structures and contents as a supplementary to

classroom teaching. It can be useful for senior secondary and undergraduate students,

particularly in law programs.

Keywords: TETL; Academic writing, Argument drafting; Online Discussion.

Journal Papers

Dr. Ashita Allamraju, Associate Professor, School of Law

Allamraju, Ashita (2020). MSMES and Competition Law in India: Victims or Perpetrators.

Review of Socio-Economic Perspectives, 5(1), 23-34. 10.19275/RSEP074.

Abstract: SMEs contribute around 35-40% of the GDP of India and are key to employment

generation, sustainable development and poverty reduction. This sector is largely unorganised

and vulnerable to the dynamic external business environment. On one hand, small size of the

SMEs makes them vulnerable to anti-competitive acts of bigger enterprises including abuse of

dominant position and on the other hand, cooperation agreements amongst SMEs assist them

to compete with large enterprises. Competition Act, 2002 deals with anti-competitive

agreements and abuse of dominant position, amongst other things. The Competition Act of

India is size and type neutral.

Sch

ool

of

Law

(S

OL

)

80

This paper thus, looks at whether SMEs are perpetrators or victims of anti-competitive conduct.

This study analyses the recent anti-trust cases in India which involved SMEs and develops a

typology of anticompetitive conduct and abuse of dominance activities employed by large

corporations against SMEs and also anti-competitive conduct that SMEs may engage in.

Keywords: SMEs; Competition Law; Anti-competitive; Large corporations.

Allamraju, Ashita (2020). Review of Regulatory Challenges in Digital Platforms: Cases from

India. International Journal of Psychosocial Rehabilitation, 24(8), 8859-8874.

10.37200/IJPR/V24I8/PR280882.

Abstract: Information Communications Technology (ICT) networks and infrastructure are

now critical resources for organizations to run their business, compete and contribute

productively in the new age digital markets. ICT has supported novel commercial models

(Internet firms), 'digital platforms', by redefining delivery of goods and services and has

significantly enhanced consumer welfare. The presence of intrinsic network effects in case

digital platforms combined with the limited interoperability makes the prevalent digital

ecosystem to have monopolistic structure or at best an oligopolistic market structure. There are

regular entry and exit in digital markets, firms exercise mergers and acquisitions as a business

strategy to establish its market share or diversify into new business areas. The power of user

information (through data) enables the firms to practice price discrimination, thereby

appropriating consumer surplus. Thus, competition issues in the new age digital markets must

to be tackled by the competition authority taking cognizance that the regulatory regime does

not curb the motivation to innovate. The paper reviews the competition issues in digital

ecosystem from the Indian perspective and reflects possible solutions to safeguard competition

in the digital markets.

Keywords: Regulatory; ICT; Digital platforms; Competition; CCI; Case; India.

Allamraju, Ashita (2020). Role of managerial perception of competitive pressures in firms’

product innovation success. European Journal of Innovation Management, 1-17.

10.1108/EJIM-03-2020-0069.

Abstract: Purpose – This study explores the link between the level of importance managers

assign to competitive pressures from domestic competition, foreign competition and customers

as factors in the key business decisions related to innovation and the outcome of firms’ product

innovation efforts.

Design/methodology/approach – The research sample is taken from the Business Environment

and Enterprise Performance Survey by World Bank (2005). The relevant questions for the

study were extracted from the survey. Logistic regression models were used for analysis using

the ISLR library from R statistical software.

Findings – Managers’ consideration of customer pressure for innovation as important in key

business decisions related to innovation has a positive and sustainable effect, distinct from that

of R&D and other innovative activities, on firms’ success of product innovation efforts.

Research limitations/implications – The research acknowledges the need to verify the findings

in a multi country setting. Practical implications – This research can help mediate the

managers’ assignment of importance to certain types of competition for innovation decisions

in multi competitive environment for improved success of product innovation efforts.

Sch

ool

of

Law

(S

OL

)

81

Originality/value – Simultaneous consideration of multiple competitive pressures by managers

helps to identify the most suitable innovation activities for their respective firms and improve

the chances of success of firms’ innovation efforts.

Keywords: Managerial perception; Domestic competition; Foreign competition; Consumer

competition; Product innovation.

Dr. Garima Tiwari, Assistant Professor, School of Law

Tiwari, Garima (2020). Intersectionality of Law, Religion and Nationalism: The ‘Othering’

Of Muslim Women and Islamic Feminism in India. NUALS Law Journal, 14(43), 43.

Abstract: Contemporary India is highly complex, volatile and diversified. The universal

human rights principles and other international norms that guide secular feminists for women

empowerment, get fragmented and vernacularized when the question of religious personal laws

is raised. The Shah Bano case was a momentous feat for the rights-based demands of Muslim

women in India, and recent decisions including the Triple Talaq judgment have also

highlighted some progressive strands. However, as the discussion on public and private spaces

of personal law is scrutinized from the vantage of secularism and religious specificity, it

projects itself predominantly as a communal issue within the public domain and not merely a

question of equality or empowerment of women in the family structure. Thus, the paper studies

the “othering” of Muslim women and intersectionality of nationalism, the law, and religion in

the context of women’s rights in India, by reviewing literature, judicial decisions and an

analysis of women’s rights movements.

Keywords: Women; Muslim women; Triple Talaq; Muslim Law; Muslim women right in

India.

Tiwari, Garima (2020). The Plight of Domestic Migrant Workers in India During Covid-19

Crisis. Revista Giuridica, Anno XX - Fascicolo (Issue) 2/2020.66-74.

Abstract: Domestic migrant workers in India travel from their villages to work in bigger cities.

The lockdown as a result of the coronavirus emergency left this marginalised section of

population stranded in their cities of work- without shelter, food, livelihood and means of

transportation to return back to their villages. The article provides an assessment of the current

condition of these internal migrant workers in India during the COVID-19 crisis and provides

an analysis of their legal and human rights. It discusses the inhumane treatment afforded to

them and also highlights the emergence of social boycotting of these workers by the

community. In this light, the article also studies the response of the government and the

judiciary to deal with this situation.

Keywords: Covid-19 crisis; Migrant worker during Covid-19; Internal Migrant; Covid-19

situation; Social emergency.

Dr. Suman Luhach, Assistant Professor, School of Law

Luhach, Suman (2020). Recreating Discourse Community for Appropriating HOCs in Law

Undergraduates’ Academic Writing. IAFOR Journal of Education, 8(4), 152-170.

Abstract: Like any other discipline, academic writing is equally crucial for law undergraduates

to master. Project reports, argumentative essay writing on current socio-legal affairs and

research paper writing comprise requisites in academia for law learners. Students’

appropriation of higher order concerns in academic writing is a major challenge for teachers,

Sch

ool

of

Law

(S

OL

)

82

as the physical classroom discourse community is typically passive and does not give enough

opportunities to students to think critically about their writing processes.

The teacher is expected to provide feedback to students on their writing, which often leads to

the creation of only one feedback centre, restriction of the scope for varied perceptions and

formation of multiple small discourses where the teacher is the central point of reference in

every discourse. Consequentially, students can fail to develop self/peer-critiques in the ongoing

discourse. The present paper has its focus on the recreation of discourse communities using a

learning management system at the Law School, Bennett University, India, to promote peer-

to-peer learning for honing higher order concerns in academic writing. The paper investigates

how law students behave whilst interacting in a recreated online discourse community, benefit

through peer feedback, and enhance their knowledge of the academic writing genre of

argumentative essays, its subject matter and rhetoric involved. The methodological

triangulation of pre-test/post-test analysis, student survey and conceptual content analysis of

students’ interaction transcripts support recreation of online discourse communities in

academic writing instruction.

Keywords: Academic writing; Argumentative essay writing; Discourse community; Higher

order concerns; Law undergraduates.

Luhach, Suman & Tiwari, Garima (2020). Exploring Polysemy Through Prototypical

Theory for Teaching Legal English in Contracts. Journal on English Language Teaching, 10(4),

28-35. 10.26634/jelt.10.4.17081

Abstract: Young law students often get confused over the appropriateness and logic of using

different terms in contracts and contractual clauses. This improper understanding of the right

usage in the initial years usually sustains in their profession as well. Consequently, vague terms

and ambiguities often become the root causes of contract interpretation disputes. Contract

drafting skills and a strong hold on legal English are valued highly in the legal profession.

Therefore, a systematic exposure to legal English is required in the formative years. The present

working paper explores Prototypical theory of Polysemy by Cognitive Linguists in teaching

legal English to the students of law. An introduction to the concept of 'Polysemes' and a

discourse analysis of already existing contracts for polysemes is planned for the students. This

is done to make students read some authentic legal contracts, understand and analyze them

through discourse analysis for learning contract drafting. The study includes references to

different types of contracts and clauses like those in the contract for sale of goods, contract for

performance of services, memorandum of understanding, non-disclosure agreements,

international contracts and other commercial contracts like memorandum and articles of

association among others. The study is done to help students understand how appropriate

selection of words is crucial in contracts and to help them learn contract drafting.

Keywords: Polysemy in Contracts; Prototypical Theory; Legal English; Cognitive Linguistics;

Discourse Analysis.

Sch

ool

of

Law

(S

OL

)

83

School of Management (SOM)

Book Chapter

Dr. Nilanjan Banik, Professor, School of Management (SOM)

Banik, Nilanjan (2020). Indian Pharma Sector's Journey for the Innovation Panacea: Lessons

from Negotiations with EU and RCEP (pp. 429-461). Hart Law Book.

10.5040/9781509933754.ch-020

Abstract: The task of improving the health scenario is particularly important for India, as the

country is planning to turn itself in a global manufacturing hub through the ‘Make-in-India’

initiative launched in 2014. The Indian pharmaceutical sector, which is 3rd largest in terms of

volume and 10th largest in terms of value, is a crucial piece in the policy. The present paper

intends to analyse the scenario in India on intellectual property rights (IPR) protection, the

evolving policy orientations and competitiveness reflected through the emerging trade patterns,

with respect to the pharmaceutical sector. The paper is arranged along the following lines. First,

the health outcomes in India are briefly noted. The orientation towards innovation and IPR is

noted next. India’s recent policy framework towards pharmaceutical sector is analysed next.

Finally, the country’s recent participation in mega-regionals like Regional Comprehensive

Economic Partnership (RCEP) is commented upon next, with India’s possibilities judged

therein through select indices. Based on the discussions, the analysis finally attempts to draw

policy conclusions.

Keywords: Pharma Sector; Economic growth; RCEP.

Dr. Pankaj Kumar Medhi, Assistant Professor, School of Management (SOM)

Medhi, Pankaj Kumar (2020). Blockchain-Enabled Supply Chain Transparency, Supply

Chain Structural Dynamics, and Sustainability of Complex Global Supply Chains - A Text

Mining Analysis. Information for Efficient Decision Making: Big Data, Blockchain and

Relevance, 716. 10.1142/9789811220470_0011

Abstract: Blockchain technology has been hailed as the technology of the future, not only for

banking and finance but also for supply chain management and logistics. As lack of

transparency in global supply chains is a major risk for sustainability, blockchain offers an

attractive solution in the form of a reliable platform to create transparency and risk

management. Not considering the nascent stage of the technology, companies are investing

millions of dollars into blockchain solutions for many business problems including that of

supply chains. However, blockchain-enabled networkwide transparency and visibility also

inject new dynamics into supply chains through introduction of structural changes like

redefining what is organizational boundary, creating new resources, and a new transactional

economy for supply chain management. The structural changes also create a fundamental need

for organizations in a supply network to adapt their supply chain processes to this new and

emerging supply chain structural dynamics for organizational and network-level efficiency and

sustainability. For efficient restructuring of the supply chain processes, organizations need

clarity regarding what should be the focus of their processes for creating sources of competitive

advantage. Using topic modeling, a text mining technique, this work finds the focus areas of

supply chain processes in organizations with examples of successful application of blockchain

technology. Apart from how these organizations have integrated the strengths of blockchain in

their supply chain processes, we also provide an exhaustive theoretical explanation about how

firms can create sources of competitive advantage from blockchain technology. Identification

Sch

ool

of

Man

agem

ent

(SO

M)

84

of the focus areas will also help operations and supply chain managers planning to implement

blockchain technology and devise plans for data-centric decision-making for their SCM

processes for efficiency.

Keywords: Blockchain Technology; Supply Chain Transparency; Traceability; Trust Digital;

Ledger Smart contract.

Conference Papers

Dr. Deepika Dhingra, Assistant Professor, School of Management (SOM)

Dhingra, Deepika (2020). Time-series data analysis for Stock Market Prediction using R, 3rd

International Conference on Innovative Computing and Communication, 1-5.

Abstract: Prediction of the stock market is gathering of previous historical data of shares or

any traded securities in the market and trying to forecast the future value of the company's

share/securities. This stock prediction using historical time series data, based on certain

preconceived notions and some constant factors and hence becomes difficult to predict for long

term range. (Figlewski, 1994) (Mondal, 2014). Time series analysis like moving average,

autoregression integrated. Moving Average and Holt winters Models are being used. This paper

proposes the best fit model for stock prediction using BSE (Bombay Stock Exchange data) for

10 years.

Keywords: Stock Market; Stock prediction; Bombay stock exchange.

Dr. Shruti Ashok, Assistant Professor, School of Management (SOM)

Ashok, Shruti., Mishra, Nandita & Dhingra, Deepika (2020). Rising Eminence of

Sustainability Reporting-Evidence from India, The International Conference on Business and

Technology (ICBT 2020).

Abstract: This paper is an attempt to examine the sustainability reporting practices of the top

40 public & private Indian companies (as per the ET-500 list). Using NVivo software, each

company’s overall disclosure index is calculated on seven identified parameters. The study is

conducted by applying content analysis on the past two-year’s annual reports of all the

companies. To identify substantial variation in public and private company’s disclosure norms,

the Mann-Whitney U test was applied for statistical analysis. Results indicate a significant

difference in disclosure of environmental, human rights, social performance, society, product

responsibility, and six capitals. Public sector companies are found to be more inclined in

societal disclosures while the private sector companies are more inclined towards social

responsibility. Environmental disclosures exhibit higher values for public companies and social

performance indicators display higher values for private sector companies.

Keywords: Entrepreneurship; Business development; Economic diversity

Sch

ool

of

Man

agem

ent

(SO

M)

85

Journal Papers

Dr. Anjali Malik, Assistant Professor, School of Management (SOM)

Malik, Anjali (2020). Children in the digital world: exploring the role of parental–child

attachment features in excessive online gaming. Young Consumers, 21(3), 335-350.

10.1108/YC-01-2020-1090.

Abstract: Purpose: The marketing of immersive and competitive online gaming products has

proliferated in recent times. Consumption has also shown a substantial increase, especially

among children. Such elevated levels of gaming have adversely affected children’s overall

well-being. This paper aims to examine the role of parental attachment variables in enhancing

children’s self-control behavior in counteracting the adverse effects of excessive gaming. The

role of gender in excessive gaming is also studied.

Design/methodology/approach A conceptual framework is tested that examines the direct

relationship of features of parental attachment with excessive online gaming behavior and an

indirect effect through the mediation of a child’s self-control construct using structural equation

modeling.

Findings The findings indicate that parental attachment through self-control can play a

significant role in limiting excessive gaming behavior among vulnerable young gamers.

Excessive gaming behavior was more pronounced for boys than girls. Alienation explained

excessive gaming behavior among girls, while communication was significant for boys, but in

a reversed direction.

Research limitations/implications All possible antecedent variables from the literature, like

parental rearing style, that may further contribute to developing a comprehensive theoretical

framework could not be studied. Practical implications The study suggests that the priming of

children achieved through parental attachment relationships may help prevent excessive

gaming behavior among vulnerable young gamers.

Originality/value This study addresses the gap in the understanding of parental attachment

features related to excessive gaming among different genders. It also establishes the role of the

intervening mechanism of a child’s self-control in regulating behavior in relation to excessive

gaming in the Indian context.

Keywords: Children; Online Gaming; Excessive gaming behavior; Parental attachment; self-

control; India.

Dr. Arvind Kumar, Assistant Professor, School of Management (SOM)

Kumar, Arvind (2020). Shopping Orientations and Their Inter-Relatedness: A Study on the

Poor for CPGs. Journal of Global Marketing, 33(4), 289-304.

10.1080/08911762.2020.1733729

Abstract: The poor, with a population of about four billion and a staggering annual purchasing

power of US$6 trillion, qualify to be a significant market, particularly for the consumer-

packaged goods (CPGs). The poor spend a substantial part of their incomes on CPGs by

necessity and the quantity of CPGs consumed by them per annum, again, commensurate with

the quantity of CPGs consumed annually by the other income groups. So, the poor seem

promising to provide new growth avenues to the CPG firms provided that the firms understand

the consumer behavior of poor well. This paper extends consumer behavior literature on the

poor, which still is at a stage of nascence, on the aspect of their shopping predilections.

Sch

ool

of

Man

agem

ent

(SO

M)

86

Particularly it evaluates the price, quality and brand related shopping predilections of the poor

for CPGs and then establishes the inter-relatedness amongst them.

Keywords: Consumer packaged goods; Shopping predilections; The economically poor; The

poor.

Dr. Dinesh Jaisinghani, Assistant Professor, School of Management (SOM)

Jaisinghani, Dinesh (2020). Oil India Limited – overcoming the challenges in competency

planning. Emerald Emerging Markets Case Studies, 10(1), 1-20. 10.1108/EEMCS-09-2019-

0243.

Abstract: The case highlights the initiatives that can be taken by the management of a large

organization to bring more objectivity in promotion policies and to make the process of

succession management more scientific. After completing the case, the following teaching

objectives should be achieved. Students should be able to comprehend the industrial structure

and the key challenges faced by oil and gas industry in an emerging economy - India; students

should understand how a large organization can bring objectivity and transparency in its

promotion policy by focussing on merit; students can analyse the challenges faced by a large

organization in implementing changes in its promotion policy; and students should be able to

understand the mechanism of alignment of assessment centres with the promotion policy.

Keywords: Career development/promotion; Assesment/testing; Balanced scorecard; Human

resource management; Organizational behaviour; Competency Planning; Promotion Policy;

Oil and gas industry; Assessment and development centres; Succession planning

Jaisinghani, Dinesh (2020). CSR disclosures and profit persistence: evidence from India.

International Journal of Emerging Markets, 10.1108/IJOEM-03-2020-0246

Abstract: The purpose of the present study is to analyze the impact of corporate social

responsibility (CSR) disclosures on firms' profitability and its persistence.

Design/methodology/approach the study has been conducted for listed firms operating in India

from 2008 to 2017. Content analysis has been utilized to estimate the CSR disclosures score.

Further, dynamic panel regression has been utilized to estimate the relationship between CSR

disclosures and profit persistence.

Findings The results confirm positive profit persistence for Indian companies. The results

further show that different dimensions of CSR disclosure have differential impact on firms'

profitability. CSR dimensions concerning total community development and product-related

disclosures have a positive relationship, whereas dimensions related to environmental and

customer-related disclosures have a negative relationship with financial performance. The

results also indicate that CSR disclosures are significantly related to profit persistence.

Originality/value the study is first of its kind that analyzes the impact of CSR disclosure on

profit persistence for Indian companies. The results can provide useful implications for

managers and regulators in terms of formulation of overall CSR policies.

Keywords: CSR disclosures; Profit Persistence; Emerging Economics; Content Analysis;

Dynamic panel Regression.

Sch

ool

of

Man

agem

ent

(SO

M)

87

Dr. Mohammad Haris Minai, Assistant Professor, School of Management (SOM)

Minai, Mohammad Haris (2020). Unpacking Transformational Leadership: Dimensional

Analysis with Psychological Empowerment. Personnel Review, 49(7), 1419-1434.

10.1108/PR-10-2019-0580-

Abstract: Purpose Scholarly studies have criticized transformational leadership (TFL) for its

lack of conceptual clarity and inadequate operationalization. This study endeavors to do a

detailed examination of the dimensions of the construct to address the lack of conceptual

clarity. Further, with respect to concerns regarding operationalization, the study does an

exploratory evaluation of reconceptualized TFL's relationship with psychological

empowerment, a construct through which TFL mostly has its beneficial outcomes.

Design/methodology/approach Respondents (n = 335) from an Indian information technology

(IT) services organization report on their psychological empowerment and the transformational

behaviors of their supervisors using temporally separated (15 days) online questionnaires.

Findings As expected, the dimensions of transformational leadership are not equally salient in

influencing psychological empowerment; however, they explain variance in all dimensions of

psychological empowerment. Visioning relates to meaning and impact; inspirational

communication relates to all dimensions of empowerment; personal recognition relates to

impact and competence; finally, intellectual stimulation relates to self-determination. Contrary

to expectations, however, data did not support the relationship of intellectual stimulation and

supportive leadership on competence.

Research limitations/implications Data collected from a single organization limit the claims of

generalizability, and the use of a cross-sectional design prevents claims of causality. Given the

significant variation in relational properties of individual dimensions, scholars can use

dimensions of TFL, and therefore theorizing with these is possible.

Originality/value This paper provides additional support for the unpacking of TFL, by

hypothesizing and demonstrating the dimensional relationships between TFL and

psychological empowerment.

Keywords: Quantitative; Leadership; Empowerment.

Dr. Nilanjan Banik, Professor, School of Management (SOM)

Banik, Nilanjan (2020). India-ASEAN trade relations: Examining the Trends and Identifying

the Potential. Global Business Review, 10.1177/0972150920953546

Abstract: The Association of South East Asian Nations (ASEAN) is an important trading

partner for India. Through its Act East Policy, India seeks deeper economic integration with

ASEAN. In this article, we investigate the pattern of trade between India and ASEAN, using

comparative advantage by country (CAC) and market comparative advantage (MCA) as

criteria, which are more appropriate measures for showing comparative advantage in a specific

market. These measures indicate some major Indian exports to ASEAN are not competitive.

When domestic industries are not competitive, governments protect them through tariffs and

non-tariff barriers, which act counter to deeper economic integration. We also identify the

sectors in which India has the potential to become competitive but is not because of

distortionary domestic policy measures. These domestic constraints prohibit Indian firms from

participating in the ASEAN supply chain network, an important factor in developing deeper

economic integration. Two such sectors-namely aluminium automotive components and the

ready-made garment industry-are examined as case studies. Notwithstanding these

Sch

ool

of

Man

agem

ent

(SO

M)

88

distortionary policy measures, India still offers a bigger market to do business. The ability to

supply lower cost chemicals and affordable pharmaceutical products at the time of COVID-19

is an added plus.

Keywords: Act East Policy; Market Comparative Advantage (MCA); inverted duty structure;

Goods and Services Tax (GST).

Dr. Nisha Mary Thomas, Assistant Professor, School of Management (SOM)

Thomas, Nisha Mary (2020). Leading Determinants of Dividend Payouts in India: A

Disaggregative Analysis. International Journal of Control and Automation, 13(4), 981 - 991.

http://sersc.org/journals/index.php/IJCA/article/view/18899

Abstract: This study undertakes a comprehensive investigation of the factors that influence

dividend pay outs for Indian firms by disaggregating the firms into (i) manufacturing and

service sectors (ii) banking and financial services sector, and materials and chemicals sector

and (iii) high dividend paying firms. The results show negative association between earnings

per share and dividend pay outs, which signals that firms with lower net income pay dividends

to allay agency problem. The results also indicate that liquid firms pay lower levels of

dividends, thereby supporting Security and Exchange Board of India’s (SEBI) stance that

Indian firms have the tendency to hoard cash and not distribute the same to the investors. The

findings also reveal that profitable Indian manufacturing and service firms are inclined to

maintain lower dividend pay outs, as they would like to invest utilize earnings for good

investment opportunities. The results highlight limited ability of Indian banks and financial

sector to pay dividends probably due to the burden of non-performing assets.

Keywords: Dividend Payout; Emerging Market; India; Panel Regression.

Dr. Palakh Jain, Assistant Professor, School of Management (SOM)

Jain, Palakh (2020). MSMES and Competition Law in India: Victims or Perpetrators. Review

of Socio-Economic Perspectives, 5(1), 23-34. 10.19275/RSEP074

Abstract: SMEs contribute around 35-40% of the GDP of India and are key to employment

generation, sustainable development and poverty reduction. This sector is largely unorganised

and vulnerable to the dynamic external business environment. On one hand, small size of the

SMEs makes them vulnerable to anti-competitive acts of bigger enterprises including abuse of

dominant position and on the other hand, cooperation agreements amongst SMEs assist them

to compete with large enterprises. Competition Act, 2002 deals with anti-competitive

agreements and abuse of dominant position, amongst other things. The Competition Act of

India is size and type neutral. This paper thus, looks at whether SMEs are perpetrators or

victims of anti-competitive conduct. This study analyses the recent anti-trust cases in India

which involved SMEs and develops a typology of anticompetitive conduct and abuse of

dominance activities employed by large corporations against SMEs and also anti-competitive

conduct that SMEs may engage in.

Keywords: SMEs; Competition Law; Anti-competitive; Large corporations.

Jain, Palakh (2020). Demographic Dividend or Demographic Nightmare. International

Journal of Advanced Science and Technology, 29(11), 275-294.

http://sersc.org/journals/index.php/IJAST/article/view/19980.

Sch

ool

of

Man

agem

ent

(SO

M)

89

Abstract: Currently, India is home to 20 percent of the population of the world amounting to

1300 million people. It is anticipated that by 2024, India’s population will surpass China and

by 2050, shall have highest number of people (over a billion) in the working age population in

Asia- Pacific. This puts India at a special advantage to reap “demographic dividend” which is

the benefit in terms of economic growth accruing to any nation due to changes in the age

structure. However, the country is experiencing a stagnation in employment creation due to

increasing use of capital-intensive technology replacing human with capital and a lack of

skilled labour to efficiently perform jobs given the new wave of industry 4.0. So, there is a

possibility of this demographic dividend turning into a demographic nightmare, if this huge

human resource is not productive employed. The author conclusion is that solution to the

problem lies enhancing the quality of the workforce by promoting Information Technology,

development of infrastructure as well as investments in health and education. The low labour

force participation of women is an opportunity which can be harnessed by taking right policy

actions such as creation of non- farm jobs in the rural sector. Also, exports require the much-

needed attention for the country to become globally competitive and tackle unemployment.

Acknowledging the important role of Defence and MSME sectors in making our economy

globally competitive, adequate support should be provided to these sectors.

Keywords: Demographic dividend; Jobless growth; Employment; Unemployment; India.

Jain, Palakh & Allamraju, Ashita (2020). Review of Regulatory Challenges in Digital

Platforms: Cases from India. International Journal of Psychosocial Rehabilitation, 24(8), 8859-

8874. 10.37200/IJPR/V24I8/PR280882

Abstract: Information Communications Technology (ICT) networks and infrastructure are

now critical resources for organizations to run their business, compete and contribute

productively in the new age digital markets. ICT has supported novel commercial models

(Internet firms), 'digital platforms', by redefining delivery of goods and services and has

significantly enhanced consumer welfare. The presence of intrinsic network effects in case

digital platforms combined with the limited interoperability makes the prevalent digital

ecosystem to have monopolistic structure or at best an oligopolistic market structure. There are

regular entry and exit in digital markets, firms exercise mergers and acquisitions as a business

strategy to establish its market share or diversify into new business areas. The power of user

information (through data) enables the firms to practice price discrimination, thereby

appropriating consumer surplus. Thus, competition issues in the new age digital markets must

to be tackled by the competition authority taking cognizance that the regulatory regime does

not curb the motivation to innovate. The paper reviews the competition issues i n digital

ecosystem from the Indian perspective and reflects possible solutions to safeguard competition

in the digital markets.

Keywords: Regulatory; ICT; Digital platforms; Competition; CCI; Case; India.

Jain, Palakh, Singh, Anshika & Binani, Neharika (2020). Case Study of the No Power

Complaints Processing System at Torrent Power Ltd. Gedrag & Organisatie Review, 33(2),

1811-1913. 10.37896/GOR33.02/586.

Abstract: Torrent Power, the sole supplier of electricity for Ahmedabad region, is a reputed

player in the Indian power sector. With activities encompassing power generation, transmission

and distribution, the company is the most experienced private player in Gujarat, India. Torrent

power distributes over 10 billion units of power annually to its 1.9 million customers spread

over an area of 408 sq. km. in the cities of Ahmedabad, Surat and Gandhinagar. Torrent Power

Sch

ool

of

Man

agem

ent

(SO

M)

90

AEC Ltd. is responsible for distribution of power in Ahmedabad. The Ahmedabad region is

divided into 4 zones – Ahmedabad city, Naranpura, Amraiwadi and Gandhinagar zone. We

studied the system for processing no power complaints at the Narangpura zonal office.

Keywords: Complaints; Torrent Power; Process management; Management case.

Jain, Palakh, Singh, Utkarsh, Gupta, Vaishnavi & Jain, Aditya Raj (2020). The Sectoral

Impact of Covid-19 On Indian Economy. International Journal of Advanced Science and

Technology, 29(11), 302-315. http://sersc.org/journals/index.php/IJAST/article/view/19983.

Abstract: The objectives behind our research paper are to assess the sectoral impact of

COVID-19 on three sectors namely - MSME, Consumer Retail and E-Commerce sector, and

Aviation and Tourism sectors. The methodology that we adopted involved data collection

secondary sources like journals, case studies, e-books, and news articles that provided us with

the ability to gather information on other related strands of our paper. The key findings of our

paper are based on the evidence we derived from the three sectors. MSME sector is the worst

hit by the pandemic and deserves urgent financial and safety net from the government. Aviation

and Tourism industry faced huge financial losses due to travel constraints and would only

revive when the government introduced some extensive packages for travelers and airlines.

The Consumer, Retail, and E-commerce sector dealt with supply chain disruptions with online

businesses expecting some boost in their businesses. The online survey rendered conclusions

on how behavioral patterns of consumers changed as they became more worried about their

health, income, jobs, and other changes in other market segments. Nation-wide lockdown

deprived us of the availability of economic data, therefore we resorted to other forms of

secondary research options. COVID-19 is a new and complex phenomenon that has disturbed

the world economies on various grounds like political, economic, social, and financial.

Presently, there are very few studies evaluating the sectoral impact of this disease on the

economy. Our paper on the sectoral analysis adds value to the existing literature and opens

doors for future research.

Keywords: Sectoral analysis; India; coronavirus; Covid-19; MSME; Aviation; Tourism;

Consumer Retail; E-commerce; Economic impact; Indian economy

Dr. Pankaj Kumar Medhi, Assistant Professor, School of Management (SOM)

Medhi, Pankaj Kumar (2020). Role of managerial perception of competitive pressures in

firms’ product innovation success. European Journal of Innovation Management, 1-17.

10.1108/EJIM-03-2020-0069

Abstract: Purpose-This study explores the link between the level of importance managers

assign to competitive pressures from domestic competition, foreign competition and customers

as factors in the key business decisions related to innovation and the outcome of firms’ product

innovation efforts.

Design/methodology/approach - The research sample is taken from the Business Environment

and Enterprise Performance Survey by World Bank (2005). The relevant questions for the

study were extracted from the survey. Logistic regression models were used for analysis using

the ISLR library from R statistical software.

Findings-Managers’ consideration of customer pressure for innovation as important in key

business decisions related to innovation has a positive and sustainable effect, distinct from that

of R&D and other innovative activities, on firms’ success of product innovation efforts.

Sch

ool

of

Man

agem

ent

(SO

M)

91

Research limitations/implications – The research acknowledges the need to verify the findings

in a multi country setting. Practical implications – This research can help mediate the

managers’ assignment of importance to certain types of competition for innovation decisions

in multi competitive environment for improved success of product innovation efforts.

Originality/value - Simultaneous consideration of multiple competitive pressures by managers

helps to identify the most suitable innovation activities for their respective firms and improve

the chances of success of firms’ innovation efforts.

Keywords: Managerial perception; Domestic competition; Foreign competition; Consumer

competition; Product innovation.

Dr. Pradeep Kumar Mohanty, Assistant Professor, School of Management (SOM)

Mohanty, Pradeep Kumar (2020). Analyzing repurchase behavior and benchmarking brands:

implications for salespersons in a personal selling context. Journal of Agribusiness in

Developing and Emerging Economies, 10.1108/JADEE-05-2020-0103

Abstract: Purpose: This work reports on a study to measure tractor owners' (mostly farmers')

repurchase behavior (RPB). While earlier studies have focused on the technical aspects of the

tractors, none as yet have considered farmer intention and behavior for predicting purchase

decision. A conceptual model was built considering all possible antecedents of farmers' RPB

based on in-depth interviews and discussions with marketing managers. Interviews with

customers were used to understand these antecedents or interactions with salespersons, either

directly or indirectly during conversations or visits to stores. The authors have attempted to

develop a scale on farmers' consumption experience from the perspective of farmers.

Design/methodology/approach: The model was validated using Smart-PLS, and the best tractor

brand was identified using data envelopment analysis (DEA). At the village level, snowball

sampling was adopted to identify potential tractor owners who had repurchased the same brand

or a higher model as respondents. Findings: Findings reveal that all the paths were found to be

significant. Farmers' consumption experience (FE) seems to be the biggest predictor of RPB,

followed by image, satisfaction and trust. The newly introduced construct FE has a significant

effect on farmers' RPB. DEA results further indicate that most tractor companies function with

100% efficiency. Research limitations/implications: The study was carried out in India; it can

be extended to other countries. Also, the sample was collected from one state in India and is

cross-sectional in nature, so it cannot be generalized. Originality/value: First, the authors

developed a conceptual model considering all possible antecedents of RPB. No studies had yet

developed a scale on FE. Second, the authors created a benchmark for the various preferred

tractor brands from the farmers' perspective using DEA.

Keywords: Brand efficiency; Data envelopment analysis (DEA); Farmers' consumption

experience; Farmers' repurchase behavior; Personal selling; Tractor industry

Dr. Sangeeta Shukla, Associate Professor, School of Management (SOM)

Shukla, Sangeeta (2020). The pragmatics of Indian political apologies: Sorry, but not sorry.

Discourse & Society, 31(6), 648-669. 10.1177/0957926520939688.

Abstract: While there is a considerable body of research on the pragmatics of apology across

the globe, the Indian apology discourse has received hardly any attention from scholars.

Political apologies particularly, have been neglected as an important area of research in India.

The act of tendering public political apologies, which was almost absent from the Indian

repertoire, is an emerging trend in India. This article aims to identify the salient characteristics

Sch

ool

of

Man

agem

ent

(SO

M)

92

of Indian political apologies by contextual analysis of the apology texts and is a first of its kind

as far as known to the authors. Indian political leaders use evasion and manipulation in apology

utterances to avoid an explicit apology. The graver the transgression, the greater the hesitation

to offer an explicit apology. We suggest that the categorization of political apologies should

take into consideration the stature of the political apologizer also and not just the magnitude of

the offence, as this can have a significant impact on the apology behaviour.

Keywords: Apology; ‘face’; Politeness theory; Political discourse; Pragmatics; Public

discourse; Access options.

Dr. Shruti Ashok, Assistant Professor, School of Management (SOM)

Ashok, Shruti & Dhingra, Deepika (2020). Reverse Mortgage - A Financial Planning Tool

for The Retirees: Case Study Approach in India. South Asian Journal of Business and

Management Case, 9(3), 375-386. 10.1177%2F2277977920958668.

Abstract: Reverse mortgage has always been viewed as a product meant exclusively for the

elderly with financial hardship. However, there is a brighter side to it too. This article attempts

to project reverse mortgage as a financial planning tool through a case study analysis. Using

the coordinated strategy, this case explores how the line-of-credit option in reverse mortgage

can meet the cash needs of a retiree during market downturns, allowing his portfolio to stay

invested. The suggested strategy is a direct attack on investment risks, especially, sequence of

return risk. Using this strategy, the depreciated assets are allowed to recover their original value

and appreciate, ensuring better returns and longevity of investments.

Keywords: Ageing population; Financial planning; Longevity of investments; Retiree;

Reverse mortgage; Senior citizen.

Dr. Shweta Mittal, Assistant Professor, School of Management (SOM)

Mittal, Shweta (2020). “Yes Madam”: digitized services right here and right now. Emerald

Emerging Markets Case Studies, 10(2), 1-20. 10.1108/EEMCS-01-2020-0004.

Abstract: The case helps to understand: the working mechanisms of a digitized salon service,

with a focus on the lower- and middle-income strata. The changing scenario of the service

marketing model, with the growth in digital service models. To investigate the organisational

challenges of a digitally facilitated/based start-up and find solutions to overcome the

challenges. Case overview/synopsis “Yes Madam”-salon at home was a business enterprise,

providing beauty and wellness services at the doorstep through a mobile application and web-

based platform. The case describes the reason for opening the doorstep beauty services, its

revenue model and aims to provide quality services to lower- and middle-income strata. The

case will help students to understand the working mechanism of digitized salon services and

associated challenges; prominent ones being attracting, selecting and retaining the beauticians

and providing the standardised services. The case has examined the low-price services for the

consumers delivered by the company. The case also discussed their plans for diversification

and penetration into the untapped markets. Complexity academic level Graduates and

postgraduates. Supplementary materials Teaching Notes are available for educators only.

Subject code CSS 3: Entrepreneurship.

Keywords: Beauty parlour; Customer service; Price; Competitors; Salon; Digitised services;

Customer satisfaction; Customization; Entrepreneurship; Services marketing.

Sch

ool

of

Man

agem

ent

(SO

M)

93

Magazine Article

Dr. Palakh Jain, Assistant Professor, School of Management (SOM)

Jain, Palakh (2020). Consumerism in Times of Uncertainty: Analyzing Purchasing Behaviour

during COVID-19. IMI Konnect, 9(3), 30-41. https://imikolkata.medium.com/article-imi-

konnect-volume-9-3-2020-c1af171ffe8a

Abstract: The study uses the Unified Payments Interface (UPI) transactional data along with

credit card and debit card transactional data to develop an understanding of the peculiar

purchasing behaviour of the consumers in the Indian context. From the analysis, it is

understood that consumers tend to over purchase and also display illicit hoarding behaviour

during the initial stage of an uncertainty. As the lockdown extended and the pandemic kept

spreading, the consumers resorted to sustainable consumption behaviour.

Keywords: COVID-19; Economy; Consumers Behaviour.

Jain, Palakh (2020). Zero Defect, Zero Effect’: What it means, and what it delivers.

GovernanceNow, 1-3. https://www.governancenow.com/news/regular-story/zero-defect-zero-

effect-what-it-means-and-what-it-delivers

Abstract: In India’s pursuit for becoming globally competitive, prime minister Narendra Modi

has pitched for ‘Make in India’ with ‘Zero Defect, Zero Effect (ZED)’ culture with twin focus

on customers and on society. By zero defect, he means that the quality of the products has to

be very high and by zero effect he means that there should be no adverse effect on the

environment by manufacturing. This highlights the government’s ardent desire to pay

particular attention on manufacturing as a means to sustainable growth in order to transform

the course of economy.

Keywords: Industry 4.0; Zero defect.

Jain, Palakh (2020). India’s digital empowerment and net neutrality: Revisiting the debate:

pros and cons of open Internet. Governance Now, 1-3.

https://www.governancenow.com/views/columns/indias-digital-empowerment-and-net-

neutrality

Abstract: The Fourth Industrial Revolution has steadily amalgamated information

communication technology (ICT) in every aspect of modern human life, having a profound

impact on economic and social systems by altering business structures and consumer habits.

About two decades back, ICT infrastructure was an augmented resource for business, but today

with technical advances it has become the first layer of foundation over which other layers

fortify the strength of an organization, to equip the business with capabilities to compete and

contribute in a digital age. The United Nations also well recognizes ICT as a critical driver of

the Sustainable Development Goals and an enabler of progress on the Millennium

Development Goals.

Keywords: Revisiting the debate: Pros and cons of open Internet

Jain, Palakh (2020). Harnessing the demographic capital: how effective are skilling

programmes? GovernanceNow, 1-3.

Abstract: Probing data concerning increased job creation and the decline in unemployment

has been holding the attention of economists and been subject of discussions in several think

tanks in the preceding months. The NITI Aayog reports that 3.53 million new jobs were created

Sch

ool

of

Man

agem

ent

(SO

M)

94

between September 2017 and February 2018 and 7 million people could have been added to

the payroll in fiscal 2018. These numbers are based on the data of the Employees' Provident

Fund Organisation (EPFO) and Pension Fund Regulatory Development Authority (PFRDA).

Keywords: Economy; Labour; Employment; Skills

Payal Seth, Research Scholar, School of Management (SOM)

Seth, Payal (2020). Getting Swachh Bharat statistics right: Moving sanitation outcomes from

access to use will represent true state. Financial Express, 1-3.

Abstract: Declaration of India as an open-defecation free nation was based solely on access to

toilets. The outcome measured does not line up with the intent behind the launch of the Swachh

Bharat Mission.

Keywords: Swachh Bharat; Sanitation; National Family Health and Demographic Survey.

Working Paper

Dr. Nilanjan Banik, Professor, School of Management (SOM)

Banik, Nilanjan (2020). Influencing Factors of COVID-19 Spreading: A Case for Government

and Divine Intervention, Korea Institute for International Economic Policy, 423-442.

Abstract: The pandemic, COVID-19, is a special one. It has created a disruption, like the ones

seen in case of major wars, turning the growth trajectories of many economies’ upside downs.

What would be an ideal policy response? As countries across the world differ structurally, that

is, some countries are more industrially advanced than the others; some are more populous,

with a large dependence on agriculture and urban-informal sector; the nature of policy response

to fight against COVID-19 varies across countries. Policy responses enacted through a

combination of fiscal and monetary policies are meant to minimize loss of life and livelihoods.

Some of the countries such as South Korea and New Zealand did well in containing the crisis.

On the contrary India was not so successful. I compare and contrast the policy response of

India, benchmarking it against South Korea and New Zealand. Although COVID-19 is

spreading fast in India, the mortality rate is low. I attribute these to the exogenous factors.

These factors such as demographic profile, tropical weather, dietary habits, large vaccination

program, and ability to supply affordable drugs have been responsible for the low number of

COVID-19 deaths in India.

Keywords: COVID-19; Testing, India; South Korea; New Zealand; Fatality Rate.

Banik, Nilanjan (2020). Abolishing Pharmaceutical and Vaccine Tariffs to Promote Access.

Geneva Network, 1-14.

Abstract: Tariffs on medicines have been declining over the last twenty years, falling from a

global average of 4.9% in 2001 to 3.4% in 2018 (latest available data). » Nevertheless,

jurisdictions and customs territories continue to apply tariffs of up to 20% on medicines and

10% on vaccines (although increasing numbers of governments apply no tariffs at all).

Sch

ool

of

Man

agem

ent

(SO

M)

95

Reductions in tariffs rates are being undermined by a trend for governments to increase the

categories of imported medicines subject to tariffs, potentially to recoup revenue lost from

lowering headline tariff rates. The Covid-19 crisis reemphasises the need to reduce inflationary

trade barriers to increase access to medicines and vaccines. Expanding and updating the WTO

Pharmaceutical Agreement would be a powerful avenue to achieve this.

Keywords: Free Trade; Latin America; Pharmaceutical Tariffs; Public Health; Tariffs On

Medicines; Tariffs On Pharmaceuticals; Trade; Trade Liberalisation; Vaccines; WTO

Pharmaceutical Agreement; WTO Zero for Zero.

Sch

ool

of

Man

agem

ent

(SO

M)

96

Times School of Media (TSOM)

Book Chapters

Dr. Nithin Kalorth, Assistant Professor, Times School of Media

Kalorth, Nithin (2020). Audible voices of others and the role reversals in media systems: the

pandemic lessons. In S.R. Sanjeev (Eds), COVID-19 Infodemic: Problem prospect and

retrospect the route maps of Kerala, 240 (pp. 60-88). D C Books

Abstract: Virality being the routine of the day in the contemporary media sphere, COVID-19

stands as the first global pandemic in a net worked information age guided by artificial

intelligence, machine learning, and algorithms. Almost every global pandemic in the history,

to be specific, which affected the first world, had happened before the disproportionate

expansion of the information age enwrapped in and guided by digital social networking. Now,

the pandemic challenges put forth questions on the hegemonic information dissemination

notions, media ownership/monopoly, reach/access of ICT and balance of power enjoyed and

rested within the western media systems. The diversity (culture, language. geography, religion)

digital divide, power segregation, difference in the system, control and governance of media

channels and means of information were openly recognized and accepted as a reality of

imbalances throughout, when the whole world switched to a mode of crisis communication.

These questions encompass big challenges to western media in their dominance that too in their

economic, cultural, and social realms exerted over the Inter half of the 2oth century until now.

Here, we exhibit and argue that the control and the power of dominant western media have

been diminishing in the socio-economic and cultural context created by the pandemic.

Keywords: Westernisation as Globalisation; Kerala: a confidence building model; Mass

literacy

Kalorth, Nithin (2020). Identities and Ideologies in Tamil Cinema: Understanding within the

framework of socio-political movements in Tamil Naidu. In. G. Baskaran and Blais Johny

(Ed.) Into the Nuances of culture essays on culture studies (pp. 95-106). Yking Book.

Abstract: Writing on impact of cinema in modern societies, Daniel Learner noted "[the impact

of movies] has been massive and sustained, as in modern society, the results are highly visible."

The movies "teach new desires and new satisfactions and portray roles in which lives are lived,

and provide clues as how these roles can be enacted by others" (1958). The movie watching is

a as part of the day for the people of Tamil Nadu from the beginning of cinema in the state

(Hughes 2010) and is going on to till date (Kokilavani 2016) and Rogers explains the political

economy of Tamil film cultures in terms of the "interplay between audience reception and the

cinematic representation of political ideologies" (2009). Cinema educated and entertained the

masses of Tamil Nadu over decades. They acquired the knowledge of their identity of culture

through the films which "bear the messages" (Baskaran 1981) of the nationalism and culture.

Keywords: Social movement; Tamil Cinema; Conflict.

Dr. Tilak Jha, Assistant Professor, Times School of Media

Jha, Tilak (2020). Welfare Communication and Poverty eradication in India, China. In: Gao

J., Baikady R., Govindappa L., Cheng SL. (eds) Social Welfare in India and China. (pp. 231-

242). Palgrave Macmillan. doi.org/10.1007/978-981-15-5648-7_13

Abstract: This Chapter analyses the theoretical and practical contours of the framework of

welfare communication-read media and propaganda-in two of the world’s most populated

countries, with a fundamentally different political set up, when it comes to poverty eradication.

Tim

es S

chool

of

Med

ia (

TS

OM

)

97

It achieves this by comparing the media coverage of the flagship poverty alleviation schemes

in India and China in recent times and their successes vis a vis the most vulnerable sections of

their population. Through comparative analysis of the coverage of poverty related news,

programmes and statements made by the concerned authorities-both at the public and private

level-in India and China, this chapter unravels related dimensions, and the communication

matrix and the mechanism behind it.

Keywords: Communication; Media and propaganda; Poverty reduction India and China.

Jha, Tilak (2020). China’s Media and Covid-19: A Content Analysis of Caixin, Xinhua and

People’s Daily, In Srikanth Kondapalli and Shaheli Das (Ed.) China and Covid-19 Domestic

and External Dimensions, (pp.1-15) Pentagon Press.

Abstract: This is not the first time China has faced a virus-created health crisis. Over the past

two decades, two of the three zoonotic coronavirus outbreaks - namely the Severe Acute

Respiratory Syndrome (SARSCoV) in 2003 and Severe Acute Diarrhoea Syndrome

Coronavirus (SADS-CoV) in 2012 - began in China. Both caused worldwide pandemic and

claimed thousands of human or animal lives. A paper authored by a scientist of the Wuhan

Institute of Virology predicted in January 2019 the high likelihood of a SARS or Middle East

Respiratory Syndrome (MERS)-like coronavirus outbreaks in China.1 Ironically, the same

research centre has been accused by the US President Donald Trump to be the reason behind

the massive global damage caused by the Covid-19 outbreak.2 More importantly though,

Chinese state media's response to the Covid-19 has come under severe criticism from several

quarters. There are concerns about whether Chinese state media3, massively abetted the spread

of the Covid-19

Keywords: Xinhua; Caixin; People's Daily; Coronavirus; Covid-19; Censorship.

Journal Papers

Dr. Nithin Kalorth, Assistant Professor, Times School of Media

Kalorth, Nithin (2020). Information and User: Social Media Literacy in Digital Societies.

Journal of Content, Community & Communication Amity School of Communication, 12(6),

263-269. 10.31620/JCCC.12.20/24.

Abstract: The information flow in digital societies is discussed and analysed from more a

decade with close watch on social media networks. The shift from traditional forms of

communication to social media enables users to gratify their daily needs of information

digitally. The current paper builds on narrative analysis of selected social media active users

and their digital social engagement to understand how a user and a network of user engage with

information. To understand role of social media literacy, the current paper interviews the users

and correlated the findings with contemporary literature on social media. The results show that

social media literacy becomes pillar of information system, but it works in micro-level of

societies at crossroads of online and offline spaces.

Keywords: Social Media; Active User; Information; Misinformation; Digital Literacy.

Tim

es S

chool

of

Med

ia (

TS

OM

)

98

Kalorth, Nithin (2020). Smartphone in Journalism: Shift in News Production, Distribution,

and Consumption in Indian New Mediascape. NISCORT Journal of Media, Communication

and Research (NJMCR), 1(1), 1-7.

Abstract: The media industry is involved in a huge way after digitalization. The entry of

computers, internet and mobile phones changed the way newsrooms worked and it also

challenged the media planners and journalists both financial and workforce end. The

smartphone has affected journalists at work, editors at newsroom and readers alike. The current

paper tries to study the above-mentioned situation in the new media news production in India.

The paper features personal interviews of journalists, editors, media managers of selected new

media of India. The paper tries to build a theoretical framework to understand the capacity and

limitations of smartphones in Indian newsrooms.

Keywords: Smartphone; Mobile Media; Content Management; Digital Economy; Talent;

MoJo.

Dr. Shilpi Jha, Associate Professor, Times School of Media,

Jha, Shilpi (2020). Coverage of Indian Parliamentry elections 2019: content analysis of prime-

time news coverage of top three news channels. Mass Communicator: International journal of

Communication Studies, 14(1) 15-22. 10.5958/0973-967X.2020.00002.2

Abstract: During the last decade, no other mass media platform has changed the way it

perceives, creates and disseminates content, as News on Indian Cable Television. Private News

Channels in India have often come under severe criticism for reducing news and ground reports

to prime-time panel discussions and endless sessions of allegation and counter accusations.

General Elections, the biggest festival of any democracy and a huge visual spectacle are

supposed to bring tremendous opportunity of varied content creation and showcasing different

issues from ground reporting for understanding the real issues and concerns of voters and

capturing the pulse of the nation This is also a time for them to mobilize resources for better

coverage of pan nation news since there is a better inflow of revenue through political as well

as government advertisement. 2019 general elections, however, saw more of views created

from inside the newsroom studios than on ground coverage of news. This research picked up

top 3 news channels India based on average BARC ratings and captured the prime-time news

coverage during the period of general elections 2019, beginning with the announcements of

dates and ending at the last round of voting (11th March-18th May). The content analysis of

the top channels Aaj Tak, ABP News and India TV (selected based on weighted average of

BARC ratings during the time period) has been categorized based on them being news

dominated vs opinion dominated and ground reporting vs studio chats. Analysis of content

differentiation among the channels has also been taken into consideration

Keywords: General Elections; News Channels; News Coverage; Opinion; Content Analysis;

BARC Ratings

Tim

es S

chool

of

Med

ia (

TS

OM

)

99

Department of Library

Conference Papers:

Priyanka C. Bhatt, Staff, Department of Library

Bhatt, Priyanka C (2020). Technology adoption for different e-tailer formats: A conceptual

framework. Proceedings of the 5th NA International Conference on Industrial Engineering and

Operations Management Detroit, 2020, 586-592.

Abstract: The increasing trends in technological advancements have opened up a new horizon

of opportunities for organizations. With the penetration of mobile technologies, the aspirant

organizations have found a new medium to engage their stakeholders. Brick and mortar

retailers often seem to invest high costs on technology and infrastructure, but the need of time

emphasises to change the behavior of decision making. Various technology adoption models

(TAM) have been deployed in past which help to adopt new technologies to sync with current

systems. A new format called ‘brick and click’ retailers has flipped the traditional format of

retail models and has opened up new possibilities for upcoming technologies. This study helps

in identifying the upcoming Internet of Things (IoT) technology suitable for different formats

of retailers, i.e., e-hypermarkets, e-specialty stores, and e-convenient stores. The model

analyses assertive compatibility of IoT and Intranet of Things with the existing retail formats.

For this study we have considered various e-tailer formats operating in Asian region

specifically India, and empirical investigation is performed. The study proves significant at

global level as the structure of e-tailers are seminal throughout the world.

Keywords: Technology adaptation; Brick and Click Retailers; E-Retail Formats; Internet of

Things

Journal Papers

Dr. Shiv Singh, Assistant Librarian, Department of Library

Singh, Shiv (2020). Indian Horticulture Research Trends: A Study of PhD Researches

Conducted in Indian Universities and Institutions. Plant archives, 20(supplement 2), 3041-

3047. http://www.plantarchives.org/SPL%20ISSUE%2020-2/504__3041-3047_.pdf

Abstract: Horticulture is the most important in our daily living. Human body requires

vitamins, minerals, proteins, energy etc. for its health and all these are supplied by horticultural

crops. The significance of any field is analysed by growth of the research on that field. So, in

the present study the characteristics of the theses on horticulture from 1952 to 2017 in India

have been explored. The data was obtained from the IndCat and analysed on the various

parameters such as publishing universities, paper growth, most productive department, advisor

productivity, Keywords/subject used. The data consist of 1594 theses, among which the most

of the contributions was from Himachal Pradesh and University at the top was Dr Y S Parmar

University of Horticulture and Forestry. The top three departments are Horticulture,

Agricultural and Vegetable Crop with 1092 theses.

Singh, Shiv & Kataria, Sanjay (2020). Global Research on Scabies: A Scientometrics

Assessment of Publication Output During 2009–2018. Plant archives, 20(supplement 2), 3499-

3506. http://www.plantarchives.org/SPL%20ISSUE%2020-2/575__3499-3506_.pdf

Abstract: Scabies is one of the most common dermatological conditions faced by a

considerable proportion of population in many countries. The purpose of this study is to analyse

Dep

art

men

t of

Lib

rary

100

the literature published on scabies from 2009–2018. The data were retrieved from the Scopus

Database and computed in MS – Excel. Various scientometric techniques were used to find out

the lead productive countries in scabies research, their share of publication, top institutes,

journals, authors and their citation impact. Approximately, 2268 publications from 892 sources

were fetched and 8639 authors with a collaboration index of 4.22 contributed towards them.

Keywords: Scientometrics; Epidermal parasitic skin diseases; Scabies; R Software; Impact

ratio.

Singh, Shiv & Kataria, Sanjay (2020). Indian contribution status of the cataract research: a

bibliometric analysis. Library Philosophy and Practice (e-journal), 1-9.

https://digitalcommons.unl.edu/libphilprac/4092/

Abstract: Based on cataract research data obtained from the Scopus database, it is

comprehended that 3673 (3.93 % global share) researched articles from India on cataract had

been published till 2017, which place India on 6th position in the world in terms of research

paper output. About 62.13 % of the Indian publications appeared during the period 2008-2017.

L.V. Prasad Eye Institute India and All India Institute of Medical Sciences (AIIMS), New Delhi

was the most productive as they had been publishing the highest number of papers, i.e., 424

and 385, respectively. About 38.71% Indian publications are a result of international

collaboration with 116 countries.

Keywords: Cataract; Blindness; India; Bibliometrics.

Dep

art

men

t of

Lib

rary

101

Author Index

Name of Author Department Page No.

Aakash Warke Physics 53, 71, 72

Aditi Chauhan Physics 53

Aditya Raj Jain SOM 90

Ajay Yadav ECE 51

Amit Singhal ECE 50, 51, 56, 57, 58

Amrita Dixit ECE 52, 53, 54

Anchal Aggarwal Biotechnology 5

Aneesh Batchu MAE 69

Anirudha Poria Mathematics 62

Anjali Malik SOM 85

Ankit Kumar Pandey ECE 58, 59

Anosh Billimoria CSE 41

Anshika Singh SOM 89

Anurag Goswami CSE 9, 24, 25, 48

Apar Garg CSE 9

Argha Debnath Physics 72

Arjun Kumar ECE 51, 52, 53, 54, 59

Arnab Bose MAE 68

Arpit Bhardwaj CSE 9, 10, 19, 20, 26, 41

Arvind Kumar CSE 8, 10, 26

Arvind Kumar SOM 85

Aryan Lala ECE 54

Ashish Kumar ECE 50

Ashish Saurabh MAE 65

Ashita Allamraju LAW 79, 80, 89

Ashok Kumar ECE 52, 53, 54

Ayan Khan Physics 72, 76

Baipureddy Neeraj CSE 14

Balmukund Mishra CSE 27

Bharath Obalareddy MAE 69

Cairo Gulati CSE 26

Chitra Sharma Biotechnology 4

Dattu Burle CSE 14

Debabrata Baral LAW 77, 78, 79

Deepa Khare Biotechnology 1

Deepak Garg CSE 11, 27, 30

Deepak Rai CSE 12, 13

Deepak Singh CSE 13, 16, 28

Deepali Atheaya MAE 65, 66

102

Name of Author Department Page No.

Deepika Dhingra SOM 84, 92

Dilbag Singh CSE 29, 34

Dinesh Jaisinghani SOM 86

Divya Acharya CSE 9, 10, 19, 41

Divyaansh Devarriya CSE 26

Garima Tiwari LAW 81,82

Gaurav Singal CSE 11, 30, 31, 33

Himank Sharma MAE 70

Hiren Kumar Thakkar CSE 12, 13, 31, 32

Indrajeet Gupta CSE 33

Jagendra Singh CSE 13

K. Rama Koteswara Rao Physics 73

Kalagara Chaitanya Kumar ECE 54

Kanad Kishore Biswas CSE 44, 45

Kanak Manjari CSE 33

Krishna Thyagarajan Physics 73

Kuldeep Chaurasia CSE 8, 14, 15

Madhushi Verma CSE 15, 33

Manan Gupta ECE 53

Manika Bhardwaj CSE 20

Manjeet Kumar ECE 50, 60

Manjit Kaur CSE 29, 34

Manoj Sharma ECE 54

Mayank Dixit CSE 15

Mohammad Danish MAE 66

Mohammad Haris Minai SOM 87

Mohit Agarwal CSE 18, 23, 44, 45

Mohit Sajwan CSE 16

Nandita Mishra SOM 84

Neelam Choudhary Mathematics 62

Neelanchali Asija Bhalla MAE 66

Neharika Binani SOM 89

Neishka Srivastava CSE 41

Nilakshi Sarma Biotechnology 4

Nilanjan Banik SOM 83, 87, 94

Nisha Ahuja CSE 16, 18

Nisha Mary Thomas SOM 88

Nithin Kalorth TSOM 96, 97, 98

Palakh Jain SOM 88, 89, 90, 93

103

Name of Author Department Page No.

Pankaj Kumar Medhi SOM 83, 90

Pankaj Sharma CSE 20

Payal Seth SOM 94

Prabhakar Sathujoda MAE 66, 67, 68, 69

Pradeep Kumar Mohanty SOM 91

Priyam Das Physics 72

Priyanka C Bhatt Library 99

Pushpendra Singh ECE 51

R. Shashidhara CSE 35

Raghunath Reddy Madireddy CSE 36

Rajinder Singh Chauhan Biotechnology 1

Rama Koteswara Rao

Kamineni Physics 74

Rama S. Komaragiri ECE 54, 55

Rishav Singh CSE 37, 47

Ritesh Vyas ECE 54, 55, 56, 60

Rohit Kumar Kaliyar CSE 9, 17, 18, 25

Rupak Chakraborty CSE 37

Sahil Suijit Deshpande MAE 65

Samya Muhuri CSE 38

Sangeeta Shukla SOM 91

Sanjay Kataria Library 99, 100

Sanjay Shukla Biotechnology 5

Sanjeet Kumar Nayak CSE 38, 39

Sarika Goyal Mathematics 63, 64

Sarika Chaudhary Biotechnology 5

Saurabh Jyoti Sarma Biotechnology 2, 3, 4

Shakti Sharma CSE 19

Shikha Singh Biotechnology 4

Shilpi Jha TSOM 98

Shiv Singh Library 99, 100

Shivani Goel CSE 19, 20, 39, 40, 41, 49

Shreyansh Sharad Jain CSE 11

Shruti Ashok SOM 84, 92

Shubhra Jain Biotechnology 5

Shweta Mittal SOM 92

Siddhi Tandon Biotechnology 5

Simranjit Singh CSE 16, 42

Smita Tiwari CSE 18, 20

Soumyendu Roy Physics 74

104

Name of Author Department Page No.

Sridhar Swaminathan CSE 20, 21, 42

Sudhir Chandra ECE 61

Suman Luhach LAW 79, 81, 82

Sumit Sharma Biotechnology 2, 3, 4

Suneet Kumar Gupta CSE 11, 22, 23, 43, 44, 45, 49

Surbhi Gupta CSE 30

Suyel Namasudra CSE 45

Swarup K. Panda Physics 74, 75

Tanmay Bhowmik CSE 23, 46

Tanveer Ahmed CSE 46, 47

Tapas Badal CSE 30, 31

Tejalal Choudhary CSE 24, 48

Thounaojam Umeshkanta

Singh Physics 75

Tilak Jha TSOM 96, 97

Tushar Bansal Civil 6

Utkarsh Jain Physics 53

Utkarsh Singh LAW 90

Vaishnavi Gupta SOM 90

Valay Kumar Jain Physics 71

Vidhi Mansharamani CSE 26

Vijaypal Singh Rathor CSE 16, 47

Vimal Chauhan MAE 66

Vinayak Ranjan MAE 65, 69, 70

Vipul Kumar Mishra CSE 14, 24, 27, 48

Virender Kumar Mehla ECE 50, 51, 58

Visalakshi Talakokula Civil 6, 7