Artificial Intelligence Research Topics for PhD Manuscripts 2021 - Phdassistance

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Artificial Intelligence Research Topics for

PhD Manuscripts 2021

Dr. Nancy Agnes, Head, Technical Operations Phdassistance, [email protected]

Keywords: Machine Learning, Artificial

Intelligence, Deep learning, Reinforcement

Learning, Robotics, Natural Language Processing,

Computer Vision, Recommender Systems, Internet

Of Things, Research Topics for PhD Manuscripts,

Artificial intelligence & machine learning, PhD

Manuscripts Writing Help, Research in Artificial

Intelligences, Artificial Intelligence Research

Topics, Best Computer Science PhD Programs, PhD

Manuscripts Help, Computer Data Science, Topics

for PhD manuscripts, PhD Research Topics,

Computer Programme.

I. INTRODUCTION

Imagine a world where knowledge isn't limited to

humans!!! A world in which computers will think and

collaborate with humans to create a more exciting

universe. Although this future is still a long way off,

Artificial Intelligence has made significant progress

in recent years. In almost every area of AI, such as

quantum computing, healthcare, autonomous

vehicles, the internet of things, robotics, and so on,

there is a lot of research going on. So much so that

the number of annual Published Research Papers on

Artificial Intelligence has increased by 90% since

1996.

Keeping this in mind, there are several sub-topics on

which you can concentrate if you want to study and

write a thesis on Artificial Intelligence. This article

covers a few of these subjects and provides a short

overview. Here some of the recent Research Topics,

1. Artificial Intelligence and Machine learning – Recent Trands

2. How AI and ML can aid healthcare systems in their response to COVID-19

3. Machine learning and artificial intelligence in haematology

4. Tackling the risk of stranded electricity assets with machine learning and artificial intelligence

II. DEEP LEARNING

Deep Learning is a type of machine learning that

learns by simulating the internal workings of the

human brain in order to process data and make

decisions. Deep Learning is a form of machine

learning that employs artificial neural networks.

These neural networks are linked in a web-like

structure, similar to the human brain's networks

(basically a condensed version of our brain!).

Artificial neural networks have a web-like structure

that allows them to process data in a nonlinear

manner, which is a major advantage over

conventional algorithms that can only process data in

a linear manner. Rank Brain, one of the variables in

the Google Search algorithm, is an example of a deep

neural network.

Recent research topics

1. Artificial intelligence & deep learning : PET and SPECT imaging

2. Hierarchical Deep Learning Neural Network (HiDeNN): A computational science and

3. engineering 4. in AI architecture. 5. AI for surgical safety: automatic assessment of

the critical view of safety in laparoscopic

cholecystectomy using Deep Learning

6. Deep learning-enabled medical computer vision

III. REINFORCEMENT LEARNING

Reinforcing Learning is an aspect of Artificial

Intelligence in which a computer learns something

in the same way as humans do. Assume the

computer is a student, for example. Over time, the

hypothetical student learns from its errors. As a

outcome of trial and error, Reinforcement Machine

Learning Algorithms learn optimal behaviour.

This means that the algorithm determines the next

way to proceed by learning behaviours based on its

current state that will increase the reward in the

future. This also works for robots, just as it does for

humans!

Google's AlphaGo Computer Programme, for

example, used Reinforcement Learning to defeat

the world champion in the game of Go (a human!)

in 2017.


1. Experimental quantum speed-up in reinforcement learning agents

2. Potential-based multiobjective reinforcement learning approaches to low-impact agents for

AI safety

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IV. ROBOTICS

Robotics is an area concerned with the creation of

humanoid robots that can assist humans and perform

several acts. In certain cases, robots can behave like

humans, but can they think like humans as well?

Kismet, a social interaction robot developed at

M.I.T.'s Artificial Intelligence Lab, is an example of

this. It understands human body language as well as

our voice and responds to them appropriately.

Another example is NASA's Robonaut, which was

designed to assist astronauts in space.


1. Regulating artificial intelligence and robotics: ethics by design in a digital society

2. Regional anaesthesia :usages of artificial intelligence and robotics in

3. Third Millennium Life Saving Smart Cyberspace Driven by AI and Robotics

V. NATURAL LANGUAGE PROCESSING

Humans can obviously communicate with each other

by speech, but now machines can as well! This is

known as Natural Language Processing, and it

involves machines analysing and understanding

language and expression as it is spoken (which means

that if you speak to a computer, it might only

respond!). Speech recognition, natural language

production, natural language translation, and other

aspects of NLP are all concerned with language. NLP

is recently very important in customer service

applications, particularly chatbots. These chatbots use

machine learning and natural language processing to

communicate with users in textual form and respond

to their questions. As a result, you get a personal

touch in your customer service experiences without

actually speaking with a human.

Here are several research papers in the field of

Natural Language Processing that have been

published. You can look at them to get more ideas for

research and thesis topics on this subject.


1. Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups:

Protocol for an Algorithm Development and

Validation Study

2. Sympathetic the temporal evolution of COVID-19 Research Through machine learning and

natural language processing

VI. COMPUTER VISION

The internet is full of images! This is the selfie age,

and taking and posting a photo has never been easier.

Each day, millions of images are uploaded to the

internet and viewed. It's important for computers to

be able to see and understand images in order to make

the most of the vast amount of images available

online. And, while humans can do this without

thinking about it, computers find it more difficult!

This is where Computer Vision enters the image.

To extract information from images, Computer

Vision utilizes Artificial Intelligence. This

knowledge may include object detection in the

image, image content recognition to group images

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together, and so on. Navigation for autonomous

vehicles using images of the surroundings is one use

of computer vision, such as AutoNav, which was

used in the Spirit and Opportunity rovers that landed

on Mars.


1. Deep learning-enabled medical computer vision 2. Artificial intelligence for surgical safety:

automatic assessment of the critical view of

safety in laparoscopic cholecystectomy using

deep learning

3. An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Video

endoscopy

VII. RECOMMENDER SYSTEMS

Do you get movie and series recommendations from

Netflix based on your previous choices or favourite

genres? This is achieved by Recommender Systems,

which offer you advice about what to do next from

the vast array of options available online. Content-

based Recommendation or even Collaborative

Filtering may be used in a Recommender System.

The content of all the products is analysed in

Content-Based Recommendation. For example, based

on Natural Language Processing performed on the

books, you might be recommended books that you

may enjoy. Collaborative Filtering, on the other hand,

analyses your past reading behaviour and then

recommends books based on it.


1. Artificial intelligence in recommender systems 2. Deep Transfer Tensor Decomposition with

Orthogonal Constraint for Recommender

Systems.

3. Recommender systems for configuration knowledge engineering

VIII. INTERNET OF THINGS

Artificial intelligence is concerned with the creation

of systems that can learn to perform human-like tasks

based on prior experience and without the need for

human interaction. The Internet of Things, on the

other hand, is a network of different devices linked to

the internet and capable of collecting and exchanging

data.

All of these IoT devices now generate a large amount

of data, which must be collected and mined in order

to produce actionable results. Artificial Intelligence

enters the picture at this stage. The Internet of Things

is used to collect and manage the massive amounts of

data that Artificial Intelligence algorithms need. As a

consequence, these algorithms transform the data into

useful actionable results that IoT devices can use.


1. Enhanced Medical Systems by using Artificial Intelligence and Internet of Things

2. Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste

Biodegradation Plants: Recent Developments

3. AIoT-Artificial Intelligence of Things

IX. CONCLUSION

In this blog discussed the recent enhancement for

artificial intelligences and their sub field. This will

help to the PhD scholar who are interested to research

in artificial intelligences domain.

REFERENCES

1. Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and

artificial intelligence in haematology. British

journal of haematology, 192(2), 239-250.

2. van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., ... & Ercole,

A. (2021). How artificial intelligence and

machine learning can help healthcare systems

respond to COVID-19. Machine Learning,

110(1), 1-14.

3. Nyangon, J. (2021). Tackling the risk of stranded electricity assets with machine learning and

artificial intelligence. In Sustainable Energy

Investment-Technical, Market and Policy

Innovations to Address Risk. IntechOpen.

4. Saha, S., Gan, Z., Cheng, L., Gao, J., Kafka, O. L., Xie, X., ... & Liu, W. K. (2021). Hierarchical

Deep Learning Neural Network (HiDeNN): An

artificial intelligence (AI) framework for

computational science and engineering.

Computer Methods in Applied Mechanics and

Engineering, 373, 113452.

5. Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., ... & Padoy, N.

(2021). Artificial intelligence for surgical safety:

automatic assessment of the critical view of

safety in laparoscopic cholecystectomy using

deep learning. Annals of Surgery.

6. Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Socher, R.

(2021). Deep learning-enabled medical computer

vision. npj Digital Medicine, 4(1), 1 9.
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