Call for Book Chapter-Elsevier Edited Book

[GC Deka]

Dear Professor/Researcher,

 Book Chapter proposal is invited for the edited book titled “Artificial Intelligence and machine Learning for Open-world Novelty”.

 Gist of the proposed book is as following:

Current research in artificial intelligence and machine Learning is based on a closed-world learning environment where the environment remains fixed and unchanged throughout the agent’s training and application session. However, these environmental conditions may not be remained fixed, rather there might be a change in input/output features, running rules, environment, or tasks in real open-world operation. The fixed environment may be prone to failure when the agents incorporate under unseen situations. To overcome the drawback of the existing closed-world model, an Open-world the learning method is required which can classify the novelty occurring in an environment. Hereby the term of Novelty refers to situations that violate implicit or explicit assumptions of the closed-world learning environment with the unchanged environmental condition throughout the training and inference.

 

The proposed book explores new methods, architectures, tools, and algorithms as well as the part of the computing approaches for Artificial Intelligence and machine learning for Open-world Novelty. Tentative topics of the proposed book are :

 Topic   1          AI and Machine Learning for Real-world problems

1.1       Introduction

1.2       Open World Novelty Hierarchy

1.3       Problems of artificial Neural Networks in real-world problems

1.4       Deep Neural Networks for Real-world problems

 Topic   2          Graph Neural Network for learning complex problems

2.1       Architecture of Graph Neural Network

2.2       Machine Learning model for training Graph Neural Network

2.3       Application of graph Neural Network

2.4       Graph Neural Network for learning complex problems

2.5       Cons and Pros of Graph Neural Network

 Topic   3          Explainable for Real-world problems

3.1       Introduction to Explainable AI

2.2       Knowledge Embedding and XAI

2.3       Learn to Explain

2.4       Explainable AI for Real-world problems

2.5       Cons and Pros of NPU accelerators

 

Topic   4          Control suite: learning for open-world problems

4.1       Introduction to  Control suite

4.2      Architecture Control Suite to generate novelty for  open-world learning environment

4.3       To Simulate an open-world learning environment

4.4       Experimental results

4.5       Bench-marking results

 

Topic   5          OODA Loop for Open-world Novelty

5.1       Introduction to  OODA Loop

5.2       OODA Loops in ICT Applications

5.3       OODA Loops for Machine Learning

5.4       Reconfiguration of OODA loop for Deep Neural Networks

5.5       OODA Loop for Open-world Novelty

 

Topic   6          Bench marking of Mobile

6.1       Introduction to the Mobile AI system

6.2       Mobile NPU architecture

6.3       Machine Learning for Mobile Devices

6.4       Machine Learning for Cloud and  Edge Devices

6.5       Bench-marking of Mobile Devices

 

Topic   7          self-supervised learning AI and Machine Learning for Real-world problems

7.1       Introduction to Self-supervised learning

7.2       Introduction to Energy based AI

7.3       Methods and algorithms for Energy-based self-supervised learning

7.4       Self-supervised learning AI and Machine Learning for Real-world problems

 

Topic   8          Graph Neural Network for Real-world problems

8.1       Introduction to Graph Neural Network

8.2       Modeling the novelty using Graph neural network

8.3       Algorithm of Graph neural network

8.4       Graph Neural Network for Real-world problems

8.5       Performance comparison with Deep Neural network

 

Topic   9          Artificial intelligence and Machine learning for irregular situations

9.1       Introduction to irregular situations

9.2       Introduction to AI and Machine learning for irregular situations

9.3       AI for Understanding environment and situations

9.4       AI for Understanding Human intentions

9.5       Applications of AI and Machine learning for irregular situations

 

Topic   10        Meta Reinforcement Learning methods for real-world problems

10.1       Introduction to Meta Reinforcement Learning

10.2       Policy-based learning methods

10.3       Models and LearningAlgorithm Related to Meta Reinforcement Learning

10.4       How to Learn in a real-world environment

10.5       Performance and experimental results

 

Publisher:

ELSEVIER

Series: Advances in Computers Serial

 

Editors

Prof Shiho Kim[Chief Editor]

School of Integrated Technology, Yonsei University, South Korea

 

Ganesh Chandra Deka

Directorate General of Training, Ministry of Skill Development and Entrepreneurship, INDIA

 

Tentative Publication Schedule

[1]  Last date for submission of Chapter proposal by Authors                30th June 2021 [1-2 page book chapter proposal in the attached format].

 [2]  Acceptance/Rejection Notice of Book Chapter Proposal to author  15th July 2020

[3]  Estimated Full Chapter Submission by Author: 15th August. 2021.

[4]  Final Acceptance/Rejection of Chapters-15th October. 2021.

[5]  Estimated Manuscript Completion Date:  31st December 2021

 

With warm regards,

Prof Shiho Kim

https://scholar.google.com/citations?hl=en&user=X3gOnQ0AAAAJ&view_op=list_works&sortby=pubdate

 GC Deka

https://scholar.google.co.in/citations?user=Qw5HblgAAAAJ&hl=en