Handbook of Machine Learning for Computational Optimization: Applications and Case Studies

CALL FOR CHAPTERS

Demystifying Technologies for Computational

Excellence: Moving Towards Society 5.0 Series – Series

Series Editors

Dr. Vikram Bali Dr. Vishal Bhatnagar

Professor and Head, CSE Professor,CSE

JSS Academy of Technical Education Noida AIACTR,New Delhi

ABOUT THE BOOK

Technology is moving at an exponential pace in this modern era of computational intelligence. To complement this, Machine Learning has emerged as one of the most promising tool for researchers and innovators to challenge themselves and think beyond the walls about new possibilities. The dependency of human processes on machine learning driven systems is encompassing all spheres of current state of the art systems. There is a huge potential in this domain to make the best use of machines in order to ensure the optimal prediction, execution and decision making. Although Machine Learning is not a new field, it has evolved with ages and the research community round the globe have made remarkable contribution for the growth and trust of applications to incorporate it. The predictive and futuristic approach which is associated with Machine Learning, makes it a promising tool for business processes as a sustainable solution. There is an ample scope in the technology to propose and devise newer algorithms which are more efficient and reliable to give machine learning an entirely new dimension in discovering certain latent domains of applications, it may support. The book will look forward to addressing the issues which can resolve the modern day computational bottom lines which need smarter and optimal machine learning based intervention to make processes even more efficient. The book will be tentatively comprising of two sections. The first section would be dealing with the innovative and improvised machine learning techniques which can complement, enrich and optimize the existing glossary of machine learning methods. Second section will deal with the application based innovative optimized machine learning solutions which will give the readers a vision of how innovation using machine learning may aid in the optimization of human and business processes.

The book chapters available (but not limited to) are given below

Section A: Innovative Learning Methodologies

Active Learning from imbalanced datasets
Quantum Machine Learning
Data Visualization
Unsupervised machine learning for networking
Machine Learning techniques for software reliability prediction
Machine Learning in fault detection and prevention.
Machine Learning for cyber security and intrusion detection.
Optimal feature selection techniques

Section B: Machine Learning in Real Time Applications

Predictive Image Quality Assessment
Machine Learning in Bioinformatics
Analysis of Biomedical data using Machine Learning
Facial recognition using machine Learning
Leveraging Computer Vision through Machine Learning
Machine Learning for optimal business process management
Machine Learning in optimal logistic management.
Clustering based fraud detection and prevention

Keydates

Abstract Submission : 15.10.2020

Abstract Notification: 30.10.2020

Full Chapter Submission: 15.12.2020

Review Notification:20.01.2021

Final Acceptance / Rejection: 01.02.2021

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