Call for Full Chapters: Advances of Machine Learning in Clean Energy and Transportation Industry

Full Chapter Submission Deadline: 15 th March, 2021

A book edited by Professor Valeriy Kharchenko (Federal Scientific Agro-engineering Center VIM, Russia), Dr. Vladimir Panchenko (Russian University of Transport, Russia), Professor Gerhard-Wilhelm Weber (Poznań University of Technology, Poland), Dr. J. Joshua Thomas (UOW Malaysia, KDU Penang University College), Dr. Pandian Vasant (University Technology Petronas, Malaysia)

This book presents the latest research of the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe.

Objective of the Book
Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in transportation industry, where technologies are also being improved from year to year – transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amount of ocean of data.

Target Audience
The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialist and practicing scientists and engineers as well compassionate global decision makers.

Recommended topics include, but are not limited to, the following:
Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.

Submission Procedure
Researchers and practitioners are invited to submit the full chapter on or before 15th March 2021. Authors will be notified by 24th April 2021 about the status of their full chapters. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Advances of Machine Learning in Clean Energy and Transportation Industry. All manuscripts undergo a double-blind peer review editorial process.

This book is scheduled to be published by Nova Science Publishers, Inc.

Guideline for manuscript preparation is given below:

Important Dates
15 th March, 2021: Full Chapter Submission
24 th April, 2021: Review Results Returned
10 th May, 2021: Final Acceptance Notification
24 th May, 2021: Final Chapter Submission

Inquiries can be forwarded to
Dr. J. Joshua Thomas, UOW Malaysia, KDU Penang University College,

Professor Valeriy Kharchenko, Federal Scientific Agro-engineering Center VIM, Russia,

Dr. Vladimir Panchenko, Russian University of Transport, Russia,

Prof. Dr. Gerhard-Wilhelm Weber, Poznan University of Technology, Poland, and METU, Ankara,

Dr. Pandian Vasant, University Technology Petronas, Malaysia,