Computational intelligence for data analysis


The book “Computational Intelligence for Sustainable Transport and Mobility – Volume 1“starts with the basics of computational intelligence technique and presents its applications to vehicular traffic forecasting, optimization, behavior analysis, traffic density estimation, etc. New technologies and methodologies are used to improve existing problems in the circulation system. Due to the development of computer intelligence methods, it is considered a powerful technique to reduce traffic, transportation and mobility problems in urban areas. In dynamic and complex situations, an adaptive mechanism is needed to enable or facilitate intelligent behavior called computational intelligence (CI) technique.

The CI technique includes Multi-Agent System (MAS), Whale Optimization, AIS, Deep Neural Networks (DNN), Fog, and Edge Computing. These CI techniques mimic human behavior and intelligence; therefore, the concept of intelligence is directly related to reasoning and decision making. These CI techniques are used to develop algorithms, models and approaches for sustainable transport, traffic and mobility operations.

The main objectives of this book are to present the new developed techniques, new technologies and computational intelligence for the forecasting of sustainable mobility and transport data, the analysis of traffic behavior, the estimation and forecasting of the traffic density, electric vehicle charging infrastructure and industry 4.0. The main objective of this book is to introduce the techniques, challenges, problems and concepts of computational intelligence to researchers, scientists and academics in general.

Readers will learn to apply computational intelligence techniques such as multi-agent systems (MAS), whale optimization, artificial intelligence (AI), deep neural networks (DNN) so that they can develop algorithms , models and approaches for sustainable transport operations. This volume is essential reading for scholars and professionals involved in courses and training programs in transportation, computing, data science, and applied machine learning.

About the authors:

Deepak Gupta received a B.Tech. from GGGSIPU, India. He received ME from Delhi Technological University, India and Ph.D. from Dr. APJ Abdul Kalam Technical University (AKTU), India. He completed his post-doctorate at the National Institute of Telecommunications of Brazil. He is co-author of more than 231 articles, including 129 SCI articles. He has written/edited 59 books, published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, DeGruyter and Katsons. He has completed 6 Indian patents. He is the organizer of the ICICC, ICDAM and DoSCI Springer lecture series. He is the recipient of the IEEE System Council 2021 Best Paper Award. He was listed in the top 2% list of the world’s top scientist/researcher databases [Table-S7-singleyr-2019]. He is also editor of the series “Elsevier Biomedical Engineering” at Elsevier, “Intelligent Biomedical Data Analysis” at De Gruyter and “Explainable AI (XAI) for Engineering Applications” at CRC Press.

Dr Suresh Chavhan (SMIEEE) is Assistant Professor at the Automotive Research Center at Vellore Institute of Technology (VIT), Vellore. He was a postdoctoral researcher who worked at the Federal University of Piaui (UFPI), Brazil. He obtained his PhD in Electrical Communications Engineering (2019) from Indian Institute of Science, Bangalore. Prior to this, he obtained his Masters (2013) and Bachelors (2011) degrees from National Institute of Technology, Surathkal and VTU, Belgaum respectively. He was awarded the IEEE Madras Section Publication Award 2021, the prestigious IEEE Systems Journal Best Paper Award 2021 with a $500 prize and an international travel grant from SERB, India. One of his ideas was shortlisted in the Top 34 for the prototype construction phase of the Mercedes-Benz Digital Challenge, India, 2020. He has published more than 30 SCI papers with very good impact factors.


Predictive modeling, artificial immune system, intelligent navigation. Data transfer, Fog and Edge Computing, Autonomous vehicles, Big Data analytics, Cloud computing, Sensors, Transport networks. Air bearings, electromagnetic accelerators, transonics, vacuum.

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