Synopsis
In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).
About the Authors
Rajeev Kumar Gupta is working as an Assistant Professor at Pandit Deendayal Energy University (An Autonomous Institution) Gandhinagar, Gujarat. He has completed his M.Tech and PhD from MANIT, Bhopal and is a recipient of the Best Young Researcher Award by RSRI in 2019. He has published more than 30 referred articles in various book chapters, conferences and international repute peer-reviewed journals of Elsevier, Springer, IEEE etc. He has a total of more than ten years of teaching experience. He is a life member of some of the reputed societies like CSI India, IAENG (Hongkong) etc. He has organized several STTP/FDP and taken several expert lectures at various institutes. He has supervised 20 M.Tech thesis and around 40 B.Tech projects in various domains. He is a TPC member and reviewer of several International Conferences. His area of interest includes Machine Learning, Deep Learning and Cloud computing.
Arti Jain (SMIEEE) is working as Assistant Professor (Sr. Grade), Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida (Uttar Pradesh), India. She has academic experience of 20+ years. She is an editorial board member of IGI Global and SciencePG. She is an advisory committee member of International Conferences held in Maldives, Turkey, and UAE. She is a reviewer of reputed International Journals and guest-edited Special Issues. She is a TPC member of several International Conferences and has chaired Special Sessions. She is an invited speaker for various workshops, seminars, FDPs, and conferences affiliated with India, China, UK, and USA. She has published 02 books, and 35 research papers in indexed International Journals, book chapters, and conferences. Her research interests include Natural language processing, machine learning, data science, deep learning, social media analytics, and data mining.
John Wang is a professor in the Department of Information Management and Business Analytics at Montclair State University, USA. Having received a scholarship award, he came to the USA and completed his PhD in operations research from Temple University. Due to his extraordinary contributions beyond a tenured full professor, Dr. Wang has been honored with two special range adjustments in 2006 and 2009, respectively. He has published over 100 refereed papers and seventeen books. He has also developed several computer software programs based on his research findings. He serves as Editor-in-Chief for ten Scopus-indexed journals, such as Int. J. of Business Analytics, Int. J. of Information Systems and Supply Chain Management, Int. J. of Information Systems in the Service Sector, Int. J. of Applied Management, Int. J. of Information and Decision Sciences, Int. J. of Data Mining, Modelling and Management, etc. He is the Editor of Encyclopedia of Business Analytics and Optimization (five-volume), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (six-volume) and the Editor of the Encyclopedia of Data Warehousing and Mining, 1st (two-volume) and 2nd (four-volume). His long-term research goal is on the synergy of operations research, data mining and cybernetics.
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