Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0

The goal of this book is to help aspiring readers and researchers understand the convergence of Big Data with the Cloud. This book presents the latest information on the adaptation and implementation of Big Data technologies in various cloud domains and Industry 4.0.

Dr. Sheetal S. Zalte-Gaikwad is an assistant professor in Computer Science Department at Shivaji University, Kolhapur, India. She pursued a Bachelor of Computer Science from Pune University, India, in 2002 and a Master of Computer Science from Pune, India, in the year 2004. She earned her Ph.D. in Mobile Adhoc Network at Shivaji University. She has 14 years of teaching experience in computer science. She has published 20+ research papers in reputed international journals and conferences, including IEEE, and it's also available online. She has also authored book chapters with Springer. Her research areas are MANET, VANET, Blockchain Security. Dr. Indranath Chatterjee is working as a Professor in the Department of Computer Engineering at Tongmyong University, Busan, South Korea. He received his Ph. D. in Computational Neuroscience from the Department of Computer Science, University of Delhi, India. His research areas include Computational Neuroscience, Schizophrenia, Medical Imaging, fMRI, and Machine learning. He has authored and edited 8 books on Computer Science and Neuroscience published by renowned international publishers. He has published numerous research papers in international journals and conferences. He is a recipient of various global awards in neuroscience. He is currently serving as a Chief Section Editor of a few renowned international journals, a member of the Editorial board of various international journals, and an Advisory board member in various "Open-Science" organizations worldwide. He is presently working on several projects for government & non-government organizations as PI/co-PI, related to medical imaging and machine learning for a broader societal impact, collaborating with several universities globally. He is an active professional member of the Association of Computing Machinery (ACM, USA), Organization of Human Brain Mapping (OHBM, USA), Federations of European Neuroscience Society (FENS, Belgium), Association for Clinical Neurology and Mental Health (ACNM, India), The Korean Society of Brain and Neural Science (KSBNS, Korea), and International Neuroinformatics Coordinating Facility (INCF, Sweden). Dr. Rajanish. K. Kamat is Dean of Computer Science and Technology, Shivaji University, Kolhapur, India. He received both B.Sc. and M.Sc. in Electronics with distinction in 1991 and 1993. Further, he completed his Mphil and Ph.D. in electronics at Goa university. Presently, he is working with the Department of Electronics and Department of Computer Science at Shivaji University, Kolhapur. He has published more than 150+ research papers in reputed international journals, including IEEE, and authored 12 books with Springer, CRC Press, and IGI global USA.

1. Big Data Based on Fuzzy Time-Series Forecasting for Stock Index Prediction. 2. Big Data-Based Time-Series Forecasting Using FbProphet for Stock Index. 3. The Impact Of Artificial Intelligence and Big Data in the Postal Sector. 4. Advances in Cloud Technologies and Future Trends. 5. Reinforcement of the Multi-Cloud Infrastructure with Edge Computing. 6. Study and Investigation of PKI-Based Blockchain Infrastructure. 7. Stock Index Forecasting Using Stacked Long Short-Term Memory (LSTM): Deep Learning and Big Data. 8. A Comparative Study and Analysis of Time-Series and Deep Learning Algorithms for Bitcoin Price Prediction. 9. Machine Learning for Healthcare. 10. Transfer Learning and Fine-Tuning-Based Early Detection of Cotton Plant Disease. 11. Recognition of Facial Expressions of Infrared Images for Lie Detection with the Use of Support Vector Machines. 12. Support Vector Machines for the Classification of Remote Sensing Images: A Review. 13. A Study on Data Cleaning of Hydrocarbon Resources under Deep Sea Water Using Imputation Technique-Based Data Science Approaches.