Disruptive Technologies for Big Data and Cloud Applications
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This book provides a written record of the synergy that already exists among the research communities and represents a solid framework in the advancement of big data and cloud computing disciplines from which new interaction will result in the future. This book is a compendium of the International Conference on Big Data and Cloud Computing (ICBDCC 2021). It includes recent advances in big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. This book primarily focuses on the application of knowledge that…mehr

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Produktbeschreibung
This book provides a written record of the synergy that already exists among the research communities and represents a solid framework in the advancement of big data and cloud computing disciplines from which new interaction will result in the future. This book is a compendium of the International Conference on Big Data and Cloud Computing (ICBDCC 2021). It includes recent advances in big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. This book primarily focuses on the application of knowledge that promotes ideas for solving the problems of society through cutting-edge technologies. The articles featured in this book provide novel ideas that contribute to the growth of world-class research and development. The contents of this book are of interest to researchers and professionals alike.

Autorenporträt
J. Dinesh Peter is currently working as an associate professor, Department of Computer Sciences Technology at Karunya University, Coimbatore. Before this, he was a full-time research scholar at the National Institute of Technology, Calicut, India, from where he received his Ph.D. in computer science and engineering. His research focus includes big data, image processing, and computer vision. He has several publications in various reputed international journals and conference papers which are widely referred to. He is a member of IEEE, CSI & IEI and has served as session chairs and delivered plenary speeches for various international conferences and workshops. Dr. Amir H. Alavi is an assistant professor in the Department of Civil and Environmental Engineering and holds a courtesy appointment in the Department of Bioengineering at the University of Pittsburgh. Dr. Alavi's research interests include structural health monitoring, multifunctional structures, advanced sensors, low-power energy harvesting, and engineering informatics. His research activities involve the implementation of self-sustained and multifunctional sensing and structural systems enhanced by engineering informatics in the fields of civil infrastructure, construction, aerospace, and biomedical engineering. Dr. Alavi has authored seven books and over 200 publications in archival journals, chapters, and conference proceedings. He has received several award certificates for his journal articles. He is among the Google Scholar 200 Most Cited Authors in Civil Engineering, Web of Science ESI's World Top 1% Scientific Minds in 2018, and the Stanford University list of Top 1% Scientists in the World 2020. Steven Fernandes is an assistant professor in the Department of Computer Science at Karunya University, Coimbatore. After earning his Ph.D. in Electronics and Communication Engineering at Karunya Institute of Technology and Science and his Masters in Microelectronics at Manipal Institute of Technology, he began his postdoctoral research at the University of Alabama, Birmingham. There, he worked on NIH-funded projects. He also conducted postdoctoral research at the University of Central Florida. This research included working on DARPA, NSF, and RBC-funded projects. His publications include research articles in highly selective artificial intelligence venues. Dr. Fernandes's current area of research is focused on using artificial intelligence techniques to extract useful patterns from big data. This includes robust computer vision applications using deep learning and computer-aided diagnosis using medical image processing.