Intelligence in Big Data Technologies-Beyond the Hype

Proceedings of ICBDCC 2019
1. Auflage 2021
ISBN: 978-981-15-5287-8
Verlag: Springer Nature Singapore

Buch, Englisch, Band 1167, 636 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 972 g

Reihe: Advances in Intelligent Systems and Computing

This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the society through cutting-edge big-data technologies. The essays featured in this proceeding provide novel ideas that contribute for the growth of world class research and development. It will be useful to researchers in the area of advanced engineering sciences.

Zielgruppe

Research

Autoren/Hrsg.

Peter, J. Dinesh

Alavi, Amir H.

Fernandes, Steven L.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

From Dew Over Cloud towards the Rainbow Ecosystem of the Future:Nature – Human – Machine.- L1 Norm SVD based Ranking Scheme: A Novel Method in Big Data Mining.- Human Annotation and Emotion Recognition for Counseling System with Cloud Environment using Deep Learning.- Enhancing Intricate details of ultrasound PCOD scan images using Tailored Anisotropic Diffusion Filter (TADF).- LSTM and GRU Deep learning Architectures for Smoke Prediction System in Indoor Environment.- A mobile based framework for detecting objects using SSD-Mobilenet in indoor environment.- Privacy Preserving Big Data Publication: (K, L) Anonymity.- Comparative Analysis of the efficacy of the EEG based Machine Learning method for the screening and diagnosing of Alcohol Use Disorder (AUD).- Smart solution for waste management: a coherent framework based on IoT and big data analytics.- Early detection of diabetes from daily routine activities: Predictive modeling based on machine learning techniques.

Über Autor(innen)

J. Dinesh Peter is currently working as an Associate Professor, Department of Computer Sciences Technology at Karunya University, Coimbatore. Prior to this, he was a full time research scholar at 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 highly cited publications in journals of national and international repute. He is a member of IEEE, CSI & IEI and has served as session chairs and delivered plenary speeches for various international conferences and workshops. Steven L. Fernandes is currently a post-doctoral researcher in the Department of Computer Science, University of Central Florida. He has previously been affiliated with the University of Alabama Birmingham, USA and the Sahyadri College of Engineering and Management, India. Dr Fernandes has authored 1 book and 40 research papers in refereed journals.
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, United States. His multidisciplinary research integrates sensing, computation, control, networking, and information systems into the civil infrastructure to create cyber-physical infrastructure systems. His research interests include smart cities, structural health monitoring, deployment of advanced sensors, energy harvesting, and civil engineering system informatics. Dr. Alavi has authored 5 books and over 170 publications in archival journals, book chapters, and conference proceedings.