J. Dinesh Peter,
Steven L. Fernandes,
Amir H. Alavi
Springer Nature Singapore
e druk, 2021
9789811552878
Intelligence in Big Data Technologies—Beyond the Hype
Proceedings of ICBDCC 2019
Specificaties
Paperback, blz.
|
Engels
Springer Nature Singapore |
e druk, 2021
ISBN13: 9789811552878
Rubricering
Onderdeel van serie
Advances in Intelligent Systems and Computing
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Specificaties
ISBN13:9789811552878
Taal:Engels
Bindwijze:paperback
Uitgever:Springer Nature Singapore
Inhoudsopgave
<div>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.</div>
