Trends of Data Science and Applications: Theory and Practices

by Siddharth Swarup Rautaray (Editor), Phani Pemmaraju (Editor), Hrushikesha Mohanty (Editor)

Overview

This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

Product Details

ISBN-13:	9789813368170
Publisher:	Springer Nature Singapore
Publication date:	03/22/2021
Series:	Studies in Computational Intelligence , #954
Edition description:	1st ed. 2021
Pages:	341
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Siddharth Swarup Rautaray is an Associate Professor at the School of Computer Engineering, KIIT-Deemed to be University, Bhubaneswar, India. He received his Ph.D. in Information Technology from the Indian Institute of Information Technology, Allahabad, U.P., India. He has over 9 years of teaching and research experience. His research interest areas are big data analytics, image processing, intelligent systems, human-computer interaction, and similar innovative areas. His research contribution includes 05 co-edited proceedings/books, over 75 research publications in reputed conferences, book chapters, and journals. As an organizing chair, he has organized 05 international conferences and has been part of different core committees of other conferences and workshops. He has delivered invited talks in several workshops and conferences.

Phani Pemmaraju is Director of Consulting Services at Infor India. He received his MCA degree from the School of Computer and InformationSciences, University of Hyderabad, Hyderabad. He has over 22 years of experience in the field of software development. He has got certifications in APICS – CPIM Certified in basics of supply chain management and Microsoft certified professional. He has been a member of the advisory board, e-governance implementation at the University of Hyderabad. He has published several research articles and delivered invited lectures in different institutes.

Hrushikesha Mohanty, Professor at the School of Computer and Information Sciences, University of Hyderabad, Hyderabad is currently on lien to serve as the Vice-Chancellor of KIIT, Deemed to be University, Bhubaneswar. His research interests include distributed computing, software engineering, and computational social science. Before joining the University of Hyderabad, he worked at Electronics Corporation of India Limited, Hyderabad for developing strategic real-time systems. Other than computer science researchpublications, he regularly writes Odia poems, stories, and a weekly Odia column MoKatha.

Table of Contents

NLP for Sentiment Computation.- Productizing an Artificial Intelligence solution for Intelligent Detail Extraction- Synergy of Symbolic and Sub-symbolic Artificial Intelligence techniques.- Digital Consumption Pattern and Impacts of Social Media: Descriptive Statistical Analysis.- Applicational Statistics in Data Science & Machine Learning.- Evolutionary algorithms based machine learning models.- Application to Predict the Impact of COVID-19 in India using Deep Learning.- Role of Data Analytics in Bio Cyber Physical Systems.- Evolution of Sentiment Analysis : Methodologies and Paradigms.- Healthcare Analytics: An advent to mitigate the risks and impacts of a Pandemic.- Image Classification for Binary Classes using Deep Convolutional Neural Network: An Experimental Study.