Data Science in Societal Applications (eBook)

The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications. 

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 10 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 06 co-edited proceedings/books, and over 110+ 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.

Manjusha Pandey is an Associate Professor at the School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, India. She has teaching and research experience of over 10 years. She received her doctoral degree from the Indian Institute of Information Technology, Allahabad, U.P., India. Her research areas are big data analytics, computer networks, intelligent systems, machine learning, and similar innovative areas. Her research contribution includes 04 co-edited proceedings/books, and over 110+ research publications in reputed conferences, book chapters, and journals.

Nhu Gia Nguyen received Ph.D. degree in computer science from Hanoi University of Science, Vietnam. Dr. Nguyen serves as Dean of the School of Computer Science at Duy Tan University. His experience includes over 22 years of teaching, and he has more than 40 publications in various conference proceedings and international journals of repute. His research interests include algorithm theory, medical imaging, network optimization, and wireless security. He has been organizing chair of (Springer LNAI) IUKM-2016 held in Vietnam. He is an associate editor in the International Journal of Synthetic Emotions (IJSE) under IGI Global.  

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The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications.