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Studies in Big Data 114 Siddharth Swarup Rautaray Manjusha Pandey Nhu Gia Nguyen Editors Data Science in Societal Applications Concepts and Implications Studies in Big Data Volume 114 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are reviewed in a single blind peer review process. Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH. All books published in the series are submitted for consideration in Web of Science. · · Siddharth Swarup Rautaray Manjusha Pandey Nhu Gia Nguyen Editors Data Science in Societal Applications Concepts and Implications Editors Siddharth Swarup Rautaray Manjusha Pandey School of Computer Engineering School of Computer Engineering Kalinga Institute of Industrial Technology Kalinga Institute of Industrial Technology Bhubaneswar, Odisha, India Bhubaneswar, Odisha, India Nhu Gia Nguyen Graduate School Duy Tan University Danang, Vietnam ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-981-19-5153-4 ISBN 978-981-19-5154-1 (eBook) https://doi.org/10.1007/978-981-19-5154-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface Data science is an amalgamation of various disciplines of research including the method ranging from empirical methods to current trends of data observation along with the application of rigorous skepticism about observations in data, resulting in interprets that involve formulating hypotheses and deducing inductions. These princi- ples of data science have been distinguished in the form of a series of steps applicable to various enterprises. The processes, algorithms, and systems to extract knowledge and insights from a huge amount of structural and unstructured data produced by these enterprises have made data science the need of the hour. The aim is to use the data as raw inputs and generate valuable data products that can be used by enterprises for their business benefits. Data science in societal applications has been established as a new horizon of the scientific field with the silver lining of research evolution in societal applications using renovations in the existing domains of statistics, computing science, and intelligence science. The application of advancements in these legacy research domains and its practical transformation in engineering, public sector enterprises, social science, and lifestyle spheres is the way forward. This adds to the ever-increasing importance of data science, both big data and small data, which when analyzed scientifically bring with them a wealth of opportunities that combine the new trends to extract, transport, pool, refine, store, analyze, and visualize data which are needed to unleash their power. This also simultaneously can be incorporated into tool-integrated development environments and workflows that are ready to be used by people in general. To have a successful career in data science emphasizing its societal applications for the benefit of the public at large is a real challenge. The desired knowledge base includes complex topics from statistics, computer science, and mathematics. In addition to that, domain-specific knowledge becomes essential for user-friendly application developments. The real-time applications of data science are added challenge that requires to be mitigated. Thus, there is a need for conjugation of data science with various real-life applications of computing and implementations. The proposed book intends to provide a range of current trends in the theoretical and practical applications of data science that are being developed and implemented by researchers, academicians, and industries. v vi Preface The book covers contributions from more than 13 renowned researchers in the domain who have proven theoretical and foundational research, platforms, methods, applications, and tools in the domain of societal applications of data science. The book will act as a base for future researchers who want to work in the domain of theory and practices in the area of data science. Also, the contents of the book focus on the social, geographical, and temporal dimensions of data science research. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, bioinformatics, social analytics, and natural language processing as included in the book. The chapters in the book also cover application- oriented perspectives, and advances in the field of data science in such areas as big data collection, searching analysis, and knowledge discovery. This book will be helpful for the students, practitioners, and researchers working in the area of data science and analytics. Bhubaneswar, India Siddharth Swarup Rautaray Bhubaneswar, India Manjusha Pandey Danang, Vietnam Nhu Gia Nguyen Acknowledgments The genesis of the book is the 2nd Data Science Conclave 2021 on Data Science in Societal Applications: Concepts and Implications held at Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar Odisha, India during October 09–10, 2021 in virtual mode. The invited speakers of the conclave have submitted the full chapters which have undergone a review process for inclu- sion in the book. Based on the reviewer comments received the authors have made necessary changes in the chapter. We extend our sincere thanks to all these invited speakers for their contributions. We also express sincere thanks to Springer for the support in publishing this book. We are thankful to the Data Science Group of the School of Computer Engineering, KIIT, Deemed to be University and the whole organizing team of Data Science Conclave 2021 for their confidence in us in shaping up this book as editors. We are thankful to Prof. Sasmita Rani Samanata, Vice Chancellor KIIT DU. who has been a great mover to the Data Science Conclave series since its beginning. We are grateful to the founder of KIIT and KISS Prof. Achuyta Samanta, the patron of the Data Science Conclave series from its inception for the endless support he has provided toward the Data Science Conclave and other events conducted by the Data Science Group of the School of Computer Engineering. Siddharth Swarup Rautaray Manjusha Pandey Nhu Gia Nguyen vii Contents Cloud GIS Model for Coastal Geospatial Big Data Analytics ........... 1 K. K. Barik, Vivek Mishra, J. R. Mohanty, Mrinal K. Debbarma, and R. K. Barik Appealing AI in Appalling Covid-19 Crisis and the Impending ......... 13 Debanjan Banerjee, Anindita Banerji, and Arunita Banerji Role of Data Science in Programmatic Advertising ................... 33 Aditya Jain, Kashyap Barua, and Monali Barbate Social Development Data and Societal Modelling: A Study in Indian Context .................................................. 47 Rabi N. Subudhi Deep Learning Trends and Inspired Systems in Natural Language Processing ........................................................ 57 Aseer Ahmad Ansari, Siddharth S. Rautaray, and Manjusha Pandey AI and IoT Enabled Smart Hospital Management Systems ............ 77 Mahendra Kumar Gourisaria, Rakshit Agrawal, Vinayak Singh, Siddharth Swarup Rautaray, and Manjusha Pandey Optimization in Agricultural Growth Using AI and Satellite Imagery .......................................................... 107 Bhaskar Goswami and Plaban Nayak Plenus Solutionem: A Complete Solution to Multi Chronic Diseases Detection and Classification ................................ 127 Aindril Kar, Harshit Anand, and Kalipada Tripathy Kochen Helfer: An AI-Based Cooking Assistant ...................... 143 Indrashis Das, Anoushka Mishra, and Shushant Ghosh ix x Contents Contemporary Digital Consumers: “Real” Versus Virtual Value Co-creation ....................................................... 161 Milena Ratajczak-Mrozek and Marcin Wieczerzycki Multiclass Prediction of Heart Disease Patients Using Big Data Analytics ......................................................... 177 Sarita Mishra, Manjusha Pandey, Siddharth Swarup Rautaray, and Sabyasachi Chakraborty