Society 5.0: Smart Future Towards Enhancing the Quality of Society (Advances in Sustainability Science and Technology) 9811921601, 9789811921605

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Advances in Sustainability Science and Technology

K. G. Srinivasa G. M. Siddesh S. R. Manisekhar   Editors

Society 5.0: Smart Future Towards Enhancing the Quality of Society

Advances in Sustainability Science and Technology Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-Sea, UK John Littlewood, School of Art & Design, Cardiff Metropolitan University, Cardiff, UK Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

The book series aims at bringing together valuable and novel scientific contributions that address the critical issues of renewable energy, sustainable building, sustainable manufacturing, and other sustainability science and technology topics that have an impact in this diverse and fast-changing research community in academia and industry. The areas to be covered are • • • • • • • • • • • • • • • • • • • • •

Climate change and mitigation, atmospheric carbon reduction, global warming Sustainability science, sustainability technologies Sustainable building technologies Intelligent buildings Sustainable energy generation Combined heat and power and district heating systems Control and optimization of renewable energy systems Smart grids and micro grids, local energy markets Smart cities, smart buildings, smart districts, smart countryside Energy and environmental assessment in buildings and cities Sustainable design, innovation and services Sustainable manufacturing processes and technology Sustainable manufacturing systems and enterprises Decision support for sustainability Micro/nanomachining, microelectromechanical machines (MEMS) Sustainable transport, smart vehicles and smart roads Information technology and artificial intelligence applied to sustainability Big data and data analytics applied to sustainability Sustainable food production, sustainable horticulture and agriculture Sustainability of air, water and other natural resources Sustainability policy, shaping the future, the triple bottom line, the circular economy

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K. G. Srinivasa · G. M. Siddesh · S. R. Manisekhar Editors

Society 5.0: Smart Future Towards Enhancing the Quality of Society

Editors K. G. Srinivasa Dr. Shyama Prasad Mukherjee International Institute of Information Technology Naya Raipur, India

G. M. Siddesh Department of ISE Ramaiah Institute of Technology Bengaluru, India

S. R. Manisekhar Department of ISE Ramaiah Institute of Technology Bengaluru, India

ISSN 2662-6829 ISSN 2662-6837 (electronic) Advances in Sustainability Science and Technology ISBN 978-981-19-2160-5 ISBN 978-981-19-2161-2 (eBook) https://doi.org/10.1007/978-981-19-2161-2 © 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

Society 5.0 is a roadmap to the super-smart society by integrating emerging technologies such as cyber-physical systems, the Internet of Things (IoT), virtual reality (VR), artificial intelligence (AI), and big data. It fills the gap between cyber and physical space by providing a balanced environment between economic and social needs, regardless of age, culture, gender, linguistic, etc. As the Internet plays a vital role in Society 5.0, the data generated is the huge and unstructured analysis and computation of these data is a challenging task. To overcome these issues, this book provides a brief introduction to the latest technology such as big data, artificial intelligence, the Internet of Things, and smart sensors for the development of a super-smart society. Subsequently, it discusses the different branches of Society 5.0 such as super-smart healthcare system, hospitality system, and transport management system. This book is divided into three parts and 13 chapters. Part A discusses the introduction to Society 5.0 and its modernization. Next, Part B highlights the importance of big data analytics and cybersecurity in Society 5.0. Finally, Part C exemplifies the different smart applications for smart future.

Part A: Society 5.0 and Modernization Chapter 1 “Introduction to Society 5.0” elaborates a broad representation behind Society 5.0 and digital transformation. It discusses on the similarities and distinctions between Industry 4.0 and Society 5.0. Finally, it provides a brief about how Society 5.0 overcomes various worldwide social issues and economic challenges. Chapter 2 “Modernization and Innovative Development in Society 5.0” highlights the concepts involving the upcoming changes in Society 5.0. How modernization and innovation work parallelly with Society 5.0. Finally, the chapter elaborates on the various case studies related to smart contract, waste management system, and artificial intelligence scenarios of Society 5.0.

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Part B: Smart Data Analytics and Cybersecurity Chapter 3 “Introduction To Smart Big Data and Real Time Analytics in Society 5.0” tells the process of digitizing, joining, and adequately utilizing smart big data analytics, medical care associations going from single-doctor workplaces to online care. They have also briefly explained about sensors, AI, and robots that will be utilized to assess and keep up with streets, extensions, passages, dams, and many more are used to minimize the accidents and maintain and improve the quality is mentioned. Chapter 4 “Conceptual Analysis and Applications of Bigdata in Smart Society” illustrates about the conceptual analysis and big data in smart society. Later it discusses the big data applications related to epidemiology, mental health, and astrophysics. Chapter 5 “Cybersecurity in Society 5.0” shows how cybersecurity will play an important role in Society 5.0 and about the common prevalent cyber-attacks as well as their prevention. It then goes on to discuss Society 5.0 in general and its human-centric approach whilst discussing the emergence, working, and the benefits of following Society 5.0. Then the focus shifts on the expected security and cybersecurity measures in Society 5.0, and the chapter concludes with three case studies: one on Japan Business Federation (Keidanren), the other one being on Hitachi’s system for Society 5.0, and the last on an article from Japan Times. Chapter 6 “Challenging Aspects of Data Preserving Algorithms in IoT Enabled Smart Societies” presents a detailed analysis of lightweight cryptographic cipher approaches on various platforms with their merits and demerits, also highlights the features of each cipher with their performance metrics parameters, and finally discusses the open research challenges related to the field of lightweight cryptography.

Part C: Smart Applications for Smart Future Chapter 7 “Smart Tutoring System for the Specially Challenged Children” tells how the proposed work helps people with hearing loss and inability to speak in Indian language. It is built to assist users to convert speech to the sequence of sign language symbols. It performs speech to text, transforms it into a suitable format, and identifies the animations required for the phrases that will help convey the desired message. This work addresses these issues of handling combined phrases using the semantics of NLP that is used to reduce the text into smaller interpretable modules. A dictation recognizer takes in speech as input through microphone enabling the proposed model to accept audio. The animations for interpreting text are created using Unity 3D tool. In the front end, a dictation recognizer is included. Chapter 8 “Blockchain Based Electronic Health Records for Smart Healthcare” discusses the current issues in using the electronic health records such as privacy,

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security, interoperability, and data quality. Securing the health records from the unauthorized users and maintaining the privacy of patient data is an essential feature that needs to be included in the HER. They presented the possible solutions to the current issues in electronic health records using the semantic storage and blockchain technology. Chapter 9 “Computational Analysis of Online Pooja Portal for Pandit Booking System: An AI and ML Based Approach for Smart Cities and High-Tech Society” provides all the pujas and puja-related facilities available at a single place. They present a web-based application which provides all the modules like Pandit booking system along with mode of Pooja selection option, destination selection, destination tracker, online Puja Samagri booking system, etc. They used algorithms like NLP, Dijkastra, BellMan Ford, LSTM, collaborative filtering (CF), etc. Chapter 10 “Food Management System in Society 5.0” discusses how the Internet, connection of various devices and advanced technology, has resulted in smart and intelligent food management system in Society 5.0 and subsequently explains how the increasing food demand can be managed. They illustrated how artificial intelligence and decision support systems facilitate the design, development, and implementation of food management activities in a smarter way. Finally, they concluded with two case studies relating to food distribution system and satellite image-based crop monitoring system. Chapter 11 “Super-Smart Healthcare System in Society 5.0” communicates about the super-smart, sustainable healthcare system in Society 5.0 whose vision will be equitable, optimized, and reliable healthcare service delivery to all the people of the society. They explained the roles and responsibilities of different stakeholders in the system. This chapter discussed the core technologies and their utility in development of such system. The different methods for healthcare data management and analysis will be described. Lastly, the ethical concerns to be taken care of regarding patient safety, data security, and well-being of people will be discussed Chapter 12 “Yagyopathy Holistic Science for Various Solutions: A Scientific Phenomenon with Modern Healthcare, QoL and Society 5.0” demonstrates the scientific evidences of Indian Agnihotra process which may be treated as an alternate therapy for curing all the challenges, faced globally in twenty-first century. It is definitely a ray of hope for central Government of India in establishment of smart cities which will be also vulnerable for health care and Industry 5.0 perspectives. They discussed the various experiments conducted by team of authors at various places of Madhya Pradesh, India, with focus to find the scientific evidences of Homa therapy. Chapter 13 “Application for Smart Cities During Pandemic—Face Mask Detection” discusses the concepts of AI, data analytics for AI, ML, deep learning, neural networks, and use of technologies for smart cities in detail. This is followed by highlighting the application of these technologies in the event of a pandemic—face mask detection, using open-source computer vision (OpenCV), TensorFlow, and Keras.

Contents

1

Introduction to Society 5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. R. Mani Sekhar, M. Akshitha, and G. M. Siddesh

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Modernization and Innovative Development in Society 5.0 . . . . . . . . S. R. Mani Sekhar, Anusha Chaturvedi, and Ankita M. Thakur

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Introduction to Smart Big Data Analytics and Smart Real-Time Analytics in Society 5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. M. Siddesh and Aishwarya S. Kulkarni

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Conceptual Analysis and Applications of Bigdata in Smart Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jamuna S. Murthy and Sanjeeva S. Chitlapalli

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Cyber-Security in Society 5.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. R. Mani Sekhar, Rudransh Pratap Singh, Lakshya Aditi Sinha, and Sunilkumar S. Manvi

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Challenging Aspects of Data Preserving Algorithms in IoT Enabled Smart Societies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. P. Sandhya and B. C. Manjith

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Smart Tutoring System for the Specially Challenged Children . . . . . 113 M. Sumana, Sahana S. Hegde, Shreehari N. Wadawadagi, D. V. Sujana, and Vaibhav Gubbi Narasimhan

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Issues and Challenges in Using Electronic Health Records for Smart Hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Krishna Prasad N. Rao and Sunilkumar S. Manvi

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Computational Analysis of Online Pooja Portal for Pandit Booking System: An AI and ML Based Approach for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Rohit Rastogi, Anjali Gupta, Anmol Pant, Nisha Gupta, Shivani Tripathi, and Utkarsh Agarwal ix

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10 Food Management System in Society 5.0 . . . . . . . . . . . . . . . . . . . . . . . . 195 K. Deepthi 11 Super-Smart Healthcare System in Society 5.0 . . . . . . . . . . . . . . . . . . . 209 Ashwini Tuppad and Shantala Devi Patil 12 Yagyopathy Holistic Science for Various Solutions: A Scientific Phenomenon with Modern Healthcare, QoL and Society 5.0 . . . . . . . 229 Rohit Rastogi, Neeti Tandon, T. Rajeshwari, Prakash Moorjani, and Sunil Malvi 13 Application for Smart Cities During Pandemic—Face Mask Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Menal Dahiya and Nikita Malik Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Editors and Contributors

About the Editors Dr. K. G. Srinivasa Professor at Dr. SPM IIIT-Naya Raipur. He received his Ph.D. in Computer Science and Engineering from Bangalore University in 2007. He is the recipient of All India Council for Technical Education—Career Award for Young Teachers, Indian Society of Technical Education—ISGITS National Award for Best Research Work Done by Young Teachers, Institution of Engineers (India)— IEI Young Engineer Award in Computer Engineering, Rajarambapu Patil National Award for Promising Engineering Teacher Award from ISTE—2012, IMS Singapore—Visiting Scientist Fellowship Award. He has published more than 100 research papers in International Conferences and Journals. He has visited many universities abroad as a visiting researcher. He has visited University of Oklahoma, USA, Iowa State University, USA, Hong Kong University, Korean University, National University of Singapore, University of British Columbia, Canada, his few prominent visits. He has authored three textbooks, namely File Structures Using C++ by TMH, Soft Computing for Data Mining Applications by LNAI Series—Springer and Guide to High Performance Computing by Springer. He has edited research monographs in the area of Cyber Physical Systems and Energy Aware Computing with CRC Press and IGI Global, respectively. He has been awarded BOYSCAST Fellowship by DST, for conducting collaborative research with Clouds Laboratory in University of Melbourne in the area of Cloud Computing. He is the principal investigator for many funded projects from AICTE, UGC, DRDO, and DST. He is the senior member of IEEE and ACM. His research areas include data mining, machine learning and cloud computing. His recent research areas include innovative teaching practices in engineering education, pedagogy; outcomes-based education, and teaching philosophy.

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Dr. G. M. Siddesh is currently working as associate professor in the Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bengaluru. He has published a good number of research papers in reputed international conferences and journals. He is a member of ISTE, IETE, etc. He has authored books on Network Data Analytics, Statistical Programming in R, Internet of Things with Springer, Oxford University Press and Cengage publishers, respectively. He has edited research monographs in the area of Cyber Physical Systems, Fog Computing and Energy Aware Computing, Bioinformatics and Artificial Intelligence for Information Management: A Healthcare Perspective with CRC Press, IGI Global and Springer publishers, respectively. His research interests include Internet of Things, Distributed Computing and Data Analytics. Dr. S. R. Manisekhar is currently an assistant professor at the Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bengaluru. He is a member of ISTE. He has published a good number of research articles and chapters. He has authored a book title Programming with R, Cengage publisher. He has also edited a book title Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications, Springer, and Artificial Intelligence for Information Management: A Healthcare Perspective, Springer. He is also an associate editor for International Journal of End-User Computing and Development. His research interests include Bioinformatics, Data Science, Machine Learning, and Software Engineering.

Contributors Utkarsh Agarwal ABES Engineering College, Vijaynagar, Ghaziabad, U.P., India M. Akshitha Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India Anusha Chaturvedi Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, Karnataka, India Sanjeeva S. Chitlapalli Department of Information Science and Engineering, B N M Institute of Technology, Bengaluru, India Menal Dahiya Department of Computer Applications, Maharaja Surajmal Institute, New Delhi, India K. Deepthi Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India Anjali Gupta ABES Engineering College, Vijaynagar, Ghaziabad, U.P., India Nisha Gupta ABES Engineering College, Vijaynagar, Ghaziabad, U.P., India Sahana S. Hegde Barclays, Bangalore, India

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Aishwarya S. Kulkarni Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India Nikita Malik Department of Computer Applications, Maharaja Surajmal Institute, New Delhi, India Sunil Malvi MP Electricty Board, Jabalpur, M.P., India B. C. Manjith Department of CSE, Indian Institute of Information Technology, Kottayam, Kerala, India Sunilkumar S. Manvi School of CSE, Reva University, Bangalore, Karnataka, India Prakash Moorjani Active Social Volunteer, GayatriShaktipeeth, Jabalpur, M.P., India Jamuna S. Murthy Department of Information Science and Engineering, B N M Institute of Technology, Bengaluru, India Vaibhav Gubbi Narasimhan GE Healthcare, Bangalore, India Anmol Pant ABES Engineering College, Vijaynagar, Ghaziabad, U.P., India Shantala Devi Patil School of Computer Science and Engineering, REVA University, Kattigenahalli, Bengaluru, India T. Rajeshwari Yagyopathy Researcher and Active Social Volunteer, Kolkata, W.B., India Krishna Prasad N. Rao School of CSE, Reva University, Bangalore, Karnataka, India; Department of CSE, NMAMIT, Nitte, Karnataka, India Rohit Rastogi Department of CSE, ABES Engineering College, Ghaziabad, U.P., India C. P. Sandhya Department of CSE, Indian Institute of Information Technology, Kottayam, Kerala, India S. R. Mani Sekhar Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, Karnataka, India G. M. Siddesh Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India Rudransh Pratap Singh Department of Electrical and Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, India Lakshya Aditi Sinha Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India D. V. Sujana Arizona State University, Tempe, AZ, USA

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M. Sumana Department of Information Science and Engineering, Ramaiah Institute of Technology, Bangalore, India Neeti Tandon Vikram University, Ujjain, M.P., India Ankita M. Thakur Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, Karnataka, India Shivani Tripathi ABES Engineering College, Vijaynagar, Ghaziabad, U.P., India Ashwini Tuppad School of Computer Science and Engineering, REVA University, Kattigenahalli, Bengaluru, India Shreehari N. Wadawadagi Bloomreach, Bangalore, India

Chapter 1

Introduction to Society 5.0 S. R. Mani Sekhar, M. Akshitha, and G. M. Siddesh

Abstract This chapter elaborates a broad representation behind Society 5.0 and digital transformation. The concept of Society 5.0 necessitates a redefinition of two types of relationships: the interaction among scientific knowledge, and the relationship between individuals and communities mediated by technology. The opening chapter presents an introduction to the notion of Society 5.0 from this standpoint. Next, it elaborates on the similarities and distinctions between Industry 4.0 and Society 5.0. Finally, the chapter provides a brief about how society 5.0 overcome various worldwide social issues and economic challenges. Keywords Society · Industrial revolution · Planning · Technology · Development

1 Introduction The Japanese Council announced the 5th Science and Technology Basic Plan in January 2016, which included “Society 5.0” as a general premise. It was highlighted as one of Japan’s strategic planning. Society 5.0 serves as an auto vision to overcome many of the worldwide social issues and economic challenges, such as an aging population in Japan. Society 5.0 [1] is a movement that tries to solve societal challenges from a different perspective. Various sections of the society would be linked, and technology shall join an artificially intelligent society with complete implementation from big data, the Internet of Things (IoT), and artificial intelligence (AI) to enable digital and physical networks to support digital and physical networks for a group of people. The information is gathered from the “real world” and refined by computers, with the findings being used in real-life situations, according to Society 5.0’s core schema. This structure is not new in and of itself. To use a common instance, air-conditioning units immediately turn on when the temperature rises above a certain level. Maintain S. R. Mani Sekhar (B) · M. Akshitha · G. M. Siddesh Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_1

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a specific temperature in a room with the thermostat. An internal microcomputer monitors the temperature of the room on a regular basis. The temperature measurement is then checked to the temperature setting that was previously established. The airflow is spontaneously triggered or deleted depending on the outcome, such as ensuring the desired temperature is maintained in the room. Many of the mechanisms we rely on in our daily lives are mechanism which is used by civilization. It is at the heart of the mechanisms that keep things running smoothly. It underpins the mechanisms that keep our houses well-supplied with energy and the trains operating on time. Electronic automatic controls are used in this system. When individuals talk about an “information society,” They are talking about a civilization where everyone has access to information. So, what sets Society 5.0 apart? In place of each system possessing a restricted scope, such as maintaining a room secure and sheltered, delivering liveliness, or assuring train arrivals are on time, Society 5.0 will feature systems that work across multiple domains. In a well-integrated manner throughout society, it is necessary to ensure satisfaction and ease. It is not sufficient to have a pleasant room temperature. We need pleasure in all aspects of our lives which involves energy, transportation, medical care, retail, education, and other elements of life. To achieve this, systems must collect a wide range of real-world data. After that, the data must be analyzed and be evaluated by complex IT systems, such as artificial intelligence (AI), because these are the only IT systems available and are capable of handling such a large amount of data. The information retrieved from such extraction should be put to use in the actual world to enhance our lives easier and more enjoyable. Isn’t this, however, earlier the case? The distinction is that in Society 5.0, the generated data will do more than just guide operations. Whether it is an air conditioner, a generator, or a railway, it will have a direct impact on our actions. In summary, Society 5.0 will include a continuous loop in which data will be collected, analyzed, and then turned into usable information. This cycle is used in the actual world, and it also acts at a societal level. This is a civilization centered on people, who are capable of solving social challenges through systems built through the merging of cyberspace and physical areas as a result of economic advancement [2]. This new social model, which is based on a unified structure, controls concerns of economic and social importance while focusing directly on people’s interests and needs [3]. From an organizational standpoint, Society 5.0 aims to develop new techniques for running personal systems, in which businesses, colleges, and governments work together to develop a cooperative operational idea strengthened by today’s societal interconnection.

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1.1 Digital Transformation in Society 5.0 The Society 5.0 model includes a number of improvements that allow for the creation of new processes and practices. These are developments in technology, economics, geopolitics, and mentality. Along with the usage of cyberspaces and their merging into physical spaces, digital transformation is bound to dramatically affect numerous parts of society, and personal life is included, including administration of government, infrastructure, and work. The vast quantity of information (big data) generated by sensors in physical locations is stored in cyberspace, allowing physical spaces and internet to be integrated. Big data is then evaluated using artificial intelligence (AI) techniques, and the solutions are then displayed to consumers in a variety of ways in physical settings using multimedia devices like displays or loudspeakers. We have arrived living in a new era, one that is globalization and the quick extension of digital technologies like the Internet of Things (IoT), artificial intelligence (AI), and robotics are producing huge societal changes. People’s views and the environment are growing extremely varied and complex. Industry 4.0, often referred to as the “4th Industrial Revolution,” the Industrial Internet, and Made in China 2025 are all examples of efforts aimed toward new digital technologies around the world. The common element that is driving such initiatives is the wave of digital transformation, and so digital transformation becomes a cornerstone of industrial policy. At the very same time, the earth is getting confronted with global concerns such as natural resource-draining, global warming, widening economic disparities, and terrorism. We are currently living in a difficult era of uncertainty, with increasing complications in all zones. As a result, it is crucial that we make full use of ICT to obtain fresh information and develop latest values by associating “people and things” and the “real and cyber” worlds as a productive mechanism for addressing societal issues, people’s lives are being enhanced, and supporting healthy economic growth. To achieve such a society through digitalization, it will be necessary to overcome these obstacles by bringing together many stakeholders in numerous zones to share a similar future vision. The United Nations adopted the 2030 Agenda for Sustainable Development in September 2015, with the Sustainable Development Goals (SDGs) at its center. It is difficult to construct a complex framework in which all nations collaborate to create a sustainable world that seeks to accomplish both economic growth and societal results. The central guiding principle is to obtain peace and security for all individuals and the world by reacting to problems in a way that “leaves no one behind.“

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1.2 Industrial Revolution Industry 4.0 [4, 5] is an industrial revolution with a blueprint announced by the German government in 2011 with the goal of improving industrial competitiveness through eminent tactics. With significant developments in the performance of production activities, the fourth Industrial Revolution, termed Industry 4.0, has a profound effect on the industrial environment. The fourth industrial revolution is projected to have a big economic impact since it supports a considerable rise in organizational productivity, as well as the creation of completely new business models, services, and goods. Unlike traditional industrial scheduling prediction, Industry 4.0 provides for realtime production making plans as well as dynamic optimization. Industry 4.0 includes the integration of independently measured and vital value chains, combining the Lean philosophy with the advancement of information and communication technologies. This fourth industrial revolution encourages the development of intelligent factories with enhanced information, disclosure, and future-oriented technology. Industry 4.0’s main advantages include specialized solutions for each industry and personalized customer interactions, the ability to produce particular products with a low intensity of manufacture, increased with ongoing supply chain changes, competition and adaptability are essential, production and operating excellence growth have increased at a faster rate., work-life balance, and lower energy costs. In some circumstances, the implementation of Industry 4.0 shows that connecting persons, systems, and things allows for an efficient, full, dynamic, and real-time network. Society 5.0 is a concept that supplements Industry 4.0 and has the potential to alter society for the greater good of humanity. This new society serves as a catalyst for social change, with the goal of having a significant impact on society at all levels, including life quality and long-term viability.

2 Industry 4.0 Industry 4.0 [4, 5] is defined as a real-time, smart, and automated network for tools, items, and, most importantly, people in the context of industrial planning. It allows for more digitalization of the entire value chain as well as real-time data transfer between people, items, and systems. Advances in Technology of global communication might be applied to machines, production workers, and logistics processes to enhance communication among all stakeholders in the process of product design, analysis of data using specified algorithms, and production control flow to enhance continual improvement.

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Three ideas can be used to explain Industry 4.0: • Because the goods have a memory that can keep records of performance information and requirements standards independently, it is feasible to solicit the appropriate resources and allocate production operations. • Intelligent Machines provide for a line of manufacturing that is versatile and flexible by replacing traditional production hierarchy with decentralized selforganization. • In order to encourage a flexible and dynamic part of the production system, the operator was enhanced with information automation. The Cyber-Physical Systems (CPS), Internet of Services (IoS), and Internet of Things (IoT), which will be a key instrument in the development of Industry 4.0, are the primary components of Industry 4.0. The CPS system, which consists of merging into a mechanical or electrical system with hardware and software intended for a purpose, is at the heart of the industry 4.0 structure.

2.1 Smart Factories Are a Result of Industry 4.0 Smart items that improve adaptability, resource conservation, and process integration are enabled by Industry 4.0. Smart Factory and Production, Smart Item, and Intelligent Buildings are examples of Industry 4.0 applications that are referred to as “Smart.“ Intelligent factories use a platform that can send data, commands, and other details between machines and clouds, as well as between machines and goods. Industrial Internet of Things (IIoT), cloud-based industrial, and creation of social products are just a few of the technologies and emerging standards that makeup Industry 4.0. With the deployment of Industry 4.0, the Industrial Internet of Things (IIoT), which emerges from the industrial systems confluence with enhanced programming, devices, and communication module, will be one of the greatest contributions to the proper running and performance of a plant. Furthermore, intelligent interconnected modules could be used to more efficiently allocate tools and other assets, electricity, and water. To take advantage of the potential prospects offered by Industry 4.0 and smart production, the complication and administration issues in the industrial sector must be resolved.

2.2 Businesses Issues Related to the Industry 4.0 The contemporary market’s high degree of competitiveness compels businesses to overcome new hurdles in terms of quality, greatest prices/costs, and delivery time. Due to contemporary global competitiveness, industry must be able to adapt to creativity and introduce new products to the market in a timely manner.

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It is essential to efficient design, and fast, and flexible techniques in order to ensure process competition throughout the value chain. The connection between operations, assets, services, and infrastructure with artificial intelligence is the most important feature to encourage fast response to changes in manufacturing. Companies will use a global network to connect data centers, machinery, and manufacturing plants from Cyber-Physical Systems (CPS). CPS is a high-tech solution for managing systems that can combine computer talents and physical assets. In addition, integrating CPS with manufacturing, logistics, and activities in present manufacturing practices will aid in the evolution of Industry 4.0 deployment in factories that have an economic impact influence. A combination of digitalization of production industry might be used to govern the life cycle of a changed product with adaptive manufacturing and distribution, which encourages the development of new industries. Control systems and manufacturing that are centralized and monolithic will give rise to solutions that can support decentralized yield and supply chain activities. In terms of modifications, decentralization, self-optimization, and automation, the system will be flexible, fast, and efficient. Intelligent machines, memory devices, and manufacturing facilities able to proactively exchange information, trigger actions, and standalone control are examples of CPSs in an industrial setting. The adoption of high-tech approaches has been aided by the industry’s ongoing growth and innovation.

3 Society 5.0 Society 5.0 is a moderate revolution that began in Japan [6] and promises to alter society by putting the individual being at the core of technical and adjustment of invention for the good of mankind The fundamental goal of Society 5.0 is to improve people’s quality of life by leveraging Industry 4.0’s capabilities. With the introduction of new technologies as an illustration of automated driving for delivering orders or drones, Japan has been moving toward Society 5.0. And meanwhile, the rest of the world is adjusting to Industry 4.0’s issues.

3.1 Movement Originated in Japan Japan is predicted to be the longest society in the world by 2050, with almost 40% of the population being over 65 years old. To address today’s societal dilemmas, modern technologies such as big data, robotics, artificial intelligence, drone delivery, and independent goods should be used. Japan’s promising growth strategy calls for training in order to achieve sustainable development [7] goals and the construction of a knowledgeable society. Japan will

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strive for the creation of a Society 5.0 services platform to facilitate interaction between education, government, and industries. As a result, Japan is taking a bold step forward in asking for a Society 5.0 in the future, with adjustments affecting all elements of society and manufacturing output. Europe will play an important role in this movement.

3.2 Computer Technology Has Evolved to Meet the Needs of This New Society Concurrent intelligence is the defining characteristic of Society 5.0, in which standard artificial intelligence ideas are enlarged to new cyber-physical-social systems (CPSS). Development of science and technology, particularly in computer science, are now contributing to the advancement of employment and society. Dual intelligence allows for successful handling of socially and engineering complicated issues, with the goal of identifying agile, targeted, and convergent solutions to comprehend ambiguous, varied, and complicated topics. Meanwhile, the world is confronted with global issues such as global warming, natural resource shortage, terrorism, and socioeconomic dispersion. Society today has invented a separate entry period of technical progress, which is the simultaneous a new beginning of technology that combines virtual and actual intelligence, as a result of significant advances in automation, electricity, data, and networking technology. The ability for individuals and things to be linked, as well as the real and virtual worlds, will allow for the proper and effective purpose of societal concerns, as well as the enhancement of the preservation of sustainable growth in the economy and the improvement of people’s quality of life. The core approaches of Society 5.0 are based on the concept of automation, which is the next step in the evolution of artificial intelligence technology as well as the overall formation for managing and controlling CPSS systems. The Japanese society [8] anticipates the emergence of a Super Smart Society, with the construction of a maintainable society in which many sorts of standards are linked through CPS and persons, can live in safety, security, and comfort. An intelligent society is a system that makes use of the possibilities of digital technology, instruments, and networks to make people’s lives better. CPS has the potential to connect various sectors, countries, regions, and societies. Society 5.0 was formed out of the emergence and development of the information society, with the goal of boosting businesses and specific abilities while also addressing social issues.

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3.3 The Upcoming Society 5.0 Employment, government administration, people’s privacy, and the industrial structure are all undergoing significant changes, and internet data must adapt to meet these needs. The implementation of Society 5.0 must take into account the integration of multiple elements like business talents, entrepreneurial spirit, and innovation policies. Technological advancements have the potential to increase living standards, but they also have the potential to adversely affect employment, wealth distribution, and information. Society 5.0 allows modern technologies such as robotics, IT, IoT, artificial intelligence, and virtual reality to be used in people’s daily life, health, and other areas of activity, whereas Industry 4.0 limits technological advancements to the industrial sector. As a result, it is up to humanity to decide which path to take and what kind of civilization we want to build in the future, given the creative technology at our disposal. Increased supply, decreased costs connected with an aging population, equitable income disparity, pollution reduction, decreased packaged foods, correction of local inequities, and other technologies for humanity’s benefit could alleviate social problems and encourage economic widening. Society 5.0 blends digital change with the inventiveness of many people to achieve sustainable development through issue resolution as well as value generation, allowing it to fulfill the United Nations proposed sustainable development objectives. Below points discuss the various similarities between Industry 4.0 and Society 5.0: • To depict work connectedness, both Engineering Revolutions highlight advanced skills such as the IoT, Smart Machines, robotics, and Knowledge Management. • Both Industry 4.0 and Society 5.0 are concerned with how individuals can interact with machines or sophisticated technologies via Intelligent Machines. • Both have the ability to multitask using a range of automated devices and other cutting-edge technology. • They both underline that work is no more contains a single hourly duty, but rather a system that operates in parallel with added processes in the modern period. • In the middle of realistically implemented progressive technology, both Industry 4.0 and Society 5.0 address sustainability engineering to ensure that nature and environment remain in excellent shape. Below points discuss the various dis-similarities between Industry 4.0 and Society 5.0: • If Industry 4.0 concentrates on how can tasks be completed instantly, Society 5.0 concentrates on how to manage duties during working hours. • In Industry 4.0, the efficiency of utilizing machinery and equipment is highlighted; in Society 5.0, the efficiency of optimizing human workers for improved machinery and technology is highlighted.

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• In Industry 4.0, communication can be computerized in a variety of ways, whereas in Society 5.0, innovative and advanced equipment or technology are employed to make work easier.

3.4 Society Problems in Society 5.0 Wealth distribution and regional disparity are becoming increasingly common. In the context of this economic boom, the social issues that must be handled have become more challenging. Reduction of greenhouse gas (GHG) emissions, increased supply, reduction in food loss, cost reduction associated with an aging population, endorsement for sustainable industrialization, wealth redistribution, and local inequality correction have all become essential in this scenario, but achieving both economy and social problem solutions at the very same time has proven challenging in the existing social system. In the midst of such massive changes in the world, emerging technologies such as IoT, AI, big data, and robotics, which can all affect the direction of a civilization, are continuing to grow. Japan intends to make Society 5.0 a fact as a new society that incorporates these new technologies throughout all industries and social activities, resulting in both financial growth and social solving problems.

3.5 Challenges and Opportunities in Society 5.0 The difficulties that the nation’s economy must deal with, such as population aging, rural depopulation, model of teaching, health, the battle in opposition to global climate change, and economic growth, can all be addressed using this approach [9]. Adopting the notion of Society 5.0 entails anticipating the future in an acceptable manner and involving the major parties (governments, universities, private firms, and citizens) in a single implementation strategy for the greater good. Society 5.0 aspires to address a number of issues [10] by going above and beyond digitalization of the economy. The challenges are rising inequality, rising demand for energy and food, Agricultural distress, lack of integration and information sharing among various departments and ministries, robotic surgeries and assisted operations, smart classrooms, and efficient measures to put down fake news quickly. It is beneficial in terms of enhancing the standard of life. People are aware of the situation about innovative products through the technique of marketing. As a result, people’s level of livelihood rises. They use such innovative products. Job generation is aided by marketing and the increase in personal wages. It is also beneficial to the country’s economic growth. Advertising’s impact is no longer limited to the outer walls of a country; instead, it has spread beyond national borders.

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Local marketing efforts can be sent to the rest of the world using satellite technology. As a result, the country’s economic potential is greatly enhanced. The country’s financial capacity grows, and new industries emerge. As a result, advertising contributes to the country’s economic prosperity. Aids in the creation of more jobs. Advertising is important for expanding job opportunities and diversifying job types. Artists, screen printers, block builders, screenplay writers, painters, and other professionals are employed by the company. In the era of Society 5.0, financial technology (fintech) is regarded as one of the most important advancements in the financial business. Fintech can help Indonesians with a variety of financial activities, including payments and transactions.

4 Conclusion Humans are at the center of transformations in Society 5.0, along with income progress, scientific advance, and conservation. The chapter has discussed how Society 5.0 establishes a framework for addressing critical social issues in society today in the local environment, as well as opportunities for addressing individual social issues through organizational activities and the development of practical advice for the promoting social responsibility in organizations. Drone delivery, artificial intelligence, big data, autonomous trucks, and robotics will all be used for the betterment of mankind in the near future, therefore combining technology with society will be critical. The structures and technology established here will undoubtedly aid in the resolution of socioeconomic issues around the world.

References 1. Cabinet office- Society 5.0, https://www8.cao.go.jp/cstp/english/society5_0/index.html 2. Potoˇcan V, Mulej M, Nedelko Z (2020) Society 5.0: Balancing of Industry 4.0, economic advancement and social problems. Kybernetes 3. Deguchi A, Hirai C, Matsuoka H, Nakano T, Oshima K, Tai M, Tani S (2020) Society 5.0 a people-centric super-smart society. Hitachi-UTokyo Laboratory (H-UTokyo Lab.), The University of Tokyo Bunkyo-ku, Tokyo, Japan. Springer open 4. Christian Manrique Valdor-Industry 4.0 and Society 5.0, https://christianmanrique.com/2019/ 02/14/industry-4-0-and-society-5-0-by-christian-manrique/ 5. ID star- Industry 4.0 and Society 5.0, https://idstar.co.id/industry-4-0-and-society-5-0-similarbut-not-the-same/ 6. Fukuyama M (2018) Society 5.0: Aiming for a new human-centered society. Japan Spotl 27(Society 5.0):47–50 7. Zengin Y, Naktiyok S, Kaygın E, Kavak O, Topçuo˘glu E (2021) An investigation upon industry 4.0 and society 5.0 within the context of sustainable development goals. Sustainability 13(5):2682 8. Narvaez Rojas C, Alomia Peñafiel GA, Loaiza Buitrago DF, Tavera Romero CA (2021) Society 5.0: A Japanese concept for a superintelligent society. Sustainability13(12):6567

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9. Pereira AG, Lima TM, Santos FC (2020) Industry 4.0 and society 5.0: opportunities and threats. Int J Recent Tech Eng 8(5):3305–3308 10. Salimova T, Vukovic N, Guskova N, Krakovskaya I (2021) Industry 4.0 and society 5.0: Challenges and opportunities, The Case Study of Russia. Smart Green City. 17(4)

Chapter 2

Modernization and Innovative Development in Society 5.0 S. R. Mani Sekhar, Anusha Chaturvedi, and Ankita M. Thakur

Abstract Modernization of society goes hand in hand with innovations made in technology. Society 5.0 is a smart society that thrives on human-centered applications of innovative technologies like IoT, blockchain, and AI. It is proposed to be a future that builds upon Industry 4.0. There is a need to ensure a proper definition of the consequences and transformations when it comes to implementing various models that help in the betterment of the daily resources accessed by the people. Society 5.0 is potentially promising as it proposes to deepen the relationship between people and technology and assures a positive impact on all aspects of society. This chapter briefly describes the concepts involving the upcoming changes in society. How modernization and innovation work parallelly with Society 5.0. Finally, the chapter elaborates on the various case studies related to Smart contracts, waste management systems, and Artificial Intelligence scenarios of Society 5.0. Keywords Society · Modernization · Cyberspace · Physical space · Artificial intelligence · IoT · Cybersecurity · Sustainable development

1 Introduction to Modernization in Society 5.0 1.1 What is Modernization in Society? Modernization refers to the transformation of a rural, traditional, and agrarian society into an urban, secular, and industrial society [1]. To put it crudely, modernization refers to the updating of a society. Modernization as a process in itself is unceasing and unrestricted, i.e., there will never be an end to modernization in a society. Thus, a society is never actually absolutely modernized at any given point. The modernization of a society is irregular and uneven which causes a modern society to consist of S. R. M. Sekhar (B) · A. Chaturvedi · A. M. Thakur Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_2

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developed regions as well as backward regions that become an unremitting source of conflict and strain [2]. The presence of unevenly modernized societies brings about a fundamental element of instability into the system of states. Modernization brings about many such challenges, nevertheless, the extraordinary and profound success of modern societies outweighs any such challenges.

1.2 Modernization of Society from 1.0 to 5.0 Before exploring the what, why, and how of Society 5.0, it is advantageous to understand how society has evolved. There are five distinct society eras that the world has experienced and continues to experience today [3]. Society 1.0, Hunter-gatherer society: As the name suggests, hunter-gatherer societies encompass humans who obtained their food by hunting, scavenging, fishing, gathering plants, and various other edibles. Although groups of hunter-gatherers still exist in our modern world, society 1.0 highlights the prehistoric societies that depended on nature’s bounties before the transition to agriculture began [4]. Society 2.0, Agrarian society: Human society started transitioning from huntinggathering to agriculture around 12,000 years ago. The word agrarian means agriculture-related. A society whose economy relies on farmlands and the production of food crops is called an agrarian society. Such societies massively depend on the climate, seasonal factors, and weather. Practicing agriculture caused human beings to settle and create communities that gave rise to a new social structure and a form of human societal organization [5]. Society 3.0, Industrial society: An industrial society is a society in which technology and machinery are used for the mass production of goods with a high capacity for division of labor and supporting a large population. It consists of a particular social structure designed to support and sustain such operations. Innumerable societies transitioned from agrarian societies to industrial societies following the Industrial Revolution in the late 1700s [6]. Society 4.0, Information society: Information society is a society in which the creation, manipulation, and distribution of information have become the most important cultural and economic activity, especially since World War II. Information societies differ from industrial societies as information is treated as a commodity because the economy employs innumerable information workers [7]. Society 5.0, Super-smart society: Super-smart society is a society that is humancentered and highlights a system that tremendously integrates physical space and cyberspace. Society 5.0 is a plan proposed as a future society that Japan should aspire to, in the 5th Science and Technology Basic Plan [8] (Fig. 1).

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Fig. 1 Modernization of society from 1.0 to 5.0

2 Smart Society 2.1 Society 4.0 Versus Society 5.0 Smart Society (5.0) collects data from the real world which is then processed by computers. The results obtained upon processing the data are then applied back to the real world. However, this is not entirely a new concept in itself. Society 4.0, better known as the Information Society, already has systems with the same schema [9]. Let’s look at an example of a thermostatic shower mixer. The role of a thermostatic shower mixer is to maintain the water temperature of the shower at a specified value by mixing the right amount of hot and cold water. A thermostatic shower mixer measures the water temperature of the shower at regular intervals and then compares it to the pre-set temperature. Furthermore, the thermostatic valve then mixes cold and hot water to adjust the temperature of the water to the pre-set value. Innumerable such systems in our society, run on the same underlying principle. Thus, the question arises, what makes Society 5.0 different from Society 4.0? In Society 4.0, individual systems operate within a limited scope by collecting data, processing it, and then applying the results in a specific real-world environment. However, in Society 5.0, such individual systems are collectively replaced with systems that operate throughout the society in an integrated manner. Therefore, these integrated systems will collect voluminous real-world data to ensure comfort in multiple spheres including but not limited to medical care, work, energy, education, transportation, etc. Furthermore, these vast arrays of data must then be processed and applied to the real-world environment for our comfort. Another noteworthy difference includes how the outcome will not only result in controlling the water temperature, railways, generators, etc. but how it will also affect our behavior and actions. In a nutshell, Society 5.0 will consist of iterative cycles in which voluminous data are collected, processed, and then converted into meaningful results in the real world at a society-wide level.

2.2 What is Cyberspace? The term Cyberspace is a concept that describes the virtual computer world. Cyberspace is an electronic medium that is used to enable data exchange and online communication by usually involving a huge computer network consisting

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of numerous worldwide subnetworks that use the TCP/IP protocol [10]. This welldesigned interface is utilized by a wide range of participants. Cyberspace enables its users to exchange information, play games, conduct business, stream videos, and engage in additional other activities. Cyberspaces can be defined by human societies. The internet is used for diverse reasons including entertainment, education, e-commerce, etc. Cyberspaces are created whenever the internet is used for a particular reason. For example, much earlier, meetings used to be conducted offline within a room/hall, i.e., in the physical world. However, in recent times, the need for meeting offline has been partially eliminated by the existence of platforms that enable online meetings. This depicts how society has used its abilities to create cyberspaces based on several use cases of its own. Cyberspaces are ever-evolving and exponentially growing with the increasing access to the internet from personal devices such as phones, laptops, televisions, etc.

2.3 What Is Physical Space? Physical space refers to the three linear dimensions in which material objects exist and events occur. Physical space can be simplified as the real world. The real world is made up of everything that is considered to be real such as trees, books, people, etc. Computer systems are also real. Therefore, the word “physical” is used to draw the difference between space and cyberspace.

2.4 Merging Physical Space with Cyberspace As discussed earlier, Society 5.0 will consist of iterative cycles in which voluminous data are collected, processed, and then converted into meaningful results at a societywide level. The voluminous arrays of data that are collected and the results that are found are applied in the real world, i.e., the physical space. The data is analyzed and real-world solutions are modeled by the computer systems in the computer networks within cyberspace. The computer systems in cyberspace must be similar in structure to the physical space which is the real world [9]. Let’s look at the example of the thermostatic shower mixer. The microcomputer executes a program to gather the value of the temperature of the water which is then compared with the pre-set temperature. Based on the difference, the ratio of cold to hot water is changed accordingly. Upon modernization, such thermostatic shower mixers can also detect the position of the person in the shower and set the pressure of the flow of water along with the angle of the shower. This increases the degree of comfort that this system overall offers. Thus, bringing the cyber model closer to the real-world model. This aids in achieving the goals of Society 5.0 which is to tremendously merge physical space with cyberspace to model practical solutions to real-life problem

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statements. If any problems exist in the output, the data is re-analyzed in cyberspace for further solutions to be derived. This iterative process of finding solutions, followed by adjusting and improving them based on the outcomes in the physical space is what Society 5.0 aims to achieve. Creating mirrored models of the physical space within cyberspace will help the computer systems understand how to come up with solutions, and understand its impact in the real-world environment, within its simulation itself. Thus, enabling it with the skill set to strategize based on the simulation of the real world. This is possible through the usage of IoT (Internet of things) and AI (artificial intelligence).

2.5 The Role of IoT and AI in Merging Physical Space with Cyberspace IoT enables the collection of diverse and voluminous data from physical space into cyberspace. In the case of thermostatic shower mixers, the variables whose values would be collected include but are not limited to the size of the shower room, the position of the person and his/her spatial distribution, the temperature of the water from a single or multiple showerheads, and pressure of the flow of the water. AI analyses the extensively gathered data and then creates a unique cyber model of the shower room that has the same functionalities as the real-world setup. The system develops an appropriate strategy based on the cyber model and employs it in the physical space. If the temperature of the water is different from the pre-set temperature, then the cyber model of the shower room must have some inconsistencies. The AI incorporates, notes the errors, and readjusts the cyber model to attain better results in the physical space. Through these calibration cycles, the cyber model and physical model will be indiscernible. At this point, the physical space and cyberspace have merged to a degree where they are indistinguishable.

3 Industry 4.0 and Society 5.0 3.1 What Is Industry 4.0? The federal government of Germany released a “High-Tech Strategy 2020 Action plan for Germany” which highlighted the high-tech approaches called Industrie/Industry 4.0. This high-tech plan was thought of much before Society 5.0 by a gap of five years. Industrie 4.0 was a strategic action plan which focused on using IoT in manufacturing to facilitate CPS (cyber-physical systems) which can build the value of production. Thus, promoting smart factories which are simply factories that significantly reduce the costs of manufacturing. IoT is used on devices to

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collect data from all the various stages of the manufacturing process in the physical space which is then sent to cyberspace. Artificial intelligence is then used to analyze the data that is gathered to arrive and optimum solutions. The solutions are then reflected into the physical space environment which is nothing but the control systems in the smart factory itself. Furthermore, Industry 4.0’s primary focus is manufacturing; however, the scope of this initiative is not limited to it. This initiative needs data-related standards as well as regulations, for which it is imperative to have a collaborative process involving multiple spheres such as the government, IT sector, manufacturing industries, etc.

3.2 Society 5.0 Versus Industry 4.0 Let’s look at the commonalities between Society 5.0 (proposed in Japan’s Science and Technology Basic Plan) and Industrie 4.0 (proposed by the German Federal Government as the High-Tech Strategy 2020 Action Plan for Germany). As highlighted before, both these action plans involve the usage of the Internet of Things and Artificial Intelligence. Both these plans also emphasize a collaborative approach among the government, industry, and other important institutions that are involved. While there are some obvious similarities between the two, there also exist stark differences [9]. When we take a look at the objectives of each of these plans, it is quite obvious that each plan is focused on entirely different domains. While Society 5.0’s objectives are to achieve a smart society, the objectives of Industrie 4.0 are to achieve smart factories. Thus, Society 5.0 focuses on Society at large whereas Industrie 4.0 focuses on manufacturing. While Industrie 4.0 focuses on the onset of an industrial revolution with manufacturing at its epicenter, it does not outline the impact of the revolution on the public. However, Society 5.0 is a people-centric initiative that is built with a focus on the comfort and needs of society. Thus, Society 5.0 highlights the impact of technology on the public to modernize society. Another important difference that can be highlighted is how the success of each plan is measured. In Industrie 4.0, the success of this initiative can be measured by the cost of manufacturing, where minimizing the costs would translate to it being a successful initiative. Whereas, in Society 5.0, the degree to which a super-smart society has been achieved will be the means to measure the success of this initiative. However, this involves slightly more complex performance metrics.

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4 Innovation in Society 5.0 Innovation in Society 5.0 is identified by servitization, frugal innovation, and open innovation. Let’s look at each of these in detail; [11] Servitization: The term servitization refers to industries selling products as a service rather than a one-time purchase. For example, a CD is a one-time product purchase. An example of selling products as services is an OTT media service [12]. For example, Voot, Amazon Music, Amazon Prime, etc. Servitization can be implemented in manufacturing industries where they sell services along with a purchase of a product. For example, when buying vehicles there is scheduled maintenance that is initially free. This is a perfect example of how the service model is applied to industries. Circling back to the example of OTT media services, there are multiple plans on the basis of months and years that users can avail to gain access to these platforms. Servitization ensures a continuous stream of profit for industries in a competitive market. Frugal Innovation: The term frugal innovation refers to the process of decreasing the cost and increasing the simplicity of goods and their production. This translates to eliminating the unessential features from a long-lasting good such as a laptop or a fridge [13]. This is done so as to sell these goods to underdeveloped and developing countries. Let’s look at an example to understand the crux of frugal innovations. Mansukh Prajapati is an Indian potter who invented a fridge that was completely made of clay. It was unique not only because of the materials it was made up of but also because it ran without any usage of electricity and can manage to keep fruits and vegetables fresh for multiple days. This clay fridge is a perfect example of frugal innovation as it is made at a very low cost using uncomplicated technologies. Open Innovation: Another business model is an open innovation model which promotes collaborating with organizations and individuals outside of the company. This is a completely new model from the age-old model that influenced the company’s silo mentality [14]. The open innovation model acknowledges that there are many experienced professionals with the same or better skill set who do not belong to the organization. Factoring in their inputs can only result in a better product. Companies enforce open innovation practices in various methods such as crowdsourcing challenges, innovation ecosystems, and partnerships between companies. The innovation ecosystem focuses on the betterment of humans that constitute the society. This shifts the focus of innovation from developing technology for sole advancement to developing technology with the means of building a society that brings joy and comfort to the humans inhabiting the society. Furthermore, innovation systems insist on using a collaborative approach between academia and industry that builds up cooperation and promotes open innovation. With industrialization and digitalization, we have reached the point of making the lives of the public very convenient. However, with the positive outcomes that accompany scientific and technological advancement, there also exist negative outcomes. This as previously discussed has caused distress between technologically advanced and technologically lacking regions striking an undeniable imbalance. Hence, Society 5.0 aims to achieve modernization in society as a whole and not just in specific regions which

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can be achieved if industries and enterprises collectively demonstrate leadership to uplift the society as a single unit through funding, networking, innovation, and most importantly extensive technology.

5 Economic Changes in Society 5.1 Economy and Change The economy is considered to be the backbone of a society. It is an essential component contributing to the betterment of society. The economy is constantly changing and it is said to help the people living in a society cater to their individual needs and meet their requirements. It also shows us how developed and civilized a society is, by keeping track of its needs and other attributes that define modern civilization. People interact with the economy every day which leads to an interconnected dependency [15].

5.2 What Are Some of the Changes That Have Occurred? One of the biggest changes for Society 5.0 is the increase in productivity which in turn contributes to the equity of the resources [16]. The economy plays a very important role in the concept of sustainable development of the environment. Both of these concepts focus on meeting the requirements of the people along with preserving resources for the future. Economic growth often leaves an impact on the environment and our natural resources. This could even have a negative impact on human life in the long run. That is why Society 5.0 should also expect to focus on preserving the non-renewable resources along with being able to meet the needs of the current generation. A significant improvement in the economy has been in the form of “Circular Economy” where there has been an emphasis on utilizing resources in a smart and efficient manner. This has been achieved through concepts like reusing and recycling in place of wastage. There have been some measures that have become necessary such as the reduction of greenhouse gasses and their harmful emissions, usage of alternate sources of energy, and reducing exploitation of natural resources by limiting their consumption. With new innovations and technological advancements, the products and services offered by companies can be made specific to the needs of a particular customer. This is a perfect example of utilizing the technology we have, to get the most desired and self-efficient solution to our problems [17]. These services are done depending on the requirement, in order to avoid having an oversupply of products and lack of inventories. Economic changes also involve a great deal of competition on an

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international level. This is the result of another major impact on the economy, which is, the increase in the demand for products in the markets [2].

5.3 What Is the Impact of Such Changes on Society? Economic growth is often paired with a good flow of cash when it comes to the funding of a country. This way money is being spent on services and facilities for the public in areas like health, sanitation, transport, education, and a lot more. It even opens new possibilities for trade thereby generating greater income for small and large businesses across the nation. Economic growth also leads to a considerable increase in the cost of living. The economic growth of a country is directly proportional to the currency value. A currency value is what decides the power of a nation’s resources in international markets [18]. In Society 5.0, factors like regional differences, age, language barriers, and gender will be eliminated and it will enable the production of products and provision of services that cater to the specific needs of different groups of people. This is how we will be able to achieve economic development to find solutions to problems keeping in mind the social problems as well [2].

5.4 How Can We Manage to Keep Our Economy Intact in the Future? The future economies will have a great effect on not just the generation of wealth but also on its distribution. With the evolution of the economy, different aspects of society will play new roles in order to control and use the resources, each having the capability to influence the other. The Digital economy acts as the influencer and enabler of the economies. It focuses more on inclusivity and is changing the view on concepts like value creation. Along with the conversion of resources into their economic equivalent outcomes, it also incorporates new views and ideologies on the utilization of these resources to fulfill the requirements and address the challenges faced by the people. The Sharing economy has opened up opportunities for individuals and groups to earn money even from small assets that are underused. It is very important for businesses to recognize the perks as well as the risks that arise from participating in this kind of economy. Examples where we can see such an implementation are borrowing of goods, rentals, and hiring people for their micro-skills to perform a task, in exchange for money. The Experience Economy has completely changed the interpretation and influence of companies when it comes to international markets. The companies have changed

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the way they approach a market or a buyer and even the ways to engage people in continuing with their services. Such an experience is fruitful as people’s loyalty and interests go to the experience that they have when they shop from a company. The gig economy is also known as the freelance economy. Here, independent freelancers use digital platforms to connect with different businesses and companies for providing services over a period of time. Due to the digitization of everything these days, many freelancers are offering their services to various gig platforms. The growing connections and social attributes among people are encouraging them to share their assets and services with each other. The purpose-driven economy is closely related to social entrepreneurship. There are social entrepreneurs in every field of society. They provide an innovative mindset and logical approach which is very beneficial in today’s market [19].

6 Security Issues During Innovative Development 6.1 Cybersecurity This study also recognizes the challenges of Society 5.0. It is very important to address the consequences of digitization and the risks involved with it in various fields. A very common issue among these is cybersecurity [20]. Cyber security involves defending servers, computers, mobile phones, networks, electronic devices, and data from any sort of malicious attacks [21]. The key importance of Cybersecurity involves providing protection of information or any form of data on the network. The system needs to be protected from computer threats like malware, virus, and trojan. On the other hand, the protection of devices from physical threats which could involve a superpower force is also very important. The three main concepts of cybersecurity are the availability of data, data integrity, and confidentiality. Due to the high connectivity of devices through sharing of knowledge and information, more and more systems have an increased possibility of facing cyberattacks [16].

6.2 What Is the Need for Cybersecurity? The need for cybersecurity is defined by the following: In Society 5.0, data sharing is done across various devices and systems. These systems integrate the data and distribute it all through the systems, which creates a need for greater and improved security. Since the systems involve a huge number of users accessing information continuously, there is a drastic increase in the number of user access points. These could easily be subjected to cyberattacks. There is a need

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Fig. 2 Integration of cyberspace and physical space

to be one step ahead when it comes to implementing preventive measures in a private network such as intrusion detection protocols and firewalls. There is an increasing convergence of concepts like Information technology and operational technology. In order to secure the systems from end to end, there is a need to recognize both the digital and physical components of a given device. In other words, it can be implied that the boundary between software and hardware is blurred over time and it provides a whole new scope for implementing security and privacy control protocols [22].

6.3 Impact on the Physical Space In Society 5.0, cyberspace is not just used for the exchange of information. It provides a space for computer networks to analyze and model solutions that are practical and can be put into use in the physical world. On the other hand, physical space is nothing but the real world. This is where raw data is collected and real-world solutions are implemented [9]. The cyberattack threats are not always limited only to cyberspace. If both physical space and cyberspace are integrated, cyberattacks could impact the physical space as well. Concepts like smart cities involving IoT applications have come to realize this integration wherein the services that are related to physical space could be subjected to these kinds of attacks [23]. We depend on various kinds of services for carrying out our day-to-day activities. These include services related to transport, healthcare, retail, energy, education, water, entertainment, and a lot more. Although these services seem to be separate and independent, they are all interconnected. We must ensure to learn and understand these interconnections in order to build a better and more valued society [9] (Fig. 2).

6.4 Some of the Threats Facing Society 5.0 Society 5.0 is vulnerable to cyber espionage. This is because of the increased connectivity in devices and services across the network. An act of cyber-crime can easily

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target any device and hack into their sensitive information. This could endanger the intellectual property of the people [2]. There is a lack of awareness among the people on IT security and privacy details. There is also a lack of rules and funds to strengthen the security of devices, especially in small-scale and medium-scale organizations. Industries refuse to give a high priority to security over other aspects of production. They do not want to spend funds on resolving security issues. This leads to a weak structural model of a product which is prone to cyberattacks [19]. Denial of service by a system means to render their services unavailable. Since the processes and systems are all interconnected, the servers and systems could be severely damaged in the event of a DDOS. DDOS stands for Distributed Denial of Service attack. It is a method where a network is flooded with traffic which causes a site’s normal operations to be interrupted. A consequence of this is unavailability and disturbance in the system activities [22]. Artificial intelligence raises many security concerns as it introduces learningbased processes in a new way. The learning models used can be modified by malicious users. They can also extract and pre-process the data to get the desired results for the AI models [24]. Remote access is a growing need for many businesses. It involves the exchange of data or services over the network, outside the boundaries of the corporate infrastructure. The risks involved with this are unauthorized access to data, hacking of personal information that could risk the assets of an individual, and monitoring and manipulation of data.

6.5 Cyber Security Approaches and Measures The usage of advanced technologies like Information technology, robotics, artificial intelligence, IoT, and augmented reality is recognized in Society 5.0. These technologies are extensively implemented in people’s lives, healthcare, and other fields of activity. The aspects of society that are being affected by these drastic changes in digital technology are the privacy of information, employment, public administration, and industrial structure. Therefore, Society 5.0 has to be implemented while keeping these various dimensions in mind [25]. There is a need to make people more aware of the importance of privacy and security and the role it plays in the technological development of modern devices. Organizations also need to keep coming up with policies in order to prioritize the security of their products or services. They also need to make sure to impose upgradation of their security systems for safe and successful growth [26]. Investment in a proactive approach rather than a reactive one could be beneficial. At an organizational level, this is known as a preventive approach since the enterprise would not be responding to threats and attacks after the damage has already been done. Using simple, well-known security solutions could help reduce the chances of breaches. This could involve the usage of antivirus, anti-malware, and even application protection.

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Due to the vulnerability and exposure to a huge amount of heterogeneous data, we need to ensure that data organization and storage are supported with data security. There is a need to introduce protocols that would allow processing, interpretation, and management of data [22]. Therefore, it is imperative to refer to some case studies that tell us about the strategies and ideas that have been considered for improving security in Society 5.0. These case studies will help determine the extent of implementation that has occurred within organizations, as well as explain the challenges that need to be addressed while pointing out its benefits [27].

7 Case Study: Smart Contracts 7.1 Using Smart Contracts for Supply Chain Management This case study deals with the organizational requirements and the sustainability impacts related to smart contracts. There has been a focus on filling the research gap in the field of supply chains by exploring the relationship between sustainability and smart contracts. The structure of this paper involves three main contributions which are: Theoretical developments for SCM which stands for Supply Chain Management. A new approach to categorizing relationships between smart contracts and supply chain sustainability. This paper also supports future research propositions in order to achieve sustainable smart solutions through the deployment of smart contracts. The concept of sustainability dates back to the eighteenth century. Since then there have been multiple definitions of sustainability depending on the purpose it serves. Out of these, the concept of the triple bottom line (TBL) involved different literature interpretations. The United Nations have defined 17 social development goals also known as SDG’S which addresses areas like the planet and humanity in order for companies to redesign their strategies to contribute toward the concept of sustainability. The business models of these companies are made to focus on the supply chains. Supply chains deal with the design, sales, production, delivery, storage, and other aspects of a product. It is important to improve its sustainability. There have been examples where a well-structured supply chain can benefit a company when it values creations. Therefore, the concept of SSCM has been introduced which is nothing but sustainable SCM. It intends to reduce negative effects on the environment while maintaining economic performance throughout. A plausible approach to increase transparency and availability of data in SSCM could be through the use of smart contracts. The term “Smart Contracts” was coined by Szabo, who said that smart contracts are nothing but a combination of both technologies as well as the promises that are communicated to provide a basis for their implementation.

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Smart contracts serve the purpose of a contract by combining it with new technologies like IoT and blockchain. We need to ensure that the main elements of a contract remain unchanged. These include the parties that are involved, the concerning subject of the agreement, and a set of clauses for regulation. There have been many versions of the definition of smart contracts. The main approaches to these definitions involve the definition of terms like “smart legal contracts” and “smart contract code”. These provide a very minimal definition that defines the connection with application-specific implementations. This paper predicts that the influence of smart contracts will have a potentially drastic impact on the way business models are controlled and monitored. The use of smart contracts enables the reduction of human error, the throughput time as well as the cost of production. The use of technological development in fields like IoT and blockchain is taken as a great advantage when it comes to diffusing the implementation of smart contracts in various kinds of business processes. This paper also intends to make contributions to the state of the field. It does so by framing many possible directions for further research in the future. There has been a mention of the limitations of this field due to its novelty and underlying research approach. There has also been an identification of the effects of these technologies on the sustainability of the supply chain. This has been achieved through a semistructures assessment framework. It also proposes a conceptual framework in order to define the relationship between sustainability, supply chain maturity, and the technologies involved. This paper also aims to offer future propositions regarding this research field. This work introduces a new paradigm that combines the digital and physical world and optimizes its implementation in supply chains [28].

7.2 Smart Contracts and Blockchain Technologies Used for IoT Interactions There have been a lot of applications of IoT which give rise to newer areas of technological development. There is a disadvantage of this use case where other than having limited resources, the devices could be exposed to some tampering due to lack of connectivity. This paper works with the design, development, and evaluation of solutions involving smart contracts for the secure usage of smart devices. The concepts used in this approach are Thing authentication, access control, and decentralized payments. The implementation of the solution in this paper is based on some of the already existing technologies such as the Ethereum smart contracts. IoT involves the usage of smart devices and approaches which enable interactions between people and devices and among devices to allow the existence of cyberphysical applications which is crucial in the betterment of the livelihood of the people in various aspects. The devices used in IoT are referred to as Things. This paper focuses on technologies like blockchain and smart contracts for the given approach. Blockchain is known as an append-only ledger of transactions. These transactions

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are evaluated for validation using network nodes. Whereas smart contracts are an application that comes under its umbrella. Smart contracts are used by users in order to interact with smart contracts. This is done through transactions using the address in the blockchain. The solution mentioned in this paper takes influence from existing work on technologies wherein a Thing and a user with authorization can have a shared secret key which is used for the security of the information that is exchanged. Access control provider known as ACP is used to maintain the user management system and access control for each key that is shared with a Thing. The exchange of information between a user and a Thing is done after a token has been sent to the user by the Thing. The ACPs are also responsible to authenticate users and maintain a secure transfer of data. This work uses the same concept along with an additional feature of payments that are provided to the service providers. The system overview for this project can be best explained with an example of a smart coffee machine. In this use case, there is a smart coffee machine in a building that is used by the employees and they interact with it through wifi and phones. To order a cup of coffee an employee has to follow the following steps. An employee needs to request a cup of coffee and the machine sends a token. After which, the employee authenticates with the ACP and a session key is generated. Then the employee pays for the coffee. Here, all the interactions that happen, occur between the ACP, user, and the coffee operator which uses the technologies of smart contract and blockchain. This paper introduces a solution that allows users to interact with IoT devices. The concept that it relies on involves DLTs along with access control, Thing authentication, and secure payments. The main aim of this paper is to enable users to have a verified relationship with a Thing thereby protecting them from malicious Things. This also prevents any disputes since all important information is recorded and noted down in the blockchain. This paper uses Ethereum based implementation which only proves that it can be applied in real life with the use of existing technologies. This prototype design involves the implementation of two pairs of decoupled keys, each for the operations of blockchain and for any sort of encryption. These decoupled keys give a much broader scope for this field enabling the avoidance of tracking and a lot more. Here, the smart contracts are responsibly replicated and executed to provide redundancy to the system. And blockchain adds a protection layer to the device in order to protect it from any attacks. Future work in the field is expected to recognize the potential and analyze the blockchain feature of this innovative technology. IoT applications enable interactions with the real world which serves as one of its unique properties. Blockchains technologies are very crucial in general, the interactions and interconnections between the physical world and cyber world lead to some challenges which might be difficult to overcome [29].

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8 Case Study: Solid Waste Management System 8.1 Solid Waste Management Concept in Society 5.0 and Studying the Barriers to Sustainable Waste Management Society 5.0 aims toward a super-smart society that incorporates various technologies including IoT, robotics, AI, and big data into the context of different aspects of society. Keeping in mind the perspectives of society 5.0, we need to ensure that the concept of sustainability is kept intact. The goals of sustainable development help the empowerment of the biosphere that we live in. Hence, there is a need to introduce sustainable solid waste management (SSWM). It is an integral part of human life to have a great influence on different fields like production, environment, society, and consumption. This study identifies the barriers and challenges when it comes to a sustainable solid waste management framework. It does so by considering the examples of the perspectives in Taiwan. This part also defines the interrelationships between these barriers and the proposed hierarchical structure of the same. It has been observed that Taiwan faces a lot of obstacles when it comes to environment-related challenges which endanger human life. The reason is that Taiwan has a large population density contributing to a large amount of waste generation. There have been many different methods of introducing SSWM which have proven to be very effective in ensuring proper management and disposal of waste generated. A case study is embodied by the SSWM in Taiwan. It is most suitable to properly recognize and evaluate the implementation of SSWM systems, especially in emerging countries. Taiwan as a country has undergone rapid industrialization and urbanization in the last few decades making it suitable for the case study. The diversity in various communities, immobility and unreliability, mass production, failures of data management, and insufficiency in technological applications have proven to have a negative impact on the implementation of SSWM. There are also some factors that could possibly have some influence on sustainable performance. These include uncertainty in local and global communities, technology interaction, the architecture of the cities, and problems related to policy implementation and regulation. This paper also provides a detailed description of the risks involved with improper waste disposal by giving various sources which have said the same. Some of the problems highlighted are severe health issues which in turn lead to the spreading of infectious diseases among people, environmental impacts due to dumping of waste in open fields, and a lot more. These barriers and challenges must be dealt with using new technological innovations of Society 5.0. It is very important to identify these barriers in order to take the first step toward finding a solution that can benefit the development of the country. This paper also identifies the barriers related to the Triple bottom line (TBL) along with the challenges that come with the implementation of SSWM systems.

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The main objectives that define this study are divided into three categories are identification of a set of barriers related to SSWM which also extends its understanding in Society 5.0, examining the relationship between the SSWM hierarchical structure and these uncertainties, thereby showing the dependencies within the proposed barriers, identify the barriers related to Taiwan’s SSWM practices. This study also states that it contributes to both theory and practical applications. The remainder of this study includes a literature review on the SSWM systems and discussions related to Society 5.0, proposed method, measures or barriers that have been selected, case backgrounds for these methodologies, results, and implications related to this study, and finally, a conclusion which also provides scope for future research along with pointing out the limitations of this study. SSWM is a very important concept when it comes to the management of cities and incorporating innovative technologies to fulfill societal demand along with ensuring proper waste management resolution. It plays a very essential role in the development of a city. It needs to be considered at every stage of development including the planning, design, organization, and construction of the city. The proposed method that has been introduced in this study is a hybrid method. It involves three different methodologies that have been integrated in order to produce an effective solution. These methods are the fuzzy Delphi method, a fuzzy decision-making trial and evaluation laboratory (DEMATEL), and the Choquet integral. This hybrid method aims to identify and develop a hierarchical structure. The Delphi method is used for the validation of the structural barriers that have been taken from the literature. Exploring the interrelationships between the various attributes of the SSWM that have been formed by the perceptions of humans is done through the fuzzy DEMATEL method. Choquet integral is used for the evaluation of the SSWM perspectives and the eradication of the subjective exertions of the experts. This method is also responsible for the prioritization of data along with ensuring its interdependency for optimal performance. Since there are a variety of dimensions and issues concerning decision-related problems and sustainability, there is a need for a hierarchical approach. Along with an evaluation of the TBL perspectives, this study also focuses on the technical and non-technical challenges of the SSWM in Society 5.0. The proposed hierarchical structure defining the barriers of SSWM is divided into perspectives like social, environmental, economic, and technical, which are further divided into different aspects like community uncertainty, solid waste features, cost, city architecture and infrastructure, and human resources problems. SSWM is a very important part of society as it could have some long-term effects on the environment although it could make it difficult to implement the technologies due to the lack of capabilities of the government and even the current economic conditions of the country. In this study 45 barriers which are based on eight aspects of attributes have been proposed. The hybrid method has been implemented as a combination of other known methods. There are 20 barriers belonging to seven aspects that have been accepted. And they have been incorporated into a valid SSWM hierarchical structure. The results of using this model show that aspects like uncertainty in community, architecture, and infrastructure of cities, policy and regulation issues,

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and technology interaction have a significant influence on the SSWM system in Society 5.0. The most important barriers are recognized in this study that needs to be addressed in order to improve the SSWM and achieve the perspectives of Society 5.0. These barriers belong to the categories of various sectors and communities including lack of mobility and reliability, failure in management and security of data, mass production and computation of resources, and even lack of AI application. The social and technical perspectives are declared as the most prioritized among the others when it comes to evaluating the performance of SSWM. Therefore, SSWM systems must be considered at par with the social and technical challenges which would eventually benefit and better the sustainability of the environment. This study also has certain limitations. This includes limitations in the hierarchical structure due to the usage of evaluations from experts from the literature review and due to the lack of literature for this perspective. This can be resolved through future studies which could involve the usage of practical implementation by only focusing on certain important barriers. The expert evaluation and understanding could lead to certain judgments that could have negative impacts on the results of the hybrid model. This bias could be eliminated by taking up a larger sample size. Lastly, this study also only deals with the SSWM in Taiwan. This means that it is not generalized at all thereby limiting its usability elsewhere. Therefore, this study needs to be extended to other developing countries so that they can be examined well enough to enrich the literature [30].

9 Case Study: Application of Artificial Intelligence in Different Scenarios of Society 5.0 Environment This case study deals with the application of artificial intelligence within the environment of Society 5.0. As highlighted earlier, Society 5.0 is developed on the basis of Industry 4.0, in which machines are built to reduce the workload of people, increase efficiency, and cut down the time of production of goods. The leading technologies in current times are Artificial intelligence with the help of which, further development can be made in Society 5.0. Using Artificial intelligence, the voluminous arrays of data that are collected can be converted into useful values. Discussed below are the possible applications of artificial intelligence in multiple spheres of Society 5.0 such as healthcare, manufacturing, and transportation [31].

9.1 Artificial Intelligence in Transportation Artificial intelligence can be used to obtain a well-regulated traffic management system. The Artificial intelligence methods that are used in the traffic system include

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genetic algorithms, Fuzzy Logic Models, Artificial neural networks, Simulated Annealing, and Ant Colony Optimizers [31]. These models provide excellent solutions and result in an efficient traffic management system. The benefits of this include reducing traffic congestion, a significant enhancement in the productivity of the transport system as well as the economy, and an increase in the reliability of passenger travel. At the moment, there exist several artificial intelligence applications in road transport. A recent notable innovation in automation with the help of artificial intelligence is self-driving vehicles such as personal cars, trucks, and public transportation. There are several other important artificial intelligence applications such as Intelligent Transport Systems (ITS), vehicle management system advancement, mobility as a service (MaaS), and much more.

9.2 Artificial Intelligence-Based Surveillance System for Railway Crossing Traffic Artificial intelligence can significantly improve the safety standards as well as the efficiency of Intelligent Transportation Systems. The case study [32] throws light on the implementation of increasing the degree of security and safety using deep learning methods for railway crossings which are a specific application of ITS. The paper proposes a system termed Artificial Intelligence-based Surveillance System for Railway Crossing Traffic (AISS4RCT). AISS4RCT takes the following inputs into consideration while performing detection and classification on images: the presence of vehicles, the presence of any human, tracking the trajectory of the vehicle, railway warnings, the light signaling system being used, and any railway barriers present at the railway crossings. These inputs are captured with the help of cameras that are positioned at appropriate angles so as to have a view of the entire railway crossing. The system is capable of autonomously detecting high-risk or hazardous cases at that railway crossing in real-time by utilizing GPU accelerated image processing approaches along with deep neural networks. The data that is collected from the cameras situated at the railway crossing is sent to a central server for it to be processed further and for it to be used as a notification system to trigger alerts for specific parties involved such as any emergency services, the railway operators, or even the police. The system uses best practices to ensure security and protect the privacy of the drivers or pedestrians. The communication and transmission of data are also safe and personal data is protected. The detection methods employed delivered an average recall of 89% obtained by using the YOLO tiny model method. These results demonstrate the abilities of the system to efficiently evaluate the various possible situations that might occur at railway crossings. There is further scope for expanding the system to include a notification subsystem that alerts nearby vehicles and trains that are headed toward the railway crossing when a hazardous situation has been detected.

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9.3 Artificial Intelligence in Healthcare There are innumerable applications of artificial intelligence in healthcare. Artificial intelligence can be used for diagnosing diseases in real-time and can also be further used to prevent or decrease the rate of the progression of autoimmune diseases. Artificial intelligence is even used to create robots that can help with specific tasks in surgery. Medical organizations have voluminous amounts of data including patient records and their respective medical history. Artificial intelligence can be used to manage these vast arrays of data [31]. Managing this data can be challenging as the data could be disorganized. Artificial intelligence is used to organize the data and analyze it to find patterns that can lead to improvements in medicine.

9.4 Optimization of Medical Big Data Using Deep Learning Deep learning methods are widely used in medicine in areas such as image processing and classification, speech recognition, and much more. There are a lot of restrictions when processing voluminous data in the current typical data processing analysis methods. Big data is gaining more significance as many medical organizations have large amounts of information collected [33]. Big Data has now reached all areas of healthcare. Researchers look to combine clinical databases and administrative databases to create predictive models.

9.5 Electronic Health Record The electronic health record (EHR) is a repository that catalogs electronic information about every drug, patient admission, symptoms, and diagnosis. It is one of the best clinical Big Data sources available. It is used to make numerous discoveries via clinical data mining and has significantly helped increase our understanding of the underlying genetic reasons for various diseases. Patterns of deleterious genetic variants were determined solely from the statistical analysis of phenotype comorbidities in EHR data, without any further genome sequencing of those patients [34]. Innovative projects have gained momentum with the availability of such data in the health services research domain. If Big Data technology is combined with mobile applications it can even enable remote health care services which will facilitate medical professionals to read and store the patient’s images and signals remotely. Furthermore, it can also enable them to retrieve, deliver and manage medical files for consultation and diagnosis over mobile phones.

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Electronic health care data must be of sufficient quality in order to facilitate the numerous diverse use cases. However, many electronic data sources are of suboptimal quality and not fit for particular use cases. To define the better quality of electronic data, Dixon et al. [35] proposed a new framework for data stewardship. The framework uses a systems approach to data quality with an emphasis on health outcomes.

10 Conclusion This project aims to highlight the modernization and developments made in various fields of society according to Society 5.0. Society 5.0 and its importance has been deeply focused on, as it brings together a developmental concept leading technology in a new direction. This paper discusses a detail of the evolution of society from Society 1.0 to Society 5.0, defines what entails in this new developmental plan, and covers the various areas that could be affected by such an implementation. Furthermore, there has been a mention of the impacts of Society 5.0 on the economic growth of a country as a whole and the issues related to security. Along with this, it also addresses the possible measures that could be taken in order to overcome the foreseeable issues. Also, this paper returns a discussion of realistic applications of innovative technologies that have already been made. Lastly, the case studies discussed, give an insight into the various applications and methods used for the same. This paper intends to promote further studies on the futuristic human-centered society to achieve the objectives enlisted under Society 5.0.

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Chapter 3

Introduction to Smart Big Data Analytics and Smart Real-Time Analytics in Society 5.0 G. M. Siddesh and Aishwarya S. Kulkarni

Abstract The structure of the Society 5.0 involves a methodology that would be more human-focused. The critical elements of the program incorporate adjusting the financial progression of the country with the global methodologies resolving the social issues with the advancement of a framework inside which the internet and actual space would be coordinated. This chapter discusses the use of smart Big Data analytics, Artificial Intelligence, and smart real-time analytics that will impact and resolve numerous challenges faced in sectors such as Healthcare, Education, Agriculture, and Smart city in Society 5.0. In this book chapter, the author has explained firstly about digitizing, joining, and adequately utilizing smart Big Data analytics, medical care associations going from single-doctor workplaces to online care and immediate self-care will improve the wellness of an individual. Secondly, During online examination, the activities such as question navigation, Internet Protocol (IP), answer responses, exam access login, and logout tracking can be noted and all these analyses will help to improve the overall quality of online examination. Consequently, explained the Artificial Intelligence and smart Big Data analytics for perspectives like meteorological information, crop-development information, economic situations, food patterns, and needs which will help in quality of agriculture produce and market rate of crop. Last but not the least brief explanation about Sensors, AI, and robots that will be utilized to assess and keep up with streets, extensions, passages, dams, and many more are used to minimize the accidents and maintain and improve the quality is mentioned. Keywords Artificial intelligence · Machine learning · Smart big data analytics · Smart real-time analytics · IoT · Agriculture · Healthcare · Education · Smart city · Society 5.0

G. M. Siddesh (B) · A. S. Kulkarni Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_3

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1 Overview of Smart Big Data Analytics and Smart Real-Time Analytics in Society 5.0 The Society 5.0 mega-project, which was started by the Japanese experts is a piece of the mechanical economy worldwide fundamental articulations change to set up the information economy and to communicate mankind to the civilization advancement following stage. The populace digital connection new configuration dependent on the industry 4.0 effective innovations needs to make some exceptionally created frameworks giving all the general public members the ideal arrangements where the man-made consciousness is locked in. Society 5.0 assesses human requirements through smart real-time analytics that is done by AI to execute the best computerized functional cycles, considering issues and upgrades accommodatingly, with a construction that can gather and get familiar with individual’s propensities in the advanced setting. Society 5.0 broadens straightforwardness and dynamic support in friendly issues by giving equivalent freedoms to all individuals, and by incorporating inventive innovations and society. It is at the core of open (government) information balance and free admittance to information as an asset for their change into new administrations and answers for the two residents, analysts, researchers, columnists, and business organizations. This turned out to be significantly more significant when most nations were influenced by pandemics when information permit public offices to foresee and caution residents about expected dangers. The reception of Smart real-time analytics by organizations offers them proficiency, exactness, and speed in dynamic. Also, a savvy investigation of information energizes the development of new business openings, decreasing the human part that may contrarily influence dynamics. Smart Real-time analytics applications permit organizations to handle information streams, get bits of knowledge, and follow up on information focuses promptly or before long the information enters their framework. Smart Real-time analytics have the ability to resolve issues and help dynamics in practically no time. They handle a lot of information (Big data) with high speed and low reaction times. At the point when an issue happens, organizations need to act rapidly. Smart Realtime analytics empowers prompt activity, permitting organizations to be proactive by taking advantage of lucky breaks or forestalling issues before they occur. Smart Real-time analytics can be persistent, or on-request. On-request conveys results when mentioned by the client. Persistent analytics can be customized to react consequently to specific occasions just as refreshing clients as these occasions occur. Benefits of using smart real-time data analytics in Society 5.0 are as mentioned below. i.

Creating processes in addition to Agile: As data is caught and overseen in smart real-time analytics, smart big data analytics will ultimately get rid of the requirement for broad announcing. Data the board programs present the data in a consolidated and prepared to-break down structure, so representatives who recently had this obligation of deciphering sometime later data and ordering

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reports will have additional time accessible for different errands, prompting an increment in usefulness. Rapidly refinement of operational processes: A discontent in a creative interaction can cause major issues with clients, providers, and partners. Monitoring the creation interaction in smart real-time permits associations to distinguish potential disappointments and triumphs, without letting the cycle with an issue progress forward longer than needed. Instantaneous perceptions: The clearest advantage of smart real-time data analytics is that there’s no pausing. By coordinating with data assortment frameworks like Enterprise Resource Planning (ERP) or Customer Experience Management (CEMs) tremendous measures of streaming data can be caught at the source, prepared, and envisioned right away. At the point when you contrast that with the manual preparation of data, which could require days or weeks to show significant outcomes, it is not difficult to perceive any reason why associations cannot upgrade their tasks without it.

This chapter gives brief overview of importance of smart big data analytics and importance of Artificial Intelligence/Machine Learning in Society 5.0 and case studies based on Healthcare sector, agriculture sector, Education, and smart city are explained for better understanding of use of smart big data analytics and smart real-time analytics in Society 5.0.

2 Why Use of Smart Big Data Analytics Important in Society 5.0? Smart Big data analytics contributes connotations with outfitting their data and using it to distinguish novel freedoms. Thus, stimulates further intense business moves, added effective tasks, higher assistance, and additional elated clients. The fundamental point of smart big data analytics in the community eye should not completely be on the phenomenal bulk of data, yet rather on the cost that connotations can remove from it. Employing AI innovation, an arena of data analytics known as discerning investigation confirmations the worth in a ration of data. Basically, clairvoyant examination improvements from data to foresee the manner in which people will act later on. AI identifies designs in data sets to think about the likelihood of specific results. For instance, the prescient model uses all that thought about a person to decide the probability of them purchasing a particular item, getting a specific infection, being affected by a financial pattern, or any ideal result. In light of that understanding, associations can settle on more educated choices. With smart big data analytics, we center around important data and regularly more modest data sets that can be transformed into significant data and viable results to address client and business challenges. It is with regards to investigation and understanding of data so we can settle on our dynamic and business capacities datadriven by placing data with regards to reason and setting. Smart Big data is big data

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transformed into noteworthy data that is accessible in real-time for an assortment of business results, regardless of whether it is in modern applications, data-driven showcasing, or cycle enhancement. With smart data, we are really seeing approaches to eliminate the clamor of the sheer part of Volume similarly to quick data is about the component of Velocity. In a showcasing and client experience setting, for example, smart data is for the most part seen from a hyper-personalization measurement. Benefits of smart big data analytics are mentioned below. i.

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Fast and dynamic decision: With the speed of Hadoop and in-memory investigation, joined with the capacity to examine new wellsprings of information, organizations can break down data promptly – and settle on choices dependent on what they have realized. Cost decrease: Big Data Analytics, for example, Hadoop, and cloud-based investigation bring tremendous expense benefits with regards to putting away a lot of information—in addition, they can recognize more productive methods of working together. New items and administrations: With the capacity to check client needs and fulfillment through investigation comes the ability to give clients what they need. Davenport brings up that with big data investigation, more organizations are making new items to address clients’ issues.

3 Importance of Artificial Intelligence or Machine Learning Models in Society 5.0 Today, the measure of data that is produced, by the two people and machines, far outperforms people’s capacity to assimilate, decipher, and settle on complex choices dependent on that data. Artificial Intelligence or Machine Learning structures the reason for all PC learning and is the fate of all impenetrable dynamics. AI can significantly work on the efficiencies of our work environments and can expand the work people can do. At the point when AI takes over tedious or hazardous errands, it opens up the human labor force to tackle job they are better prepared for undertakings that include inventiveness and compassion among others. In case individuals are tackling job that is more captivating for them, it could expand bliss and occupation fulfillment. With better observing and symptomatic capacities, computerized reasoning can drastically impact medical services. By working on the tasks of medical services offices and clinical associations, AI can lessen working expenses and set aside cash. Dissecting huge scope of online media and perusing conduct, organizations can make a more complete profile of clients and stream them into slender fragments of inclinations, likes, and abhorrence’s. With this degree of particularity and knowledge, organizations can settle on more educated advertising choices to advance their item or administration to those bound to change over.

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4 Case Studies This section of case study gives brief description of case studies associated with use of smart Big Data analytics, AI, and smart real-time data analytics in Healthcare sector, Education sector, Education sector, and Smart city.

4.1 Outcome of AI and Smart Big Data Analytics in Healthcare Sector in Society 5.0 The medical benefits industry by and large has delivered a ton of data, controlled by record upkeep, consistence, and managerial essentials, and patient thought. While most data are taken care of in printed variant construction, the most recent thing is toward quick digitization of these a great deal of data. Driven by obligatory essentials and the likelihood to chip away at the idea of clinical benefits movement meanwhile diminishing the costs, these tremendous measures of data (known as ‘huge data’) hold the assurance of supporting a wide extent of medical and medical benefits limits, including among others medical decision help, disease surveillance, and people wellbeing the executives. Smart Big Data analytics in Society 5.0 wellbeing administrations suggests electronic prosperity instructive assortments so huge and complex that they are problematic to manage with standard programming and furthermore gear; nor would they have the option to be successfully managed with traditional or typical data the leader’s instruments and procedures. Smart Big data analytics in clinical benefits is overwhelming a consequence of its volume just as taking into account the assortment of data types and the speed at which it ought to be supervised. The aggregate of data related to patient clinical consideration and thriving make up “tremendous data” in the clinical consideration industry. Presumably that we are living in the period of smart Big Data analytics, where we are seeing the extension of smart medical services gadgets [1]. The principal obstruction for the Healthcare stage scientists in picking the right Big Data instrument to handle unstructured data. Thusly, the flow space of examination is moved from enormous capacity to proficiently breaking down the data. This paper plans to current situation with the-workmanship smart Big Data analytics apparatuses and introduced the Intelligent Medical Platform (IMP) as a contextual investigation in managing the multimodal data. In this period of big data, high volumes of a wide assortment of important data can be effortlessly created or gathered at a high speed. All things considered, big data investigation is popular in different real-life applications and administrations (e.g., medical care) as it assists with finding helpful data and important information that are implanted in the big data. The subsequent data and information are ordinarily in printed or plain structures. Considering that “words generally cannot do a picture justice”, perception and visual analytics make a difference [2]. The author

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presents a framework for envisioning smart data—just as their connected data and information—from wellbeing data, which thusly upholds medical care analytics. The above Fig. 1 represents overall impact of use of smart real-time data analytics in Society 5.0. Medication exploration and revelation is one of the later applications for AI in medical care. By directing the latest advances in AI to streamline the drug revelation and prescription repurposing measures there is the likelihood to by and large cut both a chance to publicize new meds and their costs. Man-made brainpower allows those in planning to go through naturalistic diversions to such an extent that essential PC-driven estimations cannot. The methodology of normal talk and the limit of an AI PC to draw immediately on a tremendous data set of circumstances, infers the response to questions, decisions, or urging from an understudy can challenge to such an extent that a human cannot. Besides, the readiness program can acquire from past responses from the understudy, inferring that the troubles can be continually changed as per meet their adjusting needs. Moreover, getting ready ought to be conceivable wherever; with the power of AI embedded on a wireless, quick outfit to speed gatherings, after a problematic case in a middle or while traveling, will be possible. The above Fig. 2 explains the role of Big data, smart real-time data analytics, and Artificial Intelligence and Machine learning in Healthcare in Society 5.0. Patient data which is huge information in medical services alludes to electronic wellbeing informational collections so enormous and complex that they are troublesome (or difficult) to make do with customary programming and additionally equipment; nor would they be able to be effectively made do with conventional or normal information the executive’s instruments and strategies. Large information in medical services is overpowering a result of its volume as well as in view of the variety of information types and the speed at which it should be overseen. The entirety of information

Fig. 1 Overall impact of use of data analytics

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Fig. 2 Role of AI and smart Big data analytics in healthcare

identified with patient medical care and prosperity make up “huge information” in the medical care industry. For the Smart Big Data analytics in Society 5.0, there is, among this colossal aggregate and group of data, opportunity [3]. By discovering affiliations and getting models and examples inside the data, colossal data assessment might potentially additionally foster consideration, save lives, and lower costs. Thusly, gigantic data examination applications in clinical benefits exploit the impact in data to remove pieces of information for making better-taught decisions, and as an investigation characterization are insinuated as, nothing startling here, tremendous data assessment in clinical consideration. Smart Big Data in clinical benefits in Society 5.0 can arise from inside (e.g., electronic prosperity records, clinical decision genuinely steady organizations, CPOE, etc.) and outside sources (government sources, research focuses, pharmacies, protection offices, and HMOs, etc.) habitually in various designs (level archives,.csv, social tables, ASCII/text, etc.) and living at different regions (geographic similarly as in different clinical benefits providers’ objections) in different legacy and various applications (trade getting ready applications, databases, etc.). Assets and data types include: Biometric data: fingerprints, innate characteristics, handwriting, retinal scopes, x-shaft, and other clinical pictures, circulatory strain, heartbeat and heartbeat oximetry readings, and other near sorts of data; Humancreated data: unstructured and semi-coordinated data like EMRs, specialists note, email, and paper files; Big trade data: clinical benefits claims and other charging records logically available in semi-coordinated and unstructured designs. Additionally, AI grows the limit with regards to clinical benefits specialists to all the more probable grasp the regular models and prerequisites of people they care for, and with that understanding, they can give better analysis, course, and support for staying strong. Past separating prosperity records to help providers with perceiving continually debilitated individuals who may be at risk for an adversarial scene, AI

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can help clinicians with embracing a broader methodology for ailment the board, better coordinate consideration plans, and help patients with bettering direct and follow their somewhat long treatment programs. The data that is collected from the patients are stored in the database in cloud which can be further used for data analysis. Since the data collected is not in the format which can be used to build the AI model that is raw data is present. So, preprocessing of data takes place which is the important stage that need to be performed in careful way such that the model built should not contain noisy data. After preprocessing of data AI algorithms such as SVM, CNN, RNN, and K- Nearest Neighbor, Artificial Neural Network is used to build the predictive model to diagnose analysis to Doctors or Patients, respectively. By digitizing, joining, and enough using smart Big Data analytics, clinical consideration affiliations go from single-specialist working environments and multiprovider social events to colossal crisis facility associations and dependable thought affiliations stay to recognize basic benefits. Potential benefits join recognizing afflictions at earlier stages when they can be dealt with even more successfully and suitably; regulating unequivocal individual and people prosperity and separating clinical consideration distortion even more quickly and capably. Different requests can be tended to with gigantic smart Big Data analytics. Certain new developments or results may be expected and moreover surveyed reliant upon immense proportions of recorded data, similar to length of stay; patients who will pick elective operation; patients who likely will not benefit from an operation; troubles; patients in peril for surprising issues; patients in harm’s way or other crisis facility got affliction; sickness/disease development; patients in peril for progress in contamination states; causal parts of infirmity/ailment development and possible comorbid conditions. Smart Big Data analytics in Society 5.0 might perhaps change the way wherein clinical benefits providers use refined advancements to get information from their clinical and distinctive data vaults and make taught decisions. Later on, we’ll see the fast, all-over execution and use of tremendous data examination across the clinical consideration affiliation and the clinical benefits industry. In view of that, the couple of challenges included above, ought to be tended to. As enormous data assessment ends up being more norm, issues like guaranteeing insurance, guarding security, setting up rules and organization, and continually dealing with the instruments and advances will acquire thought. Gigantic data assessment and applications in clinical benefits are at a beginning stage of progress, but quick advances in stages and gadgets can accelerate their creating cycle.

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4.2 Enactment of Smart Big Data Analytics for Education Sector in Society 5.0 Existing advanced education is increasingly changing proportionally. Physical meeting of students, teachers, and other concerned stakeholders is gradually proceeding to the room of internet or digital space similar to distance learning. The rapid progress in communication and information technology is making severe modifications to the education industry and society. The requirement to combine the virtual and real-world, improved necessity for restricted time resources, information, and the necessity to merge education and professional work will influence the education industry in-order to make upcoming generation operate in the range of shared resources, to be adaptive to transformation. Epoch of Society 5.0 is a need that is essential to be handled by the world of education to fabricate quality or quality that is able to participate [4]. Society 5.0 will be the society where it can resolve many challenges faced in the education & society and advance the innovation produced in the industrial 4.0, the technologies that were used such as robots, big data, Internet of Things, and several refined machines, in humanizing the technological advances that can enable human work, as several capabilities and expertized as “Smart Society” or “Intelligent Society”. An exhaustive instructive framework at various levels is required, and experts are needed to create and procure abilities identified with information the executives and handling [5]. For these reasons, the instructive Society 5.0 framework advances the plan of preparing measures that work with the advancement of abilities for function as well as for the utilization of culture, variation to ceaselessly evolving conditions, responsibility for ideas, and communication with our current circumstance and with others, alongside friendly and self-awareness. There is huge amount of structured and unstructured data such as participation of students in various activities, surveys, credits analysis, attendance of students and staff, subjects selected, and many more data are generated from different resources. Educational sectors are continuously producing acute data such as the data generated from the online test conducted which will be indeed a very treasured perception of the amount of time taken by each student to answer particular question and in whole and which questions are attempted first or last and the pattern of answer a question and many more data is collected to build the AI to better understand the student and analysis of behavior of every particular student. Cautious examination of whole of these data can benefit educationalists’ progress in personalized education strategies that work up the merits and moderate the drawbacks of respective students, considerably refining the efficiency of education. Additionally, Artificial Intelligence can comfort and cultivate such adaptive teaching curricula adapted to distinct student requirements. By this time, companies are working on creating intelligent instruction design and creating a firm ground for such technologies. Data Analysis and Artificial Intelligence can benefit these students accomplish a enhanced estimation of their benefits and skill. Data Analysis of their results, performance, and contribution during their university life and not just the

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final entrance examinations can enhance to suggest their incline and regulate courses more appropriate for students. This in turn enormously benefits students’ choice of the correct course. One of the significant causes of several students dropping out in the middle or starting of curricula they thrived so intense to get keen on is that they coexisted just not groomed for the academic strains they facade in college or education sector. Additional intention is curriculum courses incompetent for their skill, aptitude, and many more. Thus, Big Data analysis is accomplished to regulate the horizontal preparations of a student formerly registering in a university and investment of their period, currency, and determination interested in it can extremely enhance student’s results. It can on a par benefit students organized for the forthcoming curriculum. Education sector is one of the extremely subtle subjects of the society that is striving with abundant glitches of its profess [6]. However, conveying value education and providing each student a quantity performing area is of the extreme significance if we are to eradicate the major of our tasks. The above facts are a noble sign of what way we can initiate influence Smart Big Data analytics and Artificial Intelligence to progress the lives of students and provide enhanced education and education sector to our forthcoming generations. Utilizing innovative analytics and numerous programming languages, applications that uphold E-learning can be created. Customized education, amplified education, game-based studying, and further digital education inventions can be applied to improvise the collaboration between teacher and student. Numerous features such as can be established which student information management, screen sharing, live feedback, video calling, offline learning, chatting, virtual assistance, and data analysis can improve the standard of learning. In Fig. 3 it gives brief explanation of overall impact of examination in Society 5.0. Facial detection, Face recognition, eye scanning, Voice detection, and Voice recognition benefit in organizing safe online examinations. With the support of Artificial

Fig. 3 Overall impact of examination in Society 5.0

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Learning and machine learning algorithms, detection of the student can be accomplished by determining the dimension of the jaw-line, brain shape, the distance among nose, mouth, ears, and additional facial estimations as per the information that has been provided to the system. Voice detection recognizes the decibel, wavelength, and pitch of the vocal sound which benefits in recognizing the student as per the information that has been supplied to the system. A safe registering procedure and the insurance of person existence during the complete assessment can be complete utilizing this. Onscreen marking of answers utilizes Real-time Programming that concurrently assesses the responses provided by the students and exhibits the outcome in no instant. Additional advantage of this AI and machine learning technique is that they also be taught, similar to humans. While there are a few faults done by system and if they are examined and modified by the educators, the system will not replicate the similar fault once more. This can benefit to undertake the lack of evaluators in institutes and can also protect an allocation of time and energy. Assessments organized on Google forms and additional such stages have an in-built that can produce the marks in no interval. As a significant situation in schooling, distance instruction is another subject looked at by the training local area under the new circumstance [7]. Taking into account the issue of “islandization” during the time spent philosophical and political schooling in distance instruction dependent on smart big data analytics innovation, a customized learning administration structure for distance training understudies’ philosophical and political courses, including data the executives, data examination layer and administration arrangement. Right away, a lot of far-off instruction course philosophical and political data is gathered through the organization, database, and different portable stages [7]. After the data is pre-processed, information diagrams and data mining procedures are utilized to mine the philosophical and political components of the course and foster customized philosophical and political learning program. Distance schooling educational program belief system and governmental issues are upheld to show understudies as per their aptitudes, and spotlight on understudies’ individualized philosophy and legislative issues learning, then, at that point further develop the showing impact of distance training belief system and governmental issues illustrations. Artificial Intelligence and Machine learning examine and trace the achievements of students in examinations and online teaching by giving educators a complete examination of a student’s performance and the performance of the whole group (batch). This benefits the teachers in devising methods and tactics to which the students are of utmost alert. This creates learning more naive for the students and it also decreases the load on teachers. Logs include the activities such as question navigation, Internet Protocol (IP), answer responses, exam access login, and logout tracking that can be noted and deposited in the essential position [8]. This guarantees security by avoiding tracking reviewing, audit trailing, and deceptions & hacking. Data analytics is benefited to obtain, gather, classify, and track the information that has been approved by the teacher in the exact location present for the teacher.

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Data encryption is the technique of safeguarding the information by avoiding the information from getting hacked. This is made by the encryption (adding of binary codes) to the present information which can be decrypted merely by the recipient. By the procedure of information encryption, exams can be accompanied in a protected method by protecting the exam data and question bank. The whole interaction among the specific conducting the exams and the server can be safeguarded to the greatest.

4.3 Functioning of Smart Real-Time Data Analytics and AL for Agriculture Sector in Society 5.0 In Society 5.0 another worth can be created through utilization of Artificial Intelligence and Smart Big Data analytics for perspectives like meteorological information, crop-development information, economic situations, food patterns, and needs. By accomplishing brilliant horticulture, an expanded chance for business and high efficiency is conceivable through arrangements like robot farm haulers and robotizing the coordination of harvest information through rambles. Different applications incorporate activity and computerizing of the executives of water and further develop its capacity dependent on climate gauging and groundwater information. This will prompt: Formulating an arrangement for farming by recognizing crop yields customized to address issues and further developing marketable strategies alongside climate conjectures, sharing encounters and information, and widening the client base. Making farming items needed by shoppers accessible to them when wanted Delivery of agrarian items to buyers when required through self-driving vehicles. Besides, for society in general, these arrangements can assist with expanding food creation, balance out supply, settle work deficiencies in agrarian regions, diminish food squander, and animate utilization. Accuracy Agriculture is the vital phrasing in agribusiness Engineering. Accuracy agribusiness can utilize inheritance data of horticulture to improve the cultivating as far as amount and quality. To improve the creation of the agribusiness, advancements, for example, big data analytics alongside data mining apparatus can utilize the heritage agrarian data to make the future expectation. This expectation can assist with upgrading the Agroeconomics. Big Data is changing the extent of the Indian farming from conventional to computerized. Big data certainly assists with settling the food security issues which the world will look at later on [9]. Big data alongside the data mining calculations might be the arrangement supplier for the worldwide issue sooner rather than later. Analytics is key achievement factor to make esteem out of these data. This paper momentarily depicts distinction between the genuine upsides of Jowar with the assessed esteem utilizing Multiple Linear. In future it very well may be accomplished for additional harvests utilizing data mining strategies like choice tree classifier with its calculation Id3, C4.5, and CART. Smart Big data analytics besides ICT in agribusiness are advancing innovations into a promising field for giving understanding from extremely enormous data

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sets and further developing usefulness and lessening venture costs. Smart Big data analytics and ICT can possibly utilize novel advancements and stages to create, gather, measure, and envision enormous data for future expectations and decisions [10]. In the accuracy of agribusiness, remote detecting gadgets assume a crucial part in data assortment and smart real-time analytics choice help. The outcomes conjecture utilizing a relapse model and big data handled by Map diminish of this review shows a significant capability of data combination in field of harvest and water the board for applications like accuracy horticulture. According to these outcomes, model foresees the temperature and precipitation in the area of contextual analysis. It proposes different choices to ranchers for choosing the harvest example and water the executives later on. It is answer for the yield the executives and calamity the board to build the increase of food creation. Smart Big data analytics in farming applications give another knowledge to give advance climate choices, further develop yield efficiency, and keep away from pointless expenses identified with collecting, and utilization of pesticides and composts [10]. Rattle off the various wellsprings of big data in accuracy horticulture utilizing ICT parts and kinds of organized and unstructured data. Additionally examined big data in accuracy farming, an ICT situation for agrarian big data, stage, its future applications, and difficulties in accuracy horticulture. Moreover, for society 5.0 overall, these arrangements can assist with expanding food creation and balance out supply, tackle the work lack issue in farming areas, decrease food squander, and invigorate utilization. Geospatial information assumes a vital part in numerous spaces and applications. Also, the utilization of spatial information can be beneficial in the field of horticulture. For example, advances like far-off detecting, GPS, and gadgets that utilize high goal geospatial information for making experiences with cutting edge examination calculations. Subsequently, here we examine a flash-based framework that can gather, learn, train, approve and imagine appropriated geospatial information. Figure 4 explains the importance and usage of AI, ML, and Data analytics which improve the state of farmers and agriculture stakeholders and impact the Society 5.0 stakeholders. The data is collected from IoT sensors such as soil type, pH, soil macro, micronutrients data, and environmental data such as temperature, rainfall, moisture humidity, and other data such as crop yield, available markets to sell the crop and many more are collected and stored in the database or cloud. All these agriculture data can be used for further processing to build the cognitive, precision, and analytical model. Using the data collected needs to build the model but since the data is in raw state it needs to be pre-processed. So that there is no noisy or null data present. After data preprocessing the data obtained will be the format that can be used to build the model. The models can be built using AI, and ML algorithms which perform data analytics on the data such that a useful information is classified and formulated, and analyzed into meaningful information which answers the query of users such as farmers or other stakeholders of agriculture sector.

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Fig. 4 Role of AI and smart data analytics in agriculture sector

Farming keen choice framework has a positive functional importance for directing rural creation, which can give logical premise to agribusiness. Smart Big data examination innovation can successfully work on the exhibition of savvy choice framework. The examination advancement of the farming smart choice framework is given [11]. The characterization of the farming choice framework is presented. The quick improvement of big data innovation gives another specialized way to the innovative work of agrarian savvy choice framework. It can adequately further develop the preparation pace and exactness of the agrarian astute choice framework and can give direction to farming creation. The utilization of smart big data examination innovation and man-made reasoning innovation in the agrarian clever choice framework is the following improvement bearing. Accuracy method of development is about adequacy and making exact data-driven decisions. It is also maybe the most unfathomable and feasible employment of IoT in cultivation. By using IoT sensors, farmers can assemble a colossal scope of estimations on each element of the field microclimate and climate: lighting, temperature, soil condition, tenacity, CO2 levels, and annoyance sicknesses. This data enables farmers to evaluate ideal proportions of water, composts, and pesticides that their yields need, diminish expenses, and raise better and better gathers [12]. The significant level sensors and imaging limits have given the farmers various better ways to deal with fabricated yields and diminishing harvest dam-age. Computerized planes which are used for sensible purposes actually have taken an abnormal

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flight. New sensors mounted on UAVs, with state-of-the-art cameras being the eyes of the client on the ground and optimal frameworks for study, data acquiring and assessment are perpetually developed and attempted. Really, the usage of raised investigations is not new to the cultivating scene. Satellites have been used for a very long time to analyze immense croplands and officer-administration anyway one more level of precision and flexibility has been gotten with these UAVs. To finish UAV flights, one does not need to depend upon the circumstance of the satellite or having the right environmental conditions and as UAV pictures are taken 400– 500 ft. beginning from the most punctual stage, they achieve better quality and give exactness. Farming is the key work field in India. Cultivating industry takes on less creative innovation contrasted with different enterprises. Data and Communication Technologies gives basic and practical procedures for ranchers to empower accuracy in horticulture. The author proposes a best-in-class model in agribusiness field which will direct the provincial ranchers to utilize Information and Communication advancements (ICT) in horticulture fields [13]. Big data analytics is utilized to further develop the harvest yield. It tends to be tweaked for accuracy horticulture to work on the nature of harvests which further develops the general creation rate. Big Data Analytics and ICT can be coordinated to improve the harvest yield. In future this can be executed in genuine field to screen the yields proficiently [13]. Big Data Analytics is utilized to foster a productive dynamic framework that goes about as a directing application for the limited scale ranchers. Classification based data determination approach helps the different kinds of clients to recover the data to their own advantage. Multivariate data investigation procedures can be utilized to examine the different boundaries influencing the harvest yield. Mass farming to stay aware of the expanding interest for food creation, progressed checking frameworks are needed to deal with a few difficulties like transitory items, food squander, capricious stockpile varieties, and tough sanitation and manageability necessities. The development of Internet of Things has given intends to gathering, preparing, and conveying data related to rural cycles. This has opened a few chances to support, further develop efficiency, and lessen squander in each progression in the food production network framework [14]. On the hand, this brought about a few new difficulties, for example, the security of the data, recording, and portrayal of data, giving real-time control, unwavering quality of the framework, and managing smart big data analytics and smart real-time data analytics. The farming enormous information is gathered from different sources not many being organizations, open web information, government official sites, and horticultural colleges. The information that is gotten is crude and boisterous in nature which should be cleaned and marked to accomplish consistency and train the model. The information which is pre-processed is presently put away in Cassandra, which is a profoundly accessible and a superior data set. When the information is put away effectively it is incorporated with Apache sparkle system which is an exceptionally adaptable bunch registering structure and

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has coordinated APIs in different dialects, for example, Java, python, and Scala likewise libraries, for example, GraphX, MLib, SparkSQL and so forth after the information is imported from Cassandra it is additionally joined with a sparkle augmentation named Geospark [15]. This is an open-source outsider augmentation that is utilized for investigating spatial information. The calculation that is utilized herein Multiple direct relapses and is finished by playing out various exchanges on the information. At long last, when the necessary model is acquired then its yield is envisioned with assistance of online json intelligent guides. This strategy for information investigation can be utilized for current climate patterns, crop yield forecasts, and perform bits of knowledge on Agricultural market information in Society 5.0. Accuracy or intellectual agribusiness and IoT development recommend working with tremendous game plans of data, which extends the amount of potential security get away from provisos that offenders can use for data theft and hacking attacks [16]. Sadly, data security in agribusiness is still, for the most part, a groundbreaking thought. Many farms, for example, use drones that convey data to develop contraptions. This contraption interfaces with the Internet anyway have little to zero security protection, similar to customer passwords or far away from access approvals. A piece of the fundamental IoT security recommendations fuses noticing data traffic, using encryption methods to get sensitive data, using AI-based security instruments to separate traces of questionable activity persistently, and taking care of data in the blockchain to ensure its reliability. To totally benefit from IoT, farmers ought to become acquainted with the data security thought, set up inward security approaches, and stick to them. Smart Big data examination is utilized to find novel arrangements, which go about as means for investigating massive data set, so it assumes a huge part for dynamic in explicit fields like horticulture [17]. In this soil and climate highlights for example normal temperature, normal stickiness, all-out precipitation, and creation yield are utilized in anticipating two classes specifically: great yield and awful yield. For this reason, a half and half classifier model is utilized in upgrading the element and the proposed approach is isolated into three stage’s viz pre-preparing highlight choice, and SVM_GWO, i.e., dim wolf enhancer alongside Support Vector Machine (SVM) arrangement is utilized to work on the exactness, accuracy, review, and F-measure. In Society 5.0, new worth can be created in the accompanying manners: through AI examination of large information comprising of different data, like meteorological information, crop-development information, economic situations, and food patterns and needs. Achieve ultra-work saving and high-creation “Smart Farming” via robotizing ranch work and saving money on work through robot farm haulers, computerizing assortment of harvest information through rambles, and mechanizing and advancing water the board dependent on climate expectation, stream information, and so on Formulate a cultivating plan by setting crop yields custom-made to needs, advancing work designs along with climate forecasts, sharing experience and ability, and growing the client base Make ranch produce wanted by customers accessible to them when wanted Deliver ranch to produce to customers when they need it through self-driving conveyance vehicles.

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4.4 Significances of Using AI and Smart Big Data Analytics in Smart City of Society 5.0 The indisputable allure of Society 5.0 is that it gives the plan to a maintainable human-driven society empowered by the most recent computerized advancements. Simultaneously, a possible way to acknowledge Society 5.0 is prominently absent. The overview was enlivened and inspired by giving such a way. For this reason, we characterize a Smart Community as a human-driven element where innovation is utilized to outfit the populace with data and administrations that they can use to advise their choices [18]. The key thought is that the residents of the Smart Community will burn through these administrations on a metered premise as per the notable pay-more only as costs arise plan of action. The authority of this environment of administrations is a Marketplace of Services that will compensate, in the undeniable way, administrations lined up with the needs a lot of the residents while debilitating the multiplication of those that are not. In the cut-off, the Smart Community we characterized will transform into Society 5.0. By then, the Marketplace of Services will turn into a stage for the co-production of administrations by a nearby participation between the residents and their administration. A Smart City utilizes information and innovation to make efficiencies, further develop maintainability, make financial turn of events and upgrade personal satisfaction factors for individuals living and working the city. The thought of a “Savvy city” is tied to the possibility that it can carry urban areas into the twenty-first century make them future confirmation and give a future-looking façade that renders urban communities appearing as though they could be a very innovative set for Guardians of the Galaxy. On a fundamental level, a Smart City ought to be perceived as an approach to build the personal satisfaction of the citizenship and further develop manageability by utilizing information and innovation. These empowering advances incorporate distributed computing and its variations (e.g., fog computing, cloudlets, and vehicular clouds), publicly supporting, Big Data analytics, sensors and sensor organizations, edge computing, IoT biological systems, and Marketplaces of Services, among numerous comparative ones. On the procedural side, the empowering advancements will be reviewed in sequential request, as more current innovations frequently impressively broaden old ones, while keeping away from a portion of their inadequacies and constraints. As a representation, edge processing is a characteristic expansion of remote sensor organizations, where individual edge gadgets are more impressive than the conventional sensor hubs. Thusly, edge figuring and sensor networks have impelled the ascent of the Internet of Things (IoT) as a varied assortment of organized gadgets. We consider sensor organizations, edge gadgets, and IoT biological systems as establishments of the Smart Community idea. Breaking down smart city data can uncover different significant realities identified with urban areas, accordingly, smart city drives have drawn much consideration from software engineering analysts just as data researchers. Breaking down smart city data in a continuous way is a significant situation for smart city data examination

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[19]. Real-time examination can alarm rising circumstances (e.g., car crashes, gusts), suggest valuable data (e.g., usable offices on schedule, courses for objections, etc. In this paper, we foster a continuous insightful framework dependent on Stream OLAP (an OLAP framework for streaming data) for smart city data examination which empowers OLAP-style analytics over streaming data and apply the logical framework for certifiable smart city data (to be specific, city office use data). Figure 5 gives brief explanation of the impact of use of smart Big data analytics, AI, and ML in Smart city of Society 5.0. The information about the city such as number of people residing in the city, number of roads in the city, companies in the city, local buses, metros, and many more such information is collected and stored in the database, all these types of information are collected in-order to improve the quality of one’s life such that they can lead a quality and technically well-equipped life for seamless services by the organizations. For example, utilizing video film assembled by street surveillance cameras previously introduced at intersections [20]. The framework naturally distinguishes vehicles and people on foot, works out directions, and concentrates outline level social elements. K-means (KNN) and choice tree calculations are then used to group six unique classes, which are additionally explored to show how they might add to passerby hazards. Given the significance of improvement of advanced AI frameworks, select an appropriate algorithmic base from its ideation. The broadly utilized possibilities in algorithmic advancement are; supervised learning, unsupervised learning, and mathematical modeling. The previous identifies with the advancement of numerical models to address an inflexible definition of the information, though the last mentioned, regulated and solo AI standards identify with learning portrayals dependent on the experience got from input information. Getting a handle on and overseeing street surface conditions utilizing information from accelerometers, what’re more, vehicle-mounted cameras. Focused on fix of vigorously dealt streets by utilizing a mix of weakening location information dependent on AI-prepared picture information and human stream examination information. Utilizing the distinction of three-dimensional point bunch information to

Fig. 5 Role of AI and smart big data analytics in smart city

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get a handle on changes over the long haul for the upkeep and the executives of streets and waterways. Exceptionally precise harm/spillage forecast utilizing AI/AI for water pipes (Note: Tests are likewise being led on gas pipes). Computation of the measure of mishap hazard considering the encompassing conditions. In ordinary keen city conditions, a wide cluster of sensors exists to catch the climate in the types of pictures, recordings, sensor readings, web-based media, text, and so on with that, due to the indeterministic idea of the climate there will be an endless number of potential circumstances that could happen in such a climate. In such savvy city conditions, the vast majority of the AI and AI strategies utilized for shrewd city-related applications utilize supervised discovery that is more qualified for deterministic circumstances which need past information which is marked with known results [21]. A critical issue in many shrewd city circumstances were even named or grouped past information for preparing AI calculations are accessible, the importance of names become old because of the quick evolving elements. On the off chance that a framework is to foster utilizing supervised AI, it will normally require an enormous number of preparing models to adapt successfully. The Society 5.0 building incorporates the useful information base and an information bank creation, which contains the appropriate and crucial social and monetary innovations, which structure a cooperative energy in tackling the genuine humankind inconveniences [21]. The innovation accessibility and their wide philanthropic application let the monetarily powerful nations establish happy living climate and gain the main places of the very learned Society 5.0 functional proliferation. The industry 4.0 computerized jump achievement in the social psyche makes the new conduct models of the populace digital cooperation where the profoundly progressed digital frameworks dispense with the hindrance, which is run of the mill for the shrewd urban areas world. The collaboration with business circles colossal web organizations agents to plan the mechanical advancements let the Society 5.0 find all ideal arrangements benefits to work with the human correspondence with power organs work measures in all branches and levels, administration business organizations, schooling organizations and other.

5 Conclusion The target of Society 5.0 is to comprehend an everyday citizen where people like existence unbounded. Monetary turn of events and mechanical new development exist thus, and not for the flourishing of a picked modest bunch. In concurrence with this thought reported by the public power, various activities have begun in Japanese academic circles and in industry. Additionally, despite the way that Society 5.0 beginnings in Japan, its inspiration is not just for the flourishing of one country. The designs and development made here will more likely than not add to settling social difficulties all throughout the planet. The goal of Society 5.0 is to comprehend an overall population where people like existence unbounded. Money-related turn of events and mechanical progression exist hence, and not for the achievement of a

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picked modest bunch. Improving Healthcare, Education, Smart city, and Agriculture Sector in Society 5.0 will improve the quality of life of individual. Use of technologies such as Artificial Intelligence, Machine Learning, and data analytics will make to achieve the goals mentioned above. Using these technologies on real-time data can be done by data analytics and building cognitive, precision, and recommendation model will suggest the user with better results in respective sectors of Society 5.0.

References 1. Akhtar U, Won Lee J, Muhammad Bilal SH, Ali T, Ali Khan W, Lee S (2020) The impact of big data in healthcare analytics. IEEE 2. Leung CK, Zhang Y, Hoi CSH, Souza J, Wodi BH (2019) Big data analysis and services: Visualization of smart data to support healthcare analytics. International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) 3. Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Raghupathi and Raghupathi Health Information Science and Systems, 4. https://datafloq.com/read/5-ways-ai-big-data-revolutionizing-education/6319 5. Rojas CN, Peñafiel GAA, Buitrago DFL, Romero CAT (2021) Society 5.0: A Japanese concept for a superintelligent society. Sustainability 6. Miwa C, Early childhood care and education practices in Japan for the era of society 5.0. Advances in Social Science, Education and Humanities Research, volume 503 7. Zhao J, Yang X, Qiao Q, Chen L (2020) Personalized learning design of ideology and politics of distance education courses based on big data. IEEE, International Conference on Progress in Informatics and Computing (PIC) 8. Liu MC, Huang YM (2017) The use of data science for education: The case of social-emotional learning. Smart Learning Environ 4 9. Lokhande SA (2021) “Sharayu Ashishkumar Lokhande”, International Conference on Emerging Smart Computing and Informatics (ESCI) 10. Bendre MR, Thool RC, Thool VR (2015) Big data in precision agriculture: weather forecasting for future farming. IEEE, 1st International Conference on Next Generation Computing Technologies 11. Zhao J-C, Guo J-X (2018) Big data analysis technology application in agricultural intelligence decision system. 3rd IEEE International Conference on Cloud Computing and Big Data Analysis 12. Harayama Y (2017) Mobilizing science, technology, and innovation to transform Japanese agriculture. Mobilizing Science, Technology, and Innovation to Transform Japanese Agriculture 13. Vandana B, Sathish Kumar S (2018) A novel approach using big data analytics to improve the crop yield in precision agriculture. IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology 14. Gawanmeh A, Parvin S, Venkatraman S, de Souza-Daw T, Kang J, Kaspi S, Jackson J (2019) A framework for integrating big data security into agricultural supply chain. IEEE, Fifth International Conference on Big Data Computing Service and Applications (BigDataService) 15. Bose D et al (2017) Big data analytics in Agriculture, preparation of papers for IEEE Transactions and Journals 16. Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: A review on crop data management. Agronomy 17. Sharma S, Rathee G, Saini H (2018) Big data analytics for crop prediction mode using optimization technique. 5th IEEE, International Conference on Parallel, Distributed and Grid Computing

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18. Foresti R, Rossi S, Magnani M, Lo Bianco CG, Delmonte N (2020) Smart society and artificial intelligence: Big data scheduling and the global standard method applied to smart maintenance. Elsevier 6(7) 19. Komamizu T, Amagasa T, Shaikh SA, Shiokawa H, Kitagawa H (2016) Towards real-time analysis of smart city data: A case study on city facility utilizations. IEEE 18th International Conference on High Performance Computing and Communications. IEEE 14th International Conference on Smart City 20. Soomro K, Bhutta N, Khan Z, Atif Tahir M (2019) Smart city big data analytics: An advanced review. WIRES Data Mining Knowledge Dis 9(6) 21. Fukuyama M (2018) Society 5.0: Aiming for a new human-centered society. Japan SPOTLIGHT

Chapter 4

Conceptual Analysis and Applications of Bigdata in Smart Society Jamuna S. Murthy and Sanjeeva S. Chitlapalli

Abstract Big data is a study that refers to dealing with datasets that are too substantial and complex for standard database software tools to handle. Big Data assists in representing data, managing data, analyzing data, searching, sharing, transferring data, encapsulating data, querying data, updating data, information privacy, and the data source. Smart cities are applications of upcoming technologies in creating or enhancing the City’s infrastructure. Big data plays a pivotal role in the creation of Smart Cities. Its techniques are necessary for almost every field. In this chapter, Big data applications related to epidemiology, mental health, and astrophysics are discussed. Keywords Epidemiology · Pandemic · Personality prediction · Sentiment analysis · Behavioral sciences · Hadoop

1 Introduction Big data (BD) primarily refers to the data sets that are quite large and complicated that the typical data-processing application software cannot handle. Encapsulating data, storing data, examining data, searching, sharing, transferring data, apprehending, querying, information privacy, and updating the data source are all big data’s considerable challenges. Big Data has traditionally been described by three Vs: • Velocity—the speed with which data is acquired, processed, and manipulated. • Volume—The availability of a large amount of information. • Variety—It refers to the number of diverse sources and channels via which Big Data can be produced and released [1].

J. S. Murthy (B) · S. S. Chitlapalli Department of Information Science and Engineering, B N M Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_4

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The term “smart city” refers to the application of applied research and technology, as well as information technology, to improve and enhance the City’s infrastructure and service quality in order to boost municipal resource usage efficiency [2]. It has the potential to significantly enhance and maximize urban management, services, and people’s pleasure with city living as shown in Fig. 1. Because of its exponential growth, the Internet of Things has been intertwined with numerous sectors and has infiltrated every part of our life [3]. One of the key Internet-related domains of affairs is smart dwellings, clever colleges, intelligent medical treatment, intelligent transportation, intellectual enterprise, and intelligent agriculture [4]. Big data analytics can be used in a variety of fields ranging from medical sciences to Space explorations. Big data has been proven to be one of the crucial tools in the development of upcoming technologies. There are numerous methods and techniques of big data analytics that are implemented in numerous things all around us. The primary goal of the above chapter deals with discussion of the Big Data applications in many advancing fields such as epidemiology, mental health, and astrophysics. The contributions of big data methodologies particularly in collecting and processing data and in turn solving challenges concerning COVID-19, behavioral sciences, mental health, and astrophysics are highlighted. Big data methods for

Fig. 1 Smart city

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data collection, processing, and storage used in research and real-time applications related to specific fields are highlighted in this chapter. Big data also is necessary for collecting and analyzing data coming from unconventional sources such as social media platforms. Thus, collected data play an important role while conducting studies or developing new methods related to mental health, personality predictions, and sentiment analysis. Due to the Covid-19 pandemic and as an aftermath of the same, the world faced huge challenges, especially concerning data management and data analytics. The big data techniques in these areas are discussed in the topics below.

2 Applications of Big Data—A Blessing in Disguise During the Covid-19 Pandemic SARS-CoV2, a novel coronavirus that soon became a concern to the humankind, eventually led the World Health Organization to proclaim it as COVID-19 pandemic. The pandemic has stemmed in a huge loss of human life across the world, posing unheard-of challenges, particularly in areas associated with public health, food systems, and the workplace. It has profoundly affected many aspects of our lives, technology, and business economy being a major casualties. Due to the advancements in the medical sciences, the COVID-19 pandemic is gradually being controlled. Globally, as of January 2022, the statistics revealed that there have been more than over 352 million confirmed cases of COVID-19, including over 5,600,000 deaths, reported to WHO. Over 9,620,000,000 vaccine doses have been administered currently (Up to January 2022). Table 1 shows the statistics concerning the pandemic and displays total number of cases that are confirmed and the overall vaccine doses administered as of January 2022.

2.1 Big Data in Controlling the Pandemic Due to the magnitude of the pandemic, the government and medical institutions are now flooded with enormous amounts of data, that need to be processed with high Table 1 COVID-19 statistics as of January 2022

Country

Confirmed cases

Vaccination

India

39,799,202

1,581,796,355

United States of America

70,153,597

518,927,314

China

137,555

2,944,891,463

Canada

2,921,385

74,905,276

Total

352,796,704

9,620,105,525

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speed and accuracy [5]. With the implementation of Big Data, Government, higher medical institutions, and clinical establishments can manipulate large combination of data coming from public health surveillance, real-time epidemic outbreak monitoring, and regular scenario briefing. There are numerous sorts of Big Data in terms of epidemiology, according to their own sources as shown in Fig. 2. • Molecular Big Data—Big Data that has been derived from wet-lab procedures and OMICS-based methods such as genomics, metagenomics, metabolomics, and post-genomics specializations like proteomics. • Imaging-based Big Data—Big data procured from radionics and other datamining approaches to draw out relevant, high-dimensional data from pictures. • Sensor-based Big Data—Data obtained with the help of wearable sensors on test subjects. • Digital and Computational Big Data—Data generated from the internet, smartphones, and other mobile devices. Additionally, social Big Data that is procured from social networks and other relevant non-conventional data streams permits us to renew and recreate the early epidemiological tale causing the outbreak [5, 6]. Sun and colleagues, for instance, carried out a population-degree observational study in mainland China during the second half of January 2020 (between the 13th and 31st), tracking healthcareassociated websites, social networks, and news. The authors concluded that nonclassical datasets can help researchers to apprehend how a contamination spreads in phrases of health literacy, healthcare-promoting behaviors, and health resource consumption. Non-classical datasets and data streams, in particular, withinside the preliminary phases of an outbreak, can help in influencing the layout and implementation of powerful public healthcare initiatives [7]. Qin L., Sun Q., and co authors used a lag series of SMSI (Social media search indexes) regarding a variety of terms linked to COVID-19 and its associated symptoms, namely cough, fever, pneumonia, and chest discomfort. The authors ought to discover new COVID-19 signs, symptoms, and suggestive cases. With the usage of methods like the subset choice method, COVID-19 suspected cases can be noticed six to nine days prior, at the same time as the already shown, confirmed COVID-19 cases may be detected 10 days prior [7, 8].

Fig. 2 Classification of big data based on their sources

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2.2 Managing the Aftermath Effects of COVID-19 by Using Big Data Several companies were impacted by the COVID-19 outbreak. Many small and largescale enterprises have also closed or failed as a result of COVID-19. Organizational managers and leaders can develop a greater understanding of uncertain or unpredictable occurrences by employing analytical tools (black swan events) [9]. Businesses, for example, can use big data tools and make informed decisions about unexpected events such as choosing a specific investment market, forecasting possible risks and advancements in their respective industries, analyzing possible alliance partners and suppliers, or introducing newer products. The analytic tools of Big Data can also assist firms in dealing with the uncertainties involved with a black swan event, like labor well-being, making right entrepreneurial decisions, and supervising supply chain protection and other profound external, legal, economic, and social risks. Black swan events, including COVID-19, have the potential to increase corporate failures, and firms should use analytic tools of big data to control external risks and avoid economic failures through appropriate technical planning and business forecasting. As a result, a wide range of analytical techniques, ranging from emergency hospital operations management to supply chain resilience development, can be critical during the instant response to the pandemic and the economic recovery that immediately follows. The utilization of various big data analytics techniques—that includes predictive, descriptive, and prescriptive analytics enables business leaders, managers, and researchers to tackle some of the modern management concerns and COVID-19 issues [9].

3 Role of Big Data in Mental Health Awareness and Well-Being The overall well-being of an individual is an integration of both mental health and physical health leading to a more comprehensive approach to health management and disease prevention. The term “mental health” revolves around the rational, behavioral, and emotional well-being of an individual. In recent years, thankfully there has been progress withinside the acknowledgment of the critical role mental health performs in engaging in global expansion goals, as visible via means of the inclusion of mental health in the Sustainable Development Goals. According to the statistics, suicide is the second main cause of demise, particularly in the ones aged 15–29. People stricken by fundamental mental troubles die a long way sooner—up to two decades prior—because of avoidable bodily ailments. Some of the stats related to mental illness are shown in Fig. 3.

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Fig. 3 Total count of people that suffered from mental illnesses or substance use disorders as of global statistics—2021 (in millions)

Mental diseases are of primary concern in the current global environment. Regardless of its significance when it comes to the general population, one of the most overlooked topics is mental health and well-being. The economic expense of drug misuse and mental illness was projected as 2.5 trillion USD in 2010, and it is anticipated to more than increase by 2030. The extensive growth of social media and computational infrastructure promotes improvement of mental health and creates rapid awareness at both personal and population levels. Twitter, Facebook, and different social media systems offer to get easy access to greater naturalistic, first-character accounts of individual activity, and thoughts that could be a foremost indication of emotional well-being, making social media evaluation specifically interesting withinside the discipline of mental health. COVID-19 pandemic profoundly affected the mental health of the overall public to a large extent [10, 11]. The use of social media “big data” for health and fitness applications—in particular regarding the general public health applications—is a promptly growing scope of research. These researches are focused on digital disease detection, invigilance, and digital epidemiology [11, 12]. Researchers, Kumar and Bala employed twitter data to acquire online user reviews assisting the seeker in determining the reputation of a particular service or purchasing product. They conducted a study on Airtel in order to obtain people’s opinions on it. The Filter by Content and Filter by Location is used in filtration of the keyword. To start with, unique characters, URLs, unsolicited text, and tiny phrases are removed from tweets. Second, the closing phrases are tokenized from the tweets, and every term’s TF-IDF rating is calculated. Following records cleaning, the K nearest neighbor and Naive Bayes classification algorithms had been implemented to the

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textual content to extract features. Specific border filters are utilized by bounding filters. Despite the fact that the result accuracy of hybrid recommendation system is 76.32%, Naive Bayes has a result accuracy of 66.66%. Finally, the automatic approach is supposed to be intended for opinion mining. Another hassle is that tweeter has unstructured information, and coping with a large quantity of unstructured data is a complicated and time-ingesting operation. The problem in dealing with unstructured data is that it is highly complex to handle and this is mainly due to the lack of schema organization. Storage devices are required to store a small amount of data for processing [13]. For such data, cloud storage is the best alternative [14, 15]. The complete software is written in Python in order to determine all conceivable outcomes. Hadoop reckons on cloud computing to carry out diverse operations on distributed data in a well-organized manner. The given approach had a success rate of roughly 70%, but the authors completed these jobs by the usage of two programming languages. Extracting the tweets is done by Python code while Java is used for training the data, requiring professional programmers in both languages. It will assist clinicians in providing more precise therapy for a variety of mental diseases in minimal time and cost. Infecting this strategy gives depression identification, which may save the patient from the worst stages of mental disease. Hadoop HDFS architecture is shown in Fig. 3. Yang et al. brought an audio well-being device and a survey was implemented wherein individuals were required to talk for greater than 10 min in a silent room. The first level blanketed the assessment of the sample’s validity by asking people to finish positive questions (along with STAI, NEO-FFI, and AQ). An evaluation that became primarily based on an AQ query was administered for appropriate assessment of the individuals. On the AQ data, a classification technique was used. This kind of device has one advantage: it is conditioned properly on long-term data as opposed to short-term data; however, offline data transfer is more preferred rather than real-time data transfer (Table 2). Big data is a great challenge for mental health studies in many exceptional areas around the world and has a whole lot of objectives. Data science is a fast-increasing field with several favorable applications to mental health research, a number of which Table 2 Applications developed to tackle mental illnesses Techniques

Authors

Description

Hadoop

M. Kumar and A. Bala

Examining Twitter users’ perspectives on a specific commercial product with help of sentimental analysis and saving data on Hadoop

Long-term data collection and S. Yang, B. Gao, L. Jiang et al. Platform integrated with analysis using wearable app-based wearable devices devices for data envisions, monitoring, and environment sensing

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have been highlighted from this perspective. Currently, the virtual mental health revolution is surpassing clinical evaluation, and scientific groups ought to catch up. Numerous smart healthcare devices and diverse structures have been advanced to prevent the dying charge of mental patients and to prevent the patient from taking part in any illegal actions via early prediction and diagnosis [16].

4 Application of Big Data in Astrophysics According to IUPAP, Physics is a subject that deals with the study of matter, energy, and their interactions it is a global enterprise, that plays pivotal role in the future progress of humans. There’s no doubt that a lot of sectors of engineering and science are heavily data-driven or contain an oversized quantity of data that may be controlled. Given the evidence-driven nature of those two areas, the engineering and scientific domain is also one of the primary to spring to mind once addressing big data [17]. Astronomy scientists are gathering and processing huge amounts of astronomical knowledge. In reality, it is attainable that it is one in all the sectors wherever data is being collected at a rate considerably quicker than the speed at which the collective cluster of scientists can analyze [18]. With technological advancements, this case is anticipated to worsen. Considering the Sloan digital sky survey (SDSS5) project, which began in 2000, to induce a sign of however quickly astronomical data is increasing. It has collected a lot of data within the first few weeks of its commission than all data collected within the history of astronomy [19]. This vast quantity of data is collected by astronomy professionals and physicists for one goal. That is, they are seeking an answer to their questions about the universe. The answers to those queries are often found swiftly via big data analysis. Big data mining is a vital tool for processing huge data in physics and astronomy. It will assist scientists in extracting and discovering purposeful information from giant amounts of recorded data. Reporting, classification, clustering, time-series analysis, and outlier/anomaly identification are a number of the data-mining tasks. Many data-processing tools and packages are developed in recent years for execution of the data-mining activities and to solve astronomical problems (Table 3). In astronomy, new generations of telescopes which embody the Atacama Big Millimeter Array (ALMA) and the Jansky VLA also can, in addition, deliver huge quantities of data through big surveys which consist of the SDSS, ZTF, Pan-STARRS, VLT Survey Telescope (VST), etc. [21]. This data cannot be manually analyzed. In addition, Space telescopes which include the James Webb Space Telescope (JWST) [22] and different floor telescopes cited in desk three might come to be operational withinside the coming years, with considerably growing information quantity. The evaluation turns into an increasing number of complex and tough to make use of for statistics extraction as information quantity increases. As a result, it is of mile significance to develop new techniques for processing the amount and sort of astronomical big data for you to solve scientific questions primarily based on the data. For

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Table 3 List of upcoming and existing large telescope projects and their locations and descriptions Name

Location

Description

Large Synoptic Survey Telescope (LSST) [20]

Coquimbo Region, Chile

LSST is an 8.4-m telescope with a considerably large discipline of view than any present telescopes

Cherenkov Telescope Array

118 telescopes worldwide

CTA is a next-technology ground-based observatory for gamma-ray astronomy at very-excessive energies

European Extremely Large Telescope (E-ELT)

Atacama Desert, Northern Chile

The primary mirror of E-ELT is 39.3 m across

instance, distances from Earth to galaxy clusters, relative velocities of receding celestial bodies, and chemical compositions are evaluated through manner of approach in their redshifts that can be performed through the use of big data analytics. Since 2009, NASA’s Kepler spacecraft has been seeking out new planets referred to as exoplanets outside the solar system. Kepler observes stellar mild curves through the use of automated devices to understand fluctuations in star brightness. These versions suggest whether or not or now a planet has surpassed Infront of a star. If the brightness of a star seems to extrude with an ordinary period and decreases in brightness, then an exoplanet is most likely to blame. Any decline in brightness is probably close to the noise level, making detection via ordinary algorithms unfeasible. A. Vanderburg and C. J. Shallue created a brand-new method for detecting capacity planet signals in 2018 by integrating those software programs with machine learning and big data analytics. They had been capable of statistically perceiving new planets that their model noticed with high confidence [21, 23]. As discussed, the big data will continue to play a critical role in the future of cosmic exploration. Universe exploration can actually be evolved quickly by making use of contemporary software effective hardware technology that aid in the analysis and mining of large volumes of data [24, 25].

5 Conclusion As explained big data analytics can be used in a variety of fields from gathering data from social media for research to analyzing the results of an experimental setup. Big data tools helped in gaining control over the dreadful Pandemic of COVID-19. Due to COVID-19 governments and medical institutions were flooded with massive amounts of data which were managed using big data techniques. Big data also plays an important part in enabling researchers to collect data from social media and other unconventional sources. Due to this a number of new techniques to assist people with the COVID-19 pandemic and other medical disorders are developed.

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J. S. Murthy and S. S. Chitlapalli

In recent times due to social media, there has been intense growth in the research areas of mental health and well-being. In these studies, based on social media, sentiment analysis, and behavioral sciences big data plays a significant role. With the help of sentiment analysis and personality predictions (using big data methodologies) on social media platforms such as tweeter, researchers are trying to enhance the mental health of both the individual and the general population. The last section of this chapter discusses the need for big data tools in the field of astrophysics. As mentioned previously the upcoming Telescope projects such as James Webb Space Telescope (JWST) will result in new discoveries along with huge amounts of data related to the beginning of the universe. Big data analytics is necessary to manage and process the gathered data. Big data has been shown to be a vital element in the creation of emerging technology. There are several big data analytics methodologies and approaches that are used in a variety of things all around us.

References 1. Dresp-Langley B, Ekseth OK, Fesl J, Gohshi S, Kurz M, Sehring HW (2019) Occam’s Razor for big data? On detecting quality in large unstructured datasets. Appl Sci 9(15):3065 2. Ageed ZS, Zeebaree SR, Sadeeq MM, Kak SF, Rashid ZN, Salih AA, Abdullah WM (2021) A survey of data mining implementation in smart city applications. Qubahan Acad J 1(2):91–99 3. Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992. 4. Sun M, Zhang J (2020) Research on the application of block chain big data platform in the construction of new smart city for low carbon emission and green environment. Comput Commun 149:332–342 5. Jia Q, Guo Y, Wang G, Barnes SJ (2020) Big data analytics in the fight against major public health incidents (Including COVID-19): a conceptual framework. Int J Environ Res Public Health 17(17):6161 6. Zhou C, Su F, Pei T, Zhang A, Du Y, Luo B, Cao Z, Wang J, Yuan W, Zhu Y, Song C (2020) COVID-19: challenges to GIS with big data. Geogr Sustain 1(1):77–87 7. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J (2020) How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health 17(9):3176 8. Wang CJ, Ng CY, Brook RH (2020) Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 323(14):1341–1342 9. Sheng J, Amankwah-Amoah J, Khan Z, Wang X (2021) COVID-19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. Br J Manag 32(4):1164–1183 10. Hou K, Hou T, Cai L (2021) Public attention about COVID-19 on social media: an investigation based on data mining and text analysis. Pers Ind Differ 175:110701 11. Khattak A, Jamil N, Naeem MA, Mirza F (2020) Data analytics in mental healthcare. Scientific Programming 12. Abbas J, Wang D, Su Z, Ziapour A (2021) The role of social media in the advent of COVID19 pandemic: crisis management, mental health challenges and implications. Risk Manage Healthcare Policy 14:1917 13. Geirdal AØ, Ruffolo M, Leung J, Thygesen H, Price D, Bonsaksen T, Schoultz M (2021) Mental health, quality of life, wellbeing, loneliness and use of social media in a time of social

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distancing during the COVID-19 outbreak. A cross-country comparative study. J Mental Health 30(2):148–155 Thorstad R, Wolff P (2019) Predicting future mental illness from social media: a big-data approach. Behav Res Methods 51(4):1586–1600 Wongkoblap A, Vadillo MA, Curcin V (2022) Social media big data analysis for mental health research. In: Mental health in a digital world. Academic Press, pp 109–143 Jindal K, Aron R (2021) A systematic study of sentiment analysis for social media data. Mater Today: Proc Khalil M, Said M, Osman H, Ahmed B, Ahmed D, Younis N, Maher B, Osama M, Ashmawy M (2021) Big data in astronomy: from evolution to revolution Sre´ckovi´c VA, Nina A (2019) Special issue on astrophysics & geophysics: research and applications. Data 4(1):21 Garofalo M, Botta A, Ventre G (2016) Astrophysics and big data: challenges, methods, and tools. Proc Int Astron Union 12(S325):345–348 Robertson BE, Banerji M, Brough S, Davies RL, Ferguson HC, Hausen R, Kaviraj S, Newman JA, Schmidt SJ, Tyson JA, Wechsler RH (2019) Galaxy formation and evolution science in the era of the Large synoptic survey telescope. Nat Rev Phys 1(7):450–462 Lan F, Young M, Anderson L, Ynnerman A, Bock A, Borkin MA, Forbes AG, Kollmeier JA, Wang B (2021) Visualization in astrophysics: developing new methods, discovering our universe, and educating the earth. In: Computer graphics forum, vol 40, No 3, pp 635–663 Kalirai J (2018) Scientific discovery with the James Webb space telescope. Contemp Phys 59(3):251–290 Mickaelian AM (2020) Big data in astronomy: surveys, catalogs, databases and archives. Commun Byurakan Astrophys Observ 67:159–180 Taffoni G, Murante G, Tornatore L, Goz D, Borgani S, Katevenis M, Chrysos N, Marazakis M (2019) Shall numerical astrophysics step into the era of Exascale computing? arXiv preprint arXiv:1904.11720 Sharma P, Ved M, Chintalapti J, Pal SN (2021) Big data analytics and machine learning technologies for HPC applications. In: Evolving technologies for computing, communication and smart world. Springer, Singapore, pp 411–424

Chapter 5

Cyber-Security in Society 5.0 S. R. Mani Sekhar, Rudransh Pratap Singh, Lakshya Aditi Sinha, and Sunilkumar S. Manvi

Abstract Society 5.0 is a human-centric approach to living wherein there is a balance between the economic aspects and social aspects of life. It discusses how the cyber-security services must function for them to meet the requirements of Society 5.0. The chapter initially discusses cyber-security in the current scenario and about the common prevalent cyber-attacks as well as their prevention. It then goes on to discuss Society 5.0 in general and its human-centric approach while discussing the emergence, working, and the benefits of following Society 5.0. Then the focus shifts to the expected security and cyber-security measures in Society 5.0 and the chapter concludes with three case studies, one on Japan Business Federation (Keidanren) and the other one being on Hitachi’s system for Society 5.0 and the last on an article from Japan Times. Keywords Cyber-security · Society 5.0 · Physical space · Cyberspace

1 Introduction Cyber-security, computer security, or information technology security means the application of different processes, technologies, and controls for the protection of computer systems, devices, networks, programs, and data from various cybercrimes or attacks. It intends to lessen the risks of malicious attacks and guard against the unauthorized utilization of technologies, systems, and networks. S. R. M. Sekhar (B) · L. A. Sinha Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India e-mail: [email protected] R. P. Singh Department of Electrical and Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, India S. S. Manvi School of CSE, REVA University, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_5

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1.1 Why is Cyber-Security Important? Cyber-security’s basic purpose is to protect an organization’s data and sensitive information from any potential breaches. Its function is also to protect against unauthorized access, and unauthorized changes as well as protect the information present on the cloud. Thus a Cyber-Security system secures the networks and servers of an organization and keeps them away from the grasp of hackers, phishing attacks, or malware. The common types of cyber-attack methods include DDoS attacks, rootkit, malware like adware, spyware and Trojan horse, SQL injection attacks, and phishing. The need for Cyber-Security systems in the present digital time is quite evident as everything we do revolves around the internet and electronic devices and due to the emergence of various social media sites, our personal information is always at risk which makes us quite vulnerable to online attacks and a strong Cyber-Security system ensures that we can work in a secure and a protected environment. The importance of Cyber-security can be easily understood from the following Cyber-security trends of 2021 as reported by various organizations [1–9]: • • • • • • • • • • •

95% of errors are caused in cyber-security is due to human error. 88% of administrations worldwide experienced spear-phishing attempts in 2019. As of 2020, the average charge for a data breach is $3.86 million. The average time to identify a breach in 2020 was 207 days. Private information was involved in 58% of breaches in 2020. Security breaches have increased by 11% since 2018 and 67% since 2014. In 2018, average of 10,573 malicious mobile apps was blocked per day. 94% of malware is delivered by email. 1 in 13 web needs leads to malware. Phishing attacks account for more than 80% of stated security incidents. Nearly two-thirds of financial facilities businesses have over 1000 sensitive documents open to every employee. • Hackers attack every 39 s and on average, hackers attack 2244 times a day. So if these statistics and trends are to be followed one must have a Cyber-security system for their organization. The security of any company is explained by the Security Triangle which is abbreviated as CIA- Confidentiality, Integrity, and Availability and the security triangle is considered the convention for security systems since the early computer systems.

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1.2 Elements of Cyber-Security There are primarily six key elements of cyber-security, namely [10]: • Application security—This is the first key element of cyber-security that helps to protect the applications during their development from any kind of cyberattack. It secures web-based applications from cyber-attacks that may exploit the vulnerabilities in its source code. This security is important to protect the assets of customers and companies. • Information security—This element is of utmost importance as information is the heart of an organization. Information here implies personal or customer details, business records, intellectual property, login credentials, etc. This type of security is used to prevent any disclosure, modification, destruction, disruption, or unauthorized access of information. • Network security—This element consists of securing the reliability and usability of data and network. It comprises various configurations and rules to protect against unauthorized access, modification, or misuse of computer resources and their network. The technologies used for this purpose include both software and hardware. • Disaster Recovery Planning—This is also known as Business continuity planning which means developing a business plan to prepare and analyze how work may be affected by cybercrimes or cyber-threats and how operations can be resumed effectively and quickly after such a disaster. This recovery planning starts at the business level and figures out the recovery strategy according to the priority of various organizational activities. • Operational security—This element is used to protect the functions and critical information of the organization. A protection mechanism is developed to protect any sensitive information by analyzing security vulnerabilities and holes and implementing appropriate countermeasures to tackle them. • End-user education—Errors by end-users have become one of the largest reasons for data breaches as they can take place anytime by anyone. Human errors occur mostly due to carelessness or ignorance of business security protocols and policies. Thus, it is crucial to train the employees of an organization about cyber-security and its importance.

1.3 Types of Cyber-Security Threats [11] • Malicious Software These are often called malware. These refer to any type of software that is used to hack users, wipe out computer operations, or obtain sensitive information. Some examples of such software include- viruses, worms, and Trojan horses [12].

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– Virus—This type of malicious software modifies computer programs when executed. It leads to corruption of data, system failure, slow computer performance, etc. causing economic damage. One can pick up computer viruses via normal day-to-day activities like opening spam mail, downloading free games, music, or videos, visiting an infected website, and many more. – Worms—This is standalone malware software that spreads to new computers by replicating itself. They have similar effects like viruses such as crashing of system, slow computer performance, missing or modified files, automatic opening and running of programs, etc. – Trojan Horse—This is commonly named Trojan, which is a malicious software pretending to be harmless. Once downloaded into the computer system, the attackers can easily hack personal information including personal identity, email passwords, and banking information. It is mostly masked as a legit program, misleading users of its actual intent. – Ransomware—This is another type of malware attack. In this, the attacker encrypts or blocks the victim’s computer files or data and demands a payment (ransom) to unlock or decrypt them. • Phishing This refers to the method of trying to obtain sensitive or personal information such as usernames, passwords, banking details, etc. using deceptive websites and emails for malicious activities [13]. The attacker makes the user believe that the message or email is something they need or want like alert from their banks or mail from their co-worker. But once the user downloads the attachment or clicks the given link, they become the victim of cyber-attack risking the security of their system. The different types of phishing include [14]: – Spear Phishing—These phishing attacks are directed at individuals and companies. First, the attackers gather information about their target and then attack to boost their chance of success. – Clone Phishing—These phishing attacks are done through emails containing a link or an attachment. The attacker creates an email with stolen personal information to trick the recipient into believing that the email is authentic. – Whaling—These are highly targeted phishing attacks that is they are aimed specifically at higher-profile people like senior executives. One example of such an attack is through social engineering techniques where the victims are deceived. • Keystroke Logging This type of attack is also known as keyboard capturing or key logging. In this attack, the actions of the user on the keyboard are recorded. The person is unaware of the fact that the keys struck by him are being monitored by someone else. There are two main key logging methods [14]: – Hardware-Based Key Loggers—These loggers are a part of the hardware of the computer system. They do not need any installed software.

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– Software Based Key Loggers—These are programs developed to perform the task of keyboard capturing. These are also used in organizations to troubleshoot technical issues with networks and computer systems. • Man-in-the-middle This is a type of cyber eavesdropping that involves attackers intercepting data between two parties who are communicating with each other and then relaying those messages. These messages often include sensitive information or intellectual property. • Denial-of-service This is a form of cyber-attack in which multiple systems flood the traffic of a computer system or network with messages, packets, or requests so that it cannot be used by legitimate traffic. • SQL Injection SQL stands for Structured Query Language. This attack causes malicious code to be written in the database server which uses SQL. The infected server can therefore leak important information to the attacker. • Password Attack Passwords are the first and the most widespread method of authentication to protect personal information. Password attackers use different decryption methods to find an individual’s password like social engineering, brute-force attack, accessing password database, dictionary attack, etc. By gaining an individual’s password, the attacker can easily access critical and confidential data from the system.

2 Cyber-Attack in Physical and Cyberspace A resource or an asset of an organization can have multiple vulnerabilities which can be exploited by any potential cyber-attack and thus the security triangle, i.e., the confidentiality, integrity, and the availability of the vulnerable resource can be compromised if those vulnerabilities are found. Cyber-attacks can be categorized into many different categories one such is the division into Active and Passive attacks. An active attack is an attack that attempts to change a system’s assets or affect the system’s operation thus an active attack comprises the integrity or the availability of the resource. A Passive attack aims to make use of resources from the system however it does not try to change the system resources hence a passive attack affects the confidentiality of a resource. Examples of Active attacks are: Denial-of-service attack, Buffer overflow, Heap overflow, Spoofing, etc. whereas examples of passive attacks are Wiretapping, Fiber tapping, idle scan, and port scan [15].

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Attacks can be carried out by an individual within the security radius, i.e., an individual who has authoritative access to the assets but may use that access in a manner that is not permitted by the organization. Such attacks are referred to as “inside attacks”. An attack carried out by someone who does not have the authoritative access to the assets, i.e., by an outsider to the organization is called “outside attacks”. Attacks can also be categorized into another two categories namely as: Syntactic attacks and Semantic attacks. The Syntactic attacks include malicious software such as Trojan horses, worms, and viruses. Trojan horse, also referred to as Trojan is a hostile piece of code or a hostile software that seems admissible but has the ability to gain control of the computer system and once it is inside the system it can perform the malicious job it was coded to do. A worm is a kind of malware that multiplies by sending copies of itself from one system to other worms are spread through software vulnerabilities and can also arrive in the form of attachments in emails or messages. Worms inflict damage by changing as well deleting system files or their purpose might also be to simply multiply and hence overload a system. A virus spreads from one system to another and can replicate itself but much like the biological metaphor from where the name is derived the computer virus needs a host to multiply thus to reproduce they need a file or a document. The virus attached to the file/document can remain dormant for a very long time in a system and for a virus to affect the system, the file/document containing the virus needs to be run by the user, and once it is executed the virus will affect the system in which it is present and other systems linked to the infected system. Semantic attacks, on the other hand, are the ones where use of modified information about a reliable resource is done to damage the reliability of the resource.

3 Methods to Prevent Cyber-Attack in Physical and Cyberspace While discussing of the ways to deal with cyber-security, it is a good policy to consider three possible stages of protection against attacks [16]: • Prevention This stage of protection’s main approach is to create the system to be safe from any attack in the first place and if done properly attacks may prove to be in vain as they will not cause any damage. • Consequence Management Response and Recovery are the two sub-stages of this stage of protection. Recovery as the word suggests is about building up the IT resources so that an organization can function normally as soon as possible. Common tasks which come under recovery are: removal of the hostile unit, surveying the damage caused by the hostile entity, and rebuilding the system back to its pre-attack state.

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• Incident Management and Damage Limitations The work of this stage of protection is to provide signs or caution that an attack is happening and the process of detection and communication is more difficult and are susceptible to false positives. Following are discussed some of the common ways suggested to prevent cyberattacks [17]: • Keep your software and systems fully up to date Mostly the cyber-attacks occur because the systems may not be fully up to date with the recent updates thus leaving the systems vulnerable for hackers to exploit and once they are able to gain access to the system it is too late to take any action thus to prevent such situations, it is advised to have a patch management system. A patch management system takes care of all software and system updates thus keeping the system up to date. • Backup your data This is one of the most logical things to do in order to protect the system’s data in the event of a cyber-attack to avoid loss of data. • Wi-Fi security As every device presently is a Wi-Fi enabled device and therein lies the danger of an attack. If any infected device connects to the organization’s network then the organization’s entire system is potentially at risk. Therefore hiding and securing the Wi-Fi networks of an organization is done to avoid such potential risks. • Employee personal accounts The policy of having different login details for each employee of the organization significantly decreases the attack fronts for the attackers to exploit. • Ensuring endpoint protection Endpoint protection is the process of protecting the endpoints/entry points of devices such as laptops and tablets which may be connected to an organization’s networks hence giving access paths to potential cyber-security issues. Endpoint protection systems shield such endpoints from such issues.

4 Introduction to Smart Society Smart Society can be defined as “A human-focused society that offsets economic progression with the goal of social issues by a system that profoundly coordinates the physical space and cyberspace.” [18]. It was planned in the 5th Science and

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Technology Basic Plan as the future of societies in Japan. It aims to establish a super smart and human-centered society with the use of technological advancements.

4.1 Chronology Society 5.0 (smart society) follows the previous four societies namely—Society 1.0 (Hunting Society), Society 2.0 (Agriculture Society), Society 3.0 (Industrial Society), and Society 4.0 (Informational Society) [19]. The First Industrial Revolution occurred from 1780 to 1820. It was one of the most important technological, social and economic transformations of all times. The rural economic system was changed from an agricultural production system to an industrial and manufacturing production system. This revolution led to invention of new machines, new forms of labor association, and new energy-generating alternatives. The Second Industrial Revolution occurred from 1870 to 1914 as a part of the initial period of globalization. It was supported by novel energy choices, like oil-generated sources and electricity, the advancement of land transportation and plane flights, automatic machinery, and the beginning of communication systems. All of these changes brought economic and industrial changes in the society, forming novel organizational development models. The Third Industrial Revolution happened in 1970. Its leaders were Japan, the United States, and the European Union. Its principle highlight was that industry automation dependent on how new energy and communication technologies combined and complemented one another. The utilization of the microprocessor and integrated electronic parts as communication and storage methods was the foundation for this new industrial society. We are at present implementing the fourth industrial revolution. It depends on several new technologies including cloud computing, robotics, Internet of Things, big data, and augmented reality for complete automation of manufacturing. It is also known as “Industry 4.0” as it is distinguished by the application of communication technologies and information to industry. [20] Industry 4.0 was mainly characterized by disruptive changes in smart industry, cyber-physical frameworks, big data, artificial intelligence, and IoT. These distinctive developments in the above discussed four industrial revolutions led to the advancement of Society 5.0.

4.2 Emergence of Society 5.0 The fourth industrial revolution enabled people to create a new society where artificial intelligence will transform the big data collected through the Internet of Things into what is now called the “new wisdom” [21]. This will solve the various challenges we face in our day-to-day life and raise our standard of living. For example, we will have access to the latest medical advancements at a lower cost, we will be freed from

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the stress of driving thus protecting us from accidents, and our human abilities will be enhanced which will help us to lead a more hassle-free life. The main principles of Society 5.0 are based on the techno-social concepts of Society 4.0 leading to a super-intelligent society [22]. It now envisions a sustainable socio-technological economic system where the collection and analysis of data are done by Artificial Intelligence, the Internet of Things, and Big Data rather than by human power. The task of processing and analyzing information was a tedious task for humans. Also, their scope of innovation and action was restricted due to several factors like aging or degree of ability. Therefore, this social reform will accomplish an innovation-centered society breaking the existing barriers of stagnation. It will give birth to a society where every individual will lead a fulfilling life, a society where everyone is kind to one another with mutual love and respect toward each other.

4.3 How Society 5.0 Works Two concepts of the fourth revolution form the basis of the fifth revolution namely, smart factory and smart urbanization. The mutual interaction between humans and machine learning forms the basis of smart factories. This is similar to the concept of a human-centered society which Society 5.0 is planning to achieve. On the other hand, smart urbanization means the transformation of the cities into smart cities where urban development strategies will be implemented by using innovative technological solutions such as the Internet of Things, new sustainable materials, new economic models, blockchain technologies, etc. [23]. In Society 5.0, a high degree of convergence is achieved between the physical space or real space and the cyberspace or virtual space. Here, a large volume of data from sensors in actual space is gathered in cyberspace or virtual space. In this cyberspace, the big data is examined by AI (artificial intelligence), and the examination results are brought back to people in actual space in different forms. This practice is different from the earlier method of collecting data via network and having it examined by humans. The new way brings new value to society and industry in manners not previously conceivable.

4.4 Benefits of Society 5.0 With the rapidly growing economy, the need for food and energy is increasing; and the lifespan of people is increasing. All this is leading to a severe increase in international competition and issues such as inequality, the unequal concentration of wealth, poverty, etc. These economic problems have in turn paved the path for many social problems related to different regions, gender, age, and community. Incorporating advanced technologies like AI, IoT, robotics, and big data in all social activities

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and services provides services and goods that address multiple latent requirements without discrepancy to balance economic development with the solutions to various social problems. This society will be centered on the needs of every person. It does not imply a future monitored and controlled by robots and AI. It simply frees humans from the daily monotonous cumbersome work that they might not be interested in or which they do not excel in. All these kinds of services will be performed by incorporating advanced technologies in social activities and industries fostering modernization to establish a new value. Thus, Society 5.0 is an integrated cyber-physical system that will bring comfort and vitality to the lives of people leading to an era of high-quality lives.

5 Security Requirements in Society 5.0 Initially, cybercrimes and cyber-attacks were limited to cyberspace. However, with the rise of the fifth revolution, physical space and cyberspace are being integrated, thus now even the physical space is in danger. Ordinarily, “security” was what was utilized to ensure the safety of cyberspace, and this was differentiated from “safety” in physical space. In the period of Society 5.0, we will think of these two spaces as one. Thus, it is important to consider both security and safety together. The amalgamation of cyberspace and physical space will indeed create a super smart society but this integration will also lead to more cyber-threats. Our personal lives and safety will be at greater risk as technology will be incorporated into every side of our physical being. For example- AI-operated vehicles, medical devices, home appliances everything can be easily hacked. Assuming services that are firmly associated with physical space are exposed to a cyber-attack, there will be individuals who experience the effect in real space, which will prompt an undesirable circumstance according to the point of view of inclusivity. Security plays an incredibly enormous job in preventing this sort of situation and forming a society in which all individuals can utilize innovative handy services securely [24]. Society 5.0 is constructed on a vast network of devices, systems, machines, and sensors. All of these components are prone to cyber-attacks and cybercrimes. For example, if a cyber-attacker hacks into an electric power plant and disrupts the working of generators present in them, the power supply providing electricity to millions of people could fail, causing not only a significant inconvenience but massive economic damage also. Several other examples can be quoted as damaging a product’s quantity or quality by altering systems of automated manufacturing processes, attacking self-driven vehicles to cause an accident, and causing explosions by attacking nuclear power plants, oil pipelines, or gas installations. Such attacks are possible by exploiting weak networks and information systems. Thus, we can conclude that security in society 5.0 is of utmost importance as humanity’s endurance and future achievement do not depend on technology only, but on its balanced, cognizant, safe, and secure incorporation into social, economic, and industrial frameworks.

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6 Cyber-Security Measures in Society 5.0 Guaranteeing cyber-security is essential to get ready for a society that solves problems using advanced technology and data. There are some measures that could be taken to prevent cyber-attacks in Society 5.0: • Raise Awareness The most fundamental step to preventing cyber-attacks is to improve awareness of such attacks among all citizens and among the top management. Top management should realize that cyber-security is the most crucial management problem. Trained specialists should be employed to help people improve their technological knowledge. IT (Information Technology) literacy should be improved throughout society starting with education at schools, training at organization level, and expanding understanding of cyber-security among top managers. • Secure Resources Policies should be developed by governments and organizations to protect sensitive information. Resources must be shared only with trusted parties and individuals. There should be a proper mechanism to share information among industries, companies, public and private sectors, etc. • Technical Measures Individual organizations should update their computer software and operating system. They should make use of anti-virus software. They should use strong passwords for their system and networks. Small and medium-sized enterprises should utilize cloud computing for the protection of their data. All organizations should use latest security technologies such as Artificial Intelligence and blockchain to do research work on the type of cyber-attacks possible and their countermeasures. • Establish Cyber-security Framework A framework should be established to supervise the above-mentioned measures like raising awareness among the public, training personnel, analyzing, and sharing data, and technical measures to increase security. There should be proper budget for each of these tasks. And there should be a single point of contact for reporting all cyber-attacks in a particular society. • Develop Legal Norms and Systems The legal norms and systems are not updated with respect to the quickly advancing technologies. Development of new legal norms and technical standards is essential to make cyberspace secure and safe. Such norms should be established internationally.

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7 Case Studies 7.1 A Call for Reinforcement of Cyber-Security to Realize Society 5.0 by Japan Business Federation (Keidanren) Keidanren has proposed reinforcement of measures of cyber-security twice in the year 2015 and 2016 and amended its Charter of Corporate Behavior in the year 2017 to make security a social responsibility among all the organizations [24]. As the world is shifting toward society 5.0, the importance of security is increasing exponentially since everything now will be linked by technology and data. Ensuring cyber-security is vital from two perspectives: for risk management and as a precondition for value creation by means of Society 5.0. Risk Management means taking measures against cyber-attacks to protect the companies. Avoiding cyber-attacks is impossible, so recovery mechanisms like raising awareness, detecting attacks in advance, preventing damage from expanding, etc. need to be implemented to guarantee business continuity. In order to create value in the cyberspace in Society 5.0, security is an important prerequisite. It helps to provide secure, worry-free services and products in the physical space. Keidanren proposed a new approach to establishing a secure society for the people. The initial step in this approach was self-help. Companies should take initiatives to secure their network. The next step was Cooperation. All the companies should find common links among themselves and together find solutions to their problems. The third step was assistance from the government. Support and information from the government are necessary to form a trusted alliance among different organizations. And the last step was forging global links across national and international borders. Apart from these, a few general strategies were implemented like changing awareness, securing resources, advancing technically, and developing a legal system. Raising awareness included spreading the importance of cyber-security throughout Japan. An ecosystem was built for training people who can take charge of handling measures of security. Younger generation was targeted for this purpose. A mechanism was devised to promptly share information in a secure manner across different organizations. This mechanism was known as 5W1H. 5W1H means organizing and standardizing place, position, type, purpose, method, timing, etc. of the information to be shared or used. The technical measures included employing multiple defense mechanisms, leveling up the research and development department for advanced security technologies, keeping a track of vulnerabilities present in the system, promoting the use of cloud computing, etc. Keidanren’s plan also included promoting investment as an important step for implementing cyber-security in Society 5.0. Acknowledging Society 5.0 requires centered investment of collected funds in data, manpower, and innovation; and formulating systems and frameworks to guarantee proficient fund flow is considered necessary. Government-affiliated organizations formulated the below strategy to integrate measures for increasing security:

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• Power to propose and choose establishment or abolishment of particular departments’ actions • Increment in workforce and financial budget • Management of faculty training, study, and distribution of data, worldwide standards, etc. • Broaden public awareness exercises • Form a single point of contact for reports and meetings regarding the cyber-attacks • Associations with physical security. And the last measure was developing legal norms for cyber-security in the Japanese legal system. Keidanren’s action plan of initiating a new way to promote change in the reinforcement of cyber-security procedures to realize Society 5.0 includes three major steps: • Advance understanding among top administration- This step includes formulating an action plan according to Keidanren Cyber-security Management Declaration and conducting training and seminars for top-level managers. • Activity relating to publicity and public relations-This step incorporates publishing briefings, newsletters, instructor manuals, etc. for the public, collaborating in events organized by government bodies, conducting reviews of network safety measures at individual companies, and distributing information to partners in Japan and abroad. • Activity to advance worldwide connections- This step suggests participating in Japan-US Policy Cooperation Dialogue on the Internet Economy, Japan-US Cyber Dialogue, etc. It also includes creating links with the World Economic Forum, etc. [24]. Thus, coordinated effort by a wide range of stakeholders will be fundamental for the reinforcement of cyber-security: organizations/associations, government, politicians, research institutes, educational institutions, media, residents, and so on. Japan’s ability in fundamental technological advancements, significance on quality, and a national propensity to work diligently will help in reinforcing security around the world. Keidanren will seek these initiatives in joint effort with the public authority and different organizations.

7.2 Trusted and Secure Security System Society 5.0 by Hitachi With the development of recent events in technology such as 5G communications, artificial intelligence, and the feeling of insecurity within the society, Hitachi has been targeting an organized construction of trust in the services and systems which support Society 5.0 and is trying to build trustworthy services and societies which reduce the impact of unforeseen events to the minimum. Thus this case study is dedicated

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to showcasing the technological advancements which aim to provide trustworthy services and also show the new blueprint of systems, society, and services that views society in terms of these three views and uses data to make them work at the same time [25]. Hitachi believes that services in Society 5.0 would not only need to provide safety and security in order to gain public trust, it must show Trust by Data, i.e., it must present a service’s credibility by maintaining its databases and show that the data present in these databases is true in itself, i.e., Trust of Data. One way of gaining Trust by data is by generating Trust in business. This can be achieved by collecting and analyzing data from technologies such as IoT devices and this way trust can be generated by verifying whether the production processes are done following the laid down procedures. Nowadays people are asked to provide their personal data in variety of areas thus increasing the need for privacy protection which has led to the development of SSI, i.e., self-sovereign identity which enables the users to manage their own data. One way to enforce SSI is via DID, i.e., Decentralized identity wherein the users control the revelation of their individual information which is present in a blockchain and as blockchain makes use of an electronic signature to verify the relationship between the user and their data which is done using a private key. However, the problem lies in the intricacy of handling this private key is an issue but with that being said Hitachi found a way to overcome this complexity of the private key by utilizing PBI, i.e., public biometric infrastructure which from the biometric information makes the electronic signatures and this saves the user from the trouble of controlling their keys as well as get rid of the risk of losing their private key or the possibility of their key being stolen. This process is a way to provide Trust in Data to the public. Another medium to provide Trust of data is via Verifiable Decentralized Secret analysis (VDS). It is used in instances where the data contains sensitive information where essential needs such as use of data and keeping tabs on its distribution pose a serious barrier to the data’s security. Hitachi was able to lower these barriers by using VDS. The VDS incorporates functional encryption and distributed processing and it also makes early detection of cyber-threats. This way security is boosted by minimizing the amount of sensitive information that is shared. Researchers at Hitachi believe that if Society 5.0 is to give a better way of life then it need not only be safe and secure. It also must create and use technologies on secured information access platforms which follow the methods discussed before thus Hitachi is working on such architectures for achieving these specifications. One such architecture suggested is the S3 architecture which sees the society in terms of three considerations. Those three considerations are: • Having the freedom to set the objectives in agreement with the shareholders of the service, i.e., society’s view. • New services are able to gain credibility swiftly, i.e., service view. • The capacity to alter the operation of the services, i.e., system view. Society view: Society 5.0 is a concept that aims to design a human-centric society that looks at different possible improvements to improve people’s quality of life. Thus to incorporate this human-centric approach, Hitachi is exploring the notion of

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QoL, i.e., quality of life. QoL looks at different facets of society such as society’s health, prevailing traffic conditions, etc. thus QoL makes the process of designing and operating services, a bit more human-centric. Hitachi also aims to keep in mind the diversity of the society thus it plans to use evaluation methods based on indicators that consider a greater proportion of shareholders and form collaborations with the decision-making bodies for the development of services. Service view: The development of digital technology has to lead to interoperation between the systems run by companies or regional governing bodies which have proven beneficiary to people and the society. With that being said there are many obstructions to warrant the availability and verification of the security of these interoperations between different systems. To counteract this, the proposed S3 architecture provides a platform for the evaluation of these services which analyses the services and collects data on various factors such as their status and operation. Based on this assessment the platform is able to assess the security of the services in the cyber-physical systems (CPSs). Thus the authentication of the innovations allows for deployment of services that provide advancements in QoL by bringing out functions once they are tested. System view: Quick implementation and delivery will be crucial in Society 5.0 but instead of waiting for new infrastructure which adapts to these changes, it is imperative that the new services are able to adapt to the functions provided by the existing infrastructure. This requires the uncomplicated unbundling as well as the rebundling of functions as well their flexibility to change so their work is not interfered with. However, this approach calls for the presently existent infrastructure’s functions to exist as software models as well as linking these models together as well. This process is labeled as operation virtualization by Hitachi and operation virtualization improves the flexibility of the services as for instance if due to some issue a part of the social infrastructure is not operating then in such scenarios the services can still run with the help of another infrastructure given by the software models. Thus, this paper on Society 5.0 tells in detail about the research and development that is currently going on the services and systems which make up the Society 5.0, and based on the research and development, Hitachi aims to use the obtained knowledge to achieve a mutual agreement on how to forge faith in Society 5.0.

7.3 Security in Society 5.0 (Article in Japan Times by Christopher Hobson and Tobias Burgers) Japan has planned major structural changes in the previous years under its former Prime Minister Abe. The most recent one was the integration of cyber and physical worlds to solve all the long-term issues that Japan has been dealing with. This integration will prompt a future in which “individuals, things and frameworks are all associated in the cyberspace and ideal outcomes obtained by AI surpassing the capabilities of people are fed back to the actual space” [26]. The amalgamation of

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digital advancements and AI into society and business absolutely holds extraordinary assurance for a country that was battling for years to find solutions for its problems of financial development, a maturing and decreasing population, and the decline of rural regions. Also, highly repetitive and monotonous jobs that require low skill-set can be progressively automated, while many other possibilities are opened up by big data for effective use of assets, accomplishing more with less. This concept of society has enormous social and economic benefits but there are some major threats that need to be looked after. The utilization of big data and artificial intelligence leads to certain dangers pertaining to surveillance and control, while the possible social repercussions of AI are just barely beginning to be comprehended. The major technological changes occurring can possibly reshape the manner in which the economy and society work, in any event, testing fundamental political and moral classifications about agency, obligation, and personhood. For example, while the use of AI and big data might assist with tending to a lesser workforce, it could likewise bring about many capable individuals being jobless or under-employed. There is a connected danger of over-dependence on technology, leaving significant shortcomings in the construction of society. AI and big data might be an aid for the society’s shortcomings; however, it brings up troublesome issues related to responsibility, privacy, and transparency. Moreover, existing administrative models are based on the understanding that people are the predominant agent. However, Society 5.0 would mix together human and technical collaboration, raising difficult problems related to liability and responsibility. These are just a portion of the difficulties that AI, deep learning, and big data lead to. Thus, as Japan advances toward Society 5.0, there is a requirement for a substantially straightforward accounting of the potential disadvantages encompassing this transition, and further finding solutions to minimize some of the potential risks on the way.

8 Conclusion The chapter initially gives a brief introduction to cyber-security and then iterates about the importance of cyber-security as well as cites the recent trends and statistics related to cyber-security. The discussion thereupon moves on to the elements of cybersecurity and the common cyber-attacks prevalent currently such as Trojan horses and DDoS. The chapter moves on to explain cyber-attacks in physical and cyberspace and the methods which must be followed to deal with them. This is followed by the introduction of Society 5.0 where emergence of Society 5.0 from the previous four societies which came before this is explained. Society 5.0 as discussed in the chapter is a human-centric society and the benefit of Society 5.0 is that aims to automate the common tasks so that the society as a whole can live a comfortable life and it aims to achieve this automation of tasks by using the technologies such as AI, blockchain, and IoT. Due to the increasingly autonomous nature of Society 5.0, the number of cyber-threats would also automatically increase thus the security requirements

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which would be needed to deal with these threats such as raising awareness, securing resources, establishing cyber-security frameworks, etc. are also proposed. This is followed up by three case studies of cyber securities in Society 5.0. The first one on Japan Business Federation (Keidanren) explains how their organization would secure Society 5.0 and their approach to achieving their aim involved measures such as self-help, assistance from governing bodies, and forming global links. The second case study on Hitachi’s vision for cyber-security in Society 5.0 elaborates on the need for trustworthy services and emphasizes Hitachi’s belief that in order to achieve a better way of life in Society 5.0 the services need not only be safe and secure they must follow the S3 architecture which views the society on three considerations, the society view, the service view, and the system view. Hitachi also elaborated on the need for maintaining a database to ensure a service’s credibility, i.e., Trust by data as well as proving the data in the databases is in itself true, i.e., Trust if data. The third case study discusses the vision of the former prime minister of Japan, Shinzo Abe on Society 5.0 and how the socio-economic problems of Japan could be overcome by Society 5.0 as well as the potential cyber-security problems which Society 5.0 would face when it is implemented. In summary, this chapter discusses cyber-security, Society 5.0, various cyberthreats involved with the advancement of the fifth industrial society, and action plans devised for implementing cyber-security to create a secure society for the people.

References 1. Alarming Cyber Security Facts and Stats, https://www.cybintsolutions.com/cyber-securityfacts-stats/ 2. State of the phish, https://www.proofpoint.com/sites/default/files/gtd-pfpt-uk-tr-state-of-thephish-2020-a4_final.pdf 3. IBM Security, https://www.ibm.com/downloads/cas/RZAX14GX 4. Data Breach Investigations Report, https://www.verizon.com/business/resources/reports/2020data-breach-investigations-report.pdf 5. Accenture Security, https://www.accenture.com/_acnmedia/PDF-96/Accenture-2019-Cost-ofCybercrime-Study-Final.pdf#zoom=50 6. Symantec Security Center, https://www.broadcom.com/support/security-center 7. CSO, https://www.csoonline.com/article/3634869/top-cybersecurity-statistics-trends-andfacts.html 8. Varonis, https://info.varonis.com/hubfs/docs/research_reports/2021-Financial-Data-Risk-Rep ort.pdf?utm_content=146358482&utm_medium=social&utm_source=twitter&hss_channel= tw-21672993 9. Hackers Attack Every 39 Seconds, https://eng.umd.edu/news/story/study-hackers-attackevery-39-secondsTechTarget 10. Li Y, Liu Q (2021) A comprehensive review study of cyber-attacks and cyber security; emerging trends and recent developments. Energy Rep 1(7):8176–8186 11. Reddy, G.N., Reddy, G.J.: A study of cyber security challenges and its emerging trends on latest technologies. arXiv preprint arXiv:1402.1842. Accessed 8 Feb 2014 12. Al-Ghamdi, M.I.: Effects of knowledge of cyber security on prevention of attacks. Mater. Today Proc. (2021)

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13. Khurana, S.A.: Review paper on cyber security. Int. J. Eng. Res. Technol. (IJERT) ISSN: 2278–0181 14. Cyberattack, https://en.wikipedia.org/wiki/Cyberattack 15. Cyberterrorism and security measures, https://www.nap.edu/read/11848/chapter/6#45 16. Leaf, https://leaf-it.com/10-ways-prevent-cyber-attacks/ 17. Society 5.0: Aiming for a New Human-centered Society, https://www.hitachi.com/rev/archive/ 2017/r2017_06/trends/index.html 18. Narvaez Rojas, C., Alomia Peñafiel, G.A., Loaiza Buitrago, D.F., Tavera Romero, C.A.: Society 5.0: A Japanese concept for a superintelligent society. Sustainability 13(12), 6567 (2021) 19. Desoutter Industrial Tools, https://www.desouttertools.com/industry-4-0/news/503/industrialrevolution-from-industry-1-0-to-industry-4-0 20. Securing Society 5.0- Overcoming the hidden threats in society’s greatest evolutionary leap, https://cyberkinetic.com/society-5/securing-society-5-introduction/ 21. Fukuyama, M.: Society 5.0: Aiming for a new human-centered society. Japan Spotlight 27(Society 5.0), 47–50 (2018) 22. NRI, https://www.nri.com/en/journal/2020/0825 23. Keidanren, A.: Call for Reinforcement of Cybersecurity To Realize Society 5.0 24. Trusted and Secure Service System for Society 5.0, https://www.hitachi.com/rev/archive/2021/ r2021_04/04b04/index.html 25. Security in Society 5.0, https://bellschool.anu.edu.au/news-events/news/6957/security-societ y-50

Chapter 6

Challenging Aspects of Data Preserving Algorithms in IoT Enabled Smart Societies C. P. Sandhya and B. C. Manjith

Abstract With the technological progress, securing information from attackers is considered a major concerning factor. Society 5.0 is a super-smart society where individuals can tackle many social issues by incorporating various industrial revolutions using smart devices. The influence of these smart devices has made the life of humankind easier and smarter. Due to the parametric changes in size and power requirements, it is tough to apply convention cryptographic procedures on these smart gadgets of Society 5.0 for information security. Generally, standard cryptographic algorithms are too energy-consuming for these embedded devices. One of the significant tools for providing security solutions to these size-limited portable devices is Lightweight Cryptography (LWC). Nowadays, researchers focus on these lightweight security algorithms to optimize maximum throughput and performance with less area and energy consumption. Hence, the current paper presents a detailed analysis of Lightweight Cryptographic cipher approaches on various platforms with their merits and demerits. Also, highlighted the features of each cipher with their performance metrics parameters. Finally, discussed the open research challenges related to the field of Lightweight Cryptography. Keywords Asymmetric · Cryptography · Feistel · Lightweight · Symmetric · Substitution-permutation network

1 Introduction One of the main drifts of the present human life is the involvement of smart technologies. These smart electronics gadgets widely pierce into people’s life which makes them very easier and smarter. The word “Society 5.0” means “a human-centered C. P. Sandhya (B) · B. C. Manjith Department of CSE, Indian Institute of Information Technology, Kottayam, Kerala, India e-mail: [email protected] B. C. Manjith e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_6

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civilization that achieves a balance of economic progress and social problem resolution through a system that tightly connects physical and digital world”. Digital-world operations of Society 5.0 using smart devices involve wireless sensors networks, actuators, Radio-Frequency IDentification (RFID) cards, smart vehicle security, smart monitoring systems, smart meters, Internet of Things (IoT) based micro machines for home automation, smart patient monitoring system, smart natural disaster prediction system, smart environment monitoring, smart agriculture, smart surveillance, and tracking system, etc. The tremendous growth in technology reflects the reduction of size and power consumption by the smart environment objects where security is the major concerning factor to be achieved. The integration of the cyber and physical world increases the risk of personal safety to cyber threats. The size reduction and lower power requirement make it complicated to implement the conventional cryptographic algorithms on these devices. Many research works on smart objects are going on to reduce the power consumption, computational cost, and execution time [1]. Still, the hardware requirements cause the cost of implementation of these devices to be raised high. In a smart environment, objects are communicating and share their sensitive information through wired and wireless mediums. The communication between these smart gadgets must be protected from malicious attacks. These smart devices have limited processing power, small volume, and low power consumption. So, the traditional cryptographic algorithms cannot offer security for these environment-constrained appliances. The conventional cryptosystem typically involves enciphering and deciphering the private messages using an undisclosed key between the independent parties. One of the main limitations of the standard method is that execution would cause the reduction in device performance. The key challenges related to the efficient implementation of conventional cryptography in smart devices are as follows [2]: • • • •

Small implementation area Limited hardware resources Restricted memory (RAM, ROM, registers) Real-time response.

To overcome the above-listed limitations, the most innovative technology called “Lightweight Cryptography (LWC)” is introduced which can be deliberately used in resource-constrained environments. The ultimate goal of LWC is to adequately provide intense security with low computational power for the sensitive IoT devices and Cyber-Physical Systems. For low-powered devices, the cryptographic method with a limited amount of energy consumption is very important [3]. The key reasons for adopting LWC for resource constraint gadgets are [4]: (i) (ii)

Efficient end-to-end communication Adaptability in smart gadgets.

Figure 1 shows the device spectrum, where a traditional algorithm generally performs well in high-end devices and Lightweight Cryptography mainly focused on the low-level smart environment devices [5].

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Fig. 1 Device spectrum

The three important design goals of lightweight cryptographic systems are performance, security, and cost. The trade-off between these design objectives is indicated in Fig. 2. It is extremely easy to enhance any one of the mentioned axes individually, but it is a massive challenge to attain a perfect balance between them. When designing a lightweight cryptosystem, we have to progressively increase the security with less area and power consumption. The design criteria for lightweight crypto algorithms involve minimized area hardware implementation, fast software implementation, flexibility, and reduced key size [6]. In some circumstances, low-end devices may forward private information to the top-end gadgets of the device range (for example sensor—aggregator). In such specific cases, Lightweight Cryptography may also be necessary to implement on both sides of the spectrum in order to interact appropriately between the smart objects

Fig. 2 Trade-off between performance, security, and cost

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C. P. Sandhya and B. C. Manjith Software based algorithm Implementation Mode Hardware based algorithm

Lightweight Cryptographic Algorithms

Symmetric encryption algorithm

Architectural Mode

Asymmetric encryption algorithm Feistel Structure Substitution Permutation Network

Fig. 3 Classification of lightweight cryptographic algorithms

which use lightweight algorithms. Briefly, the need for Lightweight Cryptography represents not just the resource-limited environment, but also the interoperability with end-end devices. The paper consists of seven sections. Section 2 includes an outline of Lightweight Cryptography along with performance metrics. Sections 3 is devoted to the classification of lightweight ciphers and Sect. 4 interprets various lightweight cryptographic methods currently available. Section 5 includes the description of existing approaches to lightweight cryptographic ciphers. Section 6, outlines the open research challenges. Lastly, Sect. 7 concludes the paper.

2 Light Weight Cryptography Lightweight Cryptography is a cryptographic sub-domain that offers effective protection for small-sized environment-constrained devices from malicious attacks with minimal power consumption. The primary aim of Lightweight Cryptography focuses on enhancing the balance between design goals along with better performance for battery-operated devices. In short, Lightweight Cryptography provides a trade-off between lightness and security. Most of the intelligent devices are the specific combinations of microcontrollers, electronic components, and various logic gates. For large applications, it is possible to implement the security procedures through conventional cryptographic ciphers using the microcontrollers with wide ranges up to 32 bits. But for small-scale applications, four bits are enough to perform the security algorithms. Since the conventional standard cryptographic rules require high processing time and power consumption, it is hard to implement in lightweight embedded systems with design constraints like performance, throughput, latency, and security. The fundamental problem related to low-end devices’ security is to typically provide maximum-security strength with less

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energy consumption with reasonable speed [7]. The main advantages of lightweight cryptographic algorithms over conventional algorithms are: • • • •

Limited key size Small block size Simple key schedule Simpler rounds.

2.1 Performance Metrices In Lightweight Cryptographic design, the security strength depends on the perfect balance between the performance and the available resources [8]. Different performance metrics, such as efficiency, latency, area, energy and power consumption, memory usage, throughput, and code size, are used to accurately measure the effectiveness of hardware/software-based implementations of lightweight cryptographic algorithms [9].

2.1.1

Hardware Implementation

In a hardware implementation, power consumption, latency, efficiency, area, throughput, and energy consumption are used as performance metrics. • Power and energy consumption: Power consumption depends on cryptographic algorithms, design principles, and implementation [10]. The major factor related to the power and energy consumption is circuit size. When we reduce the circuit size, there is a reduction in power as well as energy consumption, and cost. That means cost, power, and energy usage are directly related to each other. Power consumption is typically estimated in Watts(W), kilowatts(kW), or microwatt (µW) and is dependent on the clock frequency. Energy consumption can be precisely interpreted as the average power consumed over a period of time [11]. • Area: Gate Equivalent (GE) is used to measure the metric area as well as the complexity of the technology used for hardware implementation. Two NAND gate areas corresponds to one GE [13]. • Latency: Defines the specific time interval between the initial request of a process and the completion of the response [5]. In other words, it is the time taken to convert plaintext to ciphertext related to hardware performance and the total clock cycles per block in terms of software performance. Low latency is essential for real-time applications [14]. • Hardware Efficiency: The ratio between the area requirement and the throughput gives the efficiency of the hardware. It determines the resource usage and is measured in GE per bit per second. • Throughput: It explains the amount of output bits per time unit, i.e., the degree at which new output is produced. It can also be defined as the total output bits divided

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by the time [15]. If the processing rate of the message is higher, the performance is increased. It is evaluated in megabits-per-second (Mbps), gigabits-per-second (Gbps), or bits-per-second (bps). The equation for calculation throughput is given as [16]: Throughput = plaintext size/latency ∗ (plaintext size/8 − 1) ∗ tclock

(1)

For software implementation, it is the rate at which output is produced and expressed in terms of bytes per cycle.

2.1.2

Software Implementation

The Memory size, throughput, and program length are used as the performance metrics in software implementation [17]. • Memory size (ROM/RAM): Generally, memory is measured in kilobytes (KB). Within the CPU, some portion of the memory is fixed for Random Access Memory (RAM) and Read Only Memory (ROM). RAM provides a transient storage space for intermediate values of the computation and ROM (Read Only Memory) provides storage space for the program code algorithm. The memory size represents how much amount of RAM and ROM memory is used for executing the cryptographic algorithms. • Code size: The implementation code size represents the Line of Code (LOC) [18]. As the LOC increases, the complexity of the algorithm increases that making an algorithm more efficient (Table 1).

Table 1 Hardware and software performance metrics

Hardware implementation

Software implementation

Power consumption

Memory size

Energy consumption

Throughput

Latency

Latency

Area

Implementation code size

Hardware efficiency Throughput

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3 Classification of Lightweight Cryptographic Algorithms The standard cryptographic ciphers are not adaptable for embedded systems due to memory constraints. To overcome the limitations, lightweight cryptographic algorithms are proposed for environment-constrained devices with limited size, fewer computational requirements, less memory, and energy consumption. These ciphers provide strong security for the applications in devices with limited resources. Figure 3 shows the classification of Lightweight Cryptographic algorithms based on implementation and architectural mode. The details of the above classification is as follows [5].

3.1 Implementation Mode 3.1.1

Software-Based Algorithm

Small code size, less RAM size, low cost, and high flexibility are the essential features offered by Lightweight Cryptographic software implementation over the hardware implementation. The major disadvantage of this approach is that attackers can easily gain access and modify/alter the software to initiate an attack. Also, the software requires the regular updating process which utilizes more bandwidth and processing power of the device [5]. Speck, RoadRunner, LEA, Pride, Hummingbird, and Chaskey are few examples of software-oriented lightweight ciphers.

3.1.2

Hardware-Based Algorithm

Maximum optimization of resources is the main feature offered by hardware implementation of the cipher. Hardware algorithms are independent of operating systems and execute much faster than software. It gives importance to hiding and guarding sensitive information to make it much tougher to access [19]. PRESENT, PRINT, and KLEIN are few examples of hardware-oriented lightweight ciphers.

3.2 Architectural Mode 3.2.1

Symmetric Encryption Algorithm

The symmetric algorithm is an encryption technique in cryptography that uses the same key for encryption and decryption. It guarantees confidentiality and integrity, but not authentication. Classification of the symmetric cipher is as follows:

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Stream Cipher: In this type of cipher, each bit/byte of plaintext is encrypted one at a time. These ciphers are mainly for securing data in wireless applications [20]. Initially, a key will be provided as an input to a pseudorandom bit generator and produces an 8-bits output which is treated as a keystream. The role of this random number generator is to make the cryptanalysis more complex [21]. Block Cipher: In this cipher, instead of one bit, block of bits is encrypted one at a time. Here the block size and key size are fixed. Confusion and diffusion are the two basic properties of a secure block cipher. Confusion makes the relationship between encryption key and ciphertext very complex where the diffusion hides the statistical properties of the plaintext. That means each bit of plain text is distributed over many ciphertext bits [12]. We can classify the block cipher according to the structure as Feistel-based network and Substitution Permutation Network (SPN). Feistel networks can be further classified as classical Feistel Networks and Generalized Feistel networks [22]. Hash Cipher: It is a one-way process where the plaintext is scrambling into randomized or near-randomized ciphertext. The hash cipher converts data of arbitrary size to string fixed length. The value generated by the hash function is called the hash value. The hash functions are used to process information in several applications to achieve security goals.

3.2.2

Asymmetric Encryption Algorithm

The asymmetric uses different keys for the encipherment and decipherment process. It guarantees confidentiality, strong authentication, integrity validation, and nonrepudiation. It is again subdivided into Discrete Logarithm, RSA, Elliptic-curve cryptography (ECC), and others.

3.2.3

Feistel Structure

In this structure, encipherment and decipherment use the same algorithmic procedures. The number of processing rounds in the Feistel structure is large. Each iteration requires one-half of the data to be processed. Classical Feistel networks and Generalized Feistel Networks (GFN) are the two classifications of Feistel structures. In GFN, the input block is split up into sub-blocks and performs Feistel function to every sub-block pair followed by a cyclic shift operation.

3.2.4

Substitution Permutation Network

In this structure, one round constitutes the combination of substitution and permutation layer with keys. The substitution layer has S-box and the Permutation layer P-box for the fixed permutations operation.

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4 Various Lightweight Cryptographic Ciphers In recent years, cryptographic designers have proposed new lightweight cryptographic ciphers to provide security and confidentiality for resource-limited devices. In this section, we are discussing some of these lightweight ciphers. AES [23]: Advanced Encryption Standard (AES), is the most elegant cryptographic algorithm suitable for software implementation within a considerable amount of time on any platform. It was designed by Vincent Rijmen and Joan Daemen [24]. Its structure consists of exclusive-OR, S-box octet substitution, mixed column, and row-column rotations. It allows you to choose the key length of 128-bits, 192-bits, or 256-bits, and 10, 12, or 14 rounds, which makes this encryption much more secure than the Data Encryption Standard (DES). The 128-bits of block is used in AES. Due to the features of round number and key length, AES and its variants are suitable for Lightweight Cryptography. PRESENT [25]: PRESENT is the popular lightweight cryptographic algorithm that takes plaintext of 64 bits, and encryption key of 80 or 128 bits. Orange Labs (France), Ruhr University Bochum (Germany), and the Technical University of Denmark collaborated to develop the PRESENT cipher [26]. It is mainly used on hardware-constrained devices for providing low power and high efficiency. It uses 4-bits S-box and substitution permutation network (SPN). mCrypton [27]: mCrypton is a 64-bits block-sized lightweight cipher with 12 computational rounds. It was developed by Lim and Korkishko. It allows the users to choose any key sizes of 64, 96, and 128 bits to increase security. Each round consists of different layers namely substitution, a column-wise permutation, a column-to-row transposition, and key addition [28]. CLEFIA[29]: CLEFIA is a highly secure lightweight block encryption with 128-bits of block size and key size of either 128,192 or 256 bits. It was designed by Sony Corporation. It follows generalized Feistel network specialized types of Feistel structures. According to the number of key lengths, CLEFIA variants take 18, 22, or 26 rounds, respectively for enciphering one data block. Trivium[30]: Trivium is a lightweight cryptographic stream cipher that uses hardware to produce 264 bits of key using an 80-bit secret key and an 80-bit initial value (IV). Its internal state is made up of three shift registers of varying lengths. Following each round, each bit uses three shift registers, as well as a non-linear combination of taps and one register, to generate a single bit of ciphertext. This provides greater flexibility in implementation. Cavium [31]: Cavium is a lightweight cipher based on Trivium. It reduces the number of rounds of Trivium from 1152 to 144. To produce a secure design, it uses both non-linear and

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linear rules to make it operates faster than the equivalent linear feedback shift register (LFSR) and non-linear feedback shift register (NFSR) structures. The hardware implementation of Cavium is complex. Grain-128 [32]: Grain-128 is a Grain cipher version with 128-bits of key and 96-bits Initialization Vector, originally developed for high-level security purposes. It typically has a simple design and targets embedded devices with less power consumption and area requirement. Grain-128 is used by SQUASH as the basic module for the hash function [33]. BEAN [34]: BEAN is a new Grain-based Lightweight Cryptographic Cipher. It uses Fibonacci FCSRs with an 80-bits key length to implement. It uses S-boxes to improve diffusion qualities while also preventing cryptanalysis introduced by FCSRs. The benefit of BEAN over Grain is that it allows binary-based output without the need for extra hardware. BEAN has a shorter key generation time than Grain, making it more compactable. Because of BEAN’s weak output function [35], distinguisher attacks and state-recovery attacks can occur [36]. SAEB [37]: The LightWeight cryptographic cipher SAEB is based on Authenticated Encryption with Associated Data (AEAD) designed to give outstanding performances in platforms with limited computational resources. It provides confidentiality and authenticity. With the Minimum state size, inverse free, XOR only, and efficient handling of static associated data (AD) properties, SAEB has reduced the working memory size as well as contributed to a small program/circuit footprint. RECTANGLE [38]: It is an iterated ultra-lightweight block cipher with a block length of 64 bits and a key length of 80 or 128bits. RECTANGLE follows the bit-slice techniques and uses a substitution permutation (SP) network. One of the advantages of RECTANGLE over PRESENT is the reduced number of rounds from 31 to 25. It is efficient and more suitable for resource constraint environment devices. Figure 4 shows the categories of lightweight cipher and Table 2 shows the year in which each cipher is developed/published.

5 Existing Works The demand for lightweight cryptography is growing due to the increase in intelligent devices and a lot of work is going on in the area of symmetric and asymmetric lightweight ciphers. The following section reveals various lightweight cryptographic ciphers in the literatures and their hardware and software implementations for future reference. A 64-bit block cipher named Piccolo, is a version of generalized Feistel network with key scheduling based on permutation was proposed by Shibutani et al. [14].

97

Fig. 4 Various lightweight cryptographic ciphers

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Table 2 Details of lightweight ciphers Sl. No. Cipher

Year

1

McEliece

1978 Asymmetric –

Type

Block

2

TEA

1994 Symmetric

Tiny encryption algorithm

Block

3

XTEA

1997 Symmetric

eXtended TEA

Block

4

AES

1998 Symmetric

Advanced encryption standard

Block

5

Camellia

2000 Symmetric

-

Block

6

GRAIN

2004 Symmetric

-

Stream

7

HIGHT

2006 Symmetric

-

Block

8

MikeyV2

2006 Symmetric

Mutual irregular clocking Stream KEY stream generator version 2

9

mCrypton

2006 –

–

Block

10

PRESENT

2007 Symmetric

–

Block

11

DESL/DESX

2007 Symmetric

Data encryption standard Block lightweight (DESL)/extension of DES (DESX)

12

CLEFIA

2007 Symmetric

–

13

TRIVIUM

2008 Symmetric

–

Stream

14

KATAN/KTANTAN 2009 Symmetric

–

Block

15

Humming bird

–

Hybrid

16

LED

2011 Symmetric

Light encryption device

Block

17

TWINE

2011 Symmetric

–

Block

18

LBLOCK

2011 Symmetric

–

Block

19

PRINCE

2012 Symmetric

–

Block

20

KLEIN

2012 Symmetric

–

Block

21

ITUbee

2013 Symmetric

–

Block

22

SIMON/SPECK

2013 Symmetric

–

Block

23

NTRU

2013 Asymmetric –

Block

24

LEA

2013 Symmetric

Lightweight encryption algorithm

Stream

25

Rectangle

2014 Symmetric

–

Block

26

ECC/HECC

2014 Asymmetric iElliptic-curve cryptography (ECC) Hybrid elliptic-curve cryptography (HECC)

Block

27

Blowfish

2014 Symmetric

–

Block

28

Midori

2015 Symmetric

–

Block

2010 Symmetric

Expanded form

Block/stream

Block

(continued)

6 Challenging Aspects of Data Preserving Algorithms in IoT …

99

Table 2 (continued) Sl. No. Cipher

Year

29

QTL

2016 Symmetric

Type

Expanded form

Block/stream

–

Block

30

ANU

2016 Symmetric

–

Block

31

Piccolo

2017 Symmetric

–

Block

32

NVLC

2018 Symmetric

New variant lightweight cryptography algorithm

Block

33

SLIM

2020 Symmetric

–

Block

Piccolo provides high-security against related-key differential attacks and meet-inthe-middle attacks without any reduction in power and energy consumption as well as provides compact hardware implementation. This work requires only 60 extra gate equivalents to enable the decipherment function. The authors recommended Piccolo as the appropriate cipher for very constrained environments mainly for RFID tags and sensor nodes. De Cannière et al. [22] presented a simple block cipher algorithm for RFID tags and sensor networks named KATAN/KTANTAN. The three variants of KATAN ciphers are KATAN32, KATAN48, KATAN64 which share the same key of size 80-bits key through 254 rounds. These variants are operated on a Linear Structure Shift Register (LFSR) with a simple key scheduling algorithm whereas KTANTAN exhibits no generation operation and its key remains unchanged throughout the process. Two small hardware footprints of KATAN/KTANTAN are KATAN 802GE and KTANTAN 462 GE for RFID applications and De Cannière pointed out that due to the high bit manipulation usage, these algorithms are not suitable for software implementation. Rana et al. [39], pointed out the importance and requirement of security as well as the less processing power for the resource-constrained devices. They proposed an efficient algorithm that constitutes of 64-bits key and block size with five restricted rounds of operations. It utilizes the SPN structure and modified Feistel structure. The algorithm takes 64-bits plaintext as input and pass-through five rounds with five keys in each round and processes using G-functions. The performance and efficiency are measured from the execution cycles, Random Access Memory footprint, and binary code size of the proposed one. The eight lightweight cryptographic ciphers namely AES, PRESENT, SIMON, LEA, SPECK, SIT, RC5, HIGHT are implemented on FELICS using the AVR platform and the comparison outcome shows that the proposed method is efficient with 16.73% reduced power consumption, reduced processing power, less RAM, reduction in encryption and decryption cycles than the other existing ciphers. In the review of hardware architecture of lightweight ciphers, Kitsos et al. [40], conducted a performance evaluation of eight lightweight block ciphers namely DESL, DESXL, CURUPIRA-1, CURUPIRA-2, XTEA, PUFFIN, PRESENT and HIGHT. A proposed iterative loop architecture is used for the implementation of the ciphers and simulated using the MentorGraphics ModelSim tool to analyze the design

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and functionality. The performance evaluation metrics used are the throughput, area, power consumption, and hardware resource cost of each cipher. The analysis shows that PRESENT is suitable for RFID application with minimum power consumption of 20 µW and the CURUPIRA family has the hardware implementation with the power consumption of 118.1 µW. Irwin et al. [41] studied both the hardware and software implementation of PRESENT cipher on various platforms namely PC, BeagleBone Black, and Zedboard. Encryption and decryption of PRESENT ciphers are programmed in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) using the Xilinx Vivado software with the programming languages C and Python. The result shows that hardware execution is thousand times faster in enciphering and deciphering the data compared to the software execution in C as well as Python. The implementation in C outperforms Python with the ARM processor. A study on hardware and software implementation of various lightweight symmetric and asymmetric ciphers was conducted by Eisenbarth et al. [42]. An 8-bits AVR microcontroller is used for the implementation. Lightweight ciphers used for comparison are PRESENT, AES, HIGHT, CLEFIA, MCRYPTON, DES, DESXL, Trivium, IDEA, TEA, SEA Salsa20, LEX, and GRAIN. The metrics considered for software implementation are RAM, ROM, clock cycles, and chip area, clock cycles for hardware implementation. The result shows that PRESENT gives a poor performance whereas Lex and Salsa20 give good throughput and size with computational overhead. When Line of Code (LOC) is very high, TEA, IDEA, and PRESENT seem to be better choices. Diehl et al. [43], analyzed hardware and software implementation of six ciphers AES, SIMON, SPECK, PRESENT, LED, and TWINE in Xilinx Kintex-7 FPGA. Register Transfer Level (RTL) design is used for hardware implementation and an 8-bits soft core microprocessor is used for software implementation. Block size, key size, rounds, I/O bus width, Key bus width, Datapath width cycles/blocks, the latency per block are used as comparison metrics for hardware implementation, and for software implementation, the metrics are frequency, area, throughput, and throughput-to-area ratio[44]. The result shows that TWINE and AES provide better throughput-to-area (TP/A) ratios for hardware and software execution, respectively. Prabu et al. [45], studied lightweight cryptographic encryption and decryption algorithm based on the time complexity, message, and key size. The authors chose PRESENT, HIGHT, ITUBEE for comparison. The time complexity is calculated by considering the amount of work done by the calculation, result shows that the PRESENT has the least time complexity followed by HIGHT, ITUBEE, and PRESENT. Alizadeh et al. [46] examined the performance of four lightweight encryption ciphers used in RFID applications and they implemented the ciphers TEA, KLEIN, KATAN, and HIGHT on AVR Atmel ATtiny45 microcontroller and analyzed the performance in terms of memory efficiency, energy consumption, degree of confusion and diffusion. The analysis shows that KATAN is memory efficient and it consumes more energy than KLEIN. The security is evaluated by considering the degree of

6 Challenging Aspects of Data Preserving Algorithms in IoT …

101

confusion as well as diffusion. And KLEIN has the lowest degree of diffusion and the highest degree of confusion. A hybrid lightweight encryption technique with quickest bit permutation instruction PERMS with PRESENT S-box layer for the non-linearity was proposed by Thorat et al. [47]. ARM processor is used with Linux Platform along with Cadens tool to measure the hardware and the software performance. The overall performance of the proposed work is compared with current lightweight cryptographic ciphers and the evaluation shows that the proposed hybrid work has fewer CPU cycles with a good avalanche effect. Hong et al. [48], proposed a block cipher algorithm named LEA of 128-bits block size with 128,192 and 256-bits key length through 24, 28, and 32 rounds, respectively which is implemented on small area code and provides faster encryption process. The hardware implementation provides better throughput per area. And it requires 590 bytes of ROM and 32 bytes of RAM for 326.94 cycles per byte speed on the ARM platform. Meet-in-the-middle attack, pseudo-preimage attack, partial-matching, and initial-structure techniques are not well addressed by LEA. Satoh et al. [49], demonstrated a high-speed hardware architecture of 128-bits block ciphers AES, CAMELIA and compared it with 3-DES on ASIC standard cell libraries and Xilinx FPGA platform. Small Substitution-Box design having composite field inverters with lookup table is used for S-Box optimization. The advantages of proposed ciphers are the smallest gate counts with highest throughput. The comparison shows that the measured throughput and gate counts are same for above-mentioned algorithms but the efficiency of AES is higher than that of Camellia when the gate number is increased. It is evident that AES is better for both compact and high-speed hardware processing through analysis. The proposed circuit is efficient in terms of both speed and size. As a result, the proposed AES and Camellia block ciphers are appropriate for a wide range of hardware applications, including reduce-power mobile terminals and high-speed communication servers. Mohd et al. [50], designed and performed a scalar, pipelined FPGA implementation of HIGHT lightweight cipher on Verilog, Quartus, and Cyclone II. The main goal of the work is to minimize the hardware requirement and energy with high throughput. The experimental result shows better performance using pipeline and scalar design with the reduced number of rounds. The comparison results show that the scalar implementation requires fewer resources and power by 18% and 10%, respectively. The pipeline implementation, requires 18 times greater throughput and 60% less energy consumption. Leglise [51], evaluated the hardware efficiency of three block ciphers namely LILI-II, Helix, and Snow2.0 by implementing them on Xilinx FPGAs. The result shows that stream ciphers are not much more efficient for hardware implementation than block ciphers. Among the comparison of the existing ciphers, A5/1 is the weakest one, Helix is efficient but requires precomputations which may not be practical for the embedded on a single platform. SNOW 2.0 showed the best implementation and efficiency than LILI-II, Helix.

LFSR

De Cannière et al. [22]

Rana et al. [39]

Kitsos et al. [40]

D Irwin et al. [41]

1

2

3

4

SPN

SPN, FN

SPN, modified FN

Structure

Sl. References No.

Hardware and software

Hardware

Hardware

Hardware

Implementation

Table 3 Summarization of implementation of various ciphers

PC, BeagleBone Black and Zedboard, C, Python

MentorGraphics ModelSim

LINUX, FELICS for measurement, MATLAB for encryption testing

Synopsys design compiler version Y-2006.06, fsc0l_d_sc_tc 0.13 µm CMOS library

Tools

Efficient PRESENT encryption and decryption algorithm

Iterative loop architecture

Genetic Algorithm

Efficient KATAN/KTANTAN encryption and decryption algorithm

Methodology

Hardware implementation is faster

Appropriate ciphers for RFID application PRESENT cipher has lowestpower consumption and the more compact implementation

Less power consumption Suitable for resource constrained devices Reduce the use of processing cycle

80-bits high level algorithmic security Simple structure

Merits

(continued)

Software implementation is slow for encryption and decryption information

CURUPIRA family have high power consumption and high chip area

Moderate number of computation cycles

Low throughput High energy consumption, so not suitable for software implementation

Demerits

102 C. P. Sandhya and B. C. Manjith

SPN

Eisenbarth et al. [42]

Diehl et al. [43]

Prabu et al. [45]

Alizadeh et al. [46]

Thorat et al. [47]

5

6

7

8

9

SPN

SPN, FN

SPN, FN

SPN, FN

Structure

Sl. References No.

Table 3 (continued)

Hardware and software

Hardware

Hardware

Hardware and software

Hardware and software

Implementation

ARM cortex A15 processor is used with Linux Platform, Cadens tool

AVR Atmel ATtiny45 microcontroller, assembly language in C or Java

Ubiquition sensor network on 8-bit processor

Xilinx Kintex-7 FPGA, 8-bit soft core reconfigurable processor

8-bit AVR microcontrollers, lookup table (LUT), AVR (the Atmel ATmega128 platform), 8051 (the Chipcon CC1010 platform)

Tools

Fastest bit permutation instruction PERMS with PRESENT S-box

Efficient algorithms of KATAN, HIGHT, TEA, KLEIN cipher

For PRESENT: round-subordinate key schedule For HIGHT: Arexor and bitwiserevolutions For ITUBEE: Key whitening Technique

Register transfer level (RTL) design and efficient algorithms of SIMON, SPECK, PRESENT, LED, and TWINE, AES

Efficient encryption and decryption algorithms of symmetric and asymmetric ciphers

Methodology

Less CPU cycle, faster and area efficient Good avalanche effect

KLEIN is best for power consumption and highest degree of confusion Appropriate ciphers for RFID application

Complex for intruders to identify the key

Software implementation consume less power whereas hardware Implementation has high throughout

Lex and salsa20 provides good throughput

Merits

(continued)

Only useful for software accelerated lightweight cryptography

KATAN consumes more energy KLEIN has lowest degree of diffusion

ITUBEE has less time complexity than HIGHT and PRESENT

Hardware implementation has high power usage

PRESENT gives poor performance

Demerits

6 Challenging Aspects of Data Preserving Algorithms in IoT … 103

ARX

Hong et al. [48]

Satoh et al. [49]

Shibutani et al. [14]

Mohd et al. [50]

10

11

12

13

GFN

GFN

SPN, FN

Structure

Sl. References No.

Table 3 (continued)

Hardware

Hardware

Hardware

Hardware and software

Implementation

Scalar and pipeline architecture

Efficient Piccolo algorithm Half-word based round permutation Effective permutation for key expansion

Verilog-HDL,0.13 µm standard cell library, VCS version 2006.06, Design Compiler version 2007.03-SP3

VerilogTM, Altera FPGA Quartus-IITM, FPGA cyclone-II

Camellia, triple-DES architecture, small S-box hardware designs

Efficient LEA encryption and decryption algorithm

Methodology

0.13-µm and 0.18-µm ASIC standard cell libraries, Xilinx FPGA

Intel, AMD, ARM, ColdFire

Tools

Scalar: 18% less resources and 10% less power Pipeline: 16% less energy and 18 times higher throughput and 60% less energy consumption

High security against related-key differential attacks and meet-in-the-middle attacks, side channel attack Low demand for resource and power consumption Suitable for RFID and sensor networks

Best performance in speed and size Highest throughputs High-speed hardware implementations

High-speed software encryption on general-purpose processors Secure against all cipher attacks like differential attack, linear attack, zero correlation attack, boomerang attack, integral attack

Merits

(continued)

Pipeline consume high power than scalar design

Not efficient to share the key scheduling block for two plaintext blocks concurrently

Number of rounds affects the performance

Not efficient for Meet-in-the-middle pseudo-preimage attack, partial-matching and initial-structure techniques

Demerits

104 C. P. Sandhya and B. C. Manjith

_

14

Leglise [51]

Structure

Sl. References No.

Table 3 (continued)

Hardware

Implementation Xilinx Virtex XC2v6000ff1152-6 FPGA

Tools Fibonacci LFSR, Galois LFSR for LILI-II Efficient algorithms of Helix and Snow2.0

Methodology SNOW 2.0 showed the best implementation and efficiency

Merits

Helix requires software precomputation. Synchronization process of LILI-II is very expensive SNOW2.0 795 requires slices for a throughput of 13,781 Mbits/sec and mainly dedicated for software-oriented

Demerits

6 Challenging Aspects of Data Preserving Algorithms in IoT … 105

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The structure, implementation, tool, methodology, merits, and demerits of the literature studied are consolidated in Table 3. And the performance metrics of various ciphers are tabulated in Table 4.

6 Discussion and Open Research Challenges The main role of Lightweight Cryptography is to ensure security for resource constraint devices which is a significant challenge. The main issue yet to be addressed thoroughly is how to enhance the efficiency by minimizing the size of implementation. In this survey paper, various approaches to lightweight cryptographic ciphers are analyzed and highlighted the main features of ciphers with performance metrices. According to the increasing demand for IoT smart objects, the importance of Lightweight Cryptography is also increasing. The main focus of lightweight cryptographic designers has to analyze the computational complexity of each algorithm. Conventional cryptographic ciphers are not appropriate for these gadgets because of the size constraints. Other challenges identified are listed below: • Authentication and Data security for IoT devices: Authentication and data security are the most important factor related to smart devices. Recently available hybrid ciphers are not efficient for authentication. They utilize the maximum memory capacity of the device. And hence an efficient algorithm is required for the authentication of IoT devices with less memory usage. Data security is another challenge that provides rapid confusion and diffusion with fewer rounds [52]. • Balance between cost, performance, and security: For any resource constraints devices, the trade-off between design goals namely cost, performance, and security is an unavoidable factor. A perfect balance should be achieved between cost, performance, and security while designing the lightweight ciphers. A research direction is required to attain the great trade-off between these design goals. • Reduce the size of area and key: Another challenge is the maximum optimization of the hardware-related stream and block ciphers to provide security to tiny low-power computing devices with fewer gate equivalents and minimum cycle instruction codes. These challenges are directly related to chip power and area usage of resource-restricted devices. • Reduce the number of S-boxes: Most of the available lightweight cryptographic ciphers use numerous S-boxes to provide high-security results in the utilization of large memory space of the devices. The confusion, which is an important property of cryptography can be achieved by increasing the S-boxes along with the perfect balance of security and performance trade-off. Many researchers have developed modified S-box cryptographic algorithms for high-security purposes. But researchers can concentrate on other confusion techniques to replace s-boxes with same level of security and less memory consumption [53].

PRESENT

Kitsos et al. [40]

LEA

CAMELIA

PICCOLO

Shibutani et al. [14]

TWINE

Satoh et al. [49]

LED

Hong et al. [48]

16/24

36

SPECK

18

24/28/32

20

22

SIMON

ITUBEE

10

64

AES

6 or 8

DESXL

64

128

128

80

64

64

96

96

128

64

64

96

6 or 8

CURUPIA-2 10

DES

96

64

CURUPIA-1 1 or 23

Prabu et al. [45]

Diehl et al. [43]

Eisenbarth et al. [42]

5

–

Rana et al. [39]

64

32

16

32

254

80

128

128

80

80

80

96

96

128

184

56

96

96

80

64

80

80

21448

64000

168

2607

36

244

28

52

308

144

144

10

10

31

1228

–

–

1136

6511

3826

–

306

358

452

435

318

2168

2309

7334

8334

1704

–

462

802

237.04

290.1

76.19

122.7

1176

111

1622

1041

119

44.4

44.4

960

960

206.4

–

12.5

12.5

208.66

44.55

19.91

–

3.823

0.31

3.588

2.394

0.375

–

–

0.1308

0.1151

0.1211

–

27.05

15.58

1.13

1 9.76

3.82

10.4

145

108

129

148

109

–

–

98

117

20

40.14

0.46

0.8

No. of rounds Block size Key size Cycle/block Area (GE) Throughput (Kbps) Throughput-to-area Power (µW) ratio (Kbps/GE)

254

Cipher

Performance metrices

De Cannière et al. KATAN [22] KTANTAN

References

Table 4 Performance metrics of various ciphers

6 Challenging Aspects of Data Preserving Algorithms in IoT … 107

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• Secure against vulnerabilities: With the increasing demand for smart gadgets vulnerability attacks, on these devices also increased. Various kinds of vulnerable attacks include Control hijacking attacks, Eavesdropping, injecting crafted packets or input, etc. More research works are to be carried out in this area to secure devices and their sensitive information from vulnerable attacks. • Generation of random subkeys: Develop a technique to create random subkeys from the first key for all n rounds by reducing the number of computational rounds without affecting the performance and security level [53]. • Integrity, Availability, and Confidentiality: The basic elements of security are confidentiality, integrity, and availability. It is very challenging to make secure communication between smart devices with the above-mentioned security services. And challenges in lightweight schemes are design challenges which include reducing the key generation complexity, reducing the round function complexity, more flexibility, high performance with low cost, less power consumption, etc. [54]. • Physical security: Physical security is the most challenging in IoT devices, researchers must work to generate the suitable small key in public-key cryptosystems which provide the secure data transmission along with Confidentiality, integrity, and availability [55].

7 Conclusion Lightweight Cryptography is an emerging trend for smart embedded devices. The hardware/software-based implementations of lightweight cryptographic algorithms are researched and analyzed in this review article. Every algorithm has its advantages and disadvantages according to the type of platform and implementation. In the end, the research direction issues in the field of Lightweight Cryptography are discussed and a detailed evaluation of existing lightweight ciphers has been tabulated for further reference.

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28. Mathews R, Deepa VJ (2020) Analysis of lightweight cryptographic algorithms for internet of things. Available at SSRN 3734786 29. Shirai T, Shibutani K, Akishita T, Moriai S, Iwata T (2007) The 128-Bit Blockcipher CLEFIA (Extended Abstract). In: Biryukov A (eds) Fast software encryption. FSE, Springer, Berlin 30. Shanmugam D, Annadurai S (2015) Secure implementation of stream cipher: Trivium. In: Bica I, Naccache D, Simion E (eds) Innovative security solutions for information technology and communications. Springer 31. Karmakar S, Mukhopadhyay D, Chowdhury DR (2012) CAvium—strengthening. Trivium Stream Cipher Using Cellular Automata. J Cell Autom 7:179–197 32. Hell M, Johansson T, Maximov A (2006) A stream cipher proposal, Grain-128. In: IEEE international symposium on information theory, Seattle, WA, pp 1614–1618 33. Shamir A, Kaisa N (2008) SQUASH—a new mac with provable security properties for highly constrained devices such as RFID tags. In: Fast software encryption, Springer, Lausanne, Switzerland, vol 5086, pp 144–157 34. Kumar N, Ojha S, Jain K, Lal S (2009) BEAN: a lightweight stream cipher. In: 2nd international conference on security of information and networks (SIN ’09), Gazimagusa, North Cyprus, pp 168–171 35. Agren M (2012) On some symmetric lightweight cryptographic designs. Doctoral Thesis, Department of Electrical and Information Technology, Faculty of Engineering, LTH, Lund University 36. Gunathilake NA, Buchanan WJ, Asif R (2019) Next generation lightweight cryptography for smart IoT devices: implementation, challenges and applications. In: IEEE 5th world forum on internet of things (WF-IoT), pp 707–710 37. Naito Y, Matsui M, Sugawara T, Suzuki D (2018) SAEB: a lightweight blockcipher-based AEAD mode of operation. In: IACR translation cryptographic hardware and embedded systems, pp 192–217 38. Zhang W, Bao Z, Lin D, Rijmen V, Yang B, Verbauwhede I (2014) RECTANGLE: a bit-slice ultra-lightweight block cipher suitable for multiple platforms. IACR Cryptol. ePrint Arch 39. Rana S, Hossain S, Shoun HI, Kashem MA (2018) An effective lightweight cryptographic algorithm to secure resource-constrained devices. Int J Adv Comp Sci Appl (IJACSA) 40. Kitsos P, Sklavos N, Parousi M, Skodras AN (2012) A comparative study of hardware architectures for lightweight block ciphers. Comput Electr Eng 38:148–160 41. Irwin D, Liu P, Chaudhry SR, Collier M, Wang X (2018) A performance comparison of the PRESENT lightweight cryptography algorithm on different hardware platforms. In: 29th Irish signals and systems conference (ISSC), Belfast, pp 1–5 42. Eisenbarth T, Kumar S, Paar C, Poschmann A, Uhsadel L (2007) A survey of lightweightcryptography implementations. IEEE Des Test Comput 24:522–533 43. Diehl W, Farahmand F, Yalla P, Kaps J, Gaj K (2017) Comparison of hardware and software implementations of selected lightweight block ciphers. In: 27th international conference on field programmable logic and applications (FPL), pp 1–4 44. Al-Aali Y, Boussakta S (2020) Lightweight block ciphers for resource-constrained devices. In: 12th international symposium on communication systems, networks and digital signal processing (CSNDSP), pp 1–6 45. Prabu M, Nenregen R, Kumar SS (2018) Comparing lightweight algorithms for embedded system security. Int J Pure Appl Mathe 119:14191–14198 46. Alizadeh M, Salleh M, Zamani M, Shayan J, Karamizadeh S (2012) Security and performance evaluation of lightweight cryptographic algorithms in RFID. In: WSEAS conference on recent researches in communications and computers, Kos Island, Greece, pp 45–50 47. Thorat CG, Inamdar VS (2018) Implementation of new hybrid lightweight cryptosystem. Appl Comput Inform 16:195–206 48. Hong D, Lee JK, Kim DC, Kwon D, Ryu KH, Lee DG (2014) LEA: a 128-bit block cipher for fast encryption on common processors. In: Kim Y, Lee H, Perrig A (eds) WISA 2013.LNCS, vol 8267, pp 3–27. Springer, Cham

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Chapter 7

Smart Tutoring System for the Specially Challenged Children M. Sumana, Sahana S. Hegde, Shreehari N. Wadawadagi, D. V. Sujana, and Vaibhav Gubbi Narasimhan

Abstract Indian Sign Language (ISL) is used language among people with hearing loss and inability to speak in India. To interact with the deaf and dumb it is essential to learn this language and communicate efficiently through body movements. A knowledge of the sign language is required to teach children with these disabilities. The proposed system is built to assist users to converts speech to the sequence of sign language symbols. It performs speech to text, transforms it into a suitable format, and identifies the animations required for the phrases that will help convey the desired message. The model will help teachers to deliver the subjects to the students without explicit training in the sign language. Desktop version can comfortably get a good number of users. The sign language applications that are available are restricted to accepting text and are not able to handle combined phrases or Subject-object-verb patterns. All mobile applications that are available in the store do not work for audio inputs. Our work addresses these issues of handling combined phrases using the semantics of NLP that is used to reduce the text into smaller interpretable modules. A dictation recognizer takes in speech as input through microphone enabling the proposed model to accept audio. The animations for interpreting text are created using Unity 3D tool. In the front end, a dictation recognizer is included. Text pre-processing is done using NLTK in python. The pre-processing part includes elimination of the regularly used words, identification of base form of the words, handling of word M. Sumana (B) Department of Information Science and Engineering, Ramaiah Institute of Technology, Bangalore, India e-mail: [email protected] S. S. Hegde Barclays, Bangalore, India S. N. Wadawadagi Bloomreach, Bangalore, India D. V. Sujana Arizona State University, Tempe, AZ, USA V. G. Narasimhan GE Healthcare, Bangalore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_7

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meanings, and converting the given sentence into Subject-Object-Verb form. It is also necessary that the sentences of the Indian Sign Language have to be in this form. As the proposed model displays the conversations done in Indian Sign Language this can be used by teachers as well as any person who wants to communicate a given message without having any training for the same. This idea can be extended to multiple languages also, in which case languages other than English can also be used. Keywords Indian sign language · Lemmatization · Unity 3D · Dictation recognizer · Natural language processing

1 Introduction Indian Sign Language (ISL) is a commonly used language among deaf and dumb people in India. Conversations in this language include use of hand and body movements and ideas. Tutors who need to convey accurate information require training. The proposed system accepts speech and transforms them into the equivalent symbols in the sign language making it easier for the deaf and dumb to easily interpreted. The audio data collected is converted to text which is then processed. The process phrases are mapped to animations which in turn will assist the speech and hearing impaired. The teachers need not be trained to teach the subjects if this system is used. Extending the same idea for multiple languages will get a huge outreach. The desktop version can comfortably get a good number of users. The existing sign language applications are constrained to text inputs, and simple phrases and do not follow ISL rules. The proposed system follows the ISL rules extensively and also accepts speech input. Tutoring system can be extended to multiple languages and a document scanner can be embedded in it.

1.1 Motivation The deaf and dumb people have been given least priority. Not many facilities are available in making the teachers learn and understand ISL. As the result, the deaf and dumb community has difficulty in learning. According to the 2011 Indian census, there are roughly 5 million people with “hearing impairment”. In contrast to this is the number from India’s National Association of Deaf, which estimates it to be 18 million people. So, we can conclude that there are no proper statistics regarding the population of deaf and dumb in this country. Most perceiving sensory organ of the human body is the eye. Deaf and dumb can use this to their fullest advantage. But due to lack of resources and awareness among the people, they do not become an asset to themselves.

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The deaf and dumb have special schools for education. But this also means that the teachers need to be well-versed with the Indian Sign Language (ISL) in order to communicate and teach properly. According to 2017 Press Release by Indian Sign Language Research and Training Center, the number of deaf and dumb schools is 386, whereas the number of skilled teachers in this country is less. The approach discussed below will assist the mute community and enable them to learn and explore more. Also, it eases the task of the teachers to share their knowledge among the hearing loss learners. It removes the need to be aware of the Indian sign language beforehand. The mobile applications available in the market do not effectively convert each word into its respective sign and do not follow the ISL grammar. The motto of the work is to educate the deaf and dumb students in the language they understand without initial training from the teachers in ISL.

1.2 Constraints and Requirements The constraints identified are listed as follows • Ratio of English to sign language words is a large amount, i.e., the number of English words for which an equivalent ISL word exists is quite less. • Proper nouns have to be split according to their alphabets only. • Certain non-English origin words may not be interpreted correctly in the dictation recognizer. • Certain words need context recognition and some non-existent words in the dictionary need appropriate synonyms to be shown in the form of animations. • Words are being constantly added to the ISL dictionary, which also means their respective animations must also keep getting created. Unity’s Dictation recognizer feature works only on Windows platform. Software Requirements include Windows 10 or Ubuntu, Python three or above, Unity 3D, Numpy, NLTK, spacy libraries. Hardware Components include an intel i3 7th generation processor, 8 GB RAM, DDR4 2133 MHz, TB Hard Disk (7200 RPM) + 512 GB SSD, and an Intel Heatsink to keep temperature under control. If you have more than one surname, please make sure that the Volume Editor knows how you are to be listed in the author index.

1.3 Problem Statement The mute community has difficulties in receiving quality education due to the requirement of learning sign language extensively in order to teach. Due to the immense ratio of the number of deaf and dumb students to the number of qualified teachers, the mute community is being overlooked. The number of deaf and dumb achievers is also unfortunately less because of lack of resources and opportunities.

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The proposed system converts speech to sign language easily and quickly, thus eliminating the mandatory need of knowing the Indian sign language beforehand to translate speech into ISL. This can be used as a teaching tool in schools that can impart quality education to deaf and dumb students. This project hence keeps the community outreach aspect in mind and strives to give back something to the society.

1.4 Scope and Objectives This app can be used as a teaching aid in schools. If made into an android app this can fetch smartphone users as well. Can be extended to multiple languages to translate languages apart from English. Our main objectives are: • To simplify teaching in deaf and dumb schools. • To be able to translate easily and quickly to Indian Sign Language. • To eliminate the mandatory requirement of explicit learning of ISL in order to communicate with the mute community. • To provide versatile inputs, i.e., inputs in the form of both audio and text. • To bridge the communication gap between these two communities of the society: speech and sign.

2 Proposed Model This application is a desktop application that is platform-independent. It consists of an avatar through which animations are shown. The UI consists of an input field (where the normal text is displayed), and audio data can be recorded by pressing a button in the UI. The ISL equivalent of the text in subject of verb form is placed in a label component, a submit button, all of these in an environment called “Sky Box” taken from Unity’s Asset Store. The Natural Language Processing part is taken care of by the python script whereas the dictation recognizer and animations are taken care of by the C# script. C# and python codes are combined using Flask API. The audio input is taken with the help of Unity’s Dictation Recognizer. Figure 1 shows the block diagram where the interactors are a non-sign language speaker at one end and a mute person at the other end. They interact through this application which comprises speech to text conversion, text pre-processing, and animations. What the non-sign language speaker speaks is interpreted as an ISL sign in the end by the mute person, with the help of this application.

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Fig. 1 Block diagram of the application

3 Literature Review 3.1 Translation of Sign Languages Authors in [1] discuss the implementation and integration of facial expression into the Auslan Tuition System. This approach uses a 3D mesh and animation of an avatar to dynamically display the Auslan signs. However, it does not translate in real-time and also, it is not an open-source software. It is used only for Auslan Signs (Limited Database). According to Verma et al. [9], a clever character called SiSi (Say It Sign It) converts the spoken word into British Sign Language (BSL). The animated digital character or avatar shows the sentences and words as signs. However, tips are used only for BSL. Also, it is neither open source nor mobile application. According to authors in [2], a method is proposed for the fingerspelling recognition in ISL. Input in the form of gestures is processed to detect the sign, Automatic recognition and conversion of ISL gestures into text and voice is discussed in [3]. In [4], authors discuss sign language recognition system using ISL dataset creation with intraclass variance coupled with feature extraction. Jeyasheel et al. in [5] compares conventional NLP methods with Deep learning NLP methods. The output generated is a sentence, being displayed in the android application. Kumar et al. in [6], automatic computer vision-based system for Sign language recognition (ISL which can be conveyed using a single hand unlike the typical ISL which uses both the hands for gestures). Input in the form of fingerspelling video gestures is translated into their equivalent English meaning using machine learning as mentioned by authors in [7]. According to proposed model in [8], Recognition of (ISL) and translation into normal text is performed in three stages—training phase, a testing phase, and a recognition phase. Authors in [9], explore and analyze the works that have been made with automation of sign language and gesture recognition and the real-time challenges associated with them. An application aiming to mitigate the need for a human interpreter to translate English text into Sign Language is discussed in [10]. Reference [11] elaborates the usage of an algorithm to recognize various sign language signs with the help of image processing and machine learning.

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3.2 The Two Way ISL Translation Systems Anand et al. [8], executes translation of sign language into voice and reverse. The image of the signs is taken and a database is maintained that stores the signs against their meanings. This database is used for mapping signs into meanings and vice versa. But this does not support phrases and sentences, instead, it is used for basic words and symbols only. Reference [12] describes using Computer vision in Marathi Language to convert sign language to Text and Vice Versa. The large number of images taken by camera was used to identify solitary words from the Marathi sign language. This approach uses edge detection algorithm to analyze the images of Marathi signs, however, Marathi speech is not identified. It is also observed that the proposed model is applicable for just basic letters and words of Marathi sign language. Reference [13] discusses the role of lemmatization in sentence retrieval.

3.3 Current Status of ISL Interpreting Systems According to [14], Sign Language synthesizer system uses both voices for input and uses optical system to track motion. The output is in the form of an avatar. But the motion capture uses expensive devices. Reference [15] discusses the recognition of ISL in live video does the conversion of mapping the given alphabet into its corresponding symbol by taking a continuous video sequence of signs. However, this system cannot be extended to sentences. Authors in [16] propose a prototype machine translation system from text to ISL that uses syntactical analysis and LFG (Lexical functional grammar), where the end result is a set of prerecorded videos. This system cannot handle directional signs. According to [17], Continuous ISL gesture recognition and sentence formation is a gesture recognition and sentence formation system where continuous sign language sequence is split into simpler constituents using PCA, OH (orientation histogram), and gradient-based keyframe extraction. Sign language synthesizer recognizes spoken language using speech recognition, grammatically processes it into sign language, and uses a 3D avatar to communicate the same. Reference [18] discusses an approach for identification of hand poses and gestures made in the Indian sign language. Reference [19] proposes a methodology that allows the user to discuss with a person with hearing disabilities. Identification of the movements of the hands to indicate an action or a situation is discussed by authors in [20]. Reference [21] has proposed a process for allowing the deaf and people with hearing problems to understand the announcement made in railway stations.

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4 System Analysis and Design 4.1 Modules Used 1. Speech to text conversion: The input in the form of audio is captured and converted to text using Unity 3D’s Dictation Recognizer feature. The input can also be in the form of direct text, in which case the text conversion step is not required. 2. Text pre-processing: The obtained text is pre-processed to find root words (Ex.: run is the root word for runnable), form tokens (basic units which make up the sentence), remove stop words (unnecessary words with no importance to the sentence formation like is, are, etc.). The final sentence is in the SOV (subjectobject-verb) format. Python is used for carrying out these Natural Language Processing tasks. 3. Text to animation mapping: The respective animations of the various words in the text/sentence are obtained. For words for which there are no animations, the equivalent meaning is identified and animated. For words with no meanings for example ‘names’ then the word is split according to its alphabet. 4. Animation: The avatar is animated for the various ISL words and this is done using Unity. In the end, a series of animations are played (with smooth transitions) for the corresponding processed sentence. Hence a given sentence is converted into its ISL form.

4.2 Development Tools, Languages Used, and Dataset Description 1. Pycharm: It is an integrated development environment (IDE) used in computer programming, specifically for the Python language, developed by the Czech company JetBrains. It provides code analysis, a graphical debugger, an integrated unit tester, integration with version control systems (VCSes), and supports web development with Django. 2. Microsoft Visual Studio: It is an integrated development environment (IDE) from Microsoft. It is used to develop computer programs, as well as websites, web apps, web services, and mobile apps. Visual Studio uses Microsoft software development platforms to produce both native codes and managed code. Visual Studio supports 36 different programming languages. 3. Unity 3D: It is a cross-platform game engine developed by Unity Technologies. The engine can be used to create both three-dimensional and two-dimensional games as well as simulations for its many platforms. Unity engine offers a primary scripting API in C as well as drag and drops functionality. 1. Python3: It is used for pre-processing parts like lemmatization, removing stopwords, etc.

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2. C: It is used for playing animations and all the tasks associated with dictation recognizer. All the necessary words in the Indian Sign Language Dictionary are available in the dataset obtained by clicking the link www.indiansignlanguage.org. As of now, 2785 words are defined with each word falling into a particular category. It can also be observed that ISL action is attached to each word. This dictionary is appended regularly with novel words and their equivalent meanings.

4.3 Flow Diagram Figure 2 represents the flow diagram with speech/audio (or even direct text) as the input, which is converted to text using Unity’s Dictation recognizer. This is an inbuilt feature in Unity3D, where few capabilities have to be enabled which allows taking in speech input by the user through microphone. The speech-converted text is passed to the Python script where the text is pre-processed, i.e., given sentence is tokenized, on tokenization text is lemmatized (every word is brought back to its root form) thereafter stop words are removed. The base sentence, i.e., the sentence which has to be converted to ISL is obtained after sentence simplification. This base sentence is converted to SOV form, which is the final ISL sentence. The sentence which is simplified using NLTK in python language is integrated with C code which is used to map the animation, using Flask API. The mapping is done as follows: The object of the Animator class plays the animation with the same name as the ISL word. The animation is displayed in the UI which is built in unity. Figure 3 depicts a flowchart where a detailed procedure is given as to how exactly the text is pre-processed. Firstly, the input text is tokenized, i.e., the sentence is split for each of its tokens. For example, the sentence “I am going to the market” consists of six tokens. The next step is to remove the stop words, which are the unnecessary words that do not give any special meaning to the sentence. In other words, they are unwanted because the output/final meaning of the sentence does not change if they are removed. For example, in the sentence “I am going to the market”, “am” and

Fig. 2 Flow diagram of the application

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Fig. 3 Text pre-processing

“the” are the stop words. Tagger identifies the POS (parts of speech) of the word. WordNet is used to identify the words which are not in English dictionary.

5 Modeling and Implementation 5.1 Architecture Diagram The project involves four major modules. The interaction and functionalities of these modules are described in Fig. 4. The translation from speech to text is performed

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Fig. 4 Architectural diagram

using a Unity3D’s Dictation recognizer as shown in Fig. 4. The microphone is enabled by switching on its online speech recognition capabilities. The system settings are changed to “linking and typing”. The speech of the user obtained through the microphone is translated into text and projected onto the screen. Figure 5 shows the pre-processing done. The text pre-processing starts by generating tokens from sentence. The regularly used words are removed, base form of the words is identified, word synonyms are handled word meanings, and finally converted into Subject-Object-Verb form. Python and NLTK are used for text pre-processing. The process of simplifying the sentences using Python is shown in Fig. 6. On preprocessing the result obtained is simplified and transformed into an ISL sentence. The spacy module in Python is further useful in bringing the sentence to subjectobject-verb (SOV) notation by referring to the ISL grammar rules. But there are certain words in the English dictionary that do not have any sign associated with Fig. 5 Sentence pre-processing

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Fig. 6 Sentence simplification

Fig. 7 Mapper

them as per ISL. Under such a case the meaning of the word is identified and used to obtain sign. In case of words without synonyms or unavailable signs, the alphabets of the words are considered. Adjectives and adverbs are also handled as per the ISL grammar rules. Figure 7 shows the mapping of animations using Unity3D and Flask REST API. IT is observed that the generated result is according to the ISL format. The Animator objects are responsible for identifying the animations associated with each word. It also displays the animations word by word. For effective interactions between Python and C, Flask REST API is used. The GUI built in Unity 3D successfully displays these animations.

5.2 Implementation Below are the code snippets of the various important steps in the application and their explanation regarding the same. Figure 8 is about creating Dictation Recognizer object to convert the speech to text in the application and initializing animator.

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Fig. 8 Speech to text converting code using dictation recognizer

Each of the result fetched is appended to the DictationResult attribute of DicatationRecognizer. The DictationResult is processed and a corresponding text is obtained. Figure 9 is about sending request to the Flask API over the URL and passing the text entered by the user in the InputField. The response is converted to string and the animations are played for each word in the response. The word for which the animation is being played is also displayed accordingly. Figure 10 is about finding the subject, object, and verb in the given sentence. The ‘if’ condition checks whether the word is the subject of the sentence, if true, appends it to the list of subjects. The

Fig. 9 Sending the request to the flask API

Fig. 10 Code to find SOV in a given sentence

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Fig. 11 Code to remove stopwords

‘else if’ condition is about checking whether the given word is a verb, if true, appends it to the list of verbs. The last condition checks if the word is object of the sentence. Figure 11 is about creating the list of stopwords. Default list of stopwords of NLTK is not used since signs for few of the words have been defined by the Indian Sign Language Research Organization. So, few words from the stopwords are removed and a separate list of stopwords is created manually. Any word from this list that is resent in the sentence is removed.

6 Testing, Results, and Discussion There is a difference between the grammar and sentence formation rules of spoken languages to that of ISL. The application has been majorly tested for its adherence to the grammar rules of ISL. The below table shows the grammar rules that are to be followed in ISL. The above-mentioned rules are tested and the results are as depicted below. Figure 12 shows how each word is converted to alphabets in case there is no ISL sign. In the above case name is a proper noun and obviously would not include any sign in ISL. Because of this, the word is split into alphabets. Tokens without signs but with synonyms whose signs exist in the ISL dictionary are replaced with their meanings as shown in Fig. 13. The Wh-word rule is shown in Fig. 14. To effectively represent a sentence, the question words should be used at the last. And the proposed model is successful in placing the question words. It is understood that no signs exist for verbs that are used for linking and articles a/an/the. These stopwords are not necessary and have to be removed as shown as Fig. 15.

Fig. 12 Rule fingerspelling for unknown words

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Fig. 13 Replacement of word by synonym

Fig. 14 Rule Wh-questions are at the end

Fig. 15 Rule never use linking verbs

ISL only uses words in root form and in their present tense. Lemmatization is used for this purpose. In Fig. 16 ‘sold’ has been converted to ‘sell’ after lemmatization. The dictated speech is shown in the input field, which can be seen in Fig. 17. To enable the speech to text option, recording button has to be clicked. This disables the input field till dictation is stopped. Text can also be directly typed or copied into the input field. In Fig. 18 upon clicking the “Submit” button, the text in the input field is preprocessed and converted into ISL form, which is displayed in the label next to the avatar. The avatar starts showing the animations and the current animation being shown is displayed on top of the avatar in the label. For those words which are not present in the ISL dictionary, synonyms are found and that animation is shown. In case the synonyms are also not present, then the word is split into its alphabets and shown. Example: Akbar is split into ‘A’, ‘k’, ‘b’, ‘a’, ‘r’. Some words like regional

Fig. 16 Rule uses the root form of each word

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Fig. 17 UI of the application

Fig. 18 Illustration of story as an input to the application

names are not interpreted correctly by the dictation recognizer. In Fig. 19 ‘Birbal’ is misinterpreted as ‘bearable’. This is a constraint that is dependent on the speech to text conversion. Figure 20 is about console output of handling the given sentence across various steps, along with the final resulting sentence. This involves stopping word removal, obtaining SOV format, and handling interrogative words.

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Fig. 19 Recognition of names by dictation recognizer

Fig. 20 Output for each step of a given sentence

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7 Conclusion 7.1 Conclusion The system developed is open source and will significantly reduce the effort of teachers in learning sign language. Students will henceforth be able to learn in the absence of a skilled teacher. The animations are clear with smooth transitions and each animation along with its ISL form of sentence is displayed simultaneously. The dictation recognizer is a big boost to the application. The speed is also appreciable because the animations are shown immediately without any significant delays. The only latency time taken is to read the entire text and pre-process it, also which, is negligible when compared to the size.

7.2 Future Work The project can be extended to multiple languages to translate languages apart from English. This can be achieved by using translators. Also, in case of words that do not exist in the ISL dictionary, the users can be provided with an option either to split the word alphabets wise or to search for a synonym context-wise (in case of homonyms). This type of interaction with the user is essential for proceeding to search for the desired way of showing the animation for that particular word. For example, for the word “bank”, the user can choose between its animations (riverbank, a bank that lends money, etc.) or can even choose to split the alphabet.

References 1. Wong JC, Holden EJ, Lowe N, Owens RA (2003) Real-time facial expressions in the Auslan tuition system. In: Computer graphics and imaging, pp 7–12, Citeseer 2. Rokade YI, Jadav PM (2017) Indian sign language recognition system. Int J Eng Technol 9(3):189–196 3. Patel P, Patel N (2020) Comparison of various classifiers for Indian sign language recognition using state of the art features. In: Congress on intelligent systems, pp 699–712, Springer 4. Sharma S, Singh S (2021) Recognition of Indian sign language (ISL) using deep learning model. Wireless Pers Commun 1–22 5. Jeyasheeli PG, Indumathi N (2021) Sentence generation for Indian sign language using NLP, vol 18. Special Issue on Artificial Intelligence in Cloud Computing 6. Kumar A, Kumar R (2021) A novel approach for ISL alphabet recognition using extreme learning machine. Int J Inf Technol 13(1):349–357 7. Bhattacharya A, Zope V, Kumbhar K, Borwankar P, Mendes A (2019) Classification of sign language gestures using machine learning. Image 8(12) 8. Anand MS, Kumaresan A, Kumar NM (2013) An integrated two way ISL (Indian sign language) translation system–a new approach. Int J Adv Res Comp Sci 4(1)

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9. Verma VK, Srivastava S, Kumar N (2015) A comprehensive review on automation of Indian sign language. In: 2015 international conference on advances in computer engineering and applications, pp 138–142. IEEE 10. Kumar P, Kaur S (2020) Sign language generation system based on Indian sign language grammar. ACM Trans Asian Low-Resour Lang Inform Process (TALLIP) 19(4):1–26 11. Safeel M, Sukumar T, Shashank K, Arman M, Shashidhar R, Puneeth S (2020) Sign language recognition techniques-a review. In: 2020 IEEE international conference for innovation in technology (INOCON), IEEE, pp 1–9 12. Shinde A, Kagalkar R (2015) Sign language to text and vice versa recognition using computer vision in Marathi. Int J Comp Appl 975:8887 13. Ivan Boban SG, Doko A (2020) Sentence retrieval using stemming and lemmatization with different length of the queries. Adv Sci Technol Eng Syst J 5(3):349–354 14. Maarif H, Akmeliawati R, Gunawan TS (2018) Survey on language processing algorithm for sign language synthesizer. Int J Robot Mechatron 4(2):39–48 15. Singha J, Das K (2013) Recognition of Indian sign language in live video. arXiv preprint arXiv: 1306.1301 16. Dasgupta T, Basu A (2008) Prototype machine translation system from text-to-Indian sign language. In: Proceedings of the 13th international conference on intelligent user interfaces, pp 313–316 17. Tripathi K, Nandi NBG (2015) Continuous Indian sign language gesture recognition and sentence formation. Proc Comp Sci 54:523–531 18. Shenoy K, Dastane T, Rao V, Vyavaharkar D (2018) Real-time Indian sign language (ISL) recognition. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), pp 1–9. https://doi.org/10.1109/ICCCNT.2018.8493808 19. Monga H, Bhutani J, Ahuja M, Maid N, Pande H (2021). Speech Indian Sign Lang Trans. https://doi.org/10.3233/APC210172 20. Haren Amal BRP, Reny RA (2020) Hand kinesics in Indian sign language using NLP techniques with SVM based polarity. Int J Eng Adv Technol (IJEAT) 9(4):2044–2050 21. Shaikh F, Darunde S, Wahie N, Mali SG (2019) Sign language translation system for railway station announcements. In: 2019 IEEE Bombay section signature conference (IBSSC), pp 1–6

Chapter 8

Issues and Challenges in Using Electronic Health Records for Smart Hospitals Krishna Prasad N. Rao and Sunilkumar S. Manvi

Abstract In the recent years, vast developments in information technology have given rise to the concept of Smart healthcare. Smart healthcare transforms the traditional health system into more efficient, convenient, and personalized. The technical advancements have paved the way for storing patients’ medical data in the digital form known as Electronic Health Records (EHR). EHR is playing a vital role in healthcare industry and is being implemented in several countries. Using EHR has its own set of benefits. It aids the medical practitioners to provide better and more suitable treatment to the patients. In this paper, we discuss the current issues in using the electronic health records such as privacy, security, interoperability, and data quality. Securing the health records from the unauthorized users and maintaining the privacy of patient data is an essential feature that needs to be included in the EHR. Currently, sharing EHR across the healthcare organizations is not an easy task due to lack of interoperability and standards. Another issue in the EHR is the quality of patient information stored. In this paper, we also provide an insight into the blockchain technology and semantic-based storage which can be used to store and manage the EHR. Finally, discuss the possible solutions to the current issues in electronic health records using the semantic storage and blockchain technology. Keywords Blockchain · Electronic health record · Interoperability · Privacy · Security · Smart healthcare

K. P. N. Rao (B) · S. S. Manvi School of CSE, Reva University, Bangalore, Karnataka 560064, India e-mail: [email protected] S. S. Manvi e-mail: [email protected] K. P. N. Rao Department of CSE, NMAMIT, Nitte, Karnataka 574110, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_8

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1 Introduction All over the globe, the initiatives for the Smart Cities have already been in progress. The key purpose of the smart cities is to revamp the urban infrastructure at minimal costs, promote the technical innovations in different fields and thus raise the life quality of citizens. Smart healthcare, Smart society are part of the smart city initiative. Smart healthcare is an advanced health service system to connect people and health care institutions using technologies such as IoT, sensors, wearable devices, and internet. It can intelligently manage the medical ecosystem and foster the interaction between the several stakeholders of the healthcare system. Due to the continuous advancements in medical science, the conventional handwritten health records are not suitable for recording the large volumes of information. Information technology (IT) has played a key role in substantially improving the quality of healthcare through redesigning the system in healthcare. With the evolution of advanced computer-related technology in 1970s, lead to the success of storing the patient data on the computer. These health data stored electronically in the digital form is termed Electronic Health Record (EHR). Until late 2000s, healthcare systems around the world did not use EHR and relied on paper-based medical records. Medical records on paper were prone to many threats like theft, fire, unauthorized manipulation, natural disasters, etc. EHR can be described as a group of medical records of a patient which is generated during the medical tests or the hospital visits. There are various definitions of EHRs as described in Fig. 1. EHRs contain highly sensitive and critical private data which is used for diagnosis and treatment of the patients in the hospital. For example, EHRs contain heterogenous information which includes demographics of the patient, past medical reports, laboratory data, medications, vaccinations, and radiology reports. In the recent times, EHRs not only contain the patient-generated data but also hold the data generated using apps and wearable devices. Due to the use of apps and wearable devices, each individual patient is generating enormous amount of personal health data such as body temperature, heartbeat, etc. As shown in Fig. 2, EHRs store up-to-date information about the patient from various healthcare systems. EHRs are very useful in providing the better and effective treatment to the patients. The detailed health information of the patients in the EHR is used by the doctors to make fast and better decisions and thus improving the quality of treatment and care. Also, anywhere, and anytime the health information can be accessible which is an added advantage of using the EHR. The relationship between the doctor and patient has been strengthened because of the quick interactions by the EHR [1]. Due to the technological advancements, the EHRs are becoming more vulnerable to attacks by malicious users. These unauthorized users manipulate the records or misuse the patient’s data by gaining access to the EHR system using modern hacking software or tools. So, securely storing the private information and healthcare data of patients and preventing them by the unauthorized users is the present need. Nowadays even the EHRs stored on the cloud may not be secure and are vulnerable to attacks by

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Fig. 1 Some definitions of EHR [4]

professional hackers. Usually, cloud-based storage is secured by passwords which may be compromised through different hacking methods. Therefore, in the recent years features such as securing the health records and protecting the patient’s privacy are given the top priority [2]. Most of the healthcare organizations do not share EHR data with other hospitals. This hinders achieving interoperability in terms of the extraction, integration, and linking of the EHR. The key functionalities of most of the EHR systems are partially interoperable which would require some effort in the sharing the record with others. This has led to the techno-economic barriers in adopting and deploying standardized EHR systems for interoperability. Due to lack of standards in EHR, each healthcare organization has their own customized EHR systems which is the additional hurdle to the interoperability. Making EHRs interoperable with the hospitals facilitates in sharing of the data of large number of patients. Also, there is no standard mechanism in sharing the health data with researchers while protecting the identity of the patients. As a matter of good scientific practice, the EHR systems must use some process to review and assess the quality of data. Data quality issues arise from the EHR extracted data which often requires rigorous cleaning and preparation. The purpose of EHR system is to manage the healthcare transactions, assist in clinical workflows and support the billing documentation. Since the EHR data is primarily used for the storing patient data, it may not be suitable for some research cases [3].

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As per the 2018 US health data, nearly 90% of hospitals used the EHR systems which are expected to rise soon. Different stakeholders of EHR systems such as doctors, hospitals, patients, and insurance companies create, use, modify and view the records. Some EHR system allows patients to add their key information either medical or non-medical which enables the clinicians to understand and take right decision on patient’s treatment. Family health histories are also added to EHRs. They are used to evaluate the disease risk to a patient based on the inherited and social factors. The modern patient care relies on several medical devices which monitor vital signs, infuse medications, and maintain life support for patients. Data collected from various sensors, devices such as infusion pumps, and ventilators are interfaced with EHR system. This patient data is not only used for patient care but also useful for researchers to carry out the required analysis while keeping the patient details private. This chapter describes the overall challenges or the issues related to current EHR storage system used in the hospitals. Rest of the chapter is organized as follows: the section two discusses the types of data in EHR. Section three thoroughly describes the issues of EHR. In section four, the chapter discusses possible techniques and challenges for the previously described EHR issues. Section five provides a possible solution to the issues using the blockchain technology. The chapter concludes discussion of possible future directions for storing the EHRs in section six.

2 Types of EHR Data The patient data contains numerous types of data which can be linked or stored in the EHR system. Some of the common data types that are used in EHRs are identifiers and demographics of patient, family history, medications, vaccination, laboratory results, etc. [5]. EHRs also contain unstructured data (for ex: doctor’s handwritten notes) which needs to be further processed to extract the patient’s crucial data. Figure 3 lists the common data types stored in the EHR (Fig. 3). Patient’s Identifiers and Demographics: EHR system generates a unique ID in such a way that every individual patient is easily identified. Patient’s name, birth date, address, contact number, name, and contact number of the family member, which is needed in case of emergency, insurance details, etc. are the details of the patient which are collectively termed as patient identifiers. Patient’s information such as gender, age, ethnicity, education (optional), marital status, income, and nationality will be included in EHR which is known as the patient demographics. Diagnoses: Diagnosis data is an essential factor in evaluating and investigating the health of a patient. It includes information such as severity of the disease, and previous medical history. Diagnosis helps the doctors in providing effective treatment for the patient. Medications: Along with diagnosis, EHR also stores the data pertaining to the medications. It includes written prescriptions, and pharmaceutical data. These data are also useful in studying the effectiveness of medication on the patient.

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Fig. 2 Heterogeneity of data in EHR [6]

Fig. 3 List of data types in EHR

Procedures: A procedure is a process of determining or evaluating medical condition of the patient. Procedure is also known as medical test. It contains several information about laboratory procedures, surgery procedures, etc.

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Laboratory Values: Name of the lab tests, lab test type, and lab results are stored in EHR as the laboratory data. The information systems in the laboratories are the main source of laboratory data. These data may not be updated always in the EHR. Vital Signs: EHRs contain vital sign information which includes body mass index, weight, height, blood pressure, pulse rate, temperature, and respiratory rate. Incorrect units of measurement due to human errors will affect the data quality and might be necessary to pre-process it before it can be used (For ex: kilograms reported as pounds). Utilization/Cost: EHR contains utilization data such as hospitalization cost, readmission, pharmacy cost, emergency admission cost, etc. The utilization data in the EHR is useful for insurance claims processing. There are no standards specified for utilization data, but EHR incorporates the guidelines provided by the insurance companies which helps in faster claims processing.

3 Issues in EHR The government agencies and healthcare organizations are realizing the significance of EHRs in identifying the suitable treatment for the patients. Nevertheless, healthcare is facing several issues such as privacy, and security of EHR which remain a critical hurdle for their usage. Usually, the patients and healthcare providers demand the security of the EHRs. Over the years, social, political, ethical issues, and the need for standards in EHR systems have dominated the technical issues related to storage of records, record accessing, interoperability, etc.

3.1 Record Creation and Storage Standalone EHR Storage Healthcare organizations have their own IT infrastructure to store the ever-growing medical data. Most of the standalone EHR systems use centralized storage modelbased client–server architecture. It uses a relational database and is accessed through a web-based interface. In the client–server architecture, the patient health data is stored in the local server and is managed by a dedicated IT department. This means the IT team is responsible for regularly backing up all the data and protecting the servers from theft and damages such as power outrage, natural disasters, etc. Figure 4 shows the local, standalone storage model used in EHR systems [7]. The standalone EHR system is deployed locally at a physical location of the healthcare organization. This type of deployment is needed where there is no proper network infrastructure to use the cloud-based storage. The standalone EHR system typically consists of several application modules which are hosted on an application server. All the health data generated during the hospital visits are stored in a

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Fig. 4 Standalone EHR storage [8]

relational database maintained on a database server. Various departments of the organization can access the required health records using application server through a web interface. To manage the failovers (a situation where the application uses the backup servers during the downtime of primary servers), a backup of the database (backup server) is maintained. Standalone deployments are useful in scenarios where the organization is having medium patient load and network infrastructure. The standalone EHR system is more suitable for rural and remote areas due to its low system complexity and ease of deployment. The standalone architecture does not support exchange of data across multiple systems and operates in isolation. Cloud-Based EHR Storage Currently, the most popular architecture for deploying EHR systems is the cloud. In the cloud-based storage, a healthcare organization need not store the data on their local servers, instead, it is stored on the cloud provided by a vendor. This means the data is stored remotely and can be securely accessed over the internet. One of the popular cloud platforms that provide abstracted computing resources for deploying the EHR system is the Amazon Elastic Compute Cloud (EC2). For an EHR system running on Amazon EC2, a Simple Storage Service (S3) is used for storing the health records. This proprietary health system is claimed to have 99.99% of availability. Data security is a concern since the password may be compromised [9]. The issues related to centralized storage of health records can be outlined as difficulties in access management and security. Experts have realized that all the health data will be lost if the centralized storage system is destroyed. To tackle this, US defence department created ARPANET in 1969 which would remain functional even in case of an attack. Similar approach can be used to store the health data by storing the records in different places thus ensuring the protection. One might argue

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that protecting the centralized data is far easier than the other storage models. This involves a team of security experts who may define and enforces various security strategies for the system. Further, the hackers may visualize the centralized storage as a challenge and may maliciously gain access to the EHR system and manipulate the health records of the patient [9]. To maintain the completeness of the EHR, every stakeholder must be connected to the EHR system for notifying every operation to the system made by the stakeholder. For example, in drug prescription scenario, regulating the security and traffic load would be laborious task to manage. Additionally, EHR system to be effective, the centralized storage requires a systematic uniform process for all the patients. Cloud-based deployment uses a network infrastructure with high availability which allows the application modules to be hosted on high-end data centers in the central cloud. Cloud-based storage is useful for large-scale health organizations such as medical colleges, tertiary care centers, etc. In this type of storage, the application server, database server, and backup servers are located at remote data center of the cloud service provider and organizations can access the application through a web interface as shown in Fig. 5. The advantages of this architecture are high availability, and minimal local infrastructure, thus various application modules of the EHR system can be easily modified and deployed over the cloud. Periodical maintenance and operational aspects of the application are managed efficiently at the data center. Cloud-based EHR systems can communicate and exchange data easily with other systems, provided they are hosted on the common network. Any modifications to the workflows, and standards can be easily implemented due to the centralized nature of the cloud.

Fig. 5 Cloud-based EHR storage [8]

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Fig. 6 Distributed storage EHR system (master–slave architecture)

Distributed EHR Storage The demerits of using centralized storage led to the development of distributed model for the EHR systems. The distributed storage model constitutes a master server and a group of backend servers as shown in Fig. 6. The backend servers, i.e., EHR servers are different from each other but contain identical data and are hidden from the clients. A client requests the master server for a particular record. Then the master server access one of the backend servers and retrieves the required health record [9]. A distributed architecture contains EHR system nodes that can communicate health data while supporting the local deployments as well as shown in Fig. 7. The goal of distributed architecture is to aid the local server instances at several organizations and synchronize the data with a server having 24 × 7 availability hosted at a data center that can be configured based on the requirements. The local server instances are installed at healthcare organizations and own dedicated database and application server with backup server locally. The local server instances might possess load balancers for managing the client requests at server. Apart from the local instances at the healthcare organizations, a central instance is deployed that has high availability and acts as a central repository for critical health data sets. The central node acts a backbone for the healthcare infrastructure and provides web services for several portals and mobile apps. Additionally, it maintains the backup of health data and application servers of hospital nodes which are used in recovery

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Fig. 7 Distributed architecture with local deployments [8]

in case of the local instance failures. The connectivity between the hospital nodes and the central node is configurable based on the requirements. For example, large healthcare organizations can have a direct connectivity with central node through high capacity network whereas other healthcare organization nodes work in seclusion and communicate with the central node only when the need arises. EHR data constitutes several individual elements that are standardized by their specific standards. For example, DICOM is the standard for images, LOINC for laboratory codes. The standards ensure the integrity of EHRs and allow the patients to visit multiple hospitals for the better treatment.

3.2 Record Access The patient data stored in EHRs are also widely used to conduct clinical research and improve the quality of the treatment. The substantial amount of data stored in EHR systems generate an additional value when integrated and shared with other EHR systems. Data Quality Quality of data can be defined in many ways depending on the situation. Data quality is an important aspect of EHR data which assures that the data is adequate and appropriate for use. The primary issues in data quality are inconsistency, inaccurate, and incompleteness. These issues make it a tough job for querying a particular patient data for treatment and research.

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Following are the important attributes to be considered for the data quality: • Accuracy: the degree to which accurate data is recorded in EHR, is often very hard to gauge because the given variable’s actual value is not known. • Completeness: for a certain data element that is of specific interest, the amount of data missing in the EHR is used as a metric to assess the data quality in EHR systems. Also, for the research purposes, it is always better to have a clear differentiation between required (must have) data and optional data. • Timeliness: it is the total time between when the variable is first available and the time when it has appeared in the EHR. More importantly, there is a variation in the data quality in the EHRs across the various healthcare organizations. The updates made to the EHR systems also affect the data quality. For instance, a new update to the system that improves the performance may affect the record linkages and may require upgrading the other functionalities of the system. To ensure data quality, accuracy and completeness of the data must be monitored continuously. The free-form text, i.e., unstructured data poses a significant challenge in automating the EHR system. EHRs possess sizable amount of unstructured data, for example, handwritten doctor notes. The key information about the patient might be contained in the unstructured data and may not be present in the structured data. Also, the unstructured data may contain information supporting structured data or even may contain data completely contradicting structured data. The present-day tools of natural language processing (NLP) and data mining have minimal capability in obtaining the information from unstructured data accurately. Some EHR systems ask for a manual review of the unstructured data of each patient before adding to the EHR. The non-structured data hinders the use of automated phenotyping methods and increases the possibility of missing data during the extraction. Most of the EHR systems provide flexibility in entering the key data at places of choice. For instance, some EHRs have a provision for quickly entering the treatment details. This results in storing the treatment information in fragments. These fragments may not be accessible to the researchers since it includes the patient’s identity, and it is difficult to find out as well. Methods of Accessing Electronic Health Data 1.

Health Information Exchanges (HIE)

2.

HIEs have been established at independent, regional, or government levels. HIEs organize data into a central database and generates custom outputs. HIEs do not have the control over what data elements are being shared. Vendor Direct Access Like HIEs, some vendors provide network services so that third parties can directly connect to their systems. Each vendor has multiple versions and solutions of the EHR systems.

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3.

Custom Point to Point Integrations

4.

Several vendors create a specific pipelines or tunnels between the individual organizations to meet specific use cases. Such integrations are robust but they are costly and take more time to develop. Useful in providing telemedicine service. Direct-to-Portal Integration

5.

As the name says, specific connections are made to each organization’s EHR systems. These integrations require the users to provide login credentials to access the EHRs. Users have the right to disclose their data to any healthcare organization with whom they want to share the data. This integration provides reliable, fast data access but requires every healthcare provider to be integrated. Screen Scraping

6.

It is a process of collecting the data through display screen of one application and translating it to another application so that it can be displayed. Screen scraping is similar to direct-to-portal method, but it is dependent on what data is displayed on the screen. Fast Healthcare Interoperability Resources (FHIR) FHIR API is the current standard that provides the layout for exchanging the health data. FHIR has faster data access and is reliable. It requires patient’s credential to access the data.

3.3 Sharing and Interoperability The EHR data is shared among hospitals, clinics, researchers, insurance companies, and patient’s families. Sharing of data poses a significant challenge in securing and preventing unauthorized access to the patient’s healthcare data. Treatment of the patient could take a back seat if the sharing and access control mechanisms are not enabled in the right way. Sharing healthcare data is a significant step in enhancing the services provided to the patients in a health organization. For providing the better treatment to the patient, entire patient’s medical history which includes laboratory results, and side effects of medicine are very much needed in cases such as HIV, cancer, or heart diseases. The patient may visit various hospitals for getting the second opinion or may get shifted from one healthcare center to another, so sharing of the patient’s health records is a must for carrying out the therapy or surgery. Sharing becomes much harder when patient relocates from one region to another as the laws related to healthcare may differ from region to region. Interoperability Challenges Security: There are many issues pertaining to security that must be tackled to achieve interoperability among several hospitals. Interoperability involves exchanging of

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patient’s health information among the various healthcare providers. These information exchanges are controlled by patient privacy rules, which dictate how and with whom the data can be exchanged. It is the responsibility of the organization to securely transfer the accurate health data and is retrieved by recognized personnel only. Some of the challenges in security w.r.t interoperability. 1. Using secure transfer methods in sending the health data 2. Unauthorized access to or modifying the health data 3. Only authorized personnel with appropriate permission have access to the health data. Data Semantics: The clinical lexicon and laboratory coding schemes that are used in the EHR data are often termed as data semantics. Semantic interoperability is defined as the “ability to automatically interpret the information exchanged meaningfully and accurately in order to produce useful results as defined by the end-users of both systems” [10]. For instance, healthcare practitioners use disparate terminology for the same concept. The EHR systems exchanging the health records must accurately perceive that these terminologies are alike. When the health data are combined from multiple sources of various coding standards, issues related to semantic interoperability arise. Sometimes, the medication data extracted from EHR might not be sufficient for carrying out research. The significance of coding (vocabulary and terminology) standards is that they guarantee the consistency of the data stored and can be correctly interpreted by other EHR systems. Some of the worldwide accepted standards are Logical Observation Identifiers Names and Codes (LOINC) used for lab tests and vital signs, and RxNorm20 used for medications and allergies. We have several coding standards that act as a strong base for interoperability, but still there exists a gap. Some data (pathology slides, radiographic images, etc.) which are important for treatment as well as for the research might not be stored as per the lexicon and terminology specifications. Moreover, the data that are needed to research purposes may not be recorded in some of the EHR systems. For example, health records may not contain socioeconomic status of the patient. At present, the EHR systems do not have mandated coding system to be used in EHR and most of the healthcare organizations depend on the regional coding schemes for the laboratory data. This hinders the interoperability of health records across multiple sites for the lab data. Additionally, different healthcare organizations employ disparate tests to examine the same sample. Each lab test uses a distinct code for representing the results. A mechanism is needed to automatically link the lab items, such that a single query can fetch the required data from multiple EHRs. Also, due to the state and federal laws, some of the lab results are not revealed and might not be stored in the EHR. Data Format: The data formats used for storing electronic health information specify how the data is structured. This helps in integrating and interpreting the health data transferred from one system to another. To facilitate the data exchanges between several EHR systems, standard data formats such as FHIR are needed. FHIR

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supports various operations such as exchange, retrieval, and integration of health data. Since FHIR exchanges only required information rather than the whole record, faster and more efficient exchanges are possible between the EHR systems. Data Matching: A unique identification number is assigned to every individual in the countries that have national health services. In such countries, linking and aggregating the patient’s information is straightforward which raised the privacy concerns for the patient. In developed countries like United states, each healthcare provider assigns a distinct id to every individual patient. As a result, US citizens possess multiple identifiers provided by different healthcare providers. Several attempts were made to create unique health identifiers to every US citizen but took a back seat due to privacy concerns. While health data is exchanged across the healthcare organizations, patients’ demographic data is used by the matching algorithms to match the individual information. This technique is not totally perfect because few health records might not be a match or sometimes may be incorrectly matched. Due to the incorrect demographic data for example wrongly typed date of birth, misspelled names often lead to errors in comparing the patient information. This may lead to inaccurate treatments and raise the safety concerns of the patient. When the data is exchanged across the EHR systems, accurate matching must happen with the correct individual patient which ensures the full interoperability among the EHR systems. New methods are in need to enhance the quality of demographic data used in matching the patients, to improvise the efficiency of matching algorithms.

3.4 Privacy and Security The challenges related to privacy and security of healthcare data are on the rise since health industry is heavily dependent on data. The healthcare data must be secured since the data breaches occur more often. Based on a study, around 170 million data records have been compromised in the EHR systems since 2009. Another study says that there is a steady increase in the cyber-attacks on the EHR system of the hospitals. Healthcare data privacy refers to processing of patient data privately and securely and having an authorization to access the data. In addition, security means protecting the patient’s data from malicious users and intruders [5]. The current EHR systems used by the healthcare organizations are implemented as centralized architecture. They also have a data warehouse as their backbone with several data marts. In the centralized architecture, the data accumulation and processing are done in a single centralized system. Some of the benefits of centralized architecture are: better data consistency, easy to manage, greater efficiency, and patient linkage is quite straightforward if same identifier is used across the healthcare organizations. Disadvantages of centralized architecture are: single point of failure, difficulty in data exchanges between the multiple EHR systems [11]. Privacy concerns of health information have been reported by many of the surveys. As per Win [12] two thirds of customers were aware about the privacy of the health record but hardly 39% of them sensed that the health data was secure. In few of the

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cases, the responders were not concerned about the security of their health data and did not have the belief of safety of their health data [13]. 50% of the respondents were concerned in regard to the safety of their data as it travels through internet. As per the research carried out by Ancker et al. half of the responders believed that privacy of health data could be worsened due to exchanging of the health information. Several research studies showed that handling the privacy issue is necessary in successful implementation of EHR system. Whetstone and Goldsmith [14] verified that an individual had a positive influence to establish an EHR when there is confidence regarding security and privacy. Bansal et al. [15] discussed that the privacy concerns have negatively impacted the intentions of sharing the health data online. Anderson et al. [16] conducted research on the degree of willingness of the individuals in providing the access to their personal health data despite the privacy concerns. Dinev et al. [17] discussed about the weak relationship between the privacy of patient’s health data and their attitude toward the EHR. Ermakova et al. [18] conducted a study, which revealed that due to the privacy concerns individuals were reluctant in sharing their health information stored on the cloud-based model. The concerns related to privacy of EHR is more essential than the concessions when picking the healthcare service other than the secondary use case. An analysis carried out by Kuo et al. [19] confirmed that the existing concerns regarding the privacy of health data on information privacy-protective responses (IPPR) such as fabrication of patient’s personal information, refusal to give their personal information, request for removal of personal information, complaints issued directly or indirectly to the healthcare organization. Zulman et al. [20] concluded that preference of individuals in sharing their EHR varies. It depends on the type of information that is shared. King et al. [21] discussed that privacy concerns vary based on the type of information of EHR. For example, patients are more bothered about abortion, impotency, sexually transmitted diseases whereas less concerned on data such as blood pressure status, demographic data, and cancer status. The security and privacy challenges of IoT begins from the characteristics of IoT network which makes them distinct in their own style. Some of the qualities are constrained resources, variety, need for scalability, and uncontrolled environment. Currently, the tiny processor with crypto engines and adequate memory for execution of security tasks. Lafky et al. [13] proposed that security features required for IoT systems depend on the group requirements such as: network security, resilience, privacy, identity management, and trust. The authors have considered several proposed architectures for IoT systems and analyzed these architectures in terms of security initiatives. The analysis showed that various security demands were taken into account but neither of the architecture covered all the security demands [11].

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4 Techniques and Challenges 4.1 Record Storage Techniques Relational Storage Model Due to the simplicity and developer’s long experience in the relational model in the DBMS have led to extensive use of relational storage model. However, this simplification has resulted in retrieving the data using complex queries an inefficient task. Some works such as [22–24] have found various challenges in using relational storage for EHR systems. Firstly, emphasis is given to addressing the interoperability issue without concentrating on how the health data is stored and processed in the distributed and complex setting. This can be a serious problem for the healthcare stakeholders who needs the EHR system to be scalable in terms of storage and processing. Secondly, the standard relational models have a good collection of classes to construct deep hierarchies, which implies referring to different elements using the same pathways. So, if the EHR system contains a deep structure in relational model having nested tables then it is very inefficient in accessing the leaf node data in tree-type structure. Thirdly, the high join costs to store and retrieve archetype-based datasets make the relational models less optimal. Using such relational data models infers too many join operations, which results in degradation of the performance as the data size increases. The fourth reason is that to guarantee data consistency, relational storage uses fixed schema, i.e., it needs exact data field structure to be pre-designed thus making the model inflexible. The relational model may not be suitable for healthcare data since many fields are needed to store the health attributes. The health data in relational database may contain several empty fields which result in low performance and inefficient storage [25]. NoSQL Databases Scalability, flexibility, and accessibility are some of the requirements that enable the sharing of health information across the EHR systems. Previous literatures have evaluated the suitability of NoSQL databases in storing the EHRs in a distributed healthcare model [26]. These studies have shown that features of NoSQL database meet the corresponding EHR characteristics. Transitioning from relational model to NoSQL model has several significant advantages. NoSQL database has the ability to store and accessing the unstructured health data. NoSQL databases are based on the key-value pair model. The subsequent advantage of NoSQL database is that it is simple and faster to retrieve the hierarchically organized patient data through a single key. There are significant benefits such as automatic scaling, high availability, and improved performance using NoSQL data stores for health data management.

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Flexible data storage mechanisms using XML’s strengths in data interoperability and application integration have been implemented in EHR systems. Another advantage is that Stable XML tools are available which can serialize the health data entities to an XML structure [27]. XML is used as popular CDM (common data model) because of its compatibility and flexibility features. Experiments [28] show that XML-based storage is inefficient to query the standardized EHRs, particularly for queries that are population-based are sent to vast health datasets. Generally, the archetype’s design and type of the EHR database’s values automatically generate the indexes of XML database which are unreliable. Moreover, tree-structured archetypes are profound and encompasses the repeated path-segment identifiers. These problems need the storage layers to perform easy and extensive querying. In EHR systems, the key task performed is usually retrieving all the health-related data of an individual patient in lieu of a specific attribute. Therefore, these operations are document-based so that the entire document can be retrieved immediately. The relational model might not be very effective in document-based applications, contrary NoSQL model is more suitable for the document-based applications. According to the CAP (Consistency, Availability, Partition Tolerance) designing architecture, NoSQL databases sacrifice consistency to achieve the availability. NoSQL database may not meet all the properties of ACID (atomicity, consistency, isolation, durability). Usually, ACID characteristics are unimportant for storing of EHR extracts because of phenomenal health data storage schemes [29]. Health data is not overwritten when a piece of data needs to be updated, alternately, A fresh EHR extract is created with the new information is kept alongside of old extract. This way of storing the EHR data helps the health practitioners to make better decisions. This storage strategy proves that the relationship between various records and their subparts does not hinder the serviceability and reliability of the healthcare application. Integrated EHRs data modeling technique was introduced using column-based NoSQL database by considering the benefits of its versatile schema. This technique uses to read and write operations on column stores at high speeds in real-time situations. The simplicity of deploying graph databases for archetype-based EHR storage depends on several factors. Schema-free property, similarity between graph model’s labeled property and reference model (OpenEHR) are some of the factors. The reference model has graph-type architecture which includes various classes in an extensive tree hierarchy. Thus, storing it in a graph database and then mapping it to a graph model will be a straightforward task [30]. Generally, NoSQL data models use the formats that closely relates to the real representation of storing the health data. So, more emphasis on this technique will develop a way to updated and interoperable EHR system framework by leveraging the use of technologies such as big data for optimizing as well as handling the health data.

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4.2 Record Access Techniques In the healthcare domain, querying is a key aspect with respect to two perspectives. Firstly, querying the patient’s archival data and secondly, querying over a large population data for the research purposes [31]. Use of Query Languages for Accessing Health Data In the commonly used query languages like Structured Query Language (SQL), Object Query Language (OQL), users must be knowing the record structure of an EHR to prepare a suitable query for accessing the EHR system. Therefore, query languages such as SQL or OQL cannot be used straight away in accessing EHRs. Queries are represented in a language which is composite of W3C XPaths and SQL, extracted from archetypes. This query language is known as AQL (archetype query language) [32]. Using Interfaces of High-Level Query Language High-level querying is an active area of research. Several research have been carried out to improve the user level interaction. Improving the interaction facilities will enhance the standards of care. In the EHR system with a two-level architecture model, querying is different than querying an XML-based system or relational database system. The queries to be used at the user level must be made simple. Usually, the patients know only about few common parameters like body mass index (BMI), sugar levels, blood pressure status, medications, etc. and will query such parameters only. A strong query support is needed which is independent of application environment, system implementation and the programming language. There are stable tools available for converting the specification and patient health data into identical form through the XML. For querying EHRs one of the procedures is to employ Archetype Definition Language (ADL) to create storable XML output from the XML database and use XQuery. Even XQuery By Example (XQBE) can be used on top of the XML file that is generated. The problems of querying EHRs on the basis of semantic interoperability is addressed by Archetype Query Based Example (AQBE) and it provides a user-friendly interface that can be utilized by semi or fully skilled users [31]. Ontology-Based Queries Semantic search can be used for answering various research questions when data is saved on the traditional database i.e., relational database [33]. Using SPARQL Protocol and RDF Query Language (SPARQL) queries the PONTE platform allows querying on global EHR ontology [34]. A similar technique uses OQL and ontologybased mediation for the query preparation. The XOntoRank system performs the semantic-based search by deducing the semantic relationship between the terminologies in the EHR and the query keywords [35]. A framework SPARQL2XQuery enables both, i.e., conversion between ontology and XML and vice-versa [36].

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4.3 Interoperability Techniques As per Reisman [37], the main issue in the interoperability of EHRs is more of a cultural barrier rather than technical. Compared to other properties, interoperability in the healthcare needs a slight coordination and complete collaboration among the various stakeholders such as healthcare providers, patients, software vendors, legislators, etc. Earlier, the US healthcare system had a cultural distinction by use of fragmented processes and contrasting stakeholders. Also, the healthcare information generated was of competitive advantage and commodity. Blobel [38] discussed different types of EHRs, standards and implementation by considering several characteristics like architecture, requirements, and solutions. This study was aimed to provide the solutions to interoperability challenges by strengthening the EHR. Results were discussed based on comparing the standards, specifications by altering the requirements, weaknesses. The technological advancements have made the hardware compact, easily affordable and easily available. Due to this, the EHR systems in healthcare organizations are upgraded with the new ones. The use of EHR systems is becoming more common and the healthcare professionals are accustomed to use the EHR system. However, there are some practical challenges related to data security, standards, and ethical disputes. In the recent years, many healthcare organizations across the globe are incorporating the EHR system. However, the usage of EHRs in low-income countries is very few. Following are some of the solutions to interoperability: • Innovative interoperability framework An innovative framework for interoperability which helps in sharing the health data electronically across various EHR systems. The framework allows the patients to share their health data and discuss with other healthcare professionals. The framework also helps in building the knowledge about the new medications and make the health data available to the authorized persons. • Advancement in EHR and adoption In several countries EHR adoption was difficult due to different standards, architecture, and cost. Hence, there is a need for making enhancements to the EHR systems globally and help the doctors access and manage the health data easily and provide better treatment at the right time to the patients. • Blockchain-based framework A framework using blockchain may overcome the challenges such as data security, privacy, and sharing. To manage the data efficiently and increase the security to the patient’s records the blockchain technology is more suitable. Further, implementing EHR system using blockchain technology with the national and international standards would help the healthcare organizations to manage the health data more effectively and speed up the process of patient data management. [39]

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Semantic Interoperability Benefits Providing the best healthcare service to the people is the first and foremost objective of Smart healthcare system. Semantic interoperability focuses on providing effective healthcare services to the patients by allowing the healthcare organizations to work together [40]. Additionally Semantic interoperability provides following benefits: • Need for Meaningful Analysis Semantic interoperability is useful in generating meaningful analysis about the doctor/clinician’s processes. It assists in identifying and releasing excess resources for other critical works for clinicians to improve the productivity. The analytics will also help administrators to keep track of clinician’s schedules and streamline the work balance. • Clinical Process Reengineering Semantic interoperability allows to revamp the clinical procedures to make it more effective and efficient. • Resource Utilization Semantic interoperability leads to better resource utilization based on the analysis made. • Clinical Quality Monitoring Semantic interoperability provides better patient care due to efficient analysis of clinical processes. The analytics help in avoiding the issues. Semantic Interoperability Challenges Healthcare will have a new feature if the semantic interoperability can be achieved between the several EHR systems and can be used to derive the intelligence. To achieve semantic interoperability for sharing the patient’s health records across different stakeholders, following are the challenges that needs to be addressed: • Partial Data Mapping of Multiple Sources Semantic differences arise because of combining attributes that are identified in the various systems which causes incomplete data mapping. This is because of non-formalized and inconsistent structuring of information. • Need of User Involvement Identifying meaning of common and conflicting terms in healthcare system is a challenge without the user intervention. • Medical Terminologies Understanding and correct interpretation of medical terminologies.

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• Setting Standards and Guidelines A set of principles need to be established to ensure that updated rules are semantically or syntactically accurate. This is important for defining and examining the policy conflicts. • Semantic Dissimilarities in Attributes At various times it happens that, the reasoning rules are used to infer the meaning of an attribute. This forms the basis for identifying the similarities between the attributes. • Addressing Contextual Constraints Presently there are several lexicon lists built but choosing the right meaning depending on the context is still a challenge. • Platform for Semantic Interoperability There is a necessity to strengthen the data fetching techniques, artificial intelligence, and neural networks to evaluate the alikeness between distinct attributes in the profile.

4.4 Security and Privacy Techniques Physical, administrative, and technical are the three security safeguard concerns that are considered in many of the research analysis. These concerns consist of several security strategies employed by the healthcare organizations to secure the EHRs. Administrative theme comprises of techniques related to performing audits, hiring an employee to be in charge of information security. Administrative theme focuses on compliant security policies and procedures. The physical safeguard theme focuses on protecting the EHRs physically, i.e., unauthorized access to the software or hardware is not allowed. One of the techniques under physical safeguard is assigning the roles. The third category is the technical safeguards which carry out the protection for whole EHR system of the healthcare organization on the network. The technical safeguard is very much needed in ensuring the security to the EHR system of healthcare organization because most of the security breaches happen using computers and other electronic devices. Firewalls, encryption, virus checking are some of the methods used in technical safeguards. Cryptography and firewalls are the most used techniques including antivirus software, employing information security officer and the implementation is budget dependent. Liu et al. [41] discussed that physical safeguard involves physical access control like the use of locks on computer and technical protections use firewalls and encryption to protect from electronic breaches. Amer [42] realized that administrative safeguards can be made by employing an Information security officer, implementing security measures and comprehensive education. Wikina [43] discussed that administrative protections involves authorizing the release of health data of patients by a manager while installing security

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cameras would meet the physical safeguards. The healthcare organizations must incorporate new technology and consider the threats seriously since the health data is continuously targeted by hackers. Healthcare organizations admit the benefits of using RFID to achieve security and privacy. Some of the examples using RFID approaches include saving health data using RFID tags and build a access restriction for RFID tags. Only few authorized personnel have the access to the health data thus improving security and privacy. To provide protection to the IT systems of hospitals one of the technologies used is Firefox. It is effective in ensuring the protection of health data on the existing network and secure the network of a healthcare organization. Firefox can be used in both inside and outside to protect the information network of the organization from threats. The level gateways (a kind of firewall) act as a guard for the company’s network and scans the IP address of web pages for any security risks before it is passed to end-users. The status inspection firewall prevents the external access from the outside network into the intranet of the organization. Submission gateways blocks the hackers directly accessing the system and hence secures the electronic health records. These firewalls are not easy to apply because of the high costs and complexity. The healthcare organizations need to conduct a thorough analysis whether the firewall is suitable and viable for their requirements. Another category of firewalls termed as NAT (Network Address Translator) which helps in masking the intranet IP address of organization so that they are inaccessible to the external harmful users. NAT provides a barricade between the local area network and organization’s intranet. Even though firewalls ensure the security of EHRs, the four measures of the secure strategies must be essentially applied. The four steps include direction control, serviced control, behavior control and user control. Healthcare organization must undertake complete assessment on the needs, budget, and threats both internal and external prior employing any kind of firewall. Failure to do so, may negatively impact the safety of individual health records or the complete EHR system of the healthcare institution. EHRs can also be protected or secured using Cryptography. The encryption techniques have increased the EHR security when exchanging the health information. Decryption is also useful in ensuring the security of EHRs. Digital signatures are useful in protecting the integrity of the health data. Use of passwords can help in preventing the security breaches to the health records and instruct the users to frequently change the passwords. Commonly used names, phrases and dates must be avoided. Passwords must be such that it is difficult for the hacker to speculate. Restriction on access of data can be performed using the role-based control through username and password generated by system administrators. Role-based control is not effective in protecting the EHRs from the internal threats. Use of antivirus software is another commonly used security technique. Remote patient monitoring (RPM) is upcoming technology in the smart healthcare. RPM involves monitoring of the patient’s vital signs while being at home. RPM uses several sensors which sends the patient data wirelessly to a base station which is present in the patient’s house. From the base station it is transmitted to the central

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monitoring system of the healthcare provider. An alarm will be raised at healthcare provider if there are any variation from the set threshold limits and necessary actions are taken. Currently RPM is suitable for dementia, congestive heart failure and diabetes. Since the health data of patient is transmitted via internet, there may be security risks such as data misuse, data theft, snooping, etc. The significant application of technology has paved the way in integrating cloud computing for EHR systems. Although, the cloud computing technology looks promising, there are some security issues related to it. Achampong [44] indicates that there are issues when the health records are stored on far-flung servers maintained by commercial cloud providers. Healthcare organizations should implement adequate security system to their EHR systems since the security attacks have been on the raise over time. The security system is a set of mechanisms in compliance with security policies which contains possible actions, events related to the security. An IT security policy guarantees that data, software, hardware of a healthcare organization will maintain the integrity, confidentiality and is available as per the standards.

5 Blockchain-Based Solution for EHR According to Brodersen et al. [45], blockchain is defined as a “growing list of ordered transactions that are grouped together into blocks and are linked through cryptography” as shown in Fig. 8. Blockchain uses a decentralized architecture with peerto-peer network. A block is appended to the chain only after the verification of all the transaction within the block is done. Also, the block to be added must cryptographically link to the previous block to ensure the consensus across every node in the network. The process of verifying the transactions is called as mining [45]. The nodes in the network that perform mining process is known as miners. Several miners compete to create a block by solving a computationally intensive puzzle. The block created by miner who solved the puzzle first will be added to the blockchain and the

Fig. 8 Blockchain structure

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miner will be rewarded with the crypto currency. Then the new block is broadcasted across the entire network. Blockchain technology integrates several mechanisms such as cryptography, hashing that facilitates the network to reach an agreement (consensus) pertaining to creation of each block in the blockchain. The consensus guarantees that, the transactions recorded on the blockchain are tamper proof. Every transaction is saved on the blockchain which is shared across all the nodes in the network. This ensures that blockchain possess properties such as transparency, robustness and secure. Once the blocks are created and added to the blockchain, blocks can never be deleted, updated, or modified. The current health systems are facing several issues which can be addressed by using blockchain technology. Security, data integrity, privacy, control over the private data, immutability of the health records can be ensured using blockchain by establishing the consensus across several systems instead of relying on single trusted system. Various sectors such as supply chain, insurance including healthcare are thoroughly investigating the application of blockchain technology [2]. The health data is stored using the cryptographic techniques in the blocks and only the authorized users can view and read the data but cannot be modified, thus ensuring data integrity. Each user in the blockchain network possess a public key and a secret private key which act as the visible identifier. The public and private key pair are cryptographically related, i.e., the public key is generated using the private key but deducing private key through public key is not possible. Private key is a must in getting the access to the required health data on the blockchain. Sharing of medical records in a secured way, protecting the privacy of the patient and providing patient with finer control to their own health data. [10, 46] Building an access control method using blockchain to control the access to the health records and securely allow the data sharing across the several health organizations [47]. Due to the increase in need of interoperability between the EHR systems, semantic-based storage (ex: ontology-based knowledge representation) can be implemented. Then EHR systems of one provider can use the APIs (application program interface) of other EHR system to extract and share the health data. Fully or Semi-automated approaches can be used for combining the data of several EHR systems. This solution can increase the interoperability across the various healthcare organizations. Recently many hospitals are adopting blockchain for storing the patient’s health records securely. When the new record of the patient is generated during the hospital visit, it is added to blockchain network, which provides the assurance to the patients that the record cannot be modified in future. A private key is used in encrypting these health records and is stored on the blockchain, thereby allowing only authorized users to access the required data and consequently guarantees the patient’s privacy [48].

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6 Conclusion In this paper, we discussed about the current issues in managing and storing the Electronic Health Records. Despite of continuous advancements in healthcare and innovations in technology there are still some issues faced by the EHR systems. Privacy, Security, Interoperability, and the quality of data are some of the issues in the current EHR systems. Numerous research in this context, both public and private funded are being carried out to address the challenges related to the EHR. In spite of the challenges and hurdles of using EHR system, in future EHRs are likely to play a significant role in healthcare field. As discussed before, digitization of the health records allows to create horizon of opportunities for enhancing the quality of care and analyzing the medical trends, emerging technologies such as blockchain can offer several benefits for the service. The health records which are stored locally in centralized storage hinders the exchanging of the health information across the multiple organizations. Blockchain technology can pave a way to share the health data securely across the several organizations. Blockchain along with semantic-based storage could solve the issue of interoperability. However, further research is needed to explore the possibilities in this regard.

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Chapter 9

Computational Analysis of Online Pooja Portal for Pandit Booking System: An AI and ML Based Approach for Smart Cities Rohit Rastogi, Anjali Gupta, Anmol Pant, Nisha Gupta, Shivani Tripathi, and Utkarsh Agarwal Abstract India is a land of God and Goddesses, and Indians believe a lot in worshiping the Gods and following the rituals. It has certain scientifically proven benefits too. According to beliefs, there are almost 33 million Gods worshiped by Hindus. One has to visit different sites for different Puja essentials such as Pandit Bookings, Puja Samagri, Puja knowledge, Puja Locations, and then use the google maps to find the routes toward location, as one is unable to find a common platform over which one can completely rely on and get all the facilities over one place. So, it was deeply thought of having a solution to the problem by providing all the pujas and puja-related facilities available in a single place in our paperwork, wherein all kinds of puja’s, different kinds of pandit’s who specialize in their domain and all the puja samigari’s required for puja’s, destinations, destination trackers, etc. will be provided. All aforesaid is being done nowadays using technologies like Machine Learning, Artificial Intelligence through which implementation of recommendation systems, chatbots, etc. has been done. Web Development tools like Visual Studio Code, WAMP Server, etc. for frontend and backend development. Algorithms like NLP, Dijkastra, BellMan Ford, LSTM, Collaborative Filtering, (CF), etc. are being used in implementation and as a result, one may have a beautiful WEB Based Application that provides all the modules Like Pandit Booking System along with mode R. Rastogi (B) · A. Gupta · A. Pant · N. Gupta · S. Tripathi · U. Agarwal ABES Engineering College, NH-24, Delhi Hapur Bypass, Vijaynagar, Ghaziabad, U.P., India e-mail: [email protected] A. Gupta e-mail: [email protected] A. Pant e-mail: [email protected] N. Gupta e-mail: [email protected] S. Tripathi e-mail: [email protected] U. Agarwal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_9

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of pooja selection option, Destination Selection, Destination Tracker, Online Puja Samagri Booking System, etc. The present manuscript is an effort to analyze and visualize the online pooja portal development. Keywords Smart puja · Payment gateway · Chatbot · Recommendation system · Hindu rituals · Culture

1 Introduction Pandit at your door covers major rituals that include the Hawan, Shanti Vidhi, Shubh Vivaah-Weddings, Katha like satyanarayan, Inaugral Functions, Name ceremony, Vangnischaya or Sakharpude or Engagement, Thread Ceremony or Maunji-Bandhan, Laxmi-Puja, Ganesh-Puja, Anna-prashan, Ramayan-Paath, Vastushanti, Bhaagwat Katha, Sundarkand-Puja, etc.

1.1 Online Pooja Puja is basically worship and a ritual that Hindus perform to offer a devotional worship and prayer to the whole divinity, even for hosting and honoring a guest and to celebrate the event spiritually. Hinduism does not really have a solitary heavenly book that guides them through a strict practice to follow while performing all different pujas and their rituals. All things considered; Hinduism has a huge assemblage of many profound writings that basically manages enthusiasts [1].

1.2 Global Scenario of Online Puja Hinduism has many adherents across the world, i.e., 1.2 billion, which is around 15% of the complete population of the world. And Nepal has a percentage of 81.3% and India is 79.8%, here in these countries Hindus are the majority. And accompanying Christianity 31.5% and Islam have 23.2%, out of three main religions of the entire world by maximum percentage of the population the third largest religion in world after Christianity and Islam is Hinduism [2].

1.3 Multiple Worship by Hindus There are almost 33 million gods whom Hindus worship. So, throughout the population of the country, there are many ethics, faiths, and rituals followed everywhere

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around us. Indians pray to multiple Gods in multiple situations whether it is good or bad. We see many wars are in the name of God and religion in the different parts of the world. Especially in a country like ours where multiple religions are believed, nobody can wonder that the religion and spirituality are more or less a Rs. 2.5 lakh crore market in India which is nearly about $40 billion [3].

1.4 Varieties of Puja in Hindu Community Many countries apart from India follow multiple Gods and multiple pujas-related ritualistic activities, so these puja’s have a wide scope across the world and not only in India. There are many types of Pujas possible like: AkhandRamayanPaath, Hawan, Hanuman Chalisa, Satyanarayan Vrat Katha, Kaal Bhairav Puja, Kali Puja, Krishna Puja, Mahamrutinjay Jaap, Shiv Puja, Sunderkand [4].

1.5 Research Background and Problem Identification This Research is finding pandits in our society. There are so many people who want pandits with proper Security and Qualification and we are finding a solution to this problem. We searched for both pandits and the customers. Pandits did not get the customer at a good price and the customer did not find the Pandit at a good price. This is the main problem in our society [5].

1.6 Context of Research Work In this research, it can provide Puja-related Samagri and Destination and in feature may be increased E-commerce for Puja-related samagri and also for the destination it can use Google map and different algorithms. In this research, we are going through different sites like smart Puja.com, Puja-related samagri, Chatbot, etc.

1.7 Structure of the Manuscript and Organization Section 1 of this manuscript deals with introduction which creates the background of the content, global online Puja business, and then deals with research problem and objective and chapter organization. Section 2 deals with literature survey related to Chatbot, recommendation system, online location tracing of Priest, and payment gateway. Section 3 deals with experimental setup and methodology explaining all the online modules and all related

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diagrams to explain the working of the product. Section 4 demonstrates the snapshots of the software deliverables and next sections deal with related novelty, possible future scope and limitations, and conclusions. Annex section contains the key terms and definitions, additional readings, and snapshot of the python code for the important modules.

2 Literature Survey In their research paper, Ponnusamy, P. and his team members represented a selflearning system that is able to efficiently target and rectify both systemic and customer errors at runtime by means of query reformulation [6]. In their research paper on the new design, Vakili, A. his group considers settings inside the preparation set that are potential counterparts for the applicant’s reaction and looks at them in the current setting. The model develops the BERT Bi-Encoder benchmark without significantly influencing derivation speed [7]. In the research paper Chat-Bot-Kit, Sugisak, K. also, his group introduced their electronic visit device intended for the examination of PC interceded correspondence (CMC), Human-PC Connection (HCI), and Characteristic Language Preparing (NLP). In future work, they have wanted to broaden a few wizard’s help techniques for wizard-of-oz concentrates in HCI that incorporate AI and permit them to iteratively prepare models during an examination [8]. In their research paper Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser, Ilic, S. and his team established a profound learning-based discourse framework that creates mocking and silly reactions from a discussion plan point of view [9]. Bael, J. and his team members Gipp, B.; Langer, S.; Breitinger had established in their analysis of recommender system and had conferred some descriptive statistics once reviewing several articles, concerning major advancements and shortcomings of the foremost common recommendation system ideas. They have found that quite than half of the recommendation approaches applied content-based filtering (55%), cooperative filtering was solely applied by 18% of reviewed approaches, graph-based recommendations by 16%, and alternative recommendations ideas were supported by Item-centric, Hybrid, and Stereotyping recommendations. They need to know the potential reasons for ambiguity of results that were supported by powerfully cropped datasets, they provided inadequate data that makes it troublesome to re-implement the approaches, and that they speculated that minor variations to the information set can lead to strong variations in the performance [10]. Singh, P. and his team members have established their analysis of Recommendation System (RS) and provide a comprehensive study on RS covering completely different recommendation approaches and associated problems and therefore the techniques used for info retrieval. The main purpose of this paper is to identify the analysis trends within the RS. Here a summary of various recommendation approaches employed in RS like hybrid, content-based, demographic, cooperative, knowledge-based, and context-aware recommendations has been pictured [11].

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Lowry, P. B. and his team members have established in their analysis on Online Payment Gateways accustomed to facilitating E-Commerce Transactions and taking away Risk Management. This paper evaluates the literature and provides current info for IS researchers and instructors that specialize in electronic commerce and that they explore the parts of e-credit suppliers (conventional, person-to-person, and third-party) and justify however every system process one dealings [12]. Sajjanar, G. Pasha along with his team members has established in their research on Online Payment Gateways used to facilitate E-Commerce Transactions and Remove Risk Management. The current research and development show that the electronic payment system for such an electronic transaction is to be secure for participants such as Payment Gateway Server, Bank Servers and Merchant Servers, on the Internet. The primary goal of this paper is to review the asymmetric key crypto-system methodology that uses Security Protocol, the Secure Communication Tunnel Techniques that protects conventional transaction data such as account numbers, Card number, amount, and other information [13]. In this research work, authors have reviewed many research papers which are the part of their modules such as CHATBOT, RECOMMENDATION SYSTEM, PAYMENT GATEWAY, LOCATION TRACER. Chatbot: Some of the steps to create CHATBOT are: 1. 2. 3. 4. 5. 6.

Set the role and goals of the bot. Pick and evaluate a channel. Create conversation architecture. Design dialogue flow and storyboard. Integrate and collect chat data sets. Pick a platform and development approach [12].

RECOMMENDATION SYSTEM: Some major steps to creating a recommendation system are 1. 2. 3.

Collect information on users and products. Create a function that finds the comparison of all the similar products to that product. Rank and recommend the product [13].

3 Design of the Research 3.1 Work Plan of the Product Design and Research There are four student members in the research team and work has been distributed to each one. Two people are working on the front end and two are in the back end. One has to visit different sites for different Puja essentials such as Puja Samagri, Pandit’s, rituals knowledge, Puja locations, and then they have to revisit Google maps to find the routes to the locations.

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• Online Pandit Booking System, Smart Puja, Buy Puja Samagri, Puja Locations (like halls, etc.), Google Map Location Tracer to locations, Knowledge about Hindu Puja’s, Rituals & Cultures. • All the Puja Samagri is provided and items online along with booking Pandit online as per customer’s demand. As per Customer’s Demand, a dedicated Puja kit is being offered and one-to-one Puja items also. • It also provides the facility of booking Puja destinations like halls if a customer wants to book it for Puja’s. • Routes are provided to find the destinations through maps. • It can provide the option of “City Selection” once when we increase it to different cities. • It can even further provide transportation facilities once we increase it to that level.

3.2 Modules in the Portal 3.2.1

Online Pandit Booking Module

The modules provided by us is as follows:-AkhandRamayanPaath, Hawan, Hanuman Chalisa, Satyanarayan Varat Katha, Kaal Bhairav Puja, Kali Puja, Krishna Puja, Mahamrutinjay Jaap, Shiv Puja, Sunderkand.

3.2.2

Online Smart Puja

It is an online stage giving Sacred, Hassle-free Puja Services. It is a one-stage arrangement that envelops each part of Hindu strict services furnishing the best Puja experience with checked and experienced Pandits and Purohits.

3.2.3

Buy Puja Samagri Online

We will provide all the Puja Samagri items online along with booking Pandit’s online as per customer’s demand. As per Customer’s Demand, a dedicated Puja kit is being offered and one-to-one Puja items loosely also.

3.2.4

Book Online Puja Destination

Facility for booking Puja destinations like halls if a customer wants to book it for Puja is also there. Providing routes to find the destinations through maps is also made available.

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Payment Gateway

A payment gateway is a merchant service offered by a provider of e-commerce application services that authorizes E-businesses, online shops, bricks, and clicks, or conventional brick and mortar to process credit cards or direct payments [12, 13].

3.2.6

Chatbot

A ChatBot is a product application utilized through content or text-to-discourse to lead an online visit discussion as opposed to furnishing direct correspondence with a live human specialist [6–9].

3.2.7

Recommendation System

• A recommendation system is a subclass of a system for information filtering that seeks to predict a user’s “rating” or “preference” for an object. They are used in industrial applications mainly [10, 11].

3.3 Structural Diagrams 3.3.1

Use Case Diagram

A simple use case Diagram could be a description of a user’s interaction with the system that illustrates the user’s relationship with the varied use cases within which the user is concerned. Here Fig. 1 represents the utilization case Diagram of the Pandit booking website.

3.3.2

Architecture Diagrams

An architecture Diagram is a framework Diagram that is utilized to digest the general programming framework layout and the part connections, limitations, and limits. It is an important apparatus since it offers a general perspective on the product framework’s actual usage and its guide for development. Figure 2 shows the architecture diagram of the Pandit booking website.

3.3.3

Data Flow Diagram

Data multidimensional language could be a methodology for narrating the flow of knowledge through a system. The info multidimensional language conjointly offers

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Fig. 1 Use case diagram of presented application

details regarding each entity’s output input and therefore the method itself. A flow diagram may be delineated by distinctive operations supported the info.

Zero Level DFD A Context Diagram is also known as Data Flow Diagram Level 0. It is an easy and simple illustration of the analysis or entire system modeling and operation. Figure 3 shows the zero-level data flow Diagram of an online Pandit booking system.

One-Level DFD Level 0 Data Flow Diagrams (Context Diagrams) are, as previously defined, where the complete system is represented as a single operation. Level 1 Data Flow Diagram writes main sub-processes. Figure 4 represents the one-level data flow Diagram of an online Pandit booking website.

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Fig. 2 Architecture diagram of presented application

Fig. 3 Zero level data flow diagram

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Fig. 4 One-level data flow diagram

3.3.4

Activity Diagram (Registration and Login)

Diagram that visually presents a series of actions or flow of management in a very system indistinguishable from a knowledge flowchart is termed Activity Diagram. Activity Diagrams are time once used employed in business method modeling. They conjointly describe the method in a very useful case Diagram. Activities sculptured can be coincidental and consecutive. Figure 6 represents the activity Diagram of the registration of recent Pandit/ clients and therefore the login credentials of pandits/ customers (Pl. refer Fig. 5).

3.3.5

E.R. Diagram

Entity Relationship Diagram, a Diagram showing the relationship between the entity’s sets stored or kept in a database, also called ERD. Figure 6 shows the entity relationship between each and every module of the entity.

3.3.6

Class Diagram

(Pl. refer to Fig. 8) Class Diagram is the main and important building block of objectoriented modeling. Basically, it is used for general conceptual modeling of the app

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Fig. 5 Activity diagram of pandit booking website

formation, and interpreting the models into programming code for detailed modeling. Figure 7 represents the class Diagram of our booking website.

3.4 Payment Gateway A payment gateway is a technology that retailers use to accept customers’ debit or credit card transactions. The concept encompasses not only the physical cardreading machines used in brick-and-mortar retail stores but also the online store payment processing portals (Pl. refer Fig. 8). UPI: The Unified Payments Interface could be a continuous moment installment framework that encourages bank exchanges created by the National Payments

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Fig. 6 E.R. Diagram of pandit booking website

Fig. 7 Class diagram of pandit booking website

Corporation of India. The interface is worked by the depository{financial institution/bank/banking concern/banking company) of India and works by moving subsidies forthwith on a transportable stage between two financial balances.

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Fig. 8 Payment gateway (Source https://business.paytm.com/blog/know-more-about-payment-gat eway/)

3.5 Location Tracker The most popular rideshare application like - > Uber uses Waze, which was bought in 2013 by Google Maps. Google Maps is based on the DIJKSTRA’s Algorithm which was discovered by Edsger W. Dijkstra (Pl. refer Fig. 9). Here a question arises: How do we find the shortest path? There are three shortest path algorithms:

Fig. 9 GPS location tracker algorithm

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Fig. 10 GPS location tracker model [Source https://towardsdatascience.com/]

• BELLMAN FORD’s Algorithm • DIJKSTRA’s Algorithm • FLOYD WARSHALL’s Algorithm Dijkstra’s Algorithm is a greedy algorithm. This is an optimization algorithm that makes optimal choices at each and every step. Requirements • Android API level 15+ • Maps SDK via Google Play Services OR Maps SDK v3 BETA library (Pl. refer Fig. 10).

3.6 Chatbot Chatbot develops for that you can message and talk to as a human more and more businesses are using them for online customer service to resolve issues and answer the simple question. And it can use Chatbot for our project to solve the doubt asked by the user. Also try to solve all doubts automatically by Chatbot (Source URL: (https://github.com/Anmol1109/A-Simple-Chatbot)).

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Fig. 11 Chatbot flowchart [Source https://www.youtube.com/watch?v=vhV-RpphFHc]

Natural Language Processing NLP is a subfield of man-made consciousness that manages investigating understanding and creating the dialects that people utilize normally to interface with PCs in both composed and spoken settings (Pl. refer Fig. 11).

3.7 Recommendation System A recommendation system is basically a subclass of information filtering systems that predicts what a person is most likely to choose/prefer based on his/her historical preferences data. This is a very important class of machine learning algorithms that offers some relevant suggestions to user. Without looking at a user’s profile a recommendation system has the ability to predict what the particular user is most likely to prefer. This basically analyzes the already available data for making suggestions for something a user might be interested in like jobs, some videos, some shopping articles, etc. These recommendation systems are basically based on the metadata which is usually collected from a user’s history of searching or some interactions. These are

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Fig. 12 A sample recommendation system [Source https://towardsdatascience.com/]

useful alternatives to the use of search algorithms, as this even helps user to discover such items about whom they might not have found. This is generally used in applications with commercial backgrounds. It even reduces transaction costs to select and find the items in the environment of online shopping. These days it is becoming the major way for users to get exposed to a whole digital world through the eyes of their experiences, preferences, behaviors, and interests. Few examples where the recommendation system is actually in use are TV shows in your Instagram or news feeds, movies suggestions on Netflix, and some product recommendations on Amazon. Some techniques used in Recommendation System are: • Collaborative Filtering • Content-Based Filtering • Knowledge-based Systems (Pl. refer Fig. 12). (Source URL: - https://github.com/ Anmol1109/Recommendation-System).

4 Experimental Setup and Methodology Here the team has Experimented with a problem of getting Pandit’s available as and when we need them so for this, few steps were carried out, • Pandit’s were firstly hired, and are paid according to the number of puja’s he does.

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• Then on the basis of puja booking by client’s, few questions are asked from Yajman (customer) like: 1. 2. 3.

Do they want to book puja samagri? Do they want to book puja destinations? Then we direct them to the payment option.

• Then pujas were organized either at some destination or in online mode (i.e., smart puja), as per their demand. • Their experiences and feedback were saved. • Through this, further recommendation to them was made of the pujas and destinations as per their previous choices made (Pl. refer Fig. 14). This flowchart in Fig. 13 shows the flow of Modules and the way they are interlinked and communicate with each other.

Fig. 13 Flowchart of software product

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Fig. 14 Home page

5 Results and Discussions Home Page This is the main Home Page in Fig. 14 of the site which is visible through which all the modules are connected like Pandit Booking, Samagri Booking, Chatbot, Destination Booking, etc. Figure 15 is Puja Samagri Booking Module, this page allows to book Puja-related Samagri Online. This also involves the description of each and every samagri and its usage.

Fig. 15 Snapshot of Puja Samagri booking module

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Fig. 16 Sign-In page

5.1 Sign-In Page Figure 16 is a picture of Sign -In page, through this Clients and Pandit’s Sign In to their account. Figure 17 is the image of the registration page which will help the new users or new pandits to get registered on the site and proceed further with their desired task.

5.2 Chatbot Figure 18 shows the chatbot which is used as a help section for clients and users. Clients can make their work easy by having a chat with our bot if they have any queries.

Fig. 17 Registration page image

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Fig. 18 Chatbot

5.3 Recommendation System Figure 19 shows the graphical representation of our recommendation system. The recommendation and rating system are given as per the review received from the users. Figure 20 shows the Ogive curve representation of the lower quartiles, upper quartiles, and interquartile ranges. Figure 21 shows the graphical representation of the relationship between the products (Samgiri) and the number of ratings provided to our products in an ogive form. Figure 22 shows the graphical representation of the relationship between the products (samgiri) and the number of ratings provided to our products in a histogram form. Figure 23 shows the deviation of the new rating of the products from the rating of the older products which were available previously. Figure 24 shows the bar graph representation of the rating against the productID of each and every product.

Fig. 19 Bar graph for recommendation System

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Fig. 20 Ogive curve with quartiles ranges

Fig. 21 Rating of the products (Ogive)

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Fig. 22 Rating of the products (Histogram)

Fig. 23 Deviation of the rating from the mean

5.4 Payment Gateway Figure 25 is the layout of the Payment Gateway, which is used for conducting live payments in a real-world environment.

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Fig. 24 Bar graph representing the rating

Fig. 25 Payment gateway layout

5.5 Location Figure 26 shows an example default map styled using light-colored text and icons on a dark background.

6 Novelties According to beliefs, there are almost 33 million Gods that Hindus worship. One has to visit different sites for different Puja essentials such as Pandit, Puja Samagri, Puja knowledge, Puja Locations and then they visit google maps to find the routes to the location. So, one thought of a single solution to all defined problems [14]. So, a single solution was thought for all defined problems: (Pl. refer to Fig. 23).

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Fig. 26 Location map

Figure 27 shown above depicts the differences between different sites offering the same type of facilities like Online Pandit Booking, Smart Puja, etc. Online Pandit Booking System, Smart Puja, Buy Puja Samagri, Puja Locations (like halls, etc.) Google Map Location Tracer to locations, Knowledge about Hindu Puja’s, Rituals & Cultures. Providing all the Puja samagri items online along with booking Pandit online as per customer’s demand. As per Customer’s Demand Will offer dedicated Puja kit’s and one-to-one Puja items. Also, provide the facility of booking Puja destinations like halls if a customer wants to book it for Puja’s. Try to provide routes to find the destinations through maps. Provide the option of “City Selection ‘’ once when increasing it to different cities. Further provide the transportation facilities once increase to that level [15].

7 Future Research, Directions, and Limitations 7.1 Future Scope of the Research Work • One can provide the option of “City Selection” once when it increases to different cities. • Further, provide transportation facilities once it increases to that level. • People outside and across the country hire the Pandits for doing Pooja. Rituals and customs are performed everywhere in the world [16].

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Fig. 27 Novelty table describing the comparative Analysis of proposed software and other available products

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• The Product Online Pooja Portal offers the highest qualified, experienced, and well-known Pandit for you. It offers you the best Puja experience. One can provide the option of “City Selection”, after once when it has to be increased to different cities. Even further the Transportation facilities can be provided once it is expanded to that level. These services also include online Puja Samagri for Puja Items [17].

7.2 Limitations Recommendation systems have some limitations. Understanding these limitations is in order to build a successful recommendation system: Collaborative filtering systems are based on the working or action of accessible data from alike or similar users. If one were erection or building a brand-new recommendation system, they would have no user data or data set to start with. One can use content-based filtering first and then move on to the collaborative filtering approach [18, 19].

8 Conclusions One cannot carry through any puja or conduct marriage without the existence of a Pandit. The “Pandit At Your Doorstep” gives one the chance to book pandit’s, locations, samagri online for marriage and other essential favorable occasions. They assure knowledgeable and well-experienced Pandit booking anywhere in India. So, now one can enjoy wedding festivities, Akhand Ramayan Paath, Hawan, Hanuman Chalisa, Satyanarayan Vrat Katha, Kaal-Bhairav Pooja, Kaali-Puja, Krishna Pooja, Mahamrutinjay-Jaap, Shiv-Pooja, Sunderkand without staying tensed anymore. Online pandit offers all services from providing puja videos with instructions for required samagri. It also provides the facility of booking Puja destinations like halls if a customer wants to book it for Puja’s. • Routes are provided to find the destinations through maps. • The recommendation and rating system are given as per the review received from the users. • The Payment Gateway is provided, which is used for conducting live payments in a real-world environment. • The chatbot is used as a help section for clients and users. Clients can make their work easy by having a chat with our bot if they have any queries. Acknowledgements This paper and the research behind it would not have been possible without the exceptional support of our research guide, HoD-CSE, and Director ABESEC. Their enthusiasm, knowledge, and exacting attention to detail have been an inspiration and kept our work on track.

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We also do not like to miss the opportunity to acknowledge the contribution of all faculty members of the department for their kind assistance and cooperation during the development of our project. Last but not the least, we acknowledge our friends for their contribution to the completion of the project.

Annex Key Terms and Definitions Recommendations System: A recommendation system (sometimes replacing “system” with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. They are primarily used in commercial applications. Chatbot: A chatbot is a software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Online Puja: Online Puja is the act of showing reverence to a god, a spirit, or another aspect of the divine through invocations, prayers, songs, and rituals. An essential part of Puja (Online Puja) for the Hindu devotee is making a spiritual connection with the divine. Most often that contact is facilitated through an object: an element of nature, a sculpture, a vessel, a painting, or a print. NLP: Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. Dijkstra Algorithm: Dijkstra’s algorithm is an algorithm for finding the shortest paths between nodes in a graph, which may represent, for example, road networks. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later. The algorithm exists in many variants. Bellman Ford’s Algorithm: The Bellman Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. Floyd Warshall Algorithm: In computer science, the Floyd–Warshall algorithm is an algorithm for finding shortest paths in a directed weighted graph with positive or negative edge weights. A single execution of the algorithm will find the lengths of shortest paths between all pairs of vertices.

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Additional Readings Online Puja 1. 2. 3.

Article Book a Pandit online—How It Works URL https://www.smartpuja.com/ Article Pandit Vikram Kaushik Ji URL https://www.panditjiforpooja.in/contact.html Article Book puja samagri items URL https://www.amazon.in/Puja-Samagri-Items/s?k=Puja+Samagri+Items

Chatbot 1. 2. 3.

Article With ChatBot, automating customer service is a breeze URL https://www.chatbot.com/ Article Speech-to-Text URL https://cloud.google.com/ Article Chatbot: What is a Chatbot? Why are Chatbots Important? URL https://www.expert.ai/blog/chatbot/

Recommendation System 1.

2.

3.

Article How to Create Mathematical Animations like 3Blue1Brown Using Python URL https://towardsdatascience.com/ Article Towards Effective Research Paper Recommender Systems and User Modeling based on Mind Maps. URL https://arxiv.org/ftp/arxiv/papers/1703/1703.09109.pdf Article Beginner Tutorial: Recommender Systems in Python URL https://www.datacamp.com/community/tutorials/recommender-sys tems-python

DATA SET CHATBOT https://github.com/Anmol1109/CHATBOT-Dataset/blob/main/ChatterbotsDB.csv See Annex Table 1. Recommendation System https://github.com/Anmol1109/CHATBOT--Recommendation-system-Dataset Figure 28 shows the code of Home page, all other pages or Modules are connected to the Home page, In this Html,Css and Javascript are used. Figure 29 is the code for Chatbot, which is used as a help section. In this Python has been used for chatbot coding. It includes a numpy and a natural language toolkit package.

English

http://www.chatterbo tcollection.com/ima ges/aibuddy_1401466 201.jpgh

Windows desktop app which English accepts typed sentences and responds with typed and spoken replies; remembers information about you (even between sessions) and is able to have ongoing conversations in context

Simple chatbot, using pop up dialogue boxes

AI Buddy

http://www.chatterbo tcollection.com/ima ges/woomerang_130 8758260.jpg

CHAT (pop up dialogue boxes)

Language

Aaron is an animated robot English with the ability to provide links to information on Wikipedia. Aaron lives on the petamem NLP portal site

Description

[iTunes app] ChatBot is a English chatbot! You enter a question, and she will reply. Simple. This app uses Mindskate’s “Natural Response” technology to elicit

http://www.chatterbo tcollection.com/ima ges/aaron.jpg

Aaron

ChatBot (iTunes) http://www.chatterbo tcollection.com/ima ges/chatbot.jpg

Image

Name

Table 1 Some data set for Chatbot

http://aibuddy.source forge.net/

http://www.dadorac. com/retrobot.html

http://www.woomer ang.com/chat/

http://nlp.petamem. com/eng/nlp/chatbot. mpl

Homepage

http://www.chatterbo 2008 tcollection.com/item_d isplay.php?id=486

http://www.chatterbo 2011 tcollection.com/item_d isplay.php?id=3081

http://www.chatterbo 2008 tcollection.com/item_d isplay.php?id=671

(continued)

Year submitted

http://www.chatterbo 2010 tcollection.com/item_d isplay.php?id=3035

Profile

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http://www.chatterbo tcollection.com/ima ges/alex_1401478740. gif

Alex

CallMom BASIC http://www.chatterbo tcollection.com/ima ges/CallMOM_B asic_1361801314.png

Image

Name

Table 1 (continued) English

Language

[Android App] CallMom English BASIC is the next step in the evolution of artificial intelligence (AI) for mobile devices. Unlike other virtual assistants, CallMom BASIC does not rely on a remote server to process your natural language requests. The AI is built right into the app, providing a more personalized experience with greater control over your private information. CallMom BASIC utilizes the AIML S.U.P.E.R. bot, a new chatbot personality designed specifically for mobile virtual assistant applications

Alex is a chatterbot programmed to help visitors find legal information on the Jurist Web site

Description

(continued)

Year submitted

http://www.chatterbo 2008 tcollection.com/item_d isplay.php?id=511

Profile

http://callmom.pandor http://www.chatterbo 2013 abots.com/static/cal tcollection.com/item_d lmombasic/ isplay.php?id=3137

http://jurist.law.pitt. edu/alex.html

Homepage

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Name

Image

Table 1 (continued) The bot S.U.P.E.R. (Substantial Upgrade of Previous Example Robot*) provides natural language responses to your spoken input, controls activation of device features, exchanges information with other apps and web services, and generates personality responses for ordinary conversation. S.U.P.E.R., written in AIML 2.0 (Artificial Intelligence Markup Language version 2.0), includes a persistent learning capability, so the bot can learn about your preferences and the app can provide a personalized experience

Description

Language

Homepage

Profile

(continued)

Year submitted

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Image

http://www.chatterbo tcollection.com/ima ges/pixel_1308775195. jpg

http://www.chatterbo tcollection.com/ima ges/proteus_1314805 178.gif

Name

Pixel

Proteus Project

Table 1 (continued) English

Language

Members of the Proteus English Project have been doing Natural Language Processing (NLP) research at New York University since the 1960s. Our long-term goal is to build systems that automatically find the information you are looking for, pick out the most useful bits, and present it in your preferred language, at the right level of detail. One of our main challenges is to endow computers with linguistic knowledge. The kinds of knowledge that we have attempted to encode include vocabularies, morphology, syntax, semantics, grounding, genre variation, and translational equivalence. We work on both deterministic and stochastic knowledge models

Hi my name is Pixel, ask me about life in general or about Wireframe which is the company I work for

Description

http://nlp.cs.nyu.edu/

http://pandorabots. com/pandora/talk? botid=cb4cc901de35 6607

Homepage

Year submitted

http://www.chatterbo 2008 tcollection.com/item_d isplay.php?id=1269

http://www.chatterbo 2008 tcollection.com/item_d isplay.php?id=1255

Profile

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Fig. 28 Home page code

Fig. 29 Chat bot code-1

This is another page Fig. 30 showing the code of Chatbot which is used here. This is the. ipynb file which can be opened in an editor like jupyter lab. It includes a numpy and a natural language toolkit package. Figure 31 shows the code of our recommendation system which is being used in the project. This is the.ipynb file which can be opened in an editor like jupyter lab. Figure 32 is the code for location tracker, which is used as a help section. In this, Android has been used for location coding. It includes compileSDKVersion 26.

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Fig. 31 Code of recommendation system

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Fig. 32 Location tracker code

References 1. Mani Sekhar SR et al (2021) Smart retailing in digital business. In: Big data analytics: applications in business and marketing.CRC press 2. Iqbal S, Khiyal M, Khan A (2010) Design and development of secure electronic transaction on proposed electronic payment system. https://www.ijsr.net/archive/v3i3/MDIwMTMxMDk2. pdf 3. Mani Sekhar SR et al (2021) Study on traffic enforcement cameras monitoring to detect the wrong-way movement of vehicles using deep convolutional neural network. In: Computer vision and recognition systems using machine and deep learning approaches, The IET publisher ISBN-13: 978-1-83953-323-5 4. Mani Sekhar SR et al (2021) Wearable technology and artificial intelligence in psychiatric disorders. Wearable telemedicine technology for healthcare industry. Elsevier, ISBN: 9780323858540 5. Mani Sekhar SR et al (2021) Data security and privacy in 5G enabled IoT. In: Tanwar S (ed) Blockchain for 5G-Enabled IoT: the new wave for industrial automation. Springer, Berlin, ISBN 978-3-030-67489-2. https://doi.org/10.1007/978-3-030-67490-8_12 6. Ponnusamy P, Ghias A, Guo C, Sarikaya R (2020) Feedback-based self-learning in large-scale conversational AI agents. In: Proceedings of the AAAI conference on artificial intelligence. 34. 6,7 13180–13187. https://doi.org/10.1609/aaai.v34i08.7022. https://arxiv.org/pdf/1911.02557. pdf 7. Vakili Tahami A, Ghajar K, Shakery A (2020) Distilling knowledge for fast retrieval-based chat-bots. 2081–2084. pp 1–8. https://doi.org/10.1145/3397271.3401296. https://arxiv.org/pdf/ 1911.02290.pdf 8. Nicolas-Barreales G, Sujar A, Sanchez A (2021) A web-based tool for simulating molecular dynamics in cloud environments. Electronics 10:185. https://doi.org/10.3390/electronics1002 0185. https://arxiv.org/pdf/1911.00665.pdf 9. Ili´c S, Nakano R, Hajnal I (2019) Designing dialogue systems: a mean, grumpy, sarcastic chatbot in the browser. https://arxiv.org/pdf/1909.09531.pdf 10. Beel J, Gipp B, Langer S et al (2016) Research-paper recommender systems: a literature survey. Int J Digit Libr 17:305–338. https://doi.org/10.1007/s00799-015-0156-0 11. Singh P, Dutta Pramanik P, Dey A, Choudhury P (2021) Recommender systems: an overview, research trends, and future directions. Int J Bus Syst Res 15:14– 52. https://www.researchgate.net/publication/339172772_Recommender_Systems_An_Over view_Research_Trends_and_Future_Directions

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12. Lowry P, Wells T, Moody G, Humphreys S, Kettles D (2006) Online payment gateways used to facilitate e-commerce transactions and improve risk management. Commun Assoc Inf Syst 17:1–48. https://doi.org/10.17705/1CAIS.01706 13. Lowry P, Wells T, Moody G, Humphreys S, Kettles D (2006) Online payment gateways used to facilitate e-commerce transactions and improve risk management. Commun Assoc Inform Syst 17:1–48. https://doi.org/10.17705/1CAIS.01706. https://www.researchgate.net/public ation/228280377_Online_Payment_Gateways_Used_to_Facilitate_E-Commerce_Transacti ons_and_Improve_Risk_Management 14. Mani Sekhar SR et al (2020) Bioinspired techniques for data security in IoT. In: Internet of Things (IoT): concept and applications, Series-S.M.A.R.T. environments. Springer, Berlin, pp 167–187 15. Mani Sekhar SR et al (2021) Dysarthric-speech detection using transfer learning with convolutional neural networks, ICT Express, Available online 28 July 2021. https://doi.org/10.1016/ j.icte.2021.07.004 16. Siddesh GM, Mani Sekhar SR et al (2021) Distributed database management with integration of blockchain and LSTM. Int J Inform. 11(3), Article 2. Retrieval Research, IGI Global 17. Mani Sekhar SR et al (2021) Assessment and prediction of PM2.5 in Delhi in view of stubble burn from border states using collaborative learning model. Aerosol Sci Eng 5(1):44–55. https:// doi.org/10.1007/s41810-020-00083-1 18. Mani Sekhar SR et al (2019) A study of use cases for smart contracts using blockchain technology. Int J Inform Syst Soc Change 10(2):Article 2. https://doi.org/10.4018/978-1-79985351-0.ch099 19. Mani Sekhar SR et al (2019) Security and privacy issues in IoT—a platform for fog computing. In: The rise of fog computing in the digital era. IGI Global, pp 129–156

Chapter 10

Food Management System in Society 5.0 K. Deepthi

Abstract Food Management System is one of the major sectors of importance for any nation. The Internet, connection of various devices, and advanced technology have resulted in Smart and Intelligent Food Management System in Society 5.0 to meet the food demand of increasing population. Discussion is made on how Artificial Intelligence and Decision Support Systems facilitate the design, development, and implementation of food management activities in a smarter way. Finally, concluded with two case studies relating to food distribution systems and satellite image-based crop monitoring systems. Keywords Food management system · Internet of things · Artificial intelligence · Food supply chain management · Decision support systems

1 Food Management System in Society 5.0 1.1 Introduction to Food Management System (FMS) India is a largely agricultural nation with huge population thereby creating a very high demand for food, leading to encouraging the knowledge acquisition of high quality and high-yielding crops. However, compared to other developed countries, agricultural farming and animal husbandry are still lagging behind, mainly due to the lack of usage of expensive scientific sowing, irrigation methods, and natural calamities. A food management system is a streamlined process in which any country manages the consistent portions of its food management process in order to achieve its objectives. These objectives could relate to a number of different titles, including

K. Deepthi (B) Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_10

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types of food products, food product quality, processing and storage cost, environmental effects, public health benefits, food safety, and many more. The ISO standard for food management helps food management professionals to achieve the set objectives through well-defined processes and evaluation metrics. The use of information technology (IT) has facilitated the food management process/task more efficiently and intelligently in the information field. Adding to that, the networking technology is a new group of information technology that uses the internet to associate sensors, controllers, and computers together to connect people and “things”, thereby obtaining data, and enabling remote processing and intelligent network management. The emergence of new concept called Internet of Things (IoT) and availability of high-speed internet has influenced food management systems in Society 5.0. In general, IoT is an idea built on combining IT, sensor technology, wireless communication & internet technology. The connected devices can interact and collaborate with other modules/objects, giving rise to new services and applications to aim at reaching a desired common goal, i.e., smart and intelligent food management. IoT allows objects and things to get connected anytime and anywhere with anyone using any network, path, and service to learn the information gathered by themselves and other connected devices to make contextual decisions. In the near future, IoT brings devices, Internet, producer, consumer, technology, computing, and storage of services together to avail services from food management systems in a smarter way. Due to convergence, IoT applications will offer new enriched services in food industry for acquiring, storing, and analyzing nutrient food data along with Intelligent Food Packing [1, 2]. Society 5.0 demands new value generation through Artificial Intelligence (AI). The role of Artificial Intelligence (AI) is summarized in Table 1 given below under the following headings. Analysis of big data with diverse information like crop growth data, meteorological data, food trends, demands, and food trends through Artificial Intelligence (AI). Smart agriculture realization resulted in high production and minimal labor headcount through automation of farm activities. Automation through Robot tractors for collection of crops from the field, analysis of crops through drones, automation, and optimization of water management on the basis of river and weather data is one of the Table 1 Role of artificial intelligence in food industry AI In food security management

AI In food quality management

• AI For image processing and recognition

• AI For improving food quality

• AI For fertilizer management to ensure security of food safety

• AI Mathematical models for managing food quality

• AI For performing food inspection and grading along with usage of image processing techniques

• AI Machine learning techniques to increase productivity of food grains • AI for pesticide management

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significant milestones that is ever achieved. Smart farming plans may be formulated by fixing crop yield tailored to society needs, weather prediction-based agricultural activities, information retrieval, and sharing thereby extending consumer base. The ultimate motto is to make available the farm produce to reach the desired consumer exactly when needed. Society 5.0 would achieve smart and maximum delivery of food commodities to consumers through automatic decision-making self-driving vehicles. To summarize, in Society 5.0 as a whole, the smart solutions can help increase food production and supply on need basis, tackle labor-shortage issues in few regions, and consumption management by minimizing the food waste.

2 Quality Food Collection, Monitoring, and Sharing 2.1 Food Collection Food collection phase plays a major role in FMS. The choice of right suppliers, the right food items, and sustaining the relationship between purchaser and vendor will have impact on both profitability and reputation of FMS. Different methods of Food Collection: There are different ways of food collection. The choice/mode of purchasing depends on the items to be purchased, frequency of purchasing/requirement, quantity of collection, and the demand in the market. • Open market collection is preferred when purchasing is not made frequently. An assigned quality expert collects only the desired standard quality food items at the lowest possible rates. Compromise in quality is not preferred in this method during food collection. • Food collection can also happen on the contract basis by awarding the tenders based on the estimated consumption of any food item per year. The tenders are time-bound and evaluated and suppliers are bound to supply the specified quality only. • Centralized food collection method is preferred when profit-making is of most priority. It is profit-centered food collection method rather than cost centered method. • When the purchase of specific food item is involved in the fixed quantity every time and at a fixed predetermined time interval, standing order purchase is preferred. For example: Milk and Dairy Products are purchased in this manner. Dependency on a particular vendor is one of the major disadvantages of this method of food collection. • In Fortnightly/Biweekly Collection, food collection would be done based on the requirement in the coming week or fortnight and depending on the availability of storage space. For example: Grocery purchase usually happens in this manner.

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The cost, count, and weight specifications for those food items are dependent on the brand names.

2.2 Smart Food Monitoring Food monitoring plays a vital role in protecting health and providing safety to individuals. Over the last two decades, many multipurpose sensors with new technologies have been evolved aiming at developing applications to help in the field of food management. The result of integration of emerging smart innovative devices in technology for food packaging has paved the way for intelligent and smart food packaging and has progressed the food industry in an exponential manner [3]. It is expected that by 2025, intelligent IoT will become smart Intelligent IoT for offering global-level food management services through fast communication devices. According to IoT survey, the smart intelligent devices make significant contributions in the area of food collection, monitoring, protection, and consumer safety. The sensors installed in the systems collect the information in terms of various parameters thereby avoiding stale food from reaching the general public. The reliable information provided by sensors during food storage period helps to keep track of the quality and safety of food products by intense monitoring of parameter readings like the percentage of pathogen agents, temperature, gases, humidity, and storage duration. Recent research has proved that the possibility of intelligent food feature extraction has benefited the human health [3]. Internet of Things (IoT) is capable of creating network of interconnected devices. The monitoring devices installed the in the food storage industry intelligently adjust the parameters to increase the shelf life of the food products. This IoT-based smart intelligent technology is able to provide a whole lot more information that is not limited to volume, weight, aspect inspection, and color in order to evaluate the quality of packaged food product. The information collected from the various sensors is transmitted through wireless media to one or more computing systems to evaluate the quality of the product on periodic basis. The deep learning-based quality monitoring methods are also proposed to monitor decline in nutrition in food over time. The intelligent smart devices also play a vital role in cold storage and packaging industry. The highly intelligent robots replace humans to monitor the process of storing and packaging at lower temperature levels, the food and food products, dairy, medicine, and poultry products in the cold storage environment. At the same instant, humidity level in air, moisture content, light, and other sensor parameters are closely monitored to make necessary decisions. The centralized quality food monitoring system would continuously monitor the humidity and the temperature levels of vacuum-packed foods. Commercially, monitoring sensors available currently include time–temperature Integrators (TTIs), temperature data loggers, smart radio frequency identification (Smart RFIDs) and many more [4].

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2.3 Food Packaging and Sharing Packaging is a discipline that manages to provide best goods to consumers for their use. Active packaging deliberately add active compounds directly to package or on packaging material to provide the details/feature to the consumer. Intelligent packaging comprises indicators providing data about quality and protection of the product; past of package, atmosphere inside and outside the package, etc. has become boon to packaging industry [5]. Intelligent packaging system is constantly changing to progress toward becoming more cost-effective, convenient and integrated in order to deliver innovative packaging solutions [6]. Food packaging and sharing play a major role to make available all kinds of food and food products to all portions of the country and worldwide. The integration of sensors while food packaging provides consumer with intelligent and smart packaging solutions. The state of art technology computing systems analyses the information provided by advanced smart devices has led to the improvement in the safety, food quality, longer shelf life, and enhanced usability. The innovations in the packaging technology have changed the global trends and consumer preferences. The usage of Nano low-cost sensor usage in packaging and proper packaging focuses on food safety by detection and prevention of microbial growth and oxidation, food quality sensing based on aroma and flavor thereby concentrating on sustainability of food products. Proper packaging also preserves quality of food and ease of consumption for consumer. Intelligent packaging assists proper food distribution system. Lot of research is also undertaken on packaging materials to retain the nutrition and food quality for longer duration and proper disposal methods to minimize environmental hazards.

3 Food Supply Chain Management in Society 5.0 Food Systems and Supply Chains (FSCs) are still undergoing important variations in their structure and operations over the last two decades as globalization is expanding both food availability and food choice. FSCs have extended exchange relations, transparency on food roots, approaches to farming, harvest, processing as well as labor conditions. The rapid change in technology and innovation has benefited FSCs in terms of food quality, food safety, food fraud, food security, and sustainability. Also, resources like trucks, warehouse, transportation, and workforces within the food supply chain have to be used professionally to confirm the food excellence and safety through effective efforts such as optimization decisions. A systematic literature review has shown that there is a huge gap when related to Industry 5.0 approaches to the supply chain field. The Food Supply Chain (FSC) evolution toward Society 5.0 paradigm is planned to propose a set of Supply Chains to enhance the role of active stakeholders involved in leveraging smart and digital

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technologies. Japan has put forward a vision for the technology-enabled humancentric future and coined the term “Society 5.0”. There is a complete redesign of FSCs through digitization strategies development to achieve the vision of Society 5.0 and Sustainable Development Goals. As per WHO, the contribution of all stakeholders is essential in maintaining the flow of food and supplies through the supply chain. Ensuring the confidence of consumers is important for food security. As per European Food Safety Authority, there is no indication that food poses a risk to public health in relation to COVID-19.

3.1 Impact on Food Supply Chain During Pandemic COVID 19 Pandemic is not something new in the history of mankind. Mankind has seen many before and is still facing pandemic. According to WHO, the fifth pandemic is novel Coronavirus-19 which has affected every sector and every individual. Pandemic has always thrown a serious impact on the global economy. Food industry is not apart from this. Food supply chain is one such parameter that has seriously affected the economy of the entire world starting from the beginning stage, i.e., from fields to the endusers, i.e., to consumers. There was a major hindrance to food demand, production, processing, and distribution. The entire system suffered due to the restrictions imposed on movement of goods and services and monetary pressures in the food supply chain. It became a major challenge for the government to manage food production, movement of agricultural products and workers, and financially support small farmers and laborers. In addition, every nation has realized the severity of pandemic conditions and acting accordingly to meet the needs of the country thereby avoiding community spread of pandemic. From time to time recommendations, strategic preparedness, and action plans are needed from the expert committee to react smart and fast to address the challenges posed by pandemic in food supply chain stream.

3.2 Effects of Pandemic on Food Supply Chain Coronavirus disease (COVID-19), a virus of infectious form is caused by SARSCoV-2. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Well, ahead knowledge about the disease, the rate of spread, and preventive measures are the ways to slow down the transmission. In order to avoid community, spread, standard practices like work from home for employees and online classes for students were recommended for schools and colleges. However, such policies are not applicable to food industry.

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Among the various economy generating sectors, food supply chain sector is one which got impacted greatly due to pandemic COVID-19. It is observed that COVID19 has hampered the whole process from source to the destination, i.e., from growing agricultural field to the consumer. The pandemic brought in the challenges like movement restrictions of workers, changing demands of consumers, shutdown of production facilities, restricted trade policies, and monetary pressures on food supply chain. Throwing on light on those challenges posed by pandemic, a substantial consideration is made while achieving target of food production, processing, and distribution to meet the population demand [7]. Due to the movement restrictions imposed by the government on movements and transportation, shortage of field workers was a major issue, and local and migrant workers faced difficulty in conducting activities like planting, sorting, harvesting, processing, or transporting crops to markets. Other than these, the major issues faced by food industries and food supply chain were meeting demands for functional foods containing bioactive ingredients, transition of coronavirus among producers, consumers, and retailers during food supply chain management, food security concerns, and food sustainability.

4 Design and Working of Super-Smart Intelligent Food Management System The realization of the “Super Smart Society (Society 5.0)” through IoT, extracts and modularize the functions of various devices on the network in blended mode by mixing the functions of old and new systems. The main goal is to build a “service platform” that combines and coordinates these services to create new services. Food manufacturing plays a significant part in meeting the basic necessities for supporting various human activities. Once harvested, the food must be kept safely, delivered, and retailed to reach the final customers before the expiry date. As per the studies and reports, yearly around 1/3rd of the food produced has been wasted or abandoned for various reasons (1.3 billion tons approximate). Two-thirds of the food is wasted during the realization of FSCM process itself. FSCM is a significant process to save the produced food. Emergence of Information Technology (IT) combined with scientific methods has brought drastic improvements in providing smart and intelligent food processing activities like cleaning, processing, and packing to retain freshness of the foodstuffs. In spite of improved methods and technologies, addressing real-life problems of FSCM has still remained a harder challenge. Comprehensive models are desirable for making strategic decisions to accommodate emerging global situations for handling FSCM practices. According to the reviewed papers in reputed journals, Smart-Intelligent FMS aims to redesign and rationalize the supply chain in food industry in an integrated fashion. Traceability systems and decision-making systems are the two IT systems for FSCM [8].

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Implementation Many countries have formulated a vision to upgrade “Society 4.0” to achieve humancentric, technology-enabled future and renamed it with title “Society 5.0”. Digitization is an enabler for Society 5.0. Digitization of food supply chains is bringing together all the stakeholders to one platform for achieving sustainable development goals. Cutting-edge skills can be applied to the maximum extent for optimization of food value chains. The several categories of people will be involved such as youth, private-sector corporations, and technically equipped agricultural companies and need for integration of Big data, data science, AI, and essential R&D technologies to achieve smart and intelligent FMS objectives in Society 5.0. The basis of smart-intelligent framework for FSCM includes manufacturing, processing, and transformation of raw materials into finished products through major activities like agriculture, forestry, finishing, etc. A structured framework is proven to be useful handle risks in FSCM through a unified stage-by-stage processing method. Information extracted from data plays a very vital role in making food supply chain and management more efficient via analysis of enablers and barriers parameters of FSCM. A traceability system architecture may also be designed to analyze the product possibly through use of bar codes, alphanumerical codes, and radio frequency identification (RFID). Integration of coding technology into the framework has equally benefited both producers and consumers. This technology can be used to trace through the supply chain by recording the temperature of the product at all stages. The major challenge here is the increased cost of bar codes and accuracy problems in applications involving a large amount of water or metal [8]. The small-scale and large-scale greenhouse agricultural production is becoming a current trend and could be easily implemented. The Agricultural-Internet of Things (A-IoT) concept exploits networking and cloud technology for increased agricultural production. The temperature, humidity, light sensors, and processors with a large data processing capability constitute the hardware part of this agricultural IoT [9]. These hardware devices are connected via short-distance wireless communication technology standards such as Bluetooth technology, WIFI, or Zigbee protocols (because of their convenient networking and low power consumption) and are extensively used on the Internet for agriculture. The combination of sensor networks with web technology forms a wireless sensor network framework for remotely controlling and monitoring data received from the various sensors. The associated hardware control communicates with a middleware system via Network Interface Converter. The middleware, a kind of software installed on a specific computer duly communicates with the hardware network and other user interfaces to remotely manage the temperature range, humidity level, and irrigation mode in the greenhouses [10]. This facilitates taking right action at the right time thereby producing high quality and quantity of food crops. The documentation of design and implementation details of each module in the system design architecture highlights the proof of meeting the requirements of low coupling and high cohesion among modules in the overall system.

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Finally, smart packaging plays a very important role in antimicrobial, oxygen scavenging, and shelf-life extension of food. Nanotechnology developments are helping in overcoming existing challenges and development of intelligent packaging by focusing mainly on oxygen, humidity, and freshness indicators.

5 Decision Support System for Smart Food Production, Processing, and Management A decision support system (DSS) is an information system for supporting business or organizational decision-making activities. DSSs help support people to make decisions about dynamic problems and for those problems that are not known in advance. DSS is extensively used in business and management. A DSS is one such system that is demanded by Food Management System for providing software solutions. A DSS serves the organization at management, operations, and planning levels to help support higher management. The DSS either fully computerized or human-powered or a combination of both tries to resolve the problems of both semi-structured and unstructured types. Recently, DSS have themselves proved to be supportive tools and have become competitive in almost all sectors of food and packaging industry [11, 12]. The success of any DSS depends on the quantity of data recorded from the food industry. The data collection has greatly amplified in the previous two decades, parallel to reduced data costs recorded through automation and computing systems. The DSS uses the data to fully fill the demands of consumers for their food products. The DSS can help in making decisions on demand for food products, type, quality, and quantity of raw materials, stages of processing and production, utilization of manpower, etc. One of the principal challenges and successes of decision support system is based on availability of relevant, real, and representative data on which to base the exact decision [12]. DSS can be used to lessen adverse environmental impact on food processing, and strengthen process and stock management considering the fact that almost all food products are highly perishable. The satellite technology integrated with DSS for Food Production can help in monitoring weather conditions thereby predicting pests and diseases in crops. Overall to summarize, timely decisions will help improve crop productivity.

6 Case Studies There are several real-life problems for which the technology has provided smart and accurate solutions. The design and development of hardware and software solutions

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provide the standard technical requisites to widely implement smart systems for food management and automate the same.

6.1 Smart Food Distribution System-Case Study Smart technique-based issue of ration cards [13]: A computer-based issuing of ration card to citizens has been implemented by various state Governments under One Nation and One Ration Card scheme. This method of online customer-friendly and less time-consuming system has resulted into a quick issue of ration cards, in turn, resulted in accurate estimate of demand and distribution. Detailed procedure [13] of the smart approach is shown in Fig. 1 is illustrated below: • • • •

Application for new ration card through online portal. Entry name as per AADHAR and registered mobile number. Submit personal details through biometric authentication. Personal details including residential address will be taken from AADHAR database.

Fig. 1 Flow chart for new ration card processing

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• Based on residential location, applicant needs to select municipal word number. • Next step is adding family members and authenticate individuals through biometric means. • Select elder female members as head of the family. • Submission of application for further processing. • Official will verify and visit applicant’s house for physical verification and approves online for issuing of the card. • Card gets delivered to residence through Post.

6.2 Earth Observation Through Satellite Images for Crop Management-Agriculture and Soil [14] Earth and space observation has always been of keen interest for experts’ way back from centuries. Collection of data manually was tedious, cumbersome, errorprone and time-consuming process due to lack of data acquisition devices. With the advancements in the field of electronics, remote sensors deployed in the remote areas along with satellite technology eased the process of data acquisition and transmission to Earth surface. Remote sensing data provide several advantages over conventional methods to help mechanisms make timely appropriate decisions, spatial depiction, and maximum coverage cost-effectively. Space data is practically used to address several critical facets such as agricultural crop area estimation, crop condition, crop yield, and production estimation based on basic soil information, crop system studies, trial crop insurance, etc. Indian Space Research (ISRO), in early eighties, conceptualized the idea of crop production forecasts via satellite aided remote sensing data collection. This led to the increase in success rate of Crop Acreage and Production Estimation (CAPE) project/assignment with the active participation of Ministry of Agriculture and Farmers’ Welfare (MoA and FW) toward crop production in designated areas/regions. To enhance the scope of this project, the FASAL (Forecasting Agricultural Output using Space, Agro-meteorology, and Land-based Observations) program was conceptualized by means of developing technology and methodology for multiple season-based forecasts data of crops at national level. A center named MAHALANOBIS National Crop Forecast Center (MNCFC) was established by MoA and FW based in New Delhi and is operational since April 2012, uses spacebased observations data for pre-harvest multiple crop production forecasts of several types of food crops at the national level. The crops covered were rice, wheat, jute, mustard, sugarcane, cotton, rabi, and kharif rice and rabi sorghum. Remote Sensing found land and yield predictions based on weather parameters or spectral indices are used to offer manufacturing predictions. The center has also extended this forecast facility for national-level assessment of Horticultural crops production and their coverage across the various agro-climatic regions in the country [14] as shown in Fig. 2.

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Fig. 2 Satellite images captured during different harvesting periods

7 Conclusion Food Management System (FMS) in Society 5.0 is technology-centered. It has undergone significant changes via technology usage to meet the growing demands of population. The emergence of various cutting-edge technologies like IoT, Big Data, sensors, and sensor networks along with their combinations have paved the way for processing the gathered data using AI algorithms for making suitable timely decisions. AI has aided at every stage of FMS like farming (by reducing time spent in fields), storing (avoiding rotting of food items for long time), smart packaging (to maintain nutritional value for a very long time), and distribution (making available at right time and place) thereby reducing crisis in terms of food, safety, quality, sustainability, and security. It was observed that Food Supply Chain management got hindered due to pandemic situation COVID-19 but sooner the technology has lifted off the problems in a very short period. The two case studies Smart techniquebased issue of ration cards and Earth Observation through satellite images for crop management- Agriculture and Soil presented in the chapter helps us understand how technology interference has eased the process of food distribution and crop management, respectively.

References 1. Bhushan S, Bohara B, Kumar P, Sharma V (2016) A new approach towards IoT by using health care-IoT and food distribution IoT. In: 2016 2nd International conference on advances in computing, communication, & automation (ICACCA) (Fall) 2016 Sep. IEEE, New York, pp 1–7 2. Ray PP, Pradhan S, Sharma RK, Rasaily A, Swaraj A, Pradhan A (2016) IoT based fruit quality measurement system. In: 2016 Online international conference on green engineering and technologies (IC-GET) 2016 Nov 19. IEEE, New York, pp 1–5 3. Popa A, Hnatiuc M, Paun M, Geman O, Hemanth DJ, Dorcea D, Son LH, Ghita S (2019) An intelligent IoT-based food quality monitoring approach using low-cost sensors. Symmetry 11(3):374 4. Mijanur Rahman AT, Kim DH, Jang HD, Yang JH, Lee SJ (2018) Preliminary study on biosensor-type time-temperature integrator for intelligent food packaging. Sensors 18(6):1949

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5. Kaur S, Puri D (2017) Active and intelligent packaging: a boon to food packaging. Int J Food Sci Nutr 2(4):15–18 6. Kumar I, Rawat J, Mohd N, Husain S (2021) Opportunities of artificial intelligence and machine learning in the food industry. J Food Qual 12:2021 7. Jaleta ME (2021) Agricultural supply chain analysis during supply chain disruptions: case of teff commodity supply chain in Ethiopia in the era of COVID-19. Sustain Agric Res 10(3):1–63 8. Zhong R, Xu X, Wang L (2017) Food supply chain management: systems, implementations, and future research. In: Industrial management & data systems, 2017 Oct 16 9. Pani SK (2022) Assessing COVID-19 and other pandemics and epidemics using computational modelling and data analysis. Springer Nature 10. Li Z, Wang J, Higgs R, Zhou L, Yuan W (2017) Design of an intelligent management system for agricultural greenhouses based on the internet of things. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) 2017 Jul 21, vol 2. IEEE, New York, pp 154–160 11. Arason S, Ásgeirsson EI, Margeirsson B, Margeirsson S, Olsen P, Stefánsson H (2010) Decision support systems for the food industry. In: Handbook on decision making 2010. Springer, Berlin, Heidelberg, pp 295–315 12. Cabnite Office. https://www.cao.go.jp/index-e.html 13. The Department of Food and Civil Supplies, Govt of Karnataka. https://aahara.kar.nic.in 14. Satellite Applications. https://isro.gov.in

Chapter 11

Super-Smart Healthcare System in Society 5.0 Ashwini Tuppad and Shantala Devi Patil

Abstract Society 5.0 is a concept of future super-smart, sustainable society that can empower economic development of its citizens while ensuring social upliftment and equality through modern technology-driven solutions. An integral part of such a human-centered society is super-smart healthcare system. The previous Information Society (Society 4.0) opened up the gates to information accessible via the Internet, which was then used for analysis by human beings. However, the Society 5.0 is more advanced that will automate the data management and analysis to derive value and solve economic as well as social problems. In this chapter, we introduce supersmart, sustainable healthcare system in Society 5.0 whose vision will be equitable, optimized, and reliable healthcare service delivery to all the people of the society. The underlying objectives of such a system will be presented. The prototypical design of super-smart healthcare system will be described to explain structure (components) as well as the functioning of proposed system built over modern technologies like Artificial Intelligence (AI), Big Data, Cloud Computing, Internet of Things (IoT). Our prototype will explain the roles and responsibilities of different stakeholders in the system. This chapter will discuss core technologies and their utility in development of such system. The different methods for healthcare data management and analysis will be described. Lastly, the ethical concerns to be taken care of regarding patient safety, data security and well-being of people will be discussed. Keywords Super-smart healthcare · Society 5.0 · Sustainable · Human-centered · Technology

A. Tuppad (B) · S. D. Patil School of Computer Science and Engineering, REVA University, Rukmini Knowledge Park, Kattigenahalli, Bengaluru, India e-mail: [email protected] S. D. Patil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_11

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1 Introduction One of the significant predictors of any sustainable and civilized society is its healthcare system. Healthcare constitutes a collection of services aimed at ensuring physical as well as mental health management and general well-being of public. A healthcare system is a conglomeration of different organizations and people, that collectively work toward providing equal access to essential medical care to the public in their need of hour and promoting individual as well as population health. In this chapter, we introduce a Super-Smart Healthcare System for the proposed human-centric Society 5.0. We briefly present the idea behind Society 5.0 to understand its foundation and underlying objectives, according to which the design and development of Super-Smart Healthcare System are proposed. Society 5.0 is a vision of hypothetical, future idealistic society proposed by Japan government as part of its Science and Technology Basic Plan. The agenda of Society 5.0 is the development of a smart society that can bring about economic growth along with societal upliftment to be achieved by integrating cyberspace and physical space [1]. The previous Society 4.0, also called Information Society placed much significance on information search and retrieval through computer networks in order to perform information analysis in different sectors of the society and derive value from it [1, 2]. This was realized with the advent of the Internet and the World Wide Web, which was characterized by a clear separation of cyberspace components and physical space consisting of the end-user. While the search and access to information pertained to cyberspace, the analysis of information particularly was performed by people/end-users. However, recently the world is witnessing tremendous growth in different areas like economy, technology, industry, healthcare, etc. which in turn have led to newer, complex social challenges. Further, the information explosion in aforementioned fields due to big data has made the information analysis by humans very challenging and limiting, unlike in Society 4.0. This has paved way for the evolution and conception of a new society, Society 5.0 that brings together cyberspace and physical space in a close loop wherein analysis of such big data is achieved by Artificial Intelligence technology to be accessible to people. Furthermore, it is proposed to address societal problems in parallel with economic reformation making it a human-centric society [1]. In the area of healthcare, there are quite a few challenges that the Japanese government is planning to address using cyber-physical model of Society 5.0. The most pressing concern faced by Japanese healthcare system is its aging population, which is fast-growing and huge in number. The national statistics on aging population reveal that the percentage of elderly people 65 years of age and above comprised 28.8% of Japan’s overall population. Furthermore, the pace of aging has rapidly increased in Japan as compared to U.S and European nations in the last decade [1, 3]. The resulting effects of such trends are manifold- the proportion of young, productive workforce that can contribute to the national economy diminishes, the healthcare expenditure spent on medical treatment and welfare of elderly substantially increases, the manpower and other resources to be reserved for healthcare management too grow

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in demand. Moreover, an alarming decrease in birthrates is also being observed, which adds to the future threat of productive population shrinkage [1, 3]. To address these challenges, Japan has proposed a Super-smart healthcare system that strives to restore a healthy lifespan and ensure reliable healthcare service delivery to all of its citizens supported by digitalization and powerful technologies. Although the aforementioned problems in healthcare system are more prominent with respect to the Japanese society, they are not, however, a confined to it since many other countries are seemingly experiencing the same trends in aging population, birthrate, and increasing healthcare resource demands and costs as shown in Fig. 1. Similar to healthcare, even other sectors of society like economy, industry, technology, and social development are facing newer, complex issues. Gradually, many nations may advance to a situation that calls for resolution of such challenges to sustain and progress. In that regard, the concept of Society 5.0 proposed by Japanese government will act as a proactive step and may guide other nations to move toward sustainable development. On the healthcare front, the proposed healthcare system model must primarily meet the strategic goal of “Expansion of healthy lifespan” [3] while providing smart solutions for disease diagnosis and management, healthcare resource provisioning, cost optimization, improvement of quality of life as well as promoting healthcare sector growth. The previous Society 4.0, i.e., Information Society has contributed

Fig. 1 Global healthcare system challenges

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Physical World

Cyber World

Data Computation results Computational Intelligence (AI and ML) Fig. 2 Integrated cyber-physical model of healthcare System 5.0

to exponential growth of data in healthcare leading to healthcare/medical big data. The type of medical data comprises structured, unstructured, or hybrid forms classified accordingly based on whether the data can be stored in a predefined format like rows and columns and has specific ways for its retrieval and processing. The innovations in computer networks and hardware have improved data processing and transfer speeds by leaps and bounds making sharing of medical data on Web even faster [4]. Moreover, the amount of medical data collected through standalone or integrated wireless sensors in wearable medical devices has added to the existing body of digital medical data [4, 5]. The open-access movement has also contributed to cross-sectional sharing and integration of medical research data including genomic, physiological, clinical, and biomarker data which otherwise was not possible earlier. The veracity of medical data generated from different sources is very significant for further processing and analysis, which is a measure of how reliable the data is in terms of degree of noise, the data quality including its consistency and integrity, etc. However, the analysis of such a vast pool of healthcare information by human experts is a daunting and tedious task. In the proposed Society 5.0, digitalization of whole healthcare system has been conceptualized to aid in new ways of storing, accessing, and managing the medical big data [4]. It is also believed to ease data processing and analysis by creating a common digital platform to share and integrate the healthcare data related to every individual in the society. The advent of modern technologies like Artificial Intelligence, Big Data, Cloud Computing, Internet of Things has empowered the healthcare system beyond the Internet era so as to provide computationally intelligent agents/algorithms to solve real-world complex healthcare problems autonomously than just offering access to medical data over the network [1, 5, 6]. This development has the potential to address multitude of issues in clinical decision-making, public health improvement, pharmacological innovation, better healthcare administration, optimized resource utilization in hospitals, and adding comfort to the lives of patients as well as healthcare personnel. Figure 2 shows integrated cyber-physical healthcare model of Society 5.0 where physical entities in

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real world interconnected by IoT provide rich integrated healthcare data for intelligent analysis by AI/ML algorithms in cyber world. We will refer to the proposed Healthcare system in Society 5.0 as Healthcare System 5.0 from here onwards. We put forth the objectives of proposed Healthcare System 5.0 in Sect. 2 and discuss strategies for meeting those objectives. In Sect. 3, a prototypical design of proposed system is presented with constituent components, the working of the system, and the roles and responsibilities of each component therein. In Sect. 4, core technologies that are used to build and realize this supersmart healthcare system are briefly discussed. Further, Sect. 5 presents the different steps and techniques for healthcare data management and analysis. The main aim of our super-smart healthcare system is to build sustainable, smart, and human-centered healthcare infrastructure that will serve and improve the health of the society at large. In that case, its design will be incomplete if ethical concerns with respect to patient’s safety, confidentiality of sensitive medical data, and integrity are not addressed. Section 6 will discuss important ethical concerns involved in the process. Finally, Sect. 7 will discuss prominent applications of proposed system.

2 Objectives of Super-Smart Healthcare System The primary objectives of Healthcare System 5.0 must be defined to be aligned with the high-level objectives of Society 5.0, of which it is an integral part. These high-level objectives include sustainable development marked by economic growth along with resolution of social challenges in parallel. The main healthcare issues highlighted by Japan as discussed in previous section are hyper-aging population, declining birth rate, and healthy population shrinkage. Other global (not limited to Japan) healthcare challenges include early diagnosis and management of chronic debilitating diseases/disorders, treatment planning including lifestyle modification and regular monitoring, improvement of quality of life by alleviation of medical costs and burden, betterment of insurance facilities, healthcare education and promotion to all economic strata of the society so on [3]. In view of the above-stated challenges, the Healthcare System 5.0 aims to utilize the contributions of Information Society 4.0 namely digitalization and cutting-edge technologies like AI, Edge Computing, Blockchain, Cloud Computing, IoT, and the like to achieve the following objectivesi.

ii.

iii.

Accomplish early diagnosis of life-threatening/debilitating diseases for possible prevention and long-term management through clinical decision support systems. Educate and promote healthy lifestyle management through diet, exercise, and behavioral habits through patient-centric apps and public health programs for healthy lifespan extension. Digitalize and integrate healthcare big data from different sources like hospitals, insurance firms, pharmacy companies, etc. that can provide newer insights

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and/or trends to optimize the usage of healthcare resources, costs, effort, and time effectively. Promote healthcare sector growth by creating offshore and local, remote medical care services that contribute to employment opportunities as well as eliminate the social dependency of elderly in emergencies.

To realize the above-stated objectives, a transformation in healthcare system structure, priorities, strategies, and processes is required. The paradigm shift in all of them can be described as belowi.

Curative Health Care to ME—BYO Health Care [7]

The proposed system will move from conventional curative healthcare practice to ME-BYO Healthcare practice, founded and put forth by Kanagawa Prefecture Japan which is home to fastest aging community within Japan. The term ME-BYO stands for “neither healthy nor sick”. ME—BYO Healthcare policy is based on this very idea/concept of transitory states of healthy and sick that any individual experiences in his/her lifetime rather than viewing them as two separate/independent health states. Unlike the conventional curative care that aimed to provide medical care to cure a patient only after the onset and health risks of a disease are observed, ME-BYO healthcare practice signifies facilitating medical care through active lifestyle management from young age, preventive/early disease diagnostic services by technological interventions, proactive patient participation in self-education and efforts for healthy living. This resolves the issues of healthcare provision for substantially large, elderly population, their dependency, and social security problems, optimal use of medical costs, resources, and efforts as well as healthcare growth. ii.

Standardized Care to Personalized Care [7]

Standardized Care refers to healthcare services or provisions jointly approved by a panel of various stakeholders involved and backed by solid medical evidence to offer best quality healthcare that is affordable to all of its citizens. The different stakeholders include physicians, pharmacists, pharmaceutical companies, insurance firms, patients, regulatory bodies, government, etc. The prime focus is to establish the essential quality standards and guidelines to be followed while healthcare services are delivered. Its counterpart, Personalized Care offers tailor-made healthcare services/provisions based on the patient’s health profile including the risks and response to treatment/intervention. The focus of this model is to provide personalized care to the patients, each of whom is treated uniquely as per their medical history and health conditions. The basic idea of unified healthcare model may not suit each individual patient, in which case personalized healthcare comes into picture. Personalized healthcare model can be adopted into Healthcare System 5.0, where a digital health platform comprised of separate patient and physician modules can be designed so that patients can remotely update their health status like vital signs/symptoms, medications, sensor readings, etc. regularly and the physician can offer/suggest healthcare services except in emergencies or situations where direct physician attention is necessary.

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Healthcare providers led to Active Patient Involvement [7]

Another characteristic of Healthcare Society 5.0 is active patient participation in health management and restoration. This gives an opportunity for an individual to proactively invests in his/her health and well-being throughout his/her lifetime through proper lifestyle conduct, timely consultations, adherence to physician treatment, and so on.

3 Prototypical Design and Working Healthcare System 5.0 can be structured to contain the three tiers with first-tier comprised of people that interact with the system, second being the data tier that houses all the healthcare and medical big data, and third, technology tier that enables computationally intelligent, sustainable Healthcare System depicted in Fig. 3. 1.

People, comprising of individuals or groups of individuals interacting with the Healthcare System for different purposes and are either direct/or indirect stakeholders of the same. Patients Healthcare professionals Software Engineers Other Stakeholders

Medical and Healthcare data

Technology To improve healthcare services

To promote Healthcare Sector growth

Fig. 3 Prototypical Design of proposed Healthcare System 5.0

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a.

b.

c.

d.

2.

Patients—The patient-centric model allows a patient to proactively take charge of their health and lifestyle through technology-enabled awareness programs like lifestyle conduct including diet, physical activity, and behavioral practice to improve one’s physical and mental well-being. Further, sensor-enabled health monitoring, and logging critical physiological measures into uniform digital platform for physician monitoring during sickness are also possible. Healthcare professionals including physicians, nursing and support staff, technicians, and pharmacists play a pivotal role in the proposed personalized healthcare system. Physicians are involved in remote monitoring, teleconsultations, treatment (except in critical cases), referrals, and working closely with clinical decision support systems developed by software engineering teams through the patient data logged online. The software development team including designers, developers, testers, analysts, database administrations as well as maintenance staff is responsible for smooth, robust operation of proposed system with backup and alternative options in place. Finally, the other stakeholders who may be affected by the changes or developments in the healthcare system include the insurance agencies, the government officials, medical regulatory bodies, environment protection boards, medical equipment manufacturing companies, etc.

Medical and Healthcare data, including structured, unstructured, and hybrid/semi-structured medical and healthcare data [4, 8]. a.

b.

c.

The structured data sources are Electronic Health Records (EHR) collected in central repositories of hospitals, health surveys, audits, clinical trials data, patient data collected by IoT sensors, and so on. Unstructured data comes from physician’s clinical notes/prescriptions, medical images from scanning modalities, patient’s audio narratives, genetic profiles, disease codes, and so on. Semi-structured data is comprised of structured as well as unstructured data like few modern EHR systems integrating clinical notes along with patient records.

Further, digital data may also be maintained by insurance firms, and pharmacies that can be utilized to gain insights into administrative planning, healthcare expenditure, drug discovery, and compatibility. Thus, data plays a very important role in Healthcare System 5.0 and is the base of all computing behind super-smart solutions. 3.

Technology The last tier of Super-smart Healthcare System 5.0 constitutes the technology that has evolved from the previous Information Society 4.0 and also has the ability to drive the current society toward economic growth with societal goodwill. The proposed healthcare system 5.0 is hi-tech in a sense that its architectural design, operation, and value generated are all mainly derived from promising computer technologies. IoT is used to connect sensors that can collect patient

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data and communicate the same for remote computation and health management. Cloud computing provisions the necessary hardware/software resources including IT infrastructure to meet the needs of highly intensive computing environment, while the edge computing can be considered to handle the network bandwidth, latency, and performance limits smoothly. The majority of the clinical decision support is derived from AI and ML technologies. To secure the communications with such vast spread, Blockchain can be applied [9]. The core technologies to be used in implementing the Healthcare System 5.0 are discussed in next section.

4 Core Constituent Technologies This section discusses the core technologies that are useful in building a super-smart, human-centric healthcare system. Internet of Things (IoT) refers to a technology characterized by a network of large pools of interconnected physical devices, each possessing its own data sensory, processing and computing capabilities that operate over the Internet or any other communication network. The idea behind IoT was to build a network of smart physical devices that ease human lives by offering real-time solutions at much faster, efficient pace than humans would do. In healthcare system, IoT applications are on the rise. Smart hospitals, smart patient monitoring systems, IoT devices like smartwatches, wearable sensors/ sensors embedded in clothing that collect physiological data as well as blood glucose, blood pressure meters that collect biomarker data are common. Further, healthcare IoT domain has evolved into more specialized subdomains like Internet of Health Things, Wearable Internet of Things, Internet of Medical Things, Cognitive Internet of Medical Things, Internet of Mobile Health Things, Internet of Nano Things, etc. [6]. Artificial Intelligence (AI) has given rise to a world of intelligent machines/agents embedded with human-like intelligence and skills to resolve complex real-world problems. Human body and mind possess many exceptional abilities like visual perception and recognition, rational thinking, decision-making, auditory signal processing, somatosensory (touch) processing, speech synthesis, language understanding, translation, and so on that dive into action every day. AI agents are designed to mimic such human-like capabilities to solve unaddressed real-world problems where either human reach is limited by physical, technical, or other kinds of barriers or the scale is huge. Healthcare AI applications are found in products like AI robots as health/surgical assistants, interactive chatbots taking on patient queries, medical history, reminding medications and future consultations to patients, tools for processing clinical notes using natural language processing, recognizing abnormalities in medical images and many more [10–12]. Blockchain also known as distributed ledger technology is an emerging field that has gained huge popularity recently. It is a decentralized, distributed database for

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recording and storage transactions over a network that is immutable in nature. The network transactions stored in fixed-sized chunks known as “blocks” are given a timestamp and the blocks are chained together based on their timestamps. This pattern results in a unique, immutable structure and an indicator of security, reliability, and integrity of communication. Blockchain technology is finding applications in wide variety of domains like finance, healthcare, education, retail, administration, and so on. Blockchain-based healthcare applications are proposed mainly for enforcing security, privacy and integrity of sensitive patient data while in storage as well as in transmission [13–16]. Machine Learning (ML), a sub-domain of AI, is based on the idea of learning from data through experience. It involves designing algorithms to identify patterns from data and recognizing the similar patterns over unseen data to predict the results. Data availability and analysis are central to ML. With digitalization of healthcare system and medical big data generation, ML has become quite popular and also has produced promising results. Some notable ML problems include medical diagnosis, medical image segmentation, precision medicine, proteomics and genomics profile study, healthcare resource and cost optimization, patient records management, hospital administration, billing, etc. Primarily, all ML problems fall under either of the two categories- classification problems (involve predicting a discrete-value output) or regression problems (involve predicting a real-valued output) [5, 12]. Cloud Computing refers to provisioning on-demand access to different computing resources remotely over the Internet, that is hosted by a third-party service provider. The computing resources may include specific software packages/modules (Software as a Service), platform for application development and deployment (Platform as a Service), or whole IT infrastructure comprised of hardware, software, and middleware components (Infrastructure as a Service). The services are charged by pay-asyou-use model, which makes cloud computing affordable and very appealing [17, 18]. Moreover, the services are deployed in four different modelsi.

ii.

iii.

iv.

Public cloud is accessible to the general public and the resources being hosted reside on service provider servers that can be shared by end-users. This leaves the model with negligible privacy guarantees. Private cloud is similar to public cloud in structure however, it is reserved for private networks that may belong to an organization. The resources are dedicated to the members of private cloud network and not accessible to anybody outside it. Community cloud model is best suited when two or more organizations with similar computing goals, requirements, security, and privacy policies agree on utilizing cloud services from a single shared cloud. Hybrid model is a combination of above three deployment models that can be used to facilitate different needs of an organization.

Edge Computing refers to distributed computing that brings computing as well as storage resources in close physical proximity to the source of data itself so that all the computing is performed over the edges of the network, contrary to the traditional

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centralized computing paradigm. Edge computing has evolved from cloud computing and IoT technologies. The massive growth of computing over a cloud due to its ondemand economic resource delivery model almost made computing accessible and affordable to any individual or organization. However, with the advent of IoT, the capabilities of cloud unraveled new limitations as the data generated at IoT devices at the network edge became overwhelming leading to latency and bandwidth issues inside a cloud network. This gave birth to edge computing which exploits distributed computing near the edge of the network while the results are finally transmitted to the central data center for backup and subsequent use. It is well received by the IT community as it addresses the inherent common issues of bandwidth adequacy, latency issues, and network failures in a robust manner [19, 20].

5 Methods for Healthcare Data Management and Analysis With the increasing number of IoT applications in healthcare, today a lot of medical and healthcare data is generated through IoT sensors/devices at very high speeds. The advances in network communications and Internet computing have also led to healthcare information exchange for betterment of facilities. A paradigm shift in the ways healthcare data was stored, handled, and utilized is evident. Healthcare big data that comes from different verified/unverified sources in different forms and with huge scale and speed is very difficult to manage. This section provides a discussion on methods for healthcare big data management and analysis. Data storage—Earlier, the data was most commonly stored in relational databases in a tabular format. Such databases were suitable for data with a known structure and type. The healthcare records were organized in the form of field-value pairs with each row denoting an individual patient’s record and columns denoting the patient attributes. With current big data scenario that not only consists of structured data, but majority of big data pool formed by unstructured text, images, audio, etc. the relational way of data management will be obsolete if we are to integrate and process all the digital healthcare data to derive value. Thus, the evolution of database technologies from relational to modern NoSQL databases has gained more attention recently. NoSQL Databases stand for “Non-SQL” (or non-relational) databases but are also called “Not Only SQL” databases that store data distinctly than the conventional relational databases with some support for query languages similar to SQL. They are characterized by simple flexible schema design, horizontal scaling over distributed nodes, and high availability. Mainly, there are four types of NoSQL databases [21–23] i. ii.

Document databases that store data in JSON-like documents format, Key-Value Databases storing data items contains key and corresponding values with values being any data type like structured field-value pairs/unstructured text, images, audio, video, clickstream data, etc.

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Column-oriented databases with data is stored column-wise unlike row-wise as in relational databases and related columns grouped under column families, Graph databases that use the concept of graph networks to denote data and the underlying relations using nodes and edges.

iv.

NoSQL databases can store different types of data and scale well to increase incoming data flexibly with very minor schema changes. They contribute to agile software development that is necessary for Healthcare System 5.0. Figure 4 depicts the data storage methodology using NoSQL databases for different kinds of healthcare data to improve healthcare services as well as growth. It is based on patient wise data integration wherein all healthcare data stored are identified by unique patient ID (PID) that will be used to operate with all the data.

Patient-wise Data Integration MRI, CT, X-Ray, Doppler, other images on cloud

Clinical Notes, Prescripons, Medical Report

Omics data (Genomics, Proteomics, Metabolomics etc.)

PID

PID

Key-value Databases to store unstructured paent data idenfied by PID as key

PID Document Databases to store structured paent data with PID as key

e-health Records, Lab parameters, other structured data (administrave, pharmacy, insurance, survey data)

Graph Databases to associate different data for determining relaonships. Excomorbidity, cost/benefit analysis, resource allocaon/illness etc.

Column family databases to group paent data

Column family Database Cardiac

Renal PID Neuro

Fig. 4 Methodology for NoSQL based healthcare data management

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The structured e-health records, as well as laboratory test values that can be stored in field-value pairs, can be stored in document databases that are most suitable for such data with PID as the key or index to individual documents related to each patient. The more complex unstructured data obtained from various medical imaging modalities are very peculiar and their features vary from one patient to the other. Such data are huge in size as well as free form. Key-value databases are more suitable for storing such data since they are able to store different kinds of data like text, numeric, images, audio, video, etc. as the value of the respective key and are more flexible, unlike document databases. The omics data like genomics, proteomics, and metabolomics contain analysis of millions of genes, proteins, and metabolites, respectively that are extremely difficult to manage. They can also be stored in key-value databases. The proposed methodology thus integrates both unstructured and structured data as mentioned above using PID by integrating them in column family databases. Column family databases can store multi-index data in the form of column families such that columns in one family are all related for one purpose. The goal is to allow analysis of patient data for disease-specific treatment and decision-making. Further, alongside graph databases that allow linking of different data entities to determine associations or relationships between them can be used to link patient data toward cost/benefit analysis, drug-drug compatibility, patient response to a treatment, comorbidities of diseases. Data Processing—The proposed Healthcare System makes use of digital healthcare data from potential sources along with medical data collected by IoT sensors to improve the healthcare services. Owing to the huge scale of such data, the data processing and analysis too very challenging. Different data processing tasks like data cleaning, data aggregation, data mining, data transformation, etc. may be involved based on the nature of data and the purpose of its use. Running such operations on healthcare big data in a reliable manner involves high stakes. The conventional techniques for data processing were achieved through computer programming for each of the specific tasks or supported by the database management system itself. Since the data size was manageable, both the approaches were acceptable. However, the same techniques cannot work for big data that we see today. The data explosion is characterized by its distributed nature, velocity, volume, and variability which requires a distributed data processing methodology/procedure while meeting the time, security, and reliability requirements. There are two primary big data processing paradigms namely stream processing and batch processing based on the processing time involved. In-stream processing, data is processed in incoming streams as soon as it is received possessing characteristics of speed and volume. Thus, only the data stream that is to be processed is temporarily stored which is replaced later by successive stream in queue. On the contrary, batch processing involves parallel processing of “batches of data” on multiple nodes that are distributed in nature. Here, the data is rather stored and processing at once using parallel and distributed processing paradigms. Proposed system will utilize both the methodologies with stream processing employed for real-time patient data processing and transmission of results during screening, day-to-day health tracking, remote health

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service delivery so on while batch processing employed for more time-consuming, complicated tasks like clinical diagnosis, prognosis, drug-drug compatibility, disease incidence/prevalence estimation, etc. Apache Hadoop is an extremely popular open-source software framework known for distributed big data processing. The two main components of it are Hadoop Distributed File System (HDFS) and Map-Reduce framework which provides distributed file management as well as distributed scalable big data processing, respectively. The computation is performed in close communication with HDFS and Map-Reduce components. HDFS contains master and slave nodes accepting queries over file system. The Map-Reduce Engine is responsible for carrying out computation over said file contents in distributed manner using JobTracker and TaskTracker components. Map-Reduce Engine divides every job/query into two tasks called Map and Reduce. Map refers to processing individual query that involves an HDFS file to transform into key-value pairs and map chunks of such pairs to multiple nodes distributed closely. The output of Map task, i.e., the key-value pairs are processed and reduced to final output in Reduce task. The Map-Reduce tasks are run in parallel over nodes in a distributed network to provide flexible, scalable, and timely big data management. Hadoop also comprises other software packages like Pig for developing Hadoop programs, Hive for query and analysis over Hadoop supported databases and file systems, HBase as a non-relational distributed database service, Zookeeper as a distributed server over cloud technology for handling reliable cloud application service requests and so on. Data Analysis—Refers to analysis of the acquired, pre-processed data to draw conclusions and generalizations from it depending on the relationships between the variables. Data analysis is an important part of any computational intelligence system and many techniques for data analysis are offered by modern technologies. Data Mining refers to mining large repositories of data using computationally intelligent algorithms to discover hidden trends/patterns and gain insights. Data mining algorithms can be utilized to analyze healthcare big data in the proposed system in areas like epidemiological studies for determining population health to devise intervention programs, healthcare policy framing, revival of medical system, precision medicine implementation based on outliers, etc. Machine Learning described in earlier sections has been extensively used for analyzing healthcare datasets for different purposes. ML and data mining intersect each other closely through the ML algorithms are developed to solve real-world problems by learning to generalize from historical data, unlike data mining techniques that are applied to systematically search datasets to discover underlying hidden patterns from the data. Data mining has more statistical perspective while ML is centered around human-like computational intelligence embedded in machines. Simulation technology provides an environment to design, develop, deploy and test an application/experiment and its performance without the need to actually reserve hardware/software resources dedicated for the same. It is very useful in high resource requirement problems that allow one to experiment with virtual computer

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hardware/software resources to gain a possible view of underlying process and possibility of success/failure of a particular solution. This helps in minimizing the cost expenditure, time investment as well as resource optimization. It is also very useful in complex settings that may involve high stakes of irreversible/irrecoverable damages like safety–critical systems as in hospitals, mission-critical systems like defense applications, or security-critical systems as in banks since it provides an opportunity to carry out a cost/benefit analysis in a virtual scenario to minimize damages. Statistical and AI-based simulation models have been proposed to analyze epidemic outbreaks, spread, and possible control measures as well as to model anatomical and physiological altercations leading to disease incidence, progression, and comorbidity. The proposed system can utilize such simulation models.

6 Ethical Concerns Healthcare System 5.0 operates in a closely integrated cyber-physical world with substantial part of healthcare workload that required human intervention is now being proposed to be accomplished using modern, hi-tech solutions. The role of technology is immense and wide-ranging in areas like medical data collection, processing, integration, clinical decision-making, automation in high resource requirement tasks like patient monitoring, complex surgeries, therapies/treatments, emergencies, and so on. Such high reliance on technology in performing health critical tasks raises many serious ethical concerns. The realization of Healthcare System 5.0 must take into account and enforce the following ethical concernsi.

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Data confidentiality—The creation of a uniform digital platform to store and integrate all healthcare data of every patient individually must be achieved by enforcing an effective authorization procedure in place so that confidential data of patients as well as those related to hospital resources, etc. cannot be misused. Confidentiality of data means allowing only authorized access to legitimate end-users, who have rights to use that data. Compromising confidentiality leads to miscreants using the sensitive data for harmful interests. Data integrity—Integrity of data is paramount when clinical decision-making is involved. It is a parameter that indicates the consistency and accuracy of the data before, during as well as after it is collected till is in use. Since Healthcare System 5.0 will be transformed by ML algorithms to discover trends and assist physicians/healthcare professionals in decision-making, hampering the data with wrongful intent can be fatal. Strong encryption mechanisms and blockchain technology can be useful in ensuring necessary data integrity standards are met. Availability—Data availability is considered an ethical concern in Healthcare System 5.0, which aims to connect humans closely with the cyberspace involving computers, sensors, network devices, storage devices, etc. to improve medical care and healthcare system optimization based on data that truly

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belongs to the patients. In such case, the data availability at all times is important not only to the patient but also to the healthcare expert, data analysts, and development teams. In situations of data security breach, often availability of data is disturbed by Denial of Service (DoS) attacks by the intruder by flooding the server with continuous queries exhausting the existing resources. Though Healthcare System 5.0 will utilize distributed computing on top of edge computing and IoT, the threats in the form of Distributed Denial of Service (DDoS) attacks have the ability to overwhelm the network by flooding individual servers with service request from multiple clients. The bandwidth, latency, and performance of network is at stake at such times. Safety—Safety in Healthcare System 5.0 would mean safeguarding the health of patients who are more likely to be treated, assessed, and monitored through gadgets and AI agents. A lapse in the devices collecting data from patients or in the communication between the peers involved in health risk assessment or physician assistance may lead to devastating health hazards, that may not be reversed. Safety is also concerned with the accuracy, reliability, and responsiveness of software applications/algorithms making clinical predictions. The safety component thus enforces accuracy, timeliness, robustness as well as reliability parameters in all cyber-physical components having an active role in maintaining/ restoring human health. Interpretability—Interpretability is one of the most popular research interests in AI and ML technologies today. With more and more AI agents and robots produced to automate everyday tasks to offer ease, comfort, and minimize burden, ubiquitous computing has emerged. Advanced ML algorithms being designed to achieve mission-critical tasks or aid in domains reliant on only highly qualified and skilled experts have taken over fields like astronomy, agriculture, medicine, finance, etc. However, the ability of both AI agents as well as ML algorithms to provide valid, convincing interpretations of the underlying assumptions/ results arrived at is very significant and cannot be neglected. Privacy—Certain physiological and biomarker data of patient are collected by IoT sensors. ML algorithms offer decision support to clinicians using data from sensors as well as digital data stored in healthcare repositories. Further, AI agents automate complex tasks to reduce the burden on healthcare system resources in terms of medical experts, costs, time, equipment, etc. In the wake of increasing elderly population, these AI agents can ensure reach of healthcare services uniformly while eliminating the problems of social security, dependency of aged people, and optimizing healthcare system resources. The highdimensional, data-driven computing by ML algorithms can be supported by clouds as well as edge computing technologies with concerning privacy, security, and performance parameters in check. Furthermore, blockchain of healthcare data can be utilized while transmission of raw data or processed results from healthcare data centers to clinician/patient sites to preserve confidentiality and integrity of patient’s data.

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7 Applications There are a number of applications of proposed super-smart Healthcare system in Society 5.0. This section enlists and briefly throws light on most popular and significant applications. i.

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Elderly Assistance and Comfort—AI agents can be developed to provide comfort and assistance to elderly population in everyday tasks that they find difficult to do without support. Smart homes with intelligent cameras, personal digital assistants, movement trackers, speech recognition, temperature regulators, remote-controlled lighting system, physiotherapy agents are some examples of such intelligent applications allowing a comfortable and cautious living for the aged population Smart Dispensaries—Smart hospitals with smart Intensive Care Units (ICUs), Neonatal Intensive Care Units (NICUs), Operation Theaters (OTs), Hospital Beds, that make use of IoT to connect the medical devices/objects with nursing staff and physicians to minimize their burden and enable patient to receive alerts are emerging nowadays. Such smart healthcare devices can be designed to contain interactive monitors that can convert complex medical signals/ information into interpretable and more readable form to be displayed to the support staff. Smart, Fast Emergency Medical Services—Smart Blood Banks, Stem Cell Banks, Breastmilk banks, Testing and Diagnostic Labs can be implemented that operate on top of cloud computing, Web to accept patient applications/consultations remotely and provide useful information about donors/needy patients to locate them and serve fast. The ML algorithms can be run at back end to predict future demands based on historical trends and NoSQL databases can be used to contain the data. IoT enable patient casualty response—Ambulance services with highly sophisticated patient monitoring systems [9], first aid, medication, AI agents for basic emergency medical attention can be provided and empowered with IoT sensors that can transmit the patient’s health in real-time to the physician while reaching the hospital can be devised. Further, hospital bed tracking, and reservation nearby to the patient’s location using GPS sensors are very useful. Remote equitable healthcare service access—Telehealth services in case of natural calamities for remote consultations that can provide best physician details as per the specialization/nearby healthcare centers, and pharmacy stores are needed. Remote healthcare service delivery to ensure physical and mental well-being for all age groups are some great applications. Smart management of hospitals—For administration and clinical decision support purposes, smart EHR systems that consolidate all kinds of patient data from outpatient details, lab tests, physician examination, previous history, and genomic-kind of data in rare medical conditions are useful and eases the

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record maintenance by eliminating non-uniformity. This system can provide hints at resources being spent, overall costs, and wastage of both to plan hospital administration wisely.

8 Conclusion The immense growth and success of Information Society 4.0 involved digitalization of Information and remote access through the Internet not only made information access and analysis to solve real-world problems a reality but also contributed to explosive amount of information growth termed the big data. However, such massive growth of data made data management and analysis even more challenging. As the world is currently moving toward more economic advancement, the social upliftment is lagging behind. Healthcare sector is particularly facing the challenges of large portion of rapidly aging population and reduced birth rate leading to marginal productive population, requiring more healthcare services for the elderly. This may lead to healthcare system exhaustion over time and increased chronic disease burden that increases with aging and social security requirements while affecting the healthcare system growth. In this chapter, we have proposed a super-smart healthcare system for Society 5.0 which uses cutting-edge technologies to address the aforementioned challenges by integrating physical and cyberspace and technology-powered healthcare data analysis. We have laid out the objectives of the proposed system, its prototypical design and working as well as the core technologies that comprise the super-smart healthcare system. The methods for healthcare big data analysis and management have been presented. Finally, the ethical concerns to be adhered to while operating in technologically advanced healthcare system to safeguard the patients are described. The potential applications of proposed healthcare system have also been discussed.

References 1. “Society 5.0”, Council for Science, Technology and Innovation, Japan. https://www8.cao.go. jp/cstp/english/society5_0/index.html 2. Narvaez Rojas C, Alomia Peñafiel GA, Loaiza Buitrago DF, Tavera Romero CA (2021) Society 5.0: a Japanese concept for a superintelligent society. Sustainability 13:6567. https://doi.org/ 10.3390/su13126567 3. Fukuyama M (2018) Society 5.0: aiming for a new human-centered society in Japan SPOTLIGHT 2:141–154 4. Bahri S, Zoghlami N, Abed M, Tavares JMRS (2019) BIG DATA for healthcare: a survey. IEEE Access 7:7397–7408. https://doi.org/10.1109/ACCESS.2018.2889180 5. Mbunge E, Muchemwa B, Jiyane S, Batani J (2021) Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies. Global Health J ISSN 24146447. https://doi.org/10.1016/j.glohj.2021.11.008

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6. Mohanta B, Das P, Patnaik S (2019) Healthcare 5.0: a paradigm shift in digital healthcare system using artificial intelligence, IOT and 5G communication. In: 2019 International conference on applied machine learning (ICAML), 2019, pp 191–196. https://doi.org/10.1109/ICAML48257. 2019.00044 7. Healthcare in Society 5.0: An Overview, Keidanren Policy & Action, Japan. https://www.kei danren.or.jp/en/policy/2018/021_overview.pdf 8. Kaur K, Rani R (2015) Managing data in healthcare information systems: many models, one solution. Computer 48(3):52–59. https://doi.org/10.1109/MC.2015.77 9. Khatri S, Alzahrani FA, Ansari MTJ, Agrawal A, Kumar R, Khan RA (2021) A systematic analysis on blockchain integration with healthcare domain: scope and challenges. IEEE Access 9:84666–84687. https://doi.org/10.1109/ACCESS.2021.3087608 10. Abbas A, Ali M, Shahid Khan M, Khan S (2016) Personalized healthcare cloud services for disease risk assessment and wellness management using social media. Pervasive Mob Comput 28:81–99 11. Brisimi TS, Xu T, Wang T, Dai W, Adams WG, Paschalidis IC (2018) Predicting chronic disease hospitalizations from electronic health records: an interpretable classification approach. Proc IEEE 106(4):690–707. https://doi.org/10.1109/JPROC.2017.2789319 12. Amin SU, Hossain MS, Muhammad G, Alhussein M, Rahman MA (2019) Cognitive smart healthcare for pathology detection and monitoring. IEEE Access 7:10745–10753. https://doi. org/10.1109/ACCESS.2019.2891390 13. Dwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 2019 42nd International conference on telecommunications and signal processing (TSP), 2019, pp 135–139. https://doi.org/10.1109/ TSP.2019.8769060 14. Ismail L, Materwala H, Zeadally S (2019) Lightweight blockchain for healthcare. IEEE Access 7:149935–149951. https://doi.org/10.1109/ACCESS.2019.2947613 15. Al-khafajiy M, Baker T, Chalmers C et al (2019) Remote health monitoring of elderly through wearable sensors. Multimed Tools Appl 78:24681–24706. https://doi.org/10.1007/s11042-0187134-7 16. Singh AP et al (2021) A novel patient-centric architectural framework for blockchain-enabled healthcare applications. IEEE Trans Industr Inf 17(8):5779–5789. https://doi.org/10.1109/TII. 2020.3037889 17. Rallapalli S, Gondkar RR, Ketavarapu UPK (2016) Impact of processing and analyzing healthcare big data on cloud computing environment by implementing Hadoop cluster. Proc Comput Sci 85:16–22, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2016.05.171 18. Mani N, Singh A, Nimmagadda SL (2020) An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Proc Comput Sci 167:850–859, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2020.03.424 19. Ejaz M, Kumar T, Kovacevic I, Ylianttila M, Harjula E (2021) Health-blockedge: blockchainedge framework for reliable low-latency digital healthcare applications. Sensors 21:2502. https://doi.org/10.3390/s21072502 20. Hartmann M, Hashmi US, Imran A (2019) Edge computing in smart health care systems: review, challenges, and research directions. Trans Emerg Tel Tech e3710. https://doi.org/10. 1002/ett.3710 21. Dautov R, Distefano S, Buyya R (2019) Hierarchical data fusion for smart healthcare. J Big Data 6:19. https://doi.org/10.1186/s40537-019-0183-6 22. Celesti A, Buzachis A, Galletta A, Fiumara G, Fazio M, Villari M (2018) Analysis of a NoSQL graph DBMS for a hospital social network. In: 2018 IEEE symposium on computers and communications (ISCC), 2018, pp 01298–01303. https://doi.org/10.1109/ISCC.2018.8538469 23. Kotsilieris T (2021) An efficient agent based data management method of NoSQL environments for health care applications. Healthcare (Basel) 9(3):322. Published 2021 Mar 13. https://doi. org/10.3390/healthcare9030322

Chapter 12

Yagyopathy Holistic Science for Various Solutions: A Scientific Phenomenon with Modern Healthcare, QoL and Society 5.0 Rohit Rastogi, Neeti Tandon, T. Rajeshwari, Prakash Moorjani, and Sunil Malvi Abstract Yagyopathy has been observed a great weapon to unprecedented health and environmental challenges to mankind. A lot of literature is available in Indian Vedic scriptures and enormous benefits have been explained. The present manuscript demonstrates the scientific evidences of Indian Agnihotra Process which may be treated as an alternate therapy for curing all the challenges, faced globally in twentyfirst century. It is definitely a ray of hope for central Govt. of India in establishment of smart cities which will be also vulnerable to healthcare and industry 5.0 perspectives. Experiments reveal that Homa therapy is also helpful in dimension of Society 5.0 and Yajna and chanting of Mantra not only cures the diseases physically but also is very effective in mental fitness, attracts rain, improved oraganicvedic farming with vital crops, increase in happiness index, bio-electrical aura and reduction in stress, anxiety, tension and anger were reported. The places of Yajna and nearby areas were positively charged and pollution level was highly controlled. The Manuscript explains the various experiments conducted by team of authors at various places in Madhya Pradesh, India with focus to find the scientific evidences of Homa therapy. Keywords Yajna · Mantra · Stress · Depression · Anxiety · Mental fitness · Radiation · Diseases · Pollution · Aura · PM level R. Rastogi (B) Department of CSE, ABES Engineering College, Ghaziabad, U.P., India e-mail: [email protected] N. Tandon Vikram University, Ujjain, M.P., India T. Rajeshwari Yagyopathy Researcher and Active Social Volunteer, Kolkata, W.B., India P. Moorjani Active Social Volunteer, GayatriShaktipeeth, Jabalpur, M.P., India S. Malvi MP Electricty Board, Jabalpur, M.P., India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_12

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1 Introduction Gurudev pt. Sri Ram Sharma Acharya ji has clearly mentioned it goes back to prapanch (Universe) from where it has come. One should go through Gayatri Mahavigyan again and again and there are many senior sadhaks (Meditators) who can guide us, one has to understand the principles and philosophy of anything you are doing, WHY? If this why is cleared, then how becomes easy. Swadhyay is Primary, when you read Vaangmay, 1) read, then learn, then study and then understand and lastly apply it. This is applicable in all endeavors, all fields [3, 4].

1.1 Guru Poornima in Indian Scriptures Salutations and prostrations at the divine feet of Sri Krishna DwaipayanaVyasa whose birthday is celebrated as “Guru Purnima”. He is also known as “Maharishi Veda Vyasa” and one among the seven Cheeranjivis* (still alive in the physical body and gives darshan to the deserving ones) and an incarnation of Lord Narayanaya (Vyasaya Vishnurupaya Vyasarupaya Vishnave). He is responsible for editing four Vedas & writing Brahmasutras. He is also the author of 18 Puranas including the greatest epic “Mahabharata”.—“Bhagavat Gita” is a portion of this great epic “Mahabharata”.— His last work is “SrimadBhagavatam” which is known as the essence of all Upanishads and Puranas. Once upon a time his hermitage known as “Samyakprash” was situated in Arya Vrat Bharat (India) in the holy banks of river Saraswati near BadrinathDham in Himalayas where did he write all his literary works in a huge cave nearby known as VyasaGupha (cave of Vyasa) with the assistance of Lord Sri Ganesh. There is also another cave nearby to his cave known as “Ganesh Gupha” (Cave of Lord Sri Ganesh). When you visit Badrinath Dham, you can visit these places in the last Indian village “Mana” in Indo-Tibet border. The teachings of Maharshi Veda Vyas—are summarized or carry mainly two messages: (1) To do good to others is the highest religion or Punyam. (2) To hurt others in thought, word or deed is Sin or Papam. ashtAdashapurAneshuvyAsasyavachanadwayam I paropakArAyapunyAyapApAya para pEdanam II Worshipful Homage and Silent Adorations to Sri Krishna DwaipayanaVyasa who is none other than Lord Sri Vishnu on this great day along with all saints, sages, seers, and the entire lineage of “Guru Parampara” (The unbroken eternal tradition of Masters and disciples). (* Sri Adi Sankaracharya had a direct darshan of Maharishi Veda Vyasa while interpreting on his literary works.)

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1.2 Subtle Power of Yajna The below Mantra is 852nd Sukta of 17th Chapter of Yajurveda, where it is clearly depicted that the micro most unit of provided oblation in Yajna can be curtailed up to 1 × 10−17 (10 to the power minus 17) whenever the fire is enlightened in Yajna. There are two beautiful and worth noted components in this Sukta (phrase). 1. 2.

The mathematical calculation of micro and subtle formation of material by Yajna The nomenclature of mathematical numbers used in Sanskrit in Vedas from very ancient times.

852: “Ima me agna, istkaDhenvahSantveka cha, dahs cha shatam cha shatam cha sahasram cha sahsramchayutamchayutam cha niyutam cha niyutam cha prayutamcharbudamnurbudam cha samudryaschamadhyamchantaschparardhaswaita me agnaishtkadhenvahsantvamutramuisshmamlloke.” O Fire God !!, these subtle istikas (provide as oblations in Havya) act as Kaamdhenu which provides desired results for us (Pl. refer Fig. 1). These Ishtikas will become one, multiplied by ten times from one, become hundred after multiplication by 10, then thousands after ten times of hundreds, then ten thousand (Ayut) after multiplication by 10 of thousands, then Niyut after 10 times of Ayut (Lakhs), Prayut (ten lakhs) is 10 times of Niyut, Koti (crores) is of 10 times of Prayut, Arbud is ten crores (Koti), Rab or abj is Neurbad (ten times of Arbud), Karab is ten

Fig. 1 The subtle power of Yajna defined by numerical countings in ancient Indian mathematics

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times of neurbad, Padma is ten times of Kharab, Mahapadm is ten times of padma, Shanku is ten times of Mahapadma (neel), Samudra is ten times of neel or shanku, Madhya is ten times of Samudra (Shankh-Padma), Aant is ten times of Madhya, Parardha is ten times of Madhya (Lakh LakhKoti) when these are connected with fire. It is prayed that these Istikas will be like Kamdhenu cows in this and other lokaas for humans. (in this Kandika, the development of subtle power of Yajna is prayed, it is wellaccepted fact of science that the most micro atoms of material make it most powerful.) The herbals are made micro fined which means atom is subdivided into 1 × 10−6 . Yajna Process distributes and divides these atoms up to one-tenth lakhs part to one-tenth lakhs part to lakhs part. This Subtleness is up to the level of 1 × 10−17 (10 to the power minus 17). That is why the micro refined elements purified by Yajna are most powerful and make the nature cycle more balanced and nutritious.

1.3 Effects of Yajna: Removal of Many Bad Effects and Anisht Through Yajna 1.3.1

Under Gruhegruhe Daily Nan-Yajna Movement

In Seven days, one can know about the effects of Yajna on Bhoot (departed soul) issues removal Ungripping from bad dreams Issues and Delay of Marriage fixing To stop the effects of Bad Muhurta Increase in business Removal of effects of Divorce and mutual unhappiness Overall Shantikunj has told the effects and benefits of Gayatri Mantra Chanting, it omits the effects of bad Karma (actions). People who go to seashore will wash their feet, those who go little ahead may catch fish, those who take deep dive will come out with pearls [1, 2]. The base of Indian culture (Bharatiya Sanskriti) is study of Veda, Puran, Geeta, when these are chanted, is just like an ocean. You get what you search for. Gayatri is Sidhdhidhatri. You can achieve all results once understand what we want (ICRP) [5].

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Fig. 2 Yagyopathy as a holistic sustainable development in ancient Indian mathematics

1.4 Healthcare 5.0: Yagyopathy as Holistic Sciences Yagya therapy has been treated now as a holistic sustainable approach to all issues. The present mankind is facing unprecedented threats and Yajna Provides the ecofriendly remedies for all (Pl. refer Figs. 2, 3, and 4).

2 Literature Review 2.1 Chandra Gayatri Mantra and Its Effects The Chandra Gayatri Mantra is effective a lot in Yagyopathy, particularly for certain cases where there are no medications or allopath becomes stagnant. Particularly with personality traits, and disorders (dual, antisocial, schizophrenic) Yagya Aahut is with Chandra Gayatri and Saraswati Gayatri work wonders. Shahnaz, a con woman was such a case, which was discussed in Yagyopathy class. This lady married 50 men duping them one by one, she was looting them with lakhs of rupees. This comes under, dual/ antisocial behavior. During the class, Ms. Rajeshwari presented the unconventional methods of divine intervention of cosmic sound energy. As there are

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Fig. 3 The preservation of cows for sustainable development and using cow products

Fig. 4 The health remedies of Ayurveda, Yajna, and cow-breed preservation are integrated

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no medicines for behavioral issues, and unless the person concerned realizes one’s mistakes and wants to rectify them, there is no way [6, 7]. These people when caught red-handed make beautiful explanations for defense (they are skilled in conversing and convincing). These can be rectified (the authors have applied in their cases, with guidance of Dr. Vandana Srivastava, Mansikdurbalta Hawan Samagri, and Chandra Gayatri Mantra. Also, the subject is sent required color waves on self to cleanse their aura.) The researchers got wonderful results of rectification. Gurudev Pt. Sri Ram Sharma Acharya ji has also written a book titled Eternity of Sound & Science of Mantras. The resultant parameters are the medicines, which were stopped with normal EEG reports and certified by psychiatrists. As the patient’s details are not allowed to be revealed. Neither have they wanted to come forward with testimonials [8, 9]. The issues can be discussed in detail without revealing the identity of the patients with a color therapist; they choose and teach how to send color waves to the family members.

2.2 Yagyopathy On Skin Rashes The subject should do Varun Vardhak Mudra and Prana Mudra. In Varun Vardhak Mudra, place your thumb tip at the root of little finger. It will increase the water level in the body and continue with Pran Mudra. 15 min. each, thrice a day. One can find the total amazing difference. One should also do simple Varun Mudra and then Pran Mudra. Later age of life, this has become a common problem and was resolved by Mudras. This Varun Vardhak Mudra should be stopped once one gets relieved [10].

2.2.1

Case Study

Our experiences in the shapes of Samskaras, habits, and tendencies are also stored up in our subconscious minds. These samskaras of the past birth are revivified and re-energized in the next birth (Bhagwat GeetaCh VI shook 43). One of the subjects who was under strong medications, for 4 years, researcher taught his wife Yagyopathy with Chandra Gayatri, and Saraswati Gayatri (This case was also discussed and medical papers sent to Dr. Vandana Srivastava). Along with that healing, meditation was taught to her. In 3 months span of time; his medicines were gradually reduced and totally stopped. With the Yagya fumigations, the children who were stubborn and used to throw tantrums became well behaved. The wife of the subject is still continuing Nano Yagya [11, 12]. This gave to researchers the idea of introducing Yagyopathy in schools when she was in Jaipur center (GayatriKunj, Murlipura). With Saraswati Gayatri Mantra, special Hawan Samagri for Mandhbuddhi/mentally retardation helps channelizing the brain energy in positive way [13, 14].

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When the researcher Ms. Rajeshwari was in Chennai, their Yagyopathy group member, Mr. Satish learned this procedure and started conducting it in many schools in Chennai and its suburbs.

2.2.2

Surya Namaskaar

Surya Namaskar is highly effective when the Mantras are done with prefix beej mantra. When a person feels very lonely, Om Mitraya Namaha, chanting helps to gain companions (tried and succeeded with my mother, when she lost my dad); Om Hiranya Garbhaya Namaha, if chanted, helps to regain vision in a blind person, tried and succeeded with a 12 years old boy who lost his vision in one eye due to severity of Chickenpox [15, 16]. Now the researcher Ms. Rajeshwari is doing, the same to remove her spectacles, with Chakshushopanishad and supplements, eye exercises, and Yagyopathy. Last week on 11 Aug. 2021, her eye doctor told her; there is no cataract in her eyes (which was detected 2 years back), and changed her spectacles to a very mild powered (previously it was high powered). Hopefully; she would be without spectacles in 3 months span [17, 18].

3 Experimental Setup and Methodology 3.1 Meetingand Yagyopathy Camp All the participants have explained the scientific aspects of Yajna before undergoing the Yagya therapy and camps for health readings (Pl. refer Figs. 5 and 6).

3.2 Protocol to Be Followed Normally the subjects are asked for a Jap of 40 days discipline of particular mantra along with Visualization. They are asked to maintain a spiritual diary, writing even small changes that occur to them. Protocols (Daily 7:54AM to 8:45 AM) 15 min. meditation 15 min. information for Yajna Science and medications 15 min. Nano Yajna 15 min. treatment by YajnaVapour therapy [19, 20]

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Fig. 5 The Yagyopathy Camp was organized at different places in MP India like Jabalpur, Ujjain, and Sendwa in 2019

3.3 Instrument Used Different Instruments used in the experiments are described below (Pl. refer Figs. 7, 8, 9, and 10) [21–23].

3.4 Yagya Kit and Samagri Used for a Holistic Health Development 3.4.1

Herbal Gomay Havan Kund

Items to make Cow Dung Based Havan Kund Cow Dung of Indian Breed Cow, 51 Herbals, Peepal leaves, Bel Leaves, Tulsi Manjari, GendeKa Fool, AkwaKePatte [24, 25].

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Fig. 6 Making aware first all the participants before experiments regarding scientific aspects of Yajna

3.4.2

HavanSamagri

Ashwagandha, Shatawar, Trifala, Bilwa, Vasha, Mulethi, Giloy, Arjun, Punanrnava, PragyaPey Supply to all Indian States, Landon, Singapore, USA, Malaysia, Japan. (Pl. refer Figs. 11, 12, 13 and 14) for live demonstration of components and Kund for Nano Yajna Kit [26, 27].

4 Results and Discussions 4.1 Event 1 27–28 July YajnaVigyaan Workshop at GayatriShaktipeethAnkpaat Road Ujjain Readings In Ujjain city, M.P. India, the Gayatri Parivaar Shaktipeeth and Diya volunteers group organized the two days’ workshop on Yajna Science on the pious passage anniversary of Dr. APJ Abdul Kalamji, ex-president of India and co-founder of Diya to tribute him on 27th and 28th july 2021.

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Fig. 7 Airveda device for measuring the AQI pre and Post Yajna

Dr. Kalam was a live symbol of the thought prediction of Yug Rishi Pt. SriRam Sharma Acharya ji on his philosophy of scientific explanation of Spiritual Fundamental sciences. Purpose of the Workshop—to motivate the common people for Yajna activity and to represent the scientific explanation and results of Yajna rituals and positive results. Workshop Coordinator:—Sh. Prakash Moorjyaniji was trainer from Jabalpur and coordinated by Ms. NeetiTandon, an active sr. Diya volunteer of Ujjain GayatriParivaar (Pl. refer to Tables 1, 2, and 3 for detailed experiments Readings on Experiments 1, 2, and 3). First Day Dated: 27th July 2021 Place: Ankpaat Road, GayatriShaktipeeth, Ujjain Time: 1 to 4 PM Participants—34 including the intellects from Gayatri Parivaar and Ujjain Community.

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Fig. 8 Happiness index meter for measuring the Galvenic skin resistance for the stress level of the concerned subject pre and post-activity

4.1.1

Experiment 1

To define the aura of a specific place. Instrument used- Aura Scanner Method: Firstly the aura was checked at four places of Gayatri Shaktipeth by Mr. Prakash Working Principal—The handles rotate to the increase positivity of special place or special person. More the angle of rotation at special place or person, more positive he or place is. The aura was checked at four places. Sadhna Kaksha (Meditation Room) Handles of Aura scanner were between 180° and 270°. YajnaShala—Handles rotation was between 270° and 360°. GauShala—The scanner handles rotated fully 360°. Outer area of temple—the handles were between 150° and 180°.

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Fig. 9 The Aura scanner for measuring the Aura of person and place pre and post-activity

4.1.2

Analysis and Discussion

Around daily Jap practice in meditation room, Yajna Activity at Yajnashala the aura was at best index. Also, it was highest and maximum at Gaushala (cow-places). That is why serving cows has been called as best for health in Indian culture. Outside the Shaktipeeth, due to the moving traffic and vehicles, the aura was least. Also, it was observed that going apart from Shaktipeeth, the aura level was decreasing.

4.1.3

Experiment 2

To identify the Happiness Index of human subjects. Instrument used- Happiness Index Meter. Working principle of Instrument: The monitor of machine informs the stress level of an individual when this machine is kept at the hand of subject. Stress level scrolls from value 1 to value 100.

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Fig. 10 EMF meter to measure the radiation of different electronic gadgets pre and post-activity

The lesser the stress level value, the better mental state, cool calm, and happy state of an individual. Method: Before Yajna activity start at 2:00 Pm, stress levels of around 13 subjects were measured by happiness index meter. After Yajna, Nadi Shodhan, KapalBhati, and Anulom Vilom Pranaayaam was done by all the subjects and then second reading was measured.

4.1.4

Results and Discussions

10 Subjects reported reduced stress levels out of 13 candidates. It proves that Yajna activity reduces the depression, stress, tension, and anxiety of humans (Pl. refer to Figs. 15 and 16 for explaining the activity).

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Fig. 11 The Nano Yagya Kit demonstration for Eco and User-friendly Yajna

Fig. 12 The cow dung based herbal cups for Nano Yagya mixed with camphor

4.1.5

Experiment 3

To measure the CO2 and particulate matter in environment and to check the Effect of Yajna on it. Instrument used—Air Veda device which measures the PM 2.5, PM 10, and CO2 , NO2 , and SO2 level of air in that area and AQI. It should be noted that the high level of particulate matter in environment and atmosphere is highly dangerous and bad for human health. Working Principle of Instrument

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Fig. 13 The Herbal Hawan Samagri mixed with various eco-friendly and nutritious components, when burned, enriches the environment Fig. 14 The burned Nano Yagya Kund with live Demonstration Image

12 Yagyopathy Holistic Science for Various Solutions: A Scientific … Table 1 Readings by air Veda instrument for AQI on 27–28 July 2021

Table 2 Readings by Aura Scanner on 27–28 July 2021

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CO2 at reception

206

PM2.5

PM10

CO2

35

50

239

64

293

596

66

255

825

50

171

921

177

1999

1081

771

1999

1280

771

1999

1505

771

1999

1410

681

1026

1141

93

183

286

578

157

257

57

89

238

47

69

223

304

425

606

476

584

922

601

816

864

346

588

1006

DhyaanKaksh

NIL

NIL

NIL

YagyaShala

35

59

235

Shiv Mandir

Aura scanner Place

Rotation angle

NeemkeSamne

45°

BelPatra

15

Room without people

15

Room with people

15 + 20

DhyaanKaksh

360

YagyaShala

360

Reception

180

Campus

180

Out of Shaktipeeth

60

GauShala

360

Shiv MandirKeSamne

180

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Table 3 Reading by happiness index meter on 27–28 July 2021 Happiness meter index reading Person name

Before

After

Dev Parmar

20

22

16

14

PremLata Parmaar

20

21

21

16

Shashi Parmar

30

38

18

19

Basant Gehlot

21

22

19

15

Santosh Gehlot

40

42

22

24

Manju Bharodiya

12

13

31

29

Puspa Bharodiya

30

34

17

19

OP Sharma

61

48

55

49

Dinesh Parmar

45

48

35

38

Ratna

27

29

19

17

Neeti

35

35

21

23

Manohar

20

21

9

11

Fig. 15 Yagyopathy workshop participants experiencing Pranayam after Nano Yajna as protocol to inhale fresh air to improve their lung capacity under supervision of Mr. Prakash Moorjani, Jabalpur, M.P., India

A range has been mentioned in this device for PM 2.5, PM 10 and CO2 level measurement, The less value in index of all these three components points out the clean and pollution-free air in the atmosphere. The readings were again noted after the half an hour of the end of Yajna and all the three parameters were being reduced after one hour, all these three parameters’ readings were even less from first reading (Pl. refer Figs. 17 and 18 for explaining the activity).

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Fig. 16 Registration process of subjects under study by Yagyopathy Volunteer Ms. NeetiTandon, Ujjain, M.P., India

Fig. 17 Performing Nano Yajna under certain protocols to start Yagyopathy experiements

Fig. 18 Air Veda device to measure the PM 2.5, PM 10, CO2 level, etc. to measure the AQI

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Fig. 19 Usage of Aura Scanner by research team to measure the aura of the place before and after the Yajna Activity

Fig. 20 Air Veda device to measure the AQI

All the results from above three experiments at both the places n workshop justify scientifically the participants got mental peace and the atmosphere was refined and positive. Also, the AQI was also cured and improved that is why Vedmurti Taponishtha Pt. Sri Ram Sharma Acharya ji has termed Yajna as *a sustainable and holistic treatment approach* (Pl. refer Figs. 19, 20, 21 and 22 for detailed experimentations).

4.2 Event 2 108 Kundeeya Gayatri Mahayagya at Jabalpur, M.P., India, 25–29 Jan 2020

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Fig. 21 Usage of happiness index device to measure the stress level of subjects

Fig. 22 Usage of Aura Scanner by research team to measure the aura of different places inside and nearby area of experiment zone

Pl. refer Fig. 23 and Table 4 and Table 5 for event descriptions.

4.3 Event 3 Yagyopathy Camp by All world Gayatriparivaar, M.P. zone at Jabalpur Shaktipeeth, Manmohan Nagar

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Fig. 23 Author Mr. Prakash explaining about Yagyopathy and Aura measurement to Dr. R. S. Sharma, dean Jabalpur, MP medical college Table 4 The readings of AQI in 108 Kundeeya Magayagya at Jabalpur during 25–29 Jan., 2020 Pre/Post

PM2.5

PM10

CO2

Place Readings are at Yagyashala at 12:05 PM

Before Yagya

50

55

300

After Yagya

44

49

282

Before Yagya

354

231

348

After Yagya

161

213

297

Before Yagya

400

970

305

28 Jan 2020 at 11:15 AM

After Yagya

382

495

276

28 Jan 2020 at 11:20 AM

97

179

298

28 Jan 2020 at 11:36 AM

78

141

268

28 Jan 2020 at 11:50 AM

201

340

500

29 Jan 2020 at 11:15 AM

187

233

275

29 Jan 2020 at 11:20 AM

148

329

368

29 Jan 2020 at 11:35 AM

141

279

298

29 Jan 2020 at 11:50 AM

109

241

250

29 Jan 2020 at 12:05 PM

87

154

135

29 Jan 2020 at 12:20 PM

Before Yagya After Yagya

Readings are at Bhojanalaya (Kitchen) at 1:05 PM

29 Jan 2020 at 10:31 AM

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Table 5 The readings of Aura in 108 Kundeeya Magayagya at Jabalpur during 25–29 Jan., 2020 Place Yagyashala (Open Area) Yagyashala (Exit) Yagyashala (Inside) Bhojnalaya (open Area)

Degree movement of Aura scanner on 26th Jan 2020 between 10:00 and 10:15 AM 60 90 105 55

Bhojnalaya (Exit)

35

Bhojnalaya (Inside)

80

Pl. refer to Figs. 24, 25, 26, 27, 28, 29, 30, 31 and 32 for event details and Tabular Images Figs. 33, 34, 35, 36, 37 and 38 for Data readings of Yagyopathy Camp at Manmohan Nagar, M.P. from 27 July-5 Aug. 2019. 37 Participants of different age groups and gender actively participated and recorded the health readings.

Fig. 24 Different postures and Asans taught and executed on the Participants in Yagypathy Camp

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Fig. 25 Nano Kit Yagaa Process simultaneously by all participants at GayatriShaktipeethmanmohanNagar, Jabalpur in Yagyopathy Camp

4.3.1

Data Readings

4.4 Event 4 Yagyopathy Camp by All world Gayatriparivaar, M.P. zone at SendhwaShaktipeeth on 30 July 2021. 34 Volunteers and meditation practitioners were doing Sadhna Meditation and their aura was checked in the camp. Readings of happiness Index and Aura Scanner were checked

4.4.1

The Aura Scanner Readings for All Was Around

Before Yagya 60°–110° After Yagya 30°–60°

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Fig. 26 Participants Practicing Deep Breathing pranayama while doing rituals to energize their vital Powers

Aura of Sh. Mewalalji was 360°

4.4.2

Happiness Readings

Before Yagya Average for all was 34–42. After Yagya Average for all 18–24. PM Level of Yagyashala PM10—54, PM2.5-94 and CO2 -134.

4.5 Event 5 Sound energy has immense frequencies, we all have a magnetic field around us, connected to Mooladhar Chakra, which is also storage of karma. As per Meta Physics the sound energy that soothes you or makes you sad works deeply creating patterns in our blood and plasma of our body. Sound energy works with magnetic field (aura) of our body and creates thought patterns. Here the song is same and patterns are different, actually as per my knowledge different sound creates different patterns.

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Fig. 27 Volunteers captured pre and post readings of different experiments while people participating in mass Yagya

Hair follicles have highest vibrations. That is why we get Goosebumps. In this video, it seems some original sound has changed, common song is repeated (Pl. refer to Fig. 6, 7, 8, and 9 for explaining the activity). Yagya was done in Copper Kund 7.6” sides with GomaySamidha as usual. Ghee was Madhusudan Ghee. Zero PM was achieved even during Havan toward the end, i.e., after 20 min of lighting fire. The levels were low even before but I had never achieved zero PM ever, so I thought I would share the results with all (Pl. refer to Figs. 39 and 40 for explaining the activity).

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Fig. 28 Doctors for capturing different medical symptoms and accordingly treatment related to Yagypathy

5 Recommendations No matter how hard one can try more than positive its side effects are going to be disastrous as all that negative energy is going to attack somewhere are not it spreading some Bangali Baba 24 h results or refund thing? No offense in saying that we never claimed and always mentioned efforts are required from within and then you do the healing if someone is going through issues. So this is different than what you do and we just cannot assure provide anyone with a short cut god forbid it will not work then what? [28, 29]. There are the cases of hakim Ji’s (illiterate doctors) claim that after his treatment, one’s kids lead him to his death he was shot point black and his wife became a widow and her two kids semi orphans, if one does not trust one can Google case was handled by Thana Mandir Marg, Delhi [30, 31]. We are not a wordsmith but yes if this kind of issue can be solved then there is no need of daily Sadhna (Meditation) and good acts Anushthan or Chandrayan. It is well said that if there is a dead dog in the well mantra or Ganga jal is not going to solve the issue dog must be removed from the well so without doing this, no one gets peace of mind.

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Fig. 29 Performing Mass Nano Yajna, individual execution but collective at one place for checking the effect of unionness

6 Novelty The present manuscript is a novel way to establish the Yajna process as therapy and remedial treatment based on scientific evidences. The different experiments like happiness measurement, aura scanning, air quality checkup show that it is an effective process for global human threats [32, 33].

7 Future Research Work and Limitations Yajna, Hawan, and Agnihotra is a complete science and a sustainable holistic systems. Ancient Indian Rishi-Muni were super-intelligent researchers and playing with human psychology, they added it with rituals. They explored the effects of Yajna on various domains which need to be proved and established today; Effect of Yajna on • Physical and Mental Fitness • Rainwater, attracting clouds • Organic farming and science of Yajna ashes for making soil fertile

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Fig. 30 Different participants doing Tratak meditation and Mantra hantingIn small groups for health care and physical fitness along with concentration

• • • • • •

Animal productivity in their yields Pollution Control Disease Control Happiness Increase Aura development Improvement in human vital power and Bioelectricity [35, 36, 41].

8 Conclusions The Available tables and graphical analysis clearly depicts that there is a drastic improvement in the health of different subjects ad their physical and mental health has improvised after exposure to Yagyopathy. The experiment depicts that the kind of personality disorders are according to Vedas, the past life Karma, that one carries forward in the next birth. As per the psychoanalysis by Sigmund Freud), a person who dies with some pain brings forward the same pain, which inexplicable in medical terms, no scanning, MRI can reveal any physical drawback, then one should go for analysis, to understand the case in one’s field either past life regression or hypnosis therapy is done [37, 38].

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Fig. 31 Different ailments recording pre and post-activity and advice were issued as per light of Ayurveda

Fig. 32 Mass Nano Yajna execution by individual young students on their school campus

In multiple personality disorders also stamped as schizophrenia, the researchers have found Yagyopathy therapy as most convenient and convincing in these cases. Continual processing of Yagyopathy twice daily reduces delusions and illusions to vast extent. The patient stops talking in the air, symptoms like talking loudly to

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Fig. 33 Readings in Yagyopathy Experiments. Part-1

oneself, and laughing for no reason stop. Our thoughts and actions are left in our subconscious minds in the form of Samskaras or impressions [39–41].

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Fig. 35 Readings in Yagyopathy Experiments. Part-3

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Fig. 38 Readings in Yagyopathy Experiments. Part-6

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Fig. 39 Specific readings of Air Veda device to show the Effects of Yajna under certain protocols

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Fig. 40 Rare case of AQI being Zero ( PM level highly rediced) after the Yajna activity in NCR, India

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Acknowledgements The team of authors would love to pay our deep sense of gratitude to the ABES Engineering College, Ghaziabad & Amity International Business School, Amity University, and Noida for arranging all the facilities, the direct-indirect supporters for their timely help and valuable suggestions, and the almighty for blessing us throughout. We would also like to extend the vote of thanks to IIT Delhi, IIT Roorkee, Dev Sanskriti Vishwavidyalaya, Haridwar, Patanjali Foundation, and Ayurveda Institute, Dehradun for their support and guidance in accomplishing our research paper.

Key Terms and Definitions Yajna—Yajna literally means “sacrifice, devotion, worship, offering”, and refers in Hinduism to any ritual done in front of a sacred fire, often with mantras. Yajna has been a Vedic tradition, described in a layer of Vedic literature called Brahmanas, as well as Yajurveda. The tradition has evolved from offering oblations and libations into sacred fire to symbolic offerings in the presence of sacred fire (Agni). Mantra—A mantra is a sacred utterance, a numinous sound, a syllable, word or phonemes, or group of words in Sanskrit believed by practitioners to have psychological and/or spiritual powers. Some mantras have a syntactic structure and literal meaning, while others do not. Jap—Jap is the meditative repetition of a mantra or a divine name. It is a practice found in Hinduism, Jainism, Sikhism, Buddhism, and Shintoism. The mantra or name may be spoken softly, enough for the practitioner to hear it, or it may be spoken within the reciter’s mind. Jap may be performed while sitting in a meditation posture while performing other activities, or as part of formal worship in group settings. Ayurveda—Ayurveda system of medicine with historical roots in the Indian subcontinent. Globalized and modernized practices derived from Ayurveda traditions are a type of alternative medicine. In countries beyond India, Ayurvedic therapies and practices have been integrated into general wellness applications and in some cases in medical use. The main classical Ayurveda texts begin with accounts of the transmission of medical knowledge from the Gods to sages, and then to human physicians. In Sushruta Samhita (Sushruta’s Compendium), Sushruta wrote that Dhanvantari, Hindu god of Ayurveda. Sanskrit—Sanskrit is an Indo-Aryan language of the ancient Indian subcontinent with a 3,500-year history. It is the primary liturgical language of Hinduism and the predominant language of most works of Hindu philosophy as well as some of the principal texts of Buddhism and Jainism. Sanskrit, in its variants and numerous dialects, was the lingua franca of ancient and medieval India. In the early 1st millennium AD, along with Buddhism and Hinduism, Sanskrit migrated to Southeast Asia, parts of East Asia, and Central Asia, emerging as a language of high culture and of local ruling elites in these regions.

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Vedic—The Vedic period or Vedic age (c. 1500–c. 500 BCE), is the period in the history of the northern Indian subcontinent between the end of the urban Indus Valley Civilization and a second urbanization which began in the central Indo-Gangetic Plain c. 600 BCE. It gets its name from the Vedas, which are liturgical texts containing details of life during this period that have been interpreted to be historical and constitute the primary sources for understanding the period. These documents, alongside the corresponding archaeological record, allow for the evolution of the Vedic culture to be traced and inferred. Energy Measurements—There is various kind of units used to measure the quantity of energy sources. The Standard unit of Energy is known to be Joule (J). Also, another mostly used energy unit is kilowatt /hour (kWh) which is basically used in electricity bills. Large measurements may also go up to terawatt/hour (TWh) or also said as billion kW/h. Other units used for measuring heat include BTU (British Thermal Unit), kilogram calorie (kg-cal), and most commonly Ton of Oil Equivalent. Actually, it represents the quantity of heat that can be obtained from a ton of oil. Energy is also measured in some other units such as British Thermal Unit (BTU), calorie, therm, etc. which varies generally according to their area of use. Machine Learning—Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Sensor and IoT—The Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers (UIDs), and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Sensors are devices that detect and respond to changes in an environment. Inputs can come from a variety of sources such as light, temperature, motion, and pressure. Sensors output valuable information and if they are connected to a network, they can share data with other connected devices and management systems. They are an integral part of the Internet of Things (IoT). There are many types of IoT sensors and an even greater number of applications and use cases. Pollution—Pollution is the introduction of contaminants into the natural environment that cause adverse change. Pollution can take the form of chemical substances or energy, such as noise, heat, or light. Pollutants, the components of pollution, can be either foreign substances/energies or naturally occurring contaminants. Pollution is often classed as point source or nonpoint source pollution. In 2015, pollution killed 9 million people in the world. The major kinds of pollution, usually classified by

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environment, are air pollution, water pollution, and land pollution. Modern society is also concerned about specific types of pollutants, such as noise pollution, light pollution, and plastic pollution. Pollution of all kinds can have negative effects on the environment and wildlife and often impacts human health and well-being.

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15. Rastogi R, Chaturvedi DK, Satya S, Arora N, Saini H, Verma H, Mehlyan K, Varshney Y (2018) Statistical analysis of EMG and GSR therapy on visual mode and SF-36 scores for chronic TTH. In: The proceedings of international Conference on 5th IEEE Uttar Pradesh Section International Conference, MMMUT Gorakhpur. https://doi.org/10.1109/UPCON.2018.859 6851 16. Rastogi R, Saxena M, Sharma SK, Muralidharan S, Beriwal VK, Singhal P, Rastogi M, Shrivastava R (2019) Evaluation of efficacy of Yagya therapy on T2-diabetes mellitus patients. In: The proceedings of The 2nd edition of international conference on industry interactive innovations in science, engineering and technology (I3SET2K19) organized by JIS College of Engineering, Kalyani, West Bengal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3514326 17. Rastogi R, Saxena M, Gupta US, Sharma S, Chaturvedi DK, Singhal P, Gupta M, Garg P, Gupta M, Maheshwari M (2019) Yajna and Mantra therapy applications on diabetic subjects: computational intelligence based experimental approach. In: The proceedings of The 2nd edition of international conference on industry interactive innovations in science, engineering and technology (I3SET2K19) organized by JIS College of Engineering, Kalyani, West Bengal. https:// papers.ssrn.com/sol3/papers.cfm?abstract_id=3515800 18. Rastogi R, Saxena M, Sharma SK, Murlidharan S, Berival VK, Jaiswal D, Sharma A, Mishra A (2019) Statistical analysis on efficacy of Yagya therapy for type-2 diabetic mellitus patients through various parameters. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D (eds) on Computational intelligence in pattern recognition (CIPR), Kalyani, West Bengal, pp 181–197. Advanced in Intelligent Systems, Computing, vol 1120, Computational intelligence in pattern, recognition, 978-981-15-2448-6. https://doi.org/10.1007/978-981-15-2449-3_15 19. Rastogi R, Chaturvedi DK, Satya S, Arora N, Yadav V, Yadav V, Sharma P, Chauhan S (2019) Statistical analysis of EMG & GSR biofeedback efficacy on different modes for chronic TTH on various indicators. Int J Adv Intell Paradigms X. Forthcoming article. https://doi.org/10. 1504/IJAIP.2019.10021825 20. Rastogi R, Chaturvedi DK, Satya S, Arora Gupta M, Verma H, Singhal P, Singh A (2019) Comparative study of trends observed during different medications by subjects under EMG &GSR biofeedback. IJITEE 8(6S):748–756. https://www.ijitee.org/download/volume-8-issue6S/.(ICSMSIC-2019, by MCA and IT Department of ABESEC, Ghaziabad. 8–9 March 2019) 21. Rastogi R, Chaturvedi DK, Satya S, Arora N, Gupta M, Singhal P, Gulati M (2019) Statistical analysis of exponential and polynomial models of EMG & GSR biofeedback for correlation between subjects’ medications movement & medication scores. IJITEE 8(6S):625–635 https://www.ijitee.org/download/volume-8-issue-6S/. (ICSMSIC-2019, by MCA and IT dept. of ABESEC, Ghaziabad. 8–9 March 2019) 22. Rastogi R, Chaturvedi DK, Satya S, Arora N, Gupta M, Saini H, Mahelyan KS, Verma H (2019) Comparative efficacy analysis of electromyography and galvanic skin resistance biofeedback on audio mode for chronic TTH on various indicators. Int J Comput Intell IoT 1(1):18–24. Available at SSRN: https://ssrn.com/abstract=3354371 23. Rastogi R, Chaturvedi DK, Satya S, Arora N, Gupta M, Sirohi H, Singh M, Verma P, Singh V (2019) Which one is best: electromyography biofeedback, efficacy analysis on audio, visual and audio-visual modes for chronic TTH on different characteristics. Int J Comput Intell IoT 1(1):25–31. Available at SSRN: https://ssrn.com/abstract=3354375 24. Rastogi R, Chaturvedi DK, Satya S, Arora N, Gupta M, Yadav V, Chauhan S, Sharma P (2019) Chronic TTH analysis by EMG & GSR biofeedback on various modes and various medical symptoms using IoT, volumes In series: Advances in ubiquitous sensing applications for healthcare, Book-Big Data Analytics for Intelligent Healthcare Management, Chap. 5, pp 87–149, Paperback ISBN: 9780128181461, Imprint: Academic Press. https://doi.org/10.1016/ B978-0-12-818146-1.00005-2 25. Rastogi R, Chaturvedi DK, Sharma P, Yadav V, Chauhan S, Gulati M, Gupta M, Singhal P (2019) Statistical resultant analysis of psychosomatic survey on various human personality indicators: statistical survey to map stress and mental health. In: Sisman-Ugur S, Kurubacak G (eds) Handbook of research on learning in the age of Transhumanism. IGI Global, Hershey, PA, pp 363–383. https://doi.org/10.4018/978-1-5225-8431-5.ch022.Copyright: ©

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2019 |Pages: 21, ISSN: 2326-8905|EISSN: 2326-8913| ISBN13: 9781522584315| ISBN10: 1522584315| EISBN13: 9781522584322. https://doi.org/10.4018/978-1-5225-8431-5.ch022 Rastogi R, Chaturvedi DK, Verma H, Mishra Y, Gupta M (2020) Identifying better? Analytical trends to check subjects’ medications using biofeedback therapies. IGL Global, Int J Appl Res Public Health Manage (IJARPHM) 5(1):Article 2. https://doi.org/10.4018/IJARPHM.202 0010102, pp 14–31, ISSN: 2639-7692|EISSN: 2639-7706. https://doi.org/10.4018/IJARPHM, https://www.igi-global.com/article/identifying-better/240753 Rastogi R, Gupta M, Chaturvedi DK (2020) Efficacy of study for correlation of TTH vs age and gender factors using EMG biofeedback technique. Int J Appl Res Public Health Manage (IJARPHM) 5(1), Article 4, pp 49–66. https://doi.org/10.4018/IJARPHM.2020010104 Rastogi R, Chaturvedi DK, Satya S, Arora N, Gupta M, Verma H, Saini H (2020) An optimized biofeedback EMG and GSR biofeedback therapy for chronic TTH on SF-36 scores of different MMBD modes on various medical symptoms. Stud Comp Intell Hybrid Mach Intell Med Image Anal, 841. 978-981-13-8929-0. https://doi.org/10.1007/978-981-13-8930-6_8 Rastogi R, Chaturvedi DK, Satya S, Arora N, Trivedi P, Singh AK, Sharma AK, Singh A (2020) Intelligent personality analysis on indicators in IoT-MMBD enabled environment. In: Tanwar S, Tyagi S Kumar N (eds) Multimedia big data computing for IoT applications: concepts, paradigms, and solutions. Springer Nature, Singapore, pp 185–215. https://doi.org/10.1007/ 978-981-13-8759-3_7 Rastogi R, Chaturvedi DK, Satya S, Arora N, Trivedi P, Gupta M, Singhal P, Gulati M (2020) MM big data applications: statistical resultant analysis of psychosomatic survey on various human personality indicators. In: Proceedings of second international conference on computational intelligence 2018. ICCI 2018 Paper as Book Chapter, Chap. 25, © Springer Nature Singapore Pte Ltd. ISBN: 978-981-13-8221-5, Online ISBN 978-981-13-8222-2. https://doi. org/10.1007/978-981-13-8222-2_25 Rastogi R, Chaturvedi DK, Gupta M, Singhal P (2020) Intelligent mental health analyzer by biofeedback: app and analysis. In: Wickramasinghe N (ed) Handbook of research on optimizing healthcare management techniques, p 27. IGI Global, Hershey, PA, pp 1–431. https://doi.org/10.4018/978-1-7998-1371-2.ISSN: 2328-1243|EISSN: 2328 126X|ISBN13: 9781799813712|ISBN10: 1799813711|EISBN13: 9781799813729. https://doi.org/10.4018/ 978-1-7998-1371-2.ch009 Rastogi R, Chaturvedi DK, Gupta M, Singhal P (2020) Surveillance of type-I & II diabetic subjects on physical characteristics: IoT and big data perspective in healthcare @NCR, India, Chap. 23, Internet of Things (IoT). In: Alam M, Shakil KA, Khan S (eds). https://doi.org/10. 1007/978-3-030-37468-6_23. ISBN: 978-3-030-37467-9 Rastogi R, Chaturvedi DK, Gupta M, Sirohi H, Gulati M, Pratyusha (2020) Analytical observations between subjects’ medications movement and medication scores correlation based on their gender and age using GSR biofeedback. In: Burgos D, Vejar M, Pozo F (eds) Intelligent application in healthcare. Pattern recognition applications in engineering. IGI Global, Hershey, PA, pp 229–257. https://doi.org/10.4018/978-1-7998-1839-7.ch010 Rastogi R, Chaturvedi DK, Gupta M (2020) Exhibiting app and analysis for biofeedback based mental health analyzer. In: Advancement of artificial intelligence in healthcare engineering as Chap. 15, Handbook of research on advancements of artificial intelligence in healthcare engineering|Copyright: © 2020, p 300, ISBN13: 9781799821205|ISBN10: 179982120X|EISBN13: 9781799821229. https://doi.org/10.4018/978-1-7998-2120-5. https:// doi.org/10.4018/978-1-7998-2120-5.ch015 Rastogi R, Chaturvedi DK, Gupta M (2020) Computational approach for personality detection on attributes: an IoT-MMBD enabled environment, Chap. 16. In: Advancement of artificial intelligence in healthcare engineering, handbook of research on advancements of artificial intelligence in healthcare engineering, Copyright: ©2020 |p 300, ISBN13: 9781799821205|ISBN10: 179982120X|EISBN13: 9781799821229. https://doi.org/10.4018/ 978-1-7998-2120-5, https://doi.org/10.4018/978-1-7998-2120-5.ch016

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36. Rastogi R, Chaturvedi DK, Gupta M (2020) Tension type headache: IOT and FOG applications in healthcare using different biofeedback. In: Handbook of research on advancements of artificial intelligence in healthcare engineering|Copyright: © 2020 |p 300, ISBN13: 9781799821205|ISBN10: 179982120X| EISBN13: 9781799821229. https://doi.org/10.4018/ 978-1-7998-2120-5, https://doi.org/10.4018/978-1-7998-2120-5.ch017 37. Rastogi R, Chaturvedi DK (2020) Tension type headache: IOT applications to cure TTH using different biofeedback: a statistical approach in healthcare. In: Taukeni SG (ed) Biopsychosocial perspectives and practices for addressing communicable and non-communicable diseases, Chap 10. IGI Global, Hershey, PA. Copyright: © 2020 |ISBN13: 9781799821397|ISBN10: 1799821390|EISBN13: 9781799821403. https://doi.org/10.4018/978-1-7998-2139-7, https:// doi.org/10.4018/978-1-7998-2139-7.ch010 38. Rastogi R, Saxena M, Maheshwari M, Garg P, Gupta M, Shrivastava R, Rastogi M, Gupta H, (2020) Yajna and mantra science bringing health and comfort to Indo-Asian public: a healthcare 4.0 approach and computational study. In: Jain V, Chatterjee J (eds) Machine learning with health care perspective. Learning and analytics in intelligent systems, vol 13. Springer, Cham, Springer Nature Switzerland AG 2020, pp 357–390. https://doi.org/10.1007/978-3-030-408503_15 39. Rastogi R, Chaturvedi DK, Satya S, Arora N (2020) Intelligent heart disease prediction on physical and mental parameters: a ML based IoT and big data application and analysis. In: Jain V, Chatterjee J (eds) Machine learning with health care perspective. Learning and analytics in intelligent systems, vol 13. Springer, Cham, Springer Nature Switzerland AG 2020, pp 199–236. https://doi.org/10.1007/978-3-030-40850-3_10 40. Rastogi R, Chaturvedi DK, Singhal P, Gupta M (2020) Investigating diabetic subjects on their correlation with TTH and CAD: a statistical approach on experimental results. In: Sandhu K (ed) Opportunities and challenges in digital healthcare innovation. ISBN13: 9781799832744|ISBN10: 1799832740|EISBN13: 9781799832751. https://doi.org/10.4018/ 978-1-7998-3274-4, https://doi.org/10.4018/978-1-7998-3274-4.ch012 41. Rastogi R, Chaturvedi DK, Singhal P, Gupta M (2020) Investigating correlation of tension type headache and diabetes: IoT perspective in health care. In: Chakerborty C (ed) IoTHT: internet of things for healthcare technologies, Chap. 4. Springer Nature, Singapore. https://doi.org/10. 1007/978-981-15-4112-4_4 42. Saxena M, Kumar B, Matharu S (2018) Impact of Yagya on particulate matters.Interdisciplinary J Yagya Res. 1:01–08. https://doi.org/10.36018/ijyr.v1i1.5

Additional Reading 43. Source: https://qz.com/1630159/bioelectricity-may-be-key-to-fighting-cancer/AND. https:// doi.org/10.1111/jtsb.12101 44. Bansal P, Kaur R, Gupta V, Kumar S, Kaur RP (2015) Is there any scientific basis of Hawan to be used in Epilepsy-Prevention/Cure? 5(2):33–45. https://doi.org/10.14581/jer.15009. 45. Source: https://www.doyou.com/how-mantras-work-39322/ 46. Source: https://blog.sivanaspirit.com/sp-gn-scientific-benefits-chanting/ 47. Source: https://www.worldpranichealing.com/en/energy/what-is-pranic-energy/AND 48. Sui CK, The ancient science and art of Pranic healing & advanced Pranic healing 49. Source: https://www.encyclopedia.com/medicine/encyclopedias-almanacs-transcripts-andmaps/bioelectricity 50. Source: Williams M (2006) Nutrition for health, Fitness and Sport, 8th ed. McGraw-Hill 51. Ferrera LA (2006) Focus on body mass index and health research. Nova Publishers 52. Source: Mills A (2009) Kirlian photography. History of Photography 33(3) 53. Source: Wikipedia. https://simple.wikipedia.org/wiki/Chakra 54. Source: https://www.chakra-anatomy.com/human-aura.html

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55. Source: Prabhat S (2010) http://www.differencebetween.net/miscellaneous/difference-bet ween-yin-and-yang/ 56. Source: https://rationalwiki.org/wiki/Quantum_consciousness 57. Source: gaiam.com/blogs/discover/how-does-meditation-affect-the-body 58. Varman H (2014) Five important levels of the human consciousness. BSIIJ 59. Grujin J (2016) What is Kirlian photography? Aura Photography Revealed. Light stalking 60. Buelteman R (2012) Shocks flowers with 80,000 volts of electricity. BSIIJ 61. Grimnes S, Martinsen G (2015) Bioimpedance and bioelectricity basics. ResearchGate 3 62. Sui CK (2012) Pranic energy: feel divinity all around you. IJITEE 63. Wisneski LA (2010) The scientific basis of integrative medicine. IJITEE 64. Chhabra G (2015) Human Aura: a new vedic approach in it. University of Petroleum and Energy Studies 65. Chig TT (1998) What is Yin Yang? always dream even when awake. Taoist Articles 66. Sia PD (2016) Mindfulness: consciousness and quantum physics. University of Padova 67. Smith JA, Suttie J, Jazaieri H, Newman KM (2018) Things we know about the science of meditation. Mindfulness Research 68. Dudeja J (2017) Scientific analysis of Mantra-based meditation and its beneficial effects: an overview. ResearchGate 69. Acharya SS (2001) The integrated science of Yagna. IIT Bombay

Rohit Rastogi received his B.E. C. S. S. Univ. Meerut, 2003. Master’s degree in CS of NITTTRChandigarh from Punjab University. Currently, he is getting a doctoral degree. From the Dayalbagh Educational Institute in Agra, India. He is an associate professor in the CSE department of ABES Engineering College, Ghaziabad, India. He has won awards in a variety of areas, including improved education, significant contributions, human value promotion, and long-term service. He keeps himself engaged in various competition events, activities, webinars, seminars, workshops, projects, and various other educational learning forums. Neeti Tandon is research Scholar in Fundamental Physics at Vikram University Ujjain. She is keen researcher in Yagyopathy. She is scientist by thought and working on the study of effect of Yajna, Mantra, and Yoga on mental patients, patients suffering with various diseases like diabetes, stress, arthritis, lever infection, and hypertension. She is also Active Volunteer of Gayatri Parivaar and Thought Transformation Movement. She keeps herself engaged in many philanthropic activities like plantation, slum area kid education, and anti-addiction movement. She is gold medalist and honors throughout in her education and obtained graduation and post-graduation in Physics Science. T. Rajeshwari a psychotherapist specializing in Nutrition and mental health. Practices freelance and has been working with children of all ages for around 35 years including as a therapist in Montessori schools. Lives in Kolkata and runs a center called Sneh Sri, which is centered on empowering women from low-income backgrounds by training them to make utility products and handicrafts. Also, a motivational speaker conducts workshops on effective parenting and healing meditation around the country. Also conducted nutrition and lifestyle management workshop and mind training workshop to engineers, different professionals, students, and homemakers. Now in a research program on “the sounds of vedic mantras and it is impact on human behavior” with a project on yagyopathy. Treatment through ancient yagya (smokelessyagya in 10 min). I am also a karmakandi attached to AWGP. Akhilawishwa Gayatripariwar. Also with GPYG Kolkata. (Gayatripariwar youth group). She is MS in psychotherapy and dip. In Nutrition.

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Prakash Moorjani is a dedicated philanthropist, social activist, and scientific personality by thoughts. He has a long experience of corporate and vast experience of 38 years at Indian Ordiance factory in MP. He took VRS ad selflessly contributed for Vichar Kranti Abhiyaan. He has also served at Shantikunj for 3 years from 2015–18 and afterward he is serving in rural areas for river cleaning, plantation drives, scientific aspects of Vedic wisdom, and self-employment creation. He visits several places of India to explain scientific spirituality and keeps himself busy in various camps and workshops of Yagyopathy. Sunil Malvi is dynamic volunteer of Gayatri Parivaar and thought transformation movement and Technical Engineer at MP Electricity Board. His passion is to establish Yajna and Its scientific effects at legal heights and to spread the awareness of common mass for this. He spares his leisure time in social activities and maintains the records of Yagyopathy based experiments.

Chapter 13

Application for Smart Cities During Pandemic—Face Mask Detection Menal Dahiya

and Nikita Malik

Abstract Human-like thinking abilities can be simulated in machines to achieve artificial intelligence (AI) for making predictions, helping make decisions, classification, etc. Algorithms for machine learning (ML) can be employed to train and deploy models into various applications. Deep learning is an ML consisting of artificial neural networks (ANNs) augmented with multiple abstraction layers, useful for processes of pattern recognition or classification, supported by large datasets. In the ongoing COVID-19 pandemic situation, one such application of AI and deep learning is the detection of face masks to help impede the transmission of infection. In this paper, firstly, the concepts of AI, data analytics for AI, ML, deep learning, neural networks, and use of technologies for smart cities are discussed in detail. This is followed by highlighting the application of these technologies in the event of a pandemic—face mask detection, using OpenCV (Open source Computer Vision), TensorFlow, and Keras, and achieving up to 99% accuracy in detection. Keywords Artificial intelligence · Deep learning · Face mask detection · COVID-19 · Smart cities · Pandemic · Data analytics · Industry 5.0

1 Introduction 1.1 Artificial Intelligence and Its Elements Human intelligence can be characterized in a manner that a machine can be designed to easily mimic its functioning and perform various tasks varying in range and complexity-involving reasoning, learning, and perception. This simulation of natural human intelligence, and the ability to think and act like humans, are programmed into machines, called Artificial Intelligence (AI). It also includes the abilities of problem-solving by rationalizing, taking decisions, and performing actions toward M. Dahiya · N. Malik (B) Department of Computer Applications, Maharaja Surajmal Institute, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2_13

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achieving a particular goal. A machine with the ability to apply intelligence to any problem, in general, is termed as AGI or Artificial General Intelligence, while a machine intelligence that not only equals, but also exceeds human’s efficiency at a task such as translator is referred to as ANI or Artificial Narrow Intelligence, and an intellect which outdo the best of human brains in every practical domain such as social skills, scientific creativity, and general wisdom, is called ASI or Artificial Super Intelligence. AI is a science and technology which is based on multiple disciplines like Mathematics, Computer Science, Engineering, Psychology, Linguistics, etc. It continues to evolve as a cross-disciplinary approach to benefit various different industries [1].

1.2 Importance and Applications of Artificial Intelligence The data quantities generated in today’s time outperform the humans’ ability to process it and make decisions based on it. The development of human intelligence associated with computer functions is the major thrust of AI, and AI, along with its logical evolution of ML (machine learning) and deep learning, forms the basis for all future computer learning and complex business decision making. The four components or pillars of building a trusted AI, which extrapolates the concepts of social knowledge that humans have built over the years, are [2]: • Fairness: It refers to minimizing the bias or the mismatch between the distribution of training data and a desired fair distribution. The data and models used for training should be bias-free (by establishing tests for bias identification and minimization) in order to avoid unfair results and determine fairness among all groups in AI systems. • Robustness: Safety and security in AI systems are the underlying factors that determine their robustness, i.e., the AI system and the data it is trained on are not vulnerable to any compromise. • Explainability: This refers to the AI system’s ability to provide meaningful explanations about how it arrived to specific decisions, or provide suggestions that are understandable to its developers as well as users. This is a little challenging to achieve because of the natural trade-off between accuracy of AI systems and their explainability, and striking the right balance between both is important for improving the trustworthiness of an AI model. • Lineage: This refers to an AI system establishing its development and maintenance history such that its lineage of configurations and training dataset, etc. can be tracked and audited throughout its lifecycle, instituting trust in the AI system. AI finds its applications in a wide range of domains, including healthcare sciences, business processes, education sector, and autonomous vehicles. AI is serving as the technology toward building an improved, better world for all where errors in the past practices and decisions can be analyzed and understood and future damages

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Fig. 1 Fields related to AI [4]

can be predicted and prevented, all while focusing on sustainable development and implementation techniques [3].

1.3 Related Fields of Artificial Intelligence AI encompasses various tools and technologies, whose application and need are the reason why AI has such a flourishing market today. As presented in Fig. 1, the related fields of machine learning, neural networks and deep learning, robotic process automation, text analytics and natural language processing, computer vision and speech recognition, virtual agents, and expert systems, together form the broad umbrella of artificial intelligence [4].

1.4 Significance of Machine Learning and Its Applications Machine Learning (ML) is a subset of AI which is largely based on the fields of mathematics and statistics, algorithms, and implementations of concepts drawn from computer science and engineering. It is essentially the science of training a machine to learn and improve at performing a certain task without explicitly programming it to do so. For any machine to properly acquire this ability there is a need for good initial data preparation, both basic and advanced learning algorithms, automatic iterative processes, scalable methods, and ensemble modeling. Learning in ML can be divided into three groups according to their purpose, i.e., supervised, unsupervised, and reinforcement. ML finds its use in applications where a sample of data representing a larger data pool can be used to build general models for making accurate predictions on that data, especially where programming certain algorithms is not feasible. Examples of common use of ML technology include recommendation systems, fraud/anomaly detections, computer vision, or speech detection. In this paper, we have worked on the facial recognition application of ML toward the pandemic situation.

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In order to fully optimize and benefit from these objectives of ML technology, certain limits need to be kept in consideration, such as: • Proper representation of the problem in a mathematical form that clearly specifies the questions one is seeking answers to through the ML system. • Preventing overfitting of data by avoiding the algorithm to learn too much from a training dataset to a point that it learns and creates non-existing rules and results in poor testing accuracy. • Providing generalized and well-prepared dataset to train on to ensure proper behavior of the ML model [5].

1.5 Smart Technology and Covid-19 In unprecedented times such as the Covid-19 pandemic, the lack of manpower to handle the massive surge of requirements in medical field brought the need to implement smart technologies even more [6]. In the healthcare sector, massive data is generated from patient particulars, treatment records, insurance details, etc. Over the years, growth in this sector and the data consequently has attracted data scientists’ attention. There is also a rising demand for personalized healthcare services using smart technologies such as wearable devices and mobile applications to enhance the quality of living and lifespan [7]. Health-associated data can therefore be mined and data analytics can be performed using the various techniques offered by AI and ML, to develop better applications for the medical and healthcare sector. In this chapter, the case of using face masks for public healthcare, especially during the ongoing Covid-19 pandemic, has been explored using ML techniques.

2 Performing Data Analytics for AI 2.1 Data Analytics, Importance, and Types Large amounts of raw data, which is comparable to crude oil these days, can be collected by any institution or person. However, mere collection is not enough and meaning needs to be extracted from this voluminous data collection for the organizations to stay in competition in today’s environment of unpredictability and complexity. “Analytics” forms the core of any process involving data refinement. It helps extract valuable information and develop insights through the application of efficient analysis, both qualitative and quantitative, over the collected data. This knowledge discovery through data analysis, interpretation, and communication can generate fact-based decisions for purposes of planning, learning, and management. Analysis consists of data transformations that can be broadly categorized as the following four families:

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• Transforming: It refers to changing the appearance of the data. Most commonly, the process of flat-file transformation is used, in which the data is put in a matrix form, ordered in rows and columns. Specialized numeric transformations can also be made, such as in rescaling, in which a numeric series’ maximum, minimum or mean values can be changed in order to make it algorithm-appropriate. • Cleansing: It refers to fixing up the imperfect data which may have some missing values or incorrect values, or may have extremes, or even be adversarial, depending on the way the data has been collected. • Inspecting: It refers to validating the data to check for any recognizable patterns or spotting any strange data elements through production of different statistics and providing various useful visualizations of data. • Monitoring: It refers to capturing the relationship between the data elements using tools such as regression, correlation, etc. to determine whether two values are truly different or related somehow. Data analytics is used in assisting in various applications across different areas such as data mining, big data analytics, etc. It can help in gaining insights and analyzing business’ value chain. Also, it can assist in enhancing the analysts’ knowledge about the industry, giving the organizations an opportunity to learn more about their business prospects [8]. The word analytics can imply different things and refer to different types such as Descriptive, Inferential, Exploratory, Diagnostic, Causal, Prescriptive, Predictive, Mechanistic data analytics. Figure 2 presents the Gartner’s Analytic Ascendancy Model which shows the four basic types of data analytics in the form of steps or

Fig. 2 The four major types of data analytics for improved decision making [10]

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levels growing linearly from one to the next. This model illustrates in a simplified form how at every stage of analytics deeper insights into the data are offered and more user engagement is required [9]. This categorization of analytics into different types or the different methods for data interpretation and communication are discussed as follows: 1.

Descriptive Analytics

Descriptive analytics focuses on summarizing what has happened or happening in an organization by manually examining the raw content to be able to perform questionanswering to get information about the situation. This is the simplest form of analytics which provides for a solid foundation and is the first step for further complex analytical processes to be carried out. It helps in exploring the data and gaining powerful insights using some basic operations of arithmetic like the maximum or minimum values, mean, median, etc. A few important tools for carrying out descriptive analytics are SPSS, MS Excel, Stata, etc. Such analytics is characterized by conventional visualizations and business intelligence in the form of generated narratives, pie charts, bar graphs, etc. An example of carrying out descriptive analytics could be for assessing a bank’s credit risk by looking into its financial performance in the past. It can give useful insights into the sales cycle, explaining what happened, but not why it happened the way it did [10]. 2.

Diagnostic Analytics

Diagnostic analytics focuses on determining why something has happened to be able to move a step forward from descriptive analytics. It takes a deeper, better look at the data and unearths further insights by drawing comparisons between historical and other data. Some of the techniques popularly used for carrying out diagnostic analytics include probabilities, drill-down operations, pattern identification, and correlations. An illustration of its use would be in conducting marketing campaigns where the customer responses can be used to assess and answer questions relating to reasons for increase or decrease in sales or the effectiveness of the campaign in different regions. The context availability and discerning of causal relationships help in developing a deeper understanding of the data but provide only limited actionable insights [10]. 3.

Predictive Analytics

Predictive analytics focuses on going beyond what happened and why, and aims at providing assessment of what is likely to happen. This likelihood of results in the future is determined by the use of historical data along with the techniques of machine learning and statistical algorithms. The importance of previous stages of data analytics comes in use here as the recognizable results are used in creating a model which is able to predict values for new or different types of data. The predictions made are only probabilities of occurrence of an event and do not ensure that it will happen. If the model is accurate, it can help in taking further decisions. Popular models for predictive analytics are those of classification and regression, and some of the

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commonly used tools are Matlab, Python, RapidMiner, etc. Example applications of predictive analytics are in demand forecasting where based on historical data, demand for the upcoming month can be predicted, or in the banking system for detecting cases of fraud and measuring credit risk levels to be able to maximize cross-sell opportunities and retain business’ valuable clients [10]. 4.

Prescriptive Analytics

Prescriptive data analytics focuses on providing insights about precise aspects of the subject, to be able to answer the questions on which action should be taken. It is the final step that builds upon predictive analytics’ results, suggesting what course of action is to be followed to be able to reach a particular outcome. Unlike the other analytics, it uses a feedback system for learning and improving the relationship between the actions prescribed and their resulting outcomes, i.e., carries out the processes of simulations and optimizations. Examples of prescriptive analytics include the popular recommendation systems such as Netflix which, based on characteristics of items or ratings from people with similar tastes, recommend other items with related properties. Other uses include such as in the healthcare industry where clinically obese patients can be managed by adding filters to know which treatments need to be focused on [10]. 5.

Exploratory Analytics

Exploratory analytics focuses on locating the outliers by finding out patterns in the data. Outliers or other features which were not anticipated in the raw data are identified using analytical methods in this approach by analyzing different environmental variables and trying to understand how they are related and cause occurrence of outlier values. This helps in getting insights and making informed decisions. For instance, sites in biological monitoring of data may have many stressors affecting them, and correlations among those are of importance before relating them to the biological response variables. For this, correlation coefficients, plotting of scatter plots and other different methods of multivariate visualizations can provide analysis of different variables to get insightful information on the variables’ relationships. 6.

Mechanistic Analytics

Mechanistic analytics focuses on making data scientists understand the procedure alterations or the variables that can result in other variables changing. Equations in physical sciences and engineering determine the mechanistic analytics’ results. It further allows determining the parameters if the equation is already known by the big data scientists. 7.

Causal Analytics

Causal analytics focuses on data scientists figuring out what would happen if a variable’s component is changed. It is an appropriate approach for huge data volumes in which big data scientists rely on several random variables for determining what might happen next even though non-random studies can be used to draw inferences from causations.

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Inferential Analytics

Inferential analytics focuses on taking world theories into account in order to ascertain some aspects of the large population. Using this analysis requires capturing from the population small information samples and on the basis of that, inferring larger populations’ parameters.

2.2 Components of Analytics Model There are four essential components of any analytics model, as outlined in Fig. 3. The model built must generate real-life situations’ results that are interpretable and meaningful, for which the human experience comes into play. Before a model can be recommended to be deployed, a qualified and professional human examines what the computers and algorithms have generated to make sure that consistent results are produced according to real-world scenarios. The Data and Algorithm components require knowledge and skills to be acquired from some data science training courses; whereas the Real-world and Ethical components require skills that are generally acquired through working on data science projects in the real-world [11]. 1.

Data Component

This component deals with everything related to data and includes the following: Sources of Data: Data can be collected from any of the following: • • • •

Designed surveys or experiments Open datasets available on public websites Organizations that mine and store huge datasets Simulations

Data Preparation and Transformation: For raw data to become usable, it needs to be pre-processed and changed to a form as follows:

Fig. 3 Components of an analytics model [11]

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• • • • • •

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Handle missing data and impute it Encode categorical data Identify predictor and target variables/features Scale data such as by normalizing it Reduce dimensions and select features Advanced data transformation methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA)

Various software which can be used for cleaning and preparing data for analysis are Python (Pandas package), R, Excel, etc. 2.

Algorithm Component

This deals with the algorithms which are applied for deriving insights from the data. These can be categorized as: • Algorithms for Descriptive Analytics: It includes packages for data visualization such as algorithms producing scatter plots, bar plots, histograms, and line graphs. Commonly used packages are Matplotlib, Ggplot2, etc. • Algorithms for Predictive Analytics: It includes algorithms for building models for prediction. Commonly used packages include TensorFlow, Sci-kit, etc. These algorithms can further be grouped into classes: – Supervised Learning for continuous variable prediction: Regression, Multiregression, Regularized regression analysis – Supervised Learning for discrete variable prediction: Logistic regression, SVM, KNN classifier, Random forest analysis – Unsupervised Learning: K-means clustering • Algorithms for Prescriptive Analytics: It includes algorithms that, based on insights drawn from data, prescribe a particular course of action to be followed. Algorithms include probabilistic modeling, monte-carlo simulations, optimization methods, etc. 3.

Real-World Component

This deals with the application of data and algorithms to the real-world problem scenarios such that meaningful outcomes are produced. The models generated must be validated against real-world cases to be considered useful. To make sense of the obtained results from the application of algorithms, inputs from humans and human experience are also beneficial. So as to stay competent in the real-world component, skills beyond those obtained from machine learning and data science training programs need to be acquired. Skills that qualify one to be a data scientist include those of demonstrating successful completion of real-world projects comprising all the stages right from problem framing to model deployment. These real-world data science projects can be acquired from internships or interviews or websites such as Kaggle projects. Working on them allows for deepening of one’s understanding of the workflow model building, data analysis, model testing, and application. It also

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aids in picking up communication, presentation, business acumen, and team-player skills additionally. 4.

Ethical Component

This deals with data scientists practicing and maintaining high levels of ethics. It is important for them to understand what their project’s outcomes will imply, and so, they should remain true to themselves and avoid any kind of fabrication in the data or the methods used to produce results that may produce some bias. All phases of data analytics, starting from data collection to model building and analysis and final testing and applications, should be free from any manipulations nor result in some misleading findings from the project [11].

2.3 Data Analytics Workflow For understanding how data analytics works, following are the steps followed for analyzing any given data, also referred to as the workflow for data analytics, as shown in Fig. 4 [12]: 1.

Preparation

Once data is captured and before it can be analyzed, it needs to be reformatted to bring it to a form suitable for computation purposes. This involves cleaning the acquired data so that it can be explored. Various sources from which data can be acquired are: • Online repositories. Data sets are available on public websites such as National Portal of India, and U.S. Census data sets.

Fig. 4 Workflow for data analytics [12]

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• On-demand streaming through APIs. Data files can be streamed, for example, the Bloomberg financial data stream, as and when needed using APIs. • Physical scientific equipment attached to computers. • Software of computers. For instance, data from web servers’ log files. • Manually entered data, such as in spreadsheet files. 2.

Analysis

Analysis is the central component of any activity of data analytics and includes writing of scripts or computer programs that derive useful insights from the data by analyzing it. The programming languages used may be R, Python, Perl, etc. for baseline and secondary modeling of data and carrying out statistical processes for its analysis. 3.

Reflection

There is frequent alternating between the reflection and analysis stages when working with data to attain useful information from it: alternatives are explored and comparisons are drawn and observations are noted. In contrast to the programming-based analysis phase, reflection phase makes use of more critical thinking as well as communication about the outputs obtained with the clients. 4.

Dissemination

This is the final phase of the data analytics workflow in which the obtained results are presented by making use of business white papers or PowerPoint presentations or written reports like internal memos. Academic papers may also be published for sharing of experiments and the results achieved.

2.4 Tools and Prerequisites for Data Analytics Some tools and fundamentals required for data analytics are discussed below: • Mathematics: Data Analytics is all but a numbers game. All analysis basically comes from mathematical functions and so, one must cultivate a positive attitude toward working with numbers. • MS Excel: Excel is a common and popularly used application in businesses for analytics. It supports all basic functions as well as complex formulas and functional skills of visualization, data mining, etc. • Basic SQL: Excel however cannot get the data directly from the source and can only slice and dice it. RDBMS (Relational Database Management Systems) like MySQL, SQL Server, etc. provide support for data collection procedures. The underlying language for all these RDBMS is SQL (Structured Query Language). • Basic Web Development: Knowledge of working with tools for internet programming like JavaScript, HTML, PHP, etc. is an added bonus when working with IoT-based or consumer-based internet companies such as AWS, Azure, IBM, etc.

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Besides the basics, in order to be a professional in data analytics, some advanced prerequisites and tools to go for are: • Hadoop: This open-source platform for cloud computing can be used on big data to perform highly parallelized operations. It allows for storage and processing of enormous data collections of any type and applications to run on them virtually with simultaneous tasks. • R Programming: Owing to its versatility, especially when it comes to statistics, R programming language, an open-source software, is very useful for the purpose of carrying out analysis for data analytics. It is efficient in supporting loops, conditionals, calculations on vectors, matrices, storage, graphical facilities, etc. • Python Programming: Another flexible and open-source language for programming and for quantitative and analytical computing. It has powerful libraries which support all sorts of data management, manipulation, and analysis. Python-based applications are considered eminent because of their agile productivity and it is significantly faster than R. • Database Proficiency Tools: Database tools like MS Access, MySQL, MongoDB, etc. all of which are underpinned by SQL, support, and are used for, data acquisition, processing, and storage. • MATLAB: A powerful, proprietary language and computing environment that allows for implementation of complex algorithms, data manipulations, matrix multiplications, and plotting of data and functions. It is a high computing language that is based on mathematics and computation. • Perl: A high-level, dynamic programming language that, with its features similar to Unix, and rich set of analysis libraries, is one of the key languages for data analysis. • Java: Java is the backbone programming language for almost every type of data science framework. Although it may not be as appropriate as R and Python for data analytics and statistical modeling, its speed and performance make it a good choice for developing large scale systems. • Julia: A new programming language that is still in its infancy and promises to be a one-stop-shop for all kinds of data analysis needs by filling the gaps of libraries and visualizations, unlike R, Python, Matlab, etc. Its potential will soon be realized and it will come into wide adoption [13].

2.5 Using Data Analytics with Machine Learning Data is available in large quantities, and so is the information derived from it. Handling such huge knowledge requires automation, leading to data processing and machine learning trends. As can be seen from Fig. 5, there is a huge overlap of machine learning with the data analytics field. Machine learning serves as a fundamental in helping reveal concealed knowledge bits, patterns, and useful insights from the overflowing data, with the aim to transform the capability of information into incentives for further investigation and business purposes.

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Fig. 5 Relationship between machine learning and data analytics [14]

Traditional data analytics uses models which are often static and provide limited functionality when it comes to unstructured and fast-addressing data such as in IoT (Internet of Things) applications. Such applications require identification of multiple sensor inputs and other external factors producing thousands of data points, and the correlations between them. Typical data analytics involves data analysis by building a model based on historical data and expert opinion to determine the relationship between variables. On the other hand, machine learning begins with outcome or target variables and then starts to look for predictor variables on its own, along with their interactions. That is, the goal wanted to be achieved is given to machine learning, and it learns from the data the important factors for achieving what is desired and for making the decisions. Also, machine learning is valuable when future events are to be accurately predicted. ML algorithms recurse over the models and improve them as more data is acquired and assimilated over time. That is, ML can make predictions and see what happens, then compare against its own predictions and accordingly adjust in order to become more accurate. ML and predictive analytics go hand in hand and it can be said that predictive analytics is made possible by machine learning. This is another reason why machine learning is highly valuable for many applications requiring decision making amid dynamic and unstructured data. Predictive analytics is not just a process but more of an approach that is driven by predictive modeling, with models including an ML algorithm. Over time, these models can be trained to deliver the business-needed results by responding to new values or data [14]. Since data is described using a statistical framework and machine learning involves data, it is clear that machine learning is built upon a statistical framework or the statistical learning theory. The statistical process can be described as a four-step activity that includes:

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• Estimating the population’s expected proportion from which data is to be obtained. This is the proportion that is to be studied. The benchmark of the estimate can be obtained from literature or prior work or by consulting experts. • Determining the confidence interval that will be used in the data analysis phase. This specifies the margin of error that is allowed since there is a certain degree of uncertainty with the samples used in empirical estimates. • Setting the confidence level, which provides the level of precision or uncertainty in the data analysis. Generally, a value of 95% confidence level is used. • Using the statistical table for estimating the size of the sample. If a large number is estimated, it can be recalculated by adjusting the confidence levels to lower values or confidence intervals to be wider in order to get a smaller sample size. Data analysis is therefore based on the samples considered from the identified population, and should therefore be of a size that is representative of it. Descriptive and Inferential statistics are the two broad fields in which statistical processes are divided. Descriptive statistics takes up a huge piece of data and analyzes it to present a digest of it such as its distribution using descriptive measures of central tendency like mean, its variations using descriptive measures of dispersion like variance, and its shape using measures like skewness. Descriptive statistics, therefore, describes the data sample and is not used to draw conclusions about the whole population based on the data sample obtained from it. To know about the differences or relationships within the data or whether it is statistically significant, inferential statistics are used. Inferential statistics also allows the generalization of the sample data results for the whole population from which the sample was obtained. Few of the models it uses for such determinations are correlation and regression models, chi-square distributions, analysis of variance (ANOVA), etc. [15].

3 Employing Deep Learning in AI 3.1 Introducing Deep Learning When we hear about deep learning, the first thing that comes to our mind is further added neurons than the former networks. We mean that complex models build on the neuron count, each layer of neurons being totally linked in multilayer networks to locally associated bundles of neurons among the layers in CNN (Convolutional Neural Network) and recurrent connections in Recurrent Neural Network (RNN). As the problem space increases, day by day, and the environment requirements are constantly transforming and improving, the functionalities of neural networks have had to change in the right direction. Essentially, a neural network (NN) or artificial neural network (ANN) is what works like the neurons in the human brain performing various tasks. NN includes different technologies of ML and deep learning as a part of AI [16]. Figure 6 shows the

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Fig. 6 Structure of neuron [16]

structure of a neuron, reflecting how a NN resembles its functionality, with various nodes working as neurons in the brain. Why Deep Learning? Over the last decade, neural networks have been the most flourishing field of research. Neural networks could implement only on a single hidden layer and still, we have been results. Now deep learning methods are coming into picture due to their success rate of handling complex problems and availability of open-source libraries making it more flexible for developers, small firms, and individuals to use these methods. There are some issues with neural networks: • • • •

Sometimes fails to converge Small changes in the weights can lead to major changes in output Data overfitting Time complexity is too high Whereas Deep Learning can overcome/improve these issues by [17]:

1.

Improving performance with Data • Deep Learning techniques get better with more data and perform better. • You can invent more data. You can use a generative model. For example, with an image data, you can invent new images by randomly shifting and rotating the images. • Rescaling of data • Transform your data, visualize it and look for outliers. • Select any feature selection methods and Reframe your problems in two or more techniques.

2.

Improving Performance with Algorithms • In Machine Learning, different approaches and algorithms are described in the decision process from data. • Spot Check Algorithm is the suite of top methods which do well and which do not. • Improve performance by taking help or stealing ideas from literature as published research is highly optimized.

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Fig. 7 Scaling of learning algorithms with amount of data [18]

3.

Improving Performance with Algorithm Tuning • Diagnose why performance is no longer improving. Evaluate model learning behavior. • Try all the different initialization methods. • Experiments with different learning rate that decreases over epochs. • Try all the activation functions and then rescale your data. • You must try different network structures and topologies. • Gradient is depending upon the batch sizes. • Stochastic Gradient Descent is the default optimization procedure.

Figure 7 shows the performance of data science techniques with increasing amounts of data, indicating how deep learning scales better in comparison to the other learning algorithms. Basically, deep learning is a type of machine learning technique that emulates the human brain’s functioning for data processing, forming patterns and, decision making. Figure 8 shows the relation between the different technologies of AI, ML, and ANN, discussed so far [19]. Deep learning techniques are continuously trying to push forward many machine learning problems in various domains.

3.2 Foundation of Neural Networks and Deep Learning There is a lot of research going on in the area of neural networks but, here we focus only on four different architectures, namely:

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Fig. 8 Relation between techniques of Soft Computing [19]

(a)

Unsupervised Pre-trained Network: i. ii.

(b) (c) (d)

Deep Belief Networks (DBN) Generative Adversarial Networks (GAN)

Convolutional Neural Network Recurrent Neural Network Recursive Neural Network

There is always a scope for advancement in architecture in terms of advances in layers, advances in neurons, and hybrid architectures also. Researchers plan toward from older to modern architecture like RNNs & CNNs. In different types of architecture, one of the major variant factors is layer types. Researchers implement new and unusual types of activation functions in layers of CNN and changed from fully linked to locally connected patches whereas RNN is better modeled as the time realm in time sequence data. Another variant factor is neuron type for example RNN created advancement in the nature of neurons tested in the LSTM (Long Short-Term Memory) networks. Next, we have seen the emergence of hybrid architectures. Before going into the explanation of architecture let’s first explore the core components of architecture [20]. The basic principles of Deep Neural Networks are: Parameters—parameters are the coefficients chosen by the model. During learning, algorithm optimizes these coefficients and returns an array as an output which minimizes the error. Activation Functions—Activation functions are mathematical functions that are associated with each neuron that is used to activate the result of one layer’s node ahead to the upper layer. These are scalar to scalar functions. For the purpose of nonlinearity in the networks, hidden neurons are used by them in network.

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There are different activation functions in Deep Network: • Linear Activation function: Basically, an identity function, where a variable known as dependent variable, is linearly associated with another variable known as independent variable. This Activation function is used in the input layer of the neural network. • Sigmoid Activation function: Activation function that converts independent variables into simple probabilities between 0 and 1. This Activation function belongs to logistic transforms and can reduce the outliers without removing them. • Tanh Activation function: It is a hyperbolic trigonometric function, shown in Eq. 1, which shows the rate of hyperbolic sine to hyperbolic cosine. The normalized range is −1 to 1. tanh(x) = Sinh(x)/Cosh(x)

(1)

• Softmax Activation function: Basically, this function is practiced on repeated data and it is a basic form of logistic regression. Softmax activation function is mostly found at the final layer of a classifier. Layers—Layers are the fundamental unit of any network. Like Feed Forward Neural Network (FFNN), input layer, hidden layers, and output layer is also defined in Deep Network in the same manner. Sub-networks in some architecture are defined by layers. We can blend two or more layers to achieve classification or regression in Deep Network. Loss Functions—These functions are basically for minimizing the error and are used to estimate the loss so that weight can be updated accordingly. 1.

Loss Functions for Regression: • Mean Squared Error Loss (MSE): When a certain result is required, we use the squared loss function. L(w, b) =

N 1  ( yˆi − yi )2 N i=1

(2)

Mean Squared Error is a convex loss function. Yet, the convex property is not satisfied when dealing with hidden layers. • Mean Absolute Error Loss (MAE): It is an alternative to MSE loss. L(w, b) =

N M 1  | yˆi j −yi j | 2N i=1 j=1

(3)

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• Mean Squared Log Error Loss (MSLE): M N 1  L(w, b) = (log yˆi j − log yi j )2 N i=1 j=1

(4)

• Mean Absolute Percentage Error Loss (MAPE):    N M 1   100 ×  yˆi j −yi j  L(w, b) = N i=1 j=1 yi j

(5)

MSE, as shown in Eq. 4, and MAE, as shown in Eq. 5, are widely used as loss functions. The MSLE and MAPE are applied on the networks where the forecasted outputs vary largely in range. 2.

3.

Loss Functions for Classification: When we build neural networks for classification problems, the main objective is usually on adding possibilities for classifications. Loss Functions for Reconstruction: Here, the network is trained to redesign its input by learning similarities and features across the dataset.

Hyperparameters—Earlier we heard about parameters, now hyperparameters that are tuning parameters for the network. These parameters make networks train advanced and rapid. These hyperparameters accord with governing optimization functions and model drafting amid training. Drafting of these parameters emphasizes ensuring that the network neither underfits nor overfits the training dataset. Optimization Methods—Parameter optimization refers to adjusting the weights to obtain a more precise output from the data. Weights of each layer represent the specific hypothesis, i.e., how they associate with the value derived from their labels. Hypothesis space can be defined as the combination of all possible weight values and we formulate the best hypothesis by using error and optimization algorithm. Search problem space increases as we input more parameters. An optimization algorithm is guiding the network about where downhill or steep direction occurs so that it knows where to stop.

3.3 Fundamental Components of Deep Networks Taking and building on the above-mentioned concepts for developing a better understanding of the Deep Networks’ building blocks: • Feed Forward Multilayer Neural Networks: Simplest Artificial Neural Network that consists of an input layer, many hidden layers, and one output layer. For multilayer Feed Forward Neural Network, we have neurons arranged into layers:

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• A simple input layer • Fully connected hidden layers (one or multiple) • An output layer (single) FFNN, a feed forward network having fully connected three layers especially input layer, hidden layer, output layers. Input data is fed into the input layer and the numbers of neurons are the same as the features input to the network. Input layers are connected to a single or multiple hidden layers. Extracted information from the training data acts as a weight value on the connections between the various layers. Finally, we get the prediction value from the output layer. Based on the classification of networks, the final output may be real-valued or a set of probabilities. Activation functions play an important role in neural networks. For classification, output layer typically uses sigmoid activation function. The same type of activation function is used by all neurons in each layer. Mostly using algorithms is Backpropagation learning algorithm that is widely used in most the applications where we can update the weights progressively and find the best solution. These are the building blocks of large Deep Networks and are used for pre-training phase in various Deep Networks. • Restricted Boltzmann Machines (RBMs): In 2012, Krizhevsky, Sutskeven, Hinton evolved a “large, deep convolutional neural network” that won the 2012 ILSVRC (Image Net Large Scale Visual Recognition Challenge) known as “Alex Net” [21]. Hinton, a great analyst at Google appreciated for leading work on DBNs and RBMs. According to Geoff, RBM can be described as a network that consists of identical units connected to each other, similar to neurons, and have the capability to make stochastic decisions. The main reasons for implementing RBMs in Deep Learning are: • • • • • •

They are great at feature extraction Classification Regression Collaborative Filtering Topic Modeling Dimensionality Reduction

In RBM, restricted implies connections among nodes of some layers are banned. Basic RBM consists of different units and weights. Basic RBM network consists of mainly two layers one is visible and another one is hidden layer. Each visible unit is joined to every hidden unit. However, units are independent in the same layer but are connected by connections with associated weights. In RBM, every neuron of the first layer is associated with weights of every single neuron of the hidden layer. Nodes of the second layer are feature detectors, learning features from the input data. Nodes of visible layers act as an “observable” as they take training vectors as input. Every single layer has a bias unit with state on and off but it is always set to “on”. Computation is performed by each node on the input and output as a result based on stochastic decisions. All connections are visible-hidden, the same type of

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Fig. 9 Reconstruction in RBMs [22]

two-layer connection is not possible like visible-visible, hidden-hidden. Nodes act like human neurons and one decision unit. They make decisions whether they pass a signal further ‘on’ or don’t pass a signal through the net “off”. “ON” means valuable data is passing through the node, i.e., important information that the network will forward and “OFF” means useless noise, corrupted data. There are parameters that connect unit for each layer to every node in the layer and the trained network knows which features/signals are correlated with which label. The methodology called pre-training using RBMs means reconstruction of primary data from a finite set of that data. Here, weights are learned through unsupervised pre-train learning. Figure 9 shows the process of reconstruction in RBMs. • Autoencoders: Autoencoders are more flexible than PCA but overall, it is similar to PCA. Autoencoders use the Backpropagation algorithm as a basic principle in an unsupervised learning environment. It usually represents data through various hidden layers so that output is close to input signal. Basic application of autoencoders is in anomaly detection, fraud detection in financial transactions, and determining the outliers in regular data [22].

3.4 Architectures of Deep Neural Network • Unsupervised Pre-trained Networks These two are unsupervised networks:

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(i) Deep Belief Networks (DBN) As the name suggests, DBN is a deep multilayer neural network including many hidden layers. Each connected pair of layers is an RBM, i.e., DBM is a stack of many RBMs. In DBN, the training process is completed in two phases: unsupervised pretraining phase and second one is supervised fine-tuning phase. Here, in this network, the input layer is raw sensory inputs and hidden layer learns abstract representations of this input. And finally, the output layer implements the network classification. In the first step of DBN, each hidden layer, i.e., an RBM is trained to reconstruct the input layer. The next RBM is trained, and this first hidden layer acts as an input layer and the RBM is trained by using the output of the first hidden layer as input. Pre-training process is continuous until each layer is pre-trained. After this, second phase starts which are fine-tuning, in this the labels are applied to the output where they give them meaning. After that, either BPNN or gradient descent algorithm is applied to train the network and complete the training process. (ii)Generative Adversarial Networks (GAN) The fundamental approach of GAN is the training of two DNN (Deep Neural Networks/ Deep Learning Models) simultaneously. One model act as a generator, that tries to generate new examples, and the second one is discriminator, which helps in classification of the examples that whether the instance is from generator or not. Basically, these two models compete with each other. This concept was coined by the famous deep learning expert Goodfellow in 2014 [23]. It was a big breakthrough in the field of deep learning and finds important applications in image generation. GANs are extending this concept in new realms such as video, and sound and generate images from text. It values unsupervised learning models to train its learning models, i.e., discriminator and generator in parallel. A key factor of GAN is that the parameter count used by the model is more minor than traditional with regard to the extent of data on that training is performed. The Discriminator is basically a standard CNN that classifies whether the result images as authentic or fake. While training GANs, our purpose is to generate similar result images depending on the training data by updating parameters. The objective is to minimize the error so that discriminator network cannot differentiate among the authentic or fake input data. The discriminator network takes input in the form of images and then gives output in the form of classification. The Generator learns distribution of training data, it can be done by any algorithm and the discriminator needs to extract features and train a binary classifier using these features. For data, which is to be passed through the discriminator’s test, such data distributions are captured by the generator, and then the probability of the sample being from a true distribution can be estimated. GAN has become one of the most active algorithms in deep learning in recent years. • Convolutional Neural Networks

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The name “convolution” originates from a mathematical operation that involves the convolution of different functions. CNN or ConvNet is built of one or more than one convolutional layer and then followed by one or more fully connected layers, known as subsampling layers. In 1998, LeCun [24] at AT&T Bell labs developed an important aspect of optical character recognition. His work is published in “Gradientbased learning applied to document recognition”. They were working on check image recognition for the financial activities. They developed the idea of image recognition known as CNN. Typically, it is multilayer neural network and it was revolutionized by the animal visual cortex. This Neural Network is popular for image recognition as we all know that images are high dimensional vectors and require large number of parameters to characterize the network. Traditional pattern recognition approaches work on handcrafted features and “simple” trainable classifier [25]. Developing these handcrafted features is very expensive and tedious task because they are highly dependent on particular applications and cannot be easily fitted to other applications. So, converting input images into feature vector loses the spatial neighborhoods and increase complexity. To overcome this problem, CNN is proposed to minimize the number of parameters and adapt the network architecture specifically to vision tasks. Figure 10 represents the basic structure of CNN. As a deep network, the initial layers recognize the features and the subsequent layers reconstruct them to create higher-level attributes of the input. For designing a CNN, the following are the basic stages: • Convolution (CONV)- First stage where input signal is received • Subsampling—After receiving input signal from above layer, the signals are smoothened to remove or reduce the sensitivity of the filters to noise

Fig. 10 Basic structure of CNN [25]

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• Activation—Like how neurons flow in our brain, this layer controls the flow of signals from layer to layer • Fully Connected (FC)—Here, all the layers are connected from a previous layer to the neurons from the next layer Advantages of CNN: • • • •

Easier to train. Many fewer parameters than fully connected parameters. Very good for image recognition and visual recognition. Once a section within a particular portion of an image is trained/ learned, the CNN can recognize that section present anywhere in the image.

Disadvantages of CNN: • Susceptible to noise • Quality and size of the training data matter a lot in CNN. • Recurrent Neural Networks It is the building block of the network architectures from which other deep learning architectures are evolved. It is very familiar where the sequence in which the information is presented is crucial, like NLP (Natural Language Processing), Speech Synthesis, Speech Translation, etc. Following steps explains how RNN works: • Train the network by taking input from an example forms a large dataset. • When doing training, network will consider that example and apply computations to it. • And expect a reasonable prediction as a result. • Comparing the result with the expected value will give us an error. • Propagating the error back will adjust the variables. • Repeat the above steps until variables are not well defined. • A prediction is obtained by following these variables to a new input. They are called “recurrent” because a fixed task is performed for every single element of a sequence, which means they are able to “memorize” the calculated information and use them to calculate the further accurate outcomes. Advantages of RNN: • • • •

Used to build industry standard chatbots Used for predicting the phonetic segments’ correct order Used to generate accurate descriptions for unlabelled images with CNN Reduce the number of parameters that we need to learn

Disadvantages of RNN: • Difficult to track long term dependencies • Cannot be stacked into very deep models

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• Recursive Neural Networks A recursive neural network is a kind of deep neural network reduced as RvNN and has been coined to learn distributed data representations of structured networks. It is a type of deep neural network which is capable of applying the same set of weights recursively over a structured input. Basically, these networks are in non-linear state. Like tree structures in Natural Language Processing, where we use parsing to complete the sentences. We can use RvNN as it has a same topology similar to tree. RvNN is also used for the prediction of structured outputs but not for structured inputs. Sentimental Analysis is one of the important operations done by NLP that can rate the sentiments in sentences. We can apply RvNN for sentiment analysis in sentences. RvNN is adaptive in nature and capable of learning deep structured scholasticism. So, sometimes they are categorized into complex inherent chains. In simpler words, we can explain RvNN that if similar set of weights is applied on a structured input repeatedly, then RvNN will generate.

4 Application of Deep Learning for Face Mask Detection During Pandemic 4.1 Need Coronavirus disease of 2019, declared by WHO (World Health Organization) as a global pandemic, is the most recent widespread virus to have affected humans in the last century [26]. It has spread uncontrollably in 2020 with more than 5 million cases in less than half a year across almost 200 countries. As this virus disease transmits infection successfully by coming in close contact, wearing face masks in public, covering the nose and mouth, has become a trend to curb the rapid spreading of the infectious disease. Before the spread of COVID-19 also, masks were worn by people to protect against health risks of exposure to air pollution, and now, scientists have proven that face masks, when worn properly, can help impede the transmission of COVID-19 infection. In some countries, it is even mandated by law for people to come out in public places only if wearing a face mask [27]. The rise of coronavirus disease has led to an escalation in scientific cooperation worldwide. ML-based AI solutions can help in dealing with COVID-19 in various ways. Toward this, a face mask detection model based on deep learning is designed and discussed here. As it is difficult to monitor and enforce the wearing of face masks among large groups of people, a face mask detector based on the concepts of deep learning and computer vision will prove to be helpful in crowded places like airports, shopping malls, etc. to monitor and detect any person breaking the law. For this purpose, it can be used in surveillance cameras as well [28]. The discussed model here incorporates the techniques of both deep learning and classical machine learning, making use of OpenCV, Keras, and TensorFlow. Feature extraction is done

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by employing deep transfer learning and is integrated with ML algorithms, to achieve the highest accuracy, taking least amount of time in training and detection processes [29].

4.2 Methodology For building the custom COVID-19 face mask detector, there are two phases involved, with each phase further having some sub-steps (as shown in Fig. 11): 1.

2.

Training: The focus in this phase is to load from the disk the dataset containing images of faces that will be used for detecting masks on face, train the ML model using Keras and TensorFlow on the input dataset, and finally serialize the classifier model back to the disk. Deployment: Once the model has been trained, it is loaded for performing detection of face masks on a sample of face images, and it will be able to classify each face as to whether it is”with mask” or “without mask”.

• Data Set: The dataset used here is created by PyImageSearch. The dataset comprises 1376 images, which were already labeled as “no mask” and “mask”.

Fig. 11 Methodology followed for building COVID-19 face mask detector using Python libraries

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Fig. 12 Dataset consisting of images labeled as either “with mask” or “without mask”

The images are extracted from different sources and have different resolutions and sizes. The majority of them were augmented by an open-source ML library called OpenCV (Open source Computer Vision). Data augmentation includes applying simple geometric transformations like rotations, translations, etc. such that new training samples are created from the original images, which helps increase the model’s generalizability. The images are classified into two categories: 690 images as “with mask” and 686 images as “without mask”. Figure 12 shows the sample dataset used. • Data pre-processing: All the raw input images are cleaned in the process being referred to as pre-processing before they can be input to the NN ML model. To change all the raw images to more clean versions, the following steps are applied: 1. 2. 3.

4.

Image resizing to pixel value of 256 × 256. Color filtering (RGB) over the channels (3 channel image is supported by the used MobileNet model) Normalizing/Scaling using PyTorch’s (a Python library for deep learning applications) build-in weights’ standard mean. Also includes images’ center cropping with the pixel value 224 × 224 × 3. Converting the images into tensors (which is similar to NumPy array)

• Fine-tuning: Fine-tuning in deep learning makes use of weights from some previous deep learning algorithm to program another similar deep learning process. The process involves the following: 1.

Loading MobileNet V2 (object detection library in OpenCV) with pretrained weights of ImageNet (a very large image database), leaving off head of network.

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3.

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Removing the FC nodes at the network’s end (which is where the actual class label predictions take place) and replacing them with newly initialized nodes. Freezing the network’s earlier base (CONV) layers for ensuring that there is no destruction of the CNN’s previously learned robust features. These base layers’ weights are not updated during the Backpropagation process, but those of the head layer is tuned. Training of only the heads of FC layer. Unfreezing some or all of the network’s convolutional layers optionally and performing a second pass of training.

• Feature Selection: The facial structure can be automatically located using certain facial landmarks such as the eyes, eyebrows, nose, mouth, and the jawline. Once the face is located in the image, the facial landmarks can be detected using Dlib library for ML, such that the face’s region of interest (ROI) can be extracted to know where a mask must be placed on the face in order to be correctly identified and classified as “with mask” image. Figure 13 shows how a face is detected in an image, by identifying the ROI, i.e., the eyes, eyebrows, nose, jawline, and the mouth for locating the facial landmarks using CV2. This may also be termed feature selection, as we are locating and selecting only the part from the image, which is the face, which is required for building the face mask detector model.

Fig. 13 Locating the face in the image by extracting the ROI using Dlib

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4.3 Implementation • To implement the deep learning network, the available framework options are TensorFlow, Keras, Caffe. Keras has been used here as it runs on Python programming language, which is easy to use and learn, and as compared to TensorFlow platform, Keras provides data parallelism and is more user-friendly [30]. • Libraries: The following library packages shown in Fig. 14 are needed to be imported when running the Python script: Following are the purposes of these library packages that are included in the Python code:

Fig. 14 Library packages needed to be imported for running the Python script

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• For providing data augmentation • For loading the MobileNet V2 classifier (this model will be fine-tuned with ImageNet weights that are pre-trained) • For building new FC layer heads • For pre-processing the input dataset • For loading the image data • For creating binary class labels, i.e., binarizing, segmenting the input dataset, and printing the classification report, Sklearn or Scikit-learn is used • For finding as well as listing the dataset images, imutils path is implemented • Matplotlib is used for plotting the training curves Figure 15 shows a snippet of Python code used for designing COVID-19 face mask detector. • Real-Time Implementation: – For detection of COVID-19 face masks in real-life, OpenCV has been used and the Python script is run to get results shown in Fig. 16: • For experimentation, face is partially covered with mask, and some other cloth (handkerchief) is wrapped around the mouth instead of face mask, to check for accuracy of detection of the developed model, as shown in Fig. 17.

4.4 Results and Discussion To create the COVID-19 face mask detector, a NN ML model with two classes has been trained on images of people wearing masks and without mask. MobileNet V2 is fine-tuned on this image dataset and a classifier, which is approximately 99% accurate, is obtained using OpenCV, TensorFlow, Keras, PyTorch, and CNN. The classifier is then implemented in real-time video stream and on images of people to detect faces of people, extract every individual face and, classify it for whether it is “with mask” or “no mask”. In real-time video stream, an accuracy of 99.99% without mask and 86.77% with mask is observed, and when image with partially covering mask on face and handkerchief covering face is used, the classifier detects as “No Mask” in both cases with accuracy of 96.89% and 83.66%, respectively. The designed COVID-19 face mask detector is computationally efficient because of the MobileNet V2 architecture, making the model easy to be deployed on embedded systems such as Raspberry Pi, Google Coral, and others. Figure 18 shows the loss/ accuracy versus epoch (a hyperparameter that indicates the number of times the learning algorithm works through the input training dataset) graph. The training accuracy/ loss curves demonstrate the high accuracy achieved by the designed model for detecting faces in real-time and classifying them correctly as with mask or without mask. It also signifies that overfitting of data has been avoided. However, the validation loss is lower than training loss and the validation accuracy is higher than training accuracy. To further improve this, actual images of

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Fig. 15 Code snippet for face mask detection

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Fig. 16 Running face mask detector in real-time, showing accuracy of 99.99% without mask and 86.77% with mask

Fig. 17 a COVID-19 Face mask detector is unable to recognize mask below the nose, which is the ROI, and detects “No Mask” with an accuracy 96.89%. b COVID-19 Face mask detector is not able to detect handkerchief and shows “No Mask” label the accuracy of 83.66%

people wearing masks should be used instead of artificially generated images that have been used here. Images that may confuse the model classifier into correctly detecting whether the person is wearing a proper face mask or not, such as images of people with a wrapped cloth-like bandana over their mouth, should also be used to better train the model for real-time application. Additionally, a dedicated object detector having two classes should be considered for training instead of a plain image classifier used here [31].

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Fig. 18 Training accuracy/loss curves of designed face mask detector

5 Conclusion and Future Work Through this paper, the concept of Artificial Intelligence has been revisited, emphasizing on Machine Learning, Data Analytics, Neural Networks, and Deep Learning. An application of deep learning- a face mask detector, which proves to be of significance in the prevalent COVID-19 pandemic times, has been modeled and discussed in this paper. OpenCV, TensorFlow, Keras, PyTorch, and CNN have been used for designing the face mask detection system to detect whether people are wearing proper face masks or not, and were tested with real-time video streams and images of people to achieve as high as 99% accuracy. As humans are advancing toward improved facial recognition algorithms, this creates scope for new and better face mask detection systems that can identify covered faces with higher accuracies. A better system can be implemented in the future by interfacing this face detection model with alarm and alerting systems to contribute toward public healthcare and safety. It can also further be integrated with a system that can implement social distancing, making a wholesome system for impeding the COVID-19 spread.

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Author Index

A Agarwal, Utkarsh, 159 Akshitha, M., 1

C Chaturvedi, Anusha, 13 Chitlapalli, Sanjeeva S., 57

D Dahiya, Menal, 275 Deepthi, K., 195

G Gupta, Anjali, 159 Gupta, Nisha, 159

H Hegde, Sahana S., 113

K Kulkarni, Aishwarya S., 35

M Malik, Nikita, 275 Malvi, Sunil, 229 Mani Sekhar, S. R., 1 Manjith, B. C., 87 Manvi, Sunilkumar S., 69, 131 Moorjani, Prakash, 229 Murthy, Jamuna S., 57

N Narasimhan, Vaibhav Gubbi, 113

P Pant, Anmol, 159 Patil, Shantala Devi, 209

R Rajeshwari, T., 229 Rao, Krishna Prasad N., 131 Rastogi, Rohit, 159, 229

S Sandhya, C. P., 87 Sekhar, S. R. Mani, 13, 69

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al. (eds.), Society 5.0: Smart Future Towards Enhancing the Quality of Society, Advances in Sustainability Science and Technology, https://doi.org/10.1007/978-981-19-2161-2

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Author Index

Siddesh, G. M., 1, 35 Singh, Rudransh Pratap, 69 Sinha, Lakshya Aditi, 69 Sujana, D. V., 113 Sumana, M., 113

Thakur, Ankita M., 13 Tripathi, Shivani, 159 Tuppad, Ashwini, 209

T Tandon, Neeti, 229

W Wadawadagi, Shreehari N., 113