De Gruyter Frontiers in Computational Intelligence. Volume 16 Semantic Intelligent Computing and Applications 9783110781595, 9783110781663, 9783110781748

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Semantic Intelligent Computing and Applications

De Gruyter Frontiers in Computational Intelligence

Edited by Siddhartha Bhattacharyya

Volume 16

Semantic Intelligent Computing and Applications Edited by Mangesh M. Ghonge, Pradeep Nijalingappa, Renjith V. Ravi, Shilpa Laddha and Pallavi Vijay Chavan

Editors Dr. Mangesh M. Ghonge A-1101, Raj Tower, Krushnai Nagar 422009 Nashik, Maharashtra India [email protected] Dr. Pradeep Nijalingappa Department of Computer Science and Engineering Bapuji Institute of Engineering and Technology Shamanur Road, Post Box 325 577004 Davangere, Karnataka India [email protected]

Dr. Shilpa Laddha Swikrut Residency, Plot 92 1-A B/h Apex Hospital Bassaiye Nagar, 431001 Aurangabad Maharashtra India [email protected] Dr. Pallavi Vijay Chavan Palava City, Dombivli East C1004 Exotica, Casa Rio 422009 Thane, Maharashtra India [email protected]

Dr. Renjith V. Ravi S/O Mr. Raveendran K. K., Vayalappalli House Kumaranalloor PO 686001 Kottayam, Kerala India [email protected]

ISBN 978-3-11-078159-5 e-ISBN (PDF) 978-3-11-078166-3 e-ISBN (EPUB) 978-3-11-078174-8 ISSN 2512-8868 Library of Congress Control Number: 2023937357 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the internet at http://dnb.dnb.de. © 2024 Walter de Gruyter GmbH, Berlin/Boston Cover image: shulz/E+/getty images Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Preface Semantic Intelligent Computing has emerged as a key factor driving innovation, efficacy, and intelligence across a wide range of applications in today's quickly changing digital ecosystem. A new era of computing has begun, one in which robots can comprehend and interpret human language and knowledge with previously unheard-of precision and depth thanks to the convergence of artificial intelligence, natural language processing, knowledge representation, and semantic technologies. This edited book, "Semantic Intelligent Computing and Applications," represents a comprehensive exploration of the latest advancements, methodologies, and applications where semantic intelligence has its impact. It amalgamates a diverse collection of contributions from different authors in academia, all of whom share a common goal: to advance the frontiers of intelligent computing through the lens of semantics. We know that the present world is Data-driven world. Semantic intelligence provides solutions for some of the most pressing challenges in the present increasingly data-driven world. It enables machines to not only process data but also comprehend its meaning, context, and nuances, thereby facilitating more accurate and contextaware decision-making. This book is encapsulated with diversified 9 chapters. Chapter 1 discusses the architecture, application, trends, and challenges of Semantic web technologies. Chapter 2 reviews the contributions of semantic technologies in various domains Chapter 3 unleashes the concepts and research issues of Geospatial semantics information modelling. Chapter 4 elaborates the present and future applications of artificial intelligence for employees’ health and safety. Chapter 5 walks through the fundamental concepts of hybrid entropy-based support vector machine with genetic algorithm for classification Chapter 6 illustrates the concept of crisp rule set considering bone disease diagnosis as a case-study. using crisp rule set theory Chapter 7 demystifies how AI can be used for extracting business methodology. Chapter 8 covers advanced business model based on Blockchain utilizing cloud services. Chapter 9 explains the evolutionary computation and streaming analytics machine learning with IoT for urban intelligent systems Our goal with this edited volume is to provide readers with a comprehensive resource that not only surveys the state-of-the-art in semantic intelligent computing but also offers insights into its practical applications across various domains. We the editors are very sure that this book will serve as a valuable reference for researchers, students, and practitioners who are passionate about harnessing the power of semantics to drive innovation and solve complex problems. We extend our heartfelt gratitude to all the authors who have contributed their expertise to this book, as well as to the reviewers who provided invaluable feedback https://doi.org/10.1515/9783110781663-202

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during the peer-review process. Their dedication and commitment to advancing the field of semantic intelligence have made this volume possible. We sincerely hope that "Semantic Intelligent Computing and Applications" will inspire new ideas, foster collaborations, and contribute to the ongoing evolution of intelligent computing in the semantic era. Editors

Contents Preface

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List of contributing authors Editors profile

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Devasis Pradhan, Prasanna Kumar Sahu, Mangesh M Ghonge, Rajeswari, Hla Myo Tun 1 Semantic web technologies: architecture, application, trends, and challenges 1 Aniket R. Singh, Pushkar P. Khadase, Somesh M. Lad, Dhanshree M. Shimpi, Mangesh M Ghonge 2 Review on role of semantic technologies in various domains 29 Ujwala Bharambe, Sangita Chaudhari & Prakash Andugula 3 Geospatial semantic information modeling: concept and research issues 41 Simrn Gupta, Rahul Patanwadia, Parth Kalkotwar, Ramchandra Mangrulkar 4 Applications of artificial intelligence for employees’ health and safety: present and future 65 M. Revathi, D. Ramyachitra 5 Hybrid entropy-based support vector machine with genetic algorithm for classification 87 Gaurav Singh, Anushka Kamalja, Ashutosh Karwa, Pallavi Chavan 6 Decision system for bone disease diagnosis using crisp rule set theory 105 Vipul Narayan, Sritha Zith Dey Babu, Manglesh M. Ghonge, Pawan Kumar Mall, Shilpi Sharma, Swapnita Srivastava, Shashank Awasthi, L.K. Tyagi 7 Extracting business methodology: using artificial intelligence-based method 123

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Inderpal Singh, Dr Anshu Sharam 8 A blockchain-based business model to promote COVID-19 via cloud services 135 R. Ganesh Babu, G. Glorindal, Sudhanshu Maurya, S. Yuvaraj, P. Karthika 9 Evolutionary computation and streaming analytics machine learning with IoT for urban intelligent systems 157 Index

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List of contributing authors Devasis Pradhan Assistant Professor Department of Electronics & Communication Engineering Acharya Institute of Technology Bangalore-560107 India Prasanna Kumar Sahu Professor Department of Electrical Engineering National Institute of Technology Rourkela -769008 India Mangesh M Ghonge Associate Professor Department of Computer Engineering Sandip Institute of Technology and Research Center Nashik India Rajeswari Professor Department of Electronics & Communication Engineering Acharya Institute of Technology Bangalore-560107 India Hla Myo Tun Professor Department of Electronic Engineering Yangon Technological University Yangon Myanmar Aniket R. Singh Department of Computer Engineering Sandip Institute of Technology and Research Center Nashik India

Somesh M. Lad Department of Computer Engineering Sandip Institute of Technology and Research Center Nashik India Dhanshree M. Shimpi Department of Computer Engineering Sandip Institute of Technology and Research Center Nashik India Pushkar P. Khadase Department of Computer Engineering Sandip Institute of Technology and Research Center Nashik India Ujwala Bharambe Thadomal Shahani Engineering College Bandra Mumbai India Sangita Chaudhari Ramrao Adik Institute of Technology Nerul Navi Mumbai India Prakash Andugula Indian Institute of Technology Bombay Mumbai India Simrn Gupta Dwarkadas J. Sanghvi College of Engineering Mumbai India Rahul Patanwadia Dwarkadas J. Sanghvi College of Engineering Mumbai India

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Dr. Ramchandra Mangrulkar Dwarkadas J. Sanghvi College of Engineering Mumbai India M. Revathi Department of Computer Science Bharathiar University Coimbatore 641046 Tamilnadu India Dr. D. Ramyachitra Department of Computer Science Bharathiar University Coimbatore 641046 Tamilnadu India Anushka Kamalja Department of Information Technology Ramrao Adik Institute of Technology Nerul, Navi Mumbai MH India Ashutosh Karwa Department of Information Technology Ramrao Adik Institute of Technology Nerul Navi Mumbai MH India Pallavi Chavan Department of Information Technology Ramrao Adik Institute of Technology Nerul Navi Mumbai MH India Inderpal Singh Assistant professor Department of Computer Science and Engineering CT Group of Institutions Jalandhar Punjab India

Dr. Anshu Sharam Assistant professor Department of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India Vipul Narayan Assistant Professor School of Computing Science & Engineering Galgotias University Greater Noida India Sritha Zith Dey Babu Department of Computer Science Chandigarh University India Swapnita Srivastava Assistant Professor School of Computing Science & Engineering Galgotias University Greater Noida India L. K. Tyagi Professor Department of Computer Science and Engineering, GL Bajaj Greater Noida India Pawan Kumar Mall Professor Department of Computer Science and Engineering, GL Bajaj Greater Noida India R. Ganesh Babu Department of Electronics and Communication Engineering SRM TRP Engineering College Tiruchirappalli TN India

List of contributing authors

G. Glorindal Department of Information and Communication Technology St.John the Baptist University Lilongwe Malawi Sudhanshu Maurya School of Computing Graphic Era Hill University Uttarakhand India

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S. Yuvaraj Department of Electronics and Communication Engineering SRM TRP Engineering College Tiruchirappalli TN India P. Karthika Department of Computer Applications Kalasalingam Academy of Research and Education TN India

Editors profile Dr. Pradeep N working as Dean Academics and Professor in Computer Science and Engineering at Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India, affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India. He is having 21 years of academic experience, which includes both teaching and research experience. He has completed Postdoc from Thu Dau Mot University, Vietnam and he was honoured as Hon. D.Eng (Honorary Doctrine Engineering) (Honoris Causa), from the Iranian Neuroscience Society FARS chapter, and Dana Brain Health Institute, Shiraz, Iran. He is a Senior Member of IEEE and having professional membership in ACM, ISTE, IEI, and IAENG. His research areas of interest include Machine Learning, Pattern Recognition, Medical Image Analysis, Knowledge Discovery Techniques, and Data Analytics. He is having 2 international patent grant and one Indian patent application published. His 8 copyright proposals are successful in getting Copyright Grant from Govt. Of India. He is the editor for 12 international books which are published by/to be published by reputed publishers Elsevier, Springer, IGI, De-Gruyter, Nova Science, Bentham Science, and Wiley. He has authored 8 books chapters which are published by Elsevier, Springer, IGI. More than 30 research articles which includes indexed journal articles, and Conference Research articles have been accepted and published. He is one of reviewer for many conferences, journals and have chaired sessions in international conferences too. Also, he is one of the Technical Committee Member for evaluating ICT projects of Davangere Smart City, Davangere, Karnataka, India and Technical Committee member in Davangere University, Davangere, Karnataka, India. Dr Renjith V Ravi is presently employed as Associate Professor and Head of the Department of Electronics and Communication Engineering and Coordinator of the Post Graduate Programmes conducting at MEA Engineering College, Kerala, India. He possesses B.Tech. degree in Electronics and Communication Engineering in, M.E. degree in Embedded System Technology and Ph.D. in Electronics and Communication Engineering. He is a member of the panel of academic auditors of APJ Abdul Kalam Technological University, Kerala and had conducted external academic auditing in various affiliated institutions under the same University. He had published several research articles in SCIE and Scopus indexed journals, Edited books and international conferences inside and outside the country. He is an academy graduate and academy mentor in Web of Science and a certified peer reviewer from Elsevier Academy. He has been serving as a reviewer for various SCIE and Scopus indexed journals from IEEE, ACM, Springer, Elsevier, Taylor & Francis, IET, Inderscience, World Scientific, IOS Press De-Gruyter and IGI Global. He has been published four edited books and currently editing three edited books from renowned international publishers. He got granted one patent, one industrial design and two copyrights. He had been awarded several outstanding achievement and outstanding service awards, and several best paper awards from international Conferences. He is a Fellow of IETE and member of IE, ISTE, CRSI, IACSIT, IAENG, SDIWC and senior member of SCIEI and SAISE and a chartered engineer certified by the Institution of Engineers (India). He has been served as the Program Committee member, Session Chair as well as reviewer of several National and International conferences conducted in India and abroad. His research areas include Image Cryptography, Image Processing, Machine Learning, Internet of Things Etc. He is currently focusing his research in the area of secure image communication using image cryptography. Dr. Shilpa Laddha is an Assistant Professor in the Department of Information Technology at the Government College of Engineering, Aurangabad. Prof. Laddha has completed her Bachelor’s and Master’s degrees in Computer Science and Engineering from Sant Gadge Baba Amravati University, Amravati and Dr. BAMU, Aurangabad, India, respectively. She did her Doctorate in Computer Science and Engineering from Sant Gadge Baba Amravati University, Amravati, India. She has more than 20 years of teaching and research experience. Her area of interest includes Neural Networks, Information Retrieval, https://doi.org/10.1515/9783110781663-205

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Data Mining, Semantic Web Mining and Ontology, and many more. She has profound expertise in taking the full-depth training of engineering students. She has authored Two books. She has Two Copyrights & Three Patents to her credit, and her many research papers are published in prominent international journals. She has presented research communications in national and international meetings and is a visiting professor to many universities. She is a reviewer for various journals and conferences. She has guided more than 200 students on their graduation projects. She is associated with the development of an innovative, integrated engineering curriculum designing for various colleges. She received the Outstanding Women (Computer Science and Engineering) Award VIWA 2020 by the Centre for Advanced Research and Design, Venus International Foundation, Chennai. She also received International Excellence “Best Research Thesis” Award at the First Universal Innovators Leadership Awards (UILA 2020) by SScbs, University of Delhi, New Delhi, in association with NIT Patna and University of Valladolid, Spain. Dr. Pallavi Vijay Chavan is Associate Professor at Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Navi Mumbai, MH, India. She has been in academics since the past 17 years and working in the area of computing theory, data science and network security. In her academic journey, she published research work in the data science and security domain with reputed publishers including Springer, Elsevier, CRC press and Inderscience. She has published 2 books, 7+ book chapters, 10+ international journal papers and 30+ international conference papers. Presently she is guiding 5 Ph.D. research scholars in the similar domain. She completed her Ph.D. from Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, MH, India in 2017. She secured the first merit position in Nagpur University for the degree of B.E. in Computer Engineering in 2003. She is recipient of research grants from UGC, CSIR and University of Mumbai. She is acting as a reviewer for Elsevier, Inderscience journals. Her firm belief is “Teaching is a mission”.

Devasis Pradhan, Prasanna Kumar Sahu, Mangesh M Ghonge, Rajeswari, Hla Myo Tun

1 Semantic web technologies: architecture, application, trends, and challenges Abstract: Semantic web (SW) and cloud innovation frameworks have been basic parts in making and sending applications in different fields. In spite of the fact that they are independent, they can be joined in different approaches to make arrangements, which has as of late been examined top to bottom. The SW has drawn in a different, however more significantly, local area of analysts, establishments, and organizations conviction that one day the SW will gigantically affect our lives as the current web has. Consequently, there is a great deal of work that has been done around here. This chapter gives an outline of the SW and what has been done so far in the SW field. Then, at that point, it features the current significant difficulties in this field. The association of this chapter is as follows: the initial segment of the chapter examines the SW key components and the spaces that increment the development of the SW. In the second part, we attempt to outline the spaces where SW innovations assume an imperative part. In the third part, we accentuate those areas that go connected at the hip with SW innovations.

1.1 Introduction The basic advancement of the main web – from an end customer’s point of view, regardless – was the hyperlink. A customer could tap on an association and right away go to the chronicle perceived in that association. In this way, the unprecedented advantage of Web 1.0 was that it separated away from the genuine accumulating and framework organization layers drew in with information exchange between two machines. This progression enabled reports to have every one of the reserves of being clearly connected with one another. Snap an association and you are there – whether Devasis Pradhan, Department of Electronics and Communication Engineering, Acharya Institute of Technology, Bangalore 560107, e-mail: [email protected] Prasanna Kumar Sahu, Department of Electrical Engineering, National Institute of Technology, Rourkela 769008, e-mail: [email protected] Mangesh M Ghonge, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India, e-mail: [email protected] Rajeswari, Department of Electronics and Communication Engineering, Acharya Institute of Technology, Bangalore 560107, e-mail: [email protected] Hla Myo Tun, Department of Electronic Engineering, Yangon Technological University, Yangon, Myanmar, e-mail: [email protected] https://doi.org/10.1515/9783110781663-001

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or not that association goes to a substitute report on a substitute machine on another association on another central area. Essentially that Web 1.0 disengaged away from the association and real layers, and the semantic web (SW) abstracts away the chronicle and application layers that drew in with the exchanging of information. The SW interfaces real factors so that instead of associating with a specific record or application, you can rather insinuate the chronicle. If that information is anytime invigorated, you can thus take advantage of the update [1, 2]. Basically SW gives an ordinary construction that grants data to be shared and reused across application, undertaking, and neighborhood. Vision of SW is to grow thoughts and norms of the web from records to data. Hence, information ought to be obtained to utilize the overall web setup, for example, uniform resource identifiers (URIs); information ought to be identified with each other additionally as accounts are at this point. Additionally, this suggests the development of a commonplace construction that licenses data risk to be taken care of normally by gadgets similarly as genuinely including revealing possible new associations among pieces of data [3]. The possibility of familiarizing semantics to journey on the web is not satisfactory in a select way. Different elements like adaptability, accessibility of content, perception, cosmology improvement and development, multilingualism, and trustworthiness of SW give a direction to the trained professional. Recent development of SW is (a) to grasp web requests and web resources remarked on close by establishment data described by ontologies and (b) to examine the organized gigantic datasets and data based on SW as a decision or enhancement of current web. Tremendous usage of SW innovations toward conceivable arrangement advantages for different spaces, for example, sensor networks, BD, CC, the IIoT, and so on [4, 5].

1.2 Web Beginning around 1989, when the Internet started to be generally utilized, numerous turns of events and changes occurred in web advances. In the principal long periods of the Internet, the web is called HTML (HyperText Markup Language). Toward the recent development of HTML where messages are fused and messages are relied upon to be acquainted uneven with the assistance of customers [4, 5]. Web 1.0 was created by Tim Berners-Lee in 1989. Clients of Web 1.0 have been segregated with purchaser thereafter shaped toward from the web programmer to customers as the forward single-course advancement. The present circumstance has restricted the client collaboration with the substance and thus expanded the requirement for the advancement of new web innovation. Figure 2.1 replicates data about the recorded improvement of web innovations. The innovative interaction that began with Web 1.0, which shapes the reason of the general association structure, has progressed from static to dynamic or Web 2.0.

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With this change, there has been progress from a record arranged plan to a humanfocused development in web systems. In this unique circumstance, informal organizations, weblogs, and video-sharing destinations have been fostered that stress common collaboration. Beginning at 2010, web development has been invigorated to Web 3.0 as a headway. Until 2010, web or web progressions, which are under the organization of the customer, have transformed into a self-directing construction [6, 7]. The present web clients are utilizing Web 2.0 and Web 3.0 innovations. In any case, it is felt that the happening to new web advances, to be specific Web 4.0 and Web 5.0 sooner rather than later, will prompt disarray in the personalities, regardless of whether these advancements are not completely perceived and taken on.

1.2.1 Web 1.0 Web 1.0 is a solitary course web development made by Tim Berners Lee (WWW), a solitary heading web advancement that makes the underlying time arranging advancement all through the planet and customers can simply get the information. In this development, which is seen as the essential time period of the web, customers can simply get the current information and access the substance given by various web servers. Since site pages are made in a static plan, there are no chances for customers to comment, contribute, or produce content. The correspondence on the destinations is confined to the customer investigating simply through joins between pages, which caused websites to depend upon HTML and require HTML code data to make another web page [6]. Web 1.0 innovation is utilized for data access and data recovery in instruction. Web 1.0 apparatuses just give data. They permit clients to look for the data they incorporate and give enrollment and client explicit fields. They cannot give the option to add to clients in the field of instruction. Consequently, it offers the likelihood to peruse the data introduced in general by the deductive methodology in the field of training [7].

1.2.2 Web 2.0 Client-based substance and wide well-disposed associations have made the improvements possible by Web 2.0 development. Video sharing, visit, worked with organizations, web applications, voice over IP, email, messaging, podcasting, picture sharing, web-logging, and various other online coordinated efforts have been recognized through Web 2.0 advancement (Naik and Shivalingaiah, 2008). Figure 1.1 gives data on the functioning design of Web 2.0 innovation [6–8]. The accompanying three components have impacted the change to Web 2.0 innovation in web advancements (Easley and Kleinberg, 2010):

Pc Era 1980–1990

Figure 1.1: Web innovation [2].

Desktop

World Wide Web

Web 1.0 1990–2000

Web

Web 2.0 2000–2010

Semantic Web Web 3.0 2010–2020

The WebOS Web 4.0 2020–2030

Web 5.0

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a) In the advancement of web composing styles for certain people to make and stay aware of shared substance; b) Being developed of individual online data (counting messages, plans, photographs, and accounts) from your own PCs to the organizations that huge associations will host and host; c) Toward development of association styles that underline online associations between people, not just between records. Web 2.0 mechanical assemblies have changed the way wherein old-style learning has created. While as of late printed dispersion is being sifted, web resources are right now being inspected to get information. The understudies or educators who need to get to the information contact the person who is the master who is enthusiastic about them through email, messaging, wikis, or web diaries. Web 2.0 can be depicted as human correspondence on the web [7, 8].

1.2.3 Web 3.0 Web 3.0 development, which is called SW, as a technique for machine-interpretable metadata and a fantasy for another item time. From the basic show of the web right up until today, the SW has turned into an express that backings “Open” information also underlines data as opposed to handling it. Brought up lately on the web, informal organizations have acquired extraordinary prevalence by individuals and social orders in various ways. Individuals can share their sentiments, musings, and thoughts through Web 3.0 innovation, as opposed to only archiving as in past web propels. With this advancement, the social web is seen as an amazing method of partner people all through the planet. Web 3.0 innovation will keep on giving instructive mentoring with individual colleagues, insightful specialists, 3D games, virtual universes, open instructive assets, and openings for better information of the board. With the assistance of canny specialists and individual colleagues, individuals will actually want to coordinate their own learning, put forward their own objectives, and settle on choices about learning content [7, 8].

1.2.4 Web 4.0 With Web 4.0, described as “Amicable Web” and communicated as the advancement of the post-2020, people will really need to help out machines and the virtual world. Taking everything into account, with the improvement of the Internet, nanotechnology,

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and man-made mindfulness applications, the machines will truly have to inspect the orders from the client in response similarly as the opposite way around. Web 4.0 definitions incorporate man-made reasoning-based canny frameworks just as virtualization and distributed storage frameworks [8]. With online shrewd working frameworks assembled altogether on virtualization, clients can play out the entirety of their activities without introducing any working framework on their PCs. In this specific circumstance, it will in general be communicated that EyeOS or GlideOS is the reason of Web 4.0. These structures license customers to use working systems and programming organizations over the Internet without presenting programming on the PC. Likewise, Web 4.0 will go to the front in conveyed registering to avoid issues achieved by neighboring circle use. Clients will actually want to store all their own information, records, or content in web-based cloud conditions [9].

1.2.5 Web 5.0 Web 5.0 development, which depends on all contraptions related to the Internet, is called excited or visionary web (Sindhu and Chezian, 2016). With this advancement, progressed computerized reasoning robots, symbols, and 3D virtual conditions are relied upon to occur in regular day-to-day existence. In expansion, with Web 5.0, multidimensional image frameworks can be utilized for day-by-day gatherings, and through the headset, customers can team up with the web content and the data will be framed by the customer’s looks [8].

1.3 Web architecture The design of the web is shockingly basic for designing ancient rarity with over a billion clients. Then again, this is most likely one of the fundamental explanations behind its prosperity. According to a usefulness viewpoint, the web gives the accompanying: a) Transport layer: A convention, HTTP (HyperText Transfer Protocol), upholds the remote admittance to content over an organization layer (TCP-IP). HTTP limits as a sales response show in a client server enrolling model. In HTTP, a web program regularly goes about as a customer, while an application running on a PC has gone about as a server [9]. b) Worldwide addressing scheme: This empowers each report to have a remarkable universally addressable identifier. For the web, this is given by URLs (Uniform Resource Locators). A URL fills the needs of both distinguishing an asset and furthermore depicting its organization area with the goal that it tends to be

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found. URIs include the two URLs and URNs (Uniform Resource Names), where URNs mean the name of an asset [9, 10]. c) Platform interface: This empowers clients to handily get to any on the web asset. On account of the web, it is HTML and web programs that decipher and show the depicted substance. HTML along these lines, a text and picture arranging language, which is distantly served by web, has applications and is utilized by web programs to show the web content [9–11].

1.4 Semantic web architecture The word “semantic technology” is basically an assortment of PC dialects and principles with general conventions and information designs, which grant an organization of information that ranges unique fields. As the name recommends, semantic innovations utilize formal semantics to give significance to advanced records. SW data set explanation additionally implies that these records can be deciphered and examined by PCs, in this manner expanding the productive and compelling use of online archives [11, 12]. The SW technology stack, as displayed in Figure 1.2, is a wide assortment of advancements and guidelines.

User interface and applications

Trust Proof

Unifying Logic Rules: RIF/SWRL

Taxonomies:RDFS Data interchange:RDF Syntax:XML Identifiers: URI

Character Set: UNICODE

Figure 1.2: Semantic web architecture [3].

Cryptography

Querying: SPARQL

Ontologies: OWL

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They are utilized during the formation of an SW for a reach of capacities and might be portrayed as follows: a) Unicode and URI characterize the norms for characterizing SW protests and approving global character sets for information portrayal. b) XML (extensible markup language): The language system that, starting around 1998, has been utilized to characterize essentially all new dialects that are utilized to trade information over the web. XML is answerable for furnishing normalized records with a usable surface language structure without putting semantic requirements on what the reports rely on. c) XML schema: A language used to characterize the design of explicit XML dialects. d) RDF (resource description framework): An adaptable language equipped for portraying a wide range of data and metadata. RDF is a basic model of information that identifies with objects and their connections. This empowers explanation information to be convenient across different stages. e) RDF schema (RDFS): A system that gives a way to indicate essential vocabularies for explicit RDF application dialects to utilize. RDFS is a language that clarifies the properties and classes of RDF assets in which individual qualities and classes at different reflection levels sum up progressive systems [11]. f) Ontology: Languages that are used to characterize vocabularies and set up the use of words and terms with regard to an explicit jargon. RDFS is a system for building ontologies and is utilized by a lot of further developed philosophy structures. The ontology layer depends on the idea of ontological ideas, properties, and connections. It additionally portrays the qualities of different terms that add to jargon development. OWL (Web Ontology Language) on this layer is normal and portrays ontological part capacities and interrelationships [12]. g) RIF: Rule interchange format is a recursive abbreviation to online principle moves for the SPARQL (sparkle protocol and RDF query language). This is used when semantical data sets for information organizations, for example, RDF and JSON are questioned [11–13]. h) Logic and proof: Logical thinking is utilized to set up the consistency and rightness of informational collections and to construe ends that are not expressly expressed yet are needed by or reliable with a known arrangement of information. Verification follows or clarifies the means of coherent thinking. i) Trust: A method for giving validation of character and proof of the reliability of information, administrations, and specialists.

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1.5 Semantic web technology SW implies the catch of associated data. SWT engages a researcher to make cloudbased data center and improvising vocabularies with the formation of rules for dealing with data. These technologies were classified into query, linked data, vocabularies, and inference.

1.5.1 Query The SW setting implies advances and conventions that can automatically recover data from the web of data. RDF is the information model for SW information, and SPARQL is the standard inquiry language for this information model. SPARQL is basically a chart coordinating with question language [14]. A SPARQL inquiry is made out of (1) a body, which is a complex RDF diagram design coordinating with articulation and (2) ahead, which is an articulation that demonstrates how to develop the response to the query. In fact, SPARQL questions depend on design parameter. Resource description framework is a notable asset (i.e., RDF significantly increases); SPARQL questions give at least one example against such connections. These triple examples are like RDF significantly increases, then again, actually at least one of the constituent asset references are factors. A SPARQL motor would return the assets for all triples that match these examples [14, 15].

1.5.2 Linked data In order to make web as data integrity toward reality, the giant proportion of data is available on the web in a standard association, reachable and reasonable by the SW contraptions. In addition, not only does the SW need permission to data, notwithstanding, associations among data should be made available to make a web of data [16]. The RDF permits anybody to depict assets, specifically web assets, like the creator, creation date, subject, and copyright of a picture. Both RDF and its composition language RDFS are suggested by W3C (worldwide consortium) for interlinking assets on the web and for encouraging interoperability among conveyed information sources. Hence, RDF depends on URIs for distinguishing assets and establishes a diagrambased information model for connecting such assets. To this end, RDF gives the central structure squares to the diagram-based information structures that are utilized by the SW [16, 17].

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1.5.3 Vocabularies Vocabularies in the SWT portray thoughts with associations used to address a particular region. They have been arranged in such a manner for a specific implementation and portray expected associations to describe possible objectives of the usage of fundamental phrase. All things considered amazingly for SW vocabulary (two or three or a colossal number of terms) are very fundamental (portraying a few thoughts in a manner of speaking). Hence, they are the fundamental design blocks for induction strategies on the SW [18]. The essential positions of vocabulary in SW are required to blend the data when ambiguities may exist on the terms used in the different instructive assortments, or when a hint of extra data may incite the disclosure of new associations. The OWL is a gathering of data depiction lingos composing different ontology [17]. The OWL lingos are depicted by a conventional semantics [18, 19]. Then again, SKOS is a typical information model for sharing and connecting information association frameworks through the web.

1.5.4 Inference In the SW, vocabularies and rule sets basically portrayed the additional information inclined to the expected result-oriented data sets. The methods draw upon data depiction procedures. Generally speaking, ontologies center around the portrayal techniques, putting complement on describing “classes” and “subclasses,” on how individual resources can be connected with such classes, and depicting the associations among classes and their cases. A set of instructions for the processed data through data center around describing a general part for finding and making new associations subject to existing ones, like reasoning projects do [19, 20]. The data center thought of derivation on the SW is to work on the nature of information blend on the web by finding new associations normally researching the substance of the data or administering data on the web in a large scale. Surmising-based methods are likewise significant in finding potential irregularities in the (coordinated) information. RIF centers on trade as opposed to characterizing a solitary one-fits-all standard language irregularities in the (coordinated) information [21].

1.6 Semantic integration SW includes numerous spaces of software engineering and has settled many issues in regards to the portrayal and extraction of data.

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1.6.1 Cloud computing Basically it conveys processing administrations like programming, servers, systems administration, and stockpiling over the Internet. Distributed computing is an exceptionally immense region for research; along these lines, the scene of distributed computing has fundamentally changed in the course of the last decade [22, 23]. Mobile Applications

Storage

Database

Server Cloud Computing

Private cloud

Public cloud

Hybrid cloud

Figure 1.3: Features of cloud computing. (source: https://www.myrasecurity.com/en/cloud-computing)

The most difficult exploration bearings for distributed computing are capacity and adaptation to internal failure techniques, shared based cloud work process framework, versatile and information driven responsibility director for general mists, administration adaptability, and interoperability of combining prevalent data handling sets into appropriated figuring organizations, consistent organizations, and data disseminated processing provide the security shielding in the cloud. Figure 1.3 shows the key features of cloud computing [23]. The mix of semantic innovations and distributed computing uses the cloud benefits as well as incorporates the field of circulated registering and allows SW technology to scale up the index in a larger scale for the data collected from data center. The use of distributed computing innovations and administrations over semantic advancements ensures enhancements the datasets in a wider manner with flexible change in end user with proper information of the end user and frameworks which will be simplified with viable information so that information or datasets save in secure manner [24].

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1.6.2 Internet of things Interfacing of IoT with SW brings an amalgam through which information can be accessed from different devices and provided to data center for further process (Figure 1.4). In excess of 25,000,000,000 contraptions were surveyed to be related with the Internet in 2015 and 60,000,000,000 in 2021. Principal assistance with protesting disclosure, tending to, and following regardless information storing, security, depiction, and exchange. IoT will basically contain various arrangements of gadgets and distinctive correspondence systems between the gadgets.

Figure 1.4: IoT features. (source: https://threatpost.com/iot-security-concerns-peaking-with-no-end-in–sight/131308/)

SWTs have been demonstrated productive in various regions in managing the heterogeneity issue in: a) connecting numerous devices in order to exchange information; b) inducing genuine information in creating savvy applications; and c) giving interoperability in informing the executives. Regardless, one of the troubles with existing IoT applications is that the contraptions are not (or little) practical with each other because their data is dependent upon prohibitive associations and they do not use typical language to explain suitable IoT data [25, 26]. SW propels are used in IoT to reduce the test in overseeing interoperability of data conveyed by devices recently used in reality. Through semantic advancements in IoT, we can manage interoperability, viable data taking care of, resource disclosure, joining, thinking, and addressing.

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1.6.3 Big data In the past 20 years huge amount of data depicted as big data which is utilize to mentioning the presence of datasets in large scale available in data centers (Figure 1.5). The Social media not only contribute in huge data collection, but also conceivable engineers to get to.

Big Data Sources

1

Figure 1.5: Big data features [28].

Primary issues such as attack on online details considering the way that assembled data which all are unplanned or scattered manner toward BDA. For this reason “SWTs” play a key enabler through which data can be framed properly with relevant details of source [27, 28]. SWTs help BD with specific affiliation and associations engaging them in making an incredibly better judgment dynamically. The SW grants them to offer buyers with prevalent answers and encounters right away from the service providers.

1.6.4 Machine learning and AI ML implies how to structure the issue and the most effective method to address the information. SW innovations give ontological foundation information and a model which frame the information in normalized manner for better security and storage.

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SW standards clarify the idea of metadata, and it additionally has an enormous type like the information model for information correspondence and combination [29]. Cosmology coordinating is an imperative period of the SW; consequently, numerous strategies are utilized for this reason, for example, probabilistic systems, theory illustrating, heuristic, and rule-based strategies with ML and graph examination where AI estimations are extraordinarily approved considering the way that they give extraordinary comparability planning between thoughts. It breaks down different resemblance evaluation and estimations toward used plan or association between two ontologies with AI computations as SW and metadata help the machine to handle the information as per their significance [29, 30].

Figure 1.6: AI/machine learning [8].

The SW achieves different assignments for AI, for example, utilizing different apparatuses to clarify and trade information with AI strategies for standard prototype, using ontological construction upgradation with AI assignment, and offering foundation realities to direct AI. AI arrangements have been created to help cosmology learning (paying little heed to the chance of not being completely programmed), profound comment (accommodating of information bases and ontologies), and comment by means of data mining. ML will continuously be used to analyze circulated information sources and keep up with thinking with questioning over the SW [26].

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1.6.5 Natural language processing The SW is hard for ordinary language age techniques to build the gigantic proportion of information. On the inverse, there is a prerequisite for SW progressions that give induction to the ML-based data processing. The NLP era gives approaches to showing semantic information in a coordinated, rational, and available way. NLP techniques address the age of data from tremendous different informational indexes [25–27]. Techniques for cosmology synopsis add to the age of synopsis information from gigantic datasets by utilizing geographical essentially toward the identification of correctness in usage data with respect to RDF. In any case, the strategies for content determination in regular language age rely upon the setting that was imparted in the objective text. The SW requires machine-interpretative semantics for taking care of literary data. Natural language procedures give significance to unstructured or chaotic information or text [26]. Cutting edge natural language frameworks can give clients touchy admittance to the wealth of the text-based information through conventional dialects. The mix of NLP and SWTs manages organized what’s more, unstructured information that are not attainable by conventional relational techniques; for instance, a machine peruser is an instrument that changes over regular language text into appropriate organized information, and the last mentioned, as per shared semantics, can be deciphered by machines. The combination of NLP and SWTs gives a climate by which we can put the intricate inquiry and get a real legitimate reply. This mix grants utilization to develop alongside NLP qualities without updating any applications that use that information. As such, the SW can work on the specific level of customary language advances and NLP can moreover help in passing on and using a prevalent SW. The veritable troubles for the expert are to draw the NLP methodology nearer to ontological planning and to develop existing NLP methodologies to mysticism-based applications [27].

1.6.6 Wireless sensor networks Sensor networks are used in numerous spaces for catching actual normal occasions and noticing the attributes of actual items. A sensor network makes a colossal proportion of data that require work on steady taking care of and interpretation by machines. Sensor networks have made a huge load of interest today in the insightful world similarly as the business. The huge challenges in consideration, accessibility, and gathering in heterogeneous sensor associations, sending approaches, and topography or neighbor revelation systems, limit estimations, secure data assortment, energy improvement, and the security and nature of organizations [28–30]. In authentic applications, sensor data will be a mix of divergent data that comes from various associations. The dealing with and comprehension of the enormous proportion of unstructured sensor data and the utilization of a dependable development

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for this sensor data are pressing bits of versatile and interoperable sensor network designing. The current data exchange plan for sensor networks depends upon syntactic models that do not give machine-sensible ramifications to the data. The semantic advances give an extra interoperable plan to sensor data and draw in machines to process and translate the emerging semantics to encourage more astute sensor organizations.

1.7 Ontologies Various methodologies are created for setting up a way of thinking to communicate the semantics of information source. Every engineering profoundly influences the authentic formalism of philosophy. Along these lines, the decision of an option is adjusted to the necessities of the created application. Ontologies are frequently considered as a proper structure used to furnish a bunch of information with semantics [31]. They not just permit to portray and address the information but also make them reasonable and shareable between various frameworks what’s more, give abilities to correspondence and interoperability between them.

1.7.1 Monoontology approach This methodology includes the utilization of a solitary conveyed theory between various data-based systems. The usage of a typical language is made arrangements for the assurance of semantics. Worldwide metaphysics interfaces every one of the wellsprings of data so that each source has a free model set up for connecting these items to the worldwide model as shown in Figure 1.7. One hindrance of this compromise structure is the need to reconsider the mysticism in case of the extension of another source. The deficit of schematic autonomy of the sources is another disadvantage as well [28].

Global Ontology

Figure 1.7: Single ontology approach.

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1.7.2 Multiontology approach The multiontology approach is used to supply every wellspring of information by a close-by theory and gives opportunity of definition to this source disregarding different ontologies as displayed in Figure 1.8. The utilization of this methodology is for the most part for the situation where it is hard to find a consensual transcendentalism made by the glaring semantic qualification between the systems [28, 29]. Nonetheless, the shortfall of a standard jargon is displayed as a significant requirement restricting correspondence between sources. To defeat this limit, a between-power arranging is set up to make a correspondence between the different terms of the ontologies.

Local Ontology

Local Ontology

Local Ontology

Figure 1.8: Multiple ontology approach.

1.7.3 Hybrid approach This methodology is to blend two procedures, which use close-by and shared ontologies. It is portrayed by a close-by transcendentalism, a common language is attempted to work with the arranging and make the different ontologies sources essentially indistinguishable. The course of action between neighborhood ontologies and shared transcendentalism is ought to be conceivable found or back [29]. Region terms and locals make this language in a way that allows these locals to foster tangled terms by going along with them with chairmen, making the terms commensurate and shareable between ontologies with basically no conflict. Figure 1.9 shows the hybrid ontology approach.

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Shared Vocabulary

Local Ontology

Local Ontology

Local Ontology

Figure 1.9: Hybrid ontology approach.

1.8 Challenges on SWT 1.8.1 Content availability To exploit the idea of SW, the current web content ought to be moved to SW content. That fuses static HTML pages, existing XML content, and dynamic substance, blended media, and web organizations. Since the establishment of the SW is at this point being created (RDFS, OIL, DAML + OIL, etc.), there is insignificant SW content available. Aside framework, specialists are right now constructing devices to help the semantic comment of web content. Such devices are significant and basic to the achievement of the SW [28, 29]. The framework of the SW is as yet being fabricated, as of now, there is minimal SW content accessible. Aside from the foundation, analysts right now fabricate apparatuses to help the semantic comment of web content. Such apparatuses are significant and basic to the accomplishment of the SW [29, 30].

1.8.2 Ontology: evolution and development Ontologies are vital to the SW since they are the transporters of the significance contained given in the jargon and semantics of the comments. This test is identified with metaphysics’ accessibility, improvement, and advancement. As we have seen,

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ontologies assume the principle part in the SW since they are the transporters of the importance contained in the SW. A work should be done in having normal broadly utilized ontologies for the SW on the arrangement of sufficient foundation for cosmology advancement, change the board, and planning, and, in this dispersed web climate, on the sufficient control of the advancement of ontologies and the comments alluding to them [30, 31]. One of the issues is the advancement of ontologies and their connection to currently explained information. Arrangement of board devices is important to keep control of the adaptations of every cosmology just as the interdependence among them and explanations. The subsequent issue is the development of essential or piece ontologies to be utilized by every one of the spaces.

1.8.3 Scaling of web content a) First challenge: Storage and organization of web pages. The “essential” SW comprises metaphysics-based explained pages whose connecting structure mirrors the construction of the WWW, that is, pages associated with others through hyperlinks. This hyperlinked arrangement doesn’t completely take advantage of the basic semantics of SW pages. b) Second challenge: Finding information in SW. A component should accommodate the simple finding of SW content considering the semantics of web assets. In this setting, a common (P2P) plan could be researched, similar to the flow arrangement of switches in the WWW, in what we could call a “semantic TCP/IP show,” the new European SW (SWAP, SW, and peer-to-peer) project is submitted this point (SW and peer-to-peer).

1.8.4 Visualization With the expanding measure of data overburden, instinctive perception of content will turn out to be increasingly significant, as clients will be progressively requesting simple acknowledgment of the pertinence motivations. Likewise, the use of semantic records and switches for the limit, affiliation, and finding of information that will require a critical stage forward in portrayal appeared differently in relation to standard site maps that address interface structures. By creating satisfactory 3D ongoing realistic portrayal innovation and taking advantage of semantic connections, an inventive three-dimensional interface could be produced consequently.

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1.8.5 Diversity in language This issue as of now customary web and ought to likewise be handled in the SW. In any case, the development of semantic data has been animated generally by the rise of Linked Data. Albeit this presents to us a bit nearer to the vision of the SW, it additionally raises new issues, for example, the requirement for managing data communicated in various normal dialects. To be sure, albeit the Web of Data can contain any sort of data in any language, it actually needs unequivocal instruments to naturally accommodate such data when it is communicated in various dialects [31–33]. a) At ontology level: It might need to utilize their local language for the improvement of the ontologies in which comments will be based. Since not all clients will be philosophy-manufacturers, this level has the most minimal need. b) At annotation level: Explanation of content can be acted in different dialects. Since more clients (particularly content suppliers) will preferably explain content over foster ontologies, legitimate help is required that permits suppliers to comment on content in their local language. c) At UI level: A large number of individuals might want to get to applicable substance in their local language independent of the source language in which explanations are introduced. Albeit right now, most substance is in English, we expect that more substance will show up in different dialects. Any SW approach ought to remember offices to get to data for a few dialects. Internationalization and limitation procedures ought to be investigated to customize data access dependent on the local language of the client.

1.8.6 Standardization of web language The SW is an arising field and the WWW consortium will deliver proposals on the dialects and innovation that will be utilized around here. To propel the best in class in the SW, it is significant that such norms show up inside this year. As currently introduced over, a layered way to deal with metaphysics language creation and comments has been embraced by the local area [34, 35].

1.8.7 Security and privacy SW applications accept and expect that the data content of assets is of top notch and can be trusted. Additionally, the security and protection of touchy data on the SW should be safeguarded. A tonne of work should be made in creating extensive answers for guaranteeing the trust, security, and protection of SW content.

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1.9 Semantic web technology: applications 1.9.1 Supply chain management As a class of issues, inventory network, the board incorporates many elements that make it ready for applying SWTs, explicitly: a) The information being overseen changes continually. b) The necessary perspectives on those information (e.g., computations and KPIs) change continually. c) A lot of cross-authoritative coordinated effort happens with huge volumes of information being passed on between providers at each level of the store network. d) Rules and guidelines change, requiring various types of information to be caught over the long run. e) Providers change over the long run and are situated in new districts and nations, perhaps requiring new dialect limitation, monetary standards, and so forth, and frequently requiring new information network to new outsider frameworks. f) SW advances give store network directors and officials the capacity to deal with all of this intricacy dependably and effectively.

1.9.2 Life science and pharmaceutical industry Life science and pharma associations need to sort out profoundly assorted, inner and outside data streams that come from a wide assortment of sources like biomedical writing, licenses, clinical preliminary reports, medical services records, particular media sources, and surprisingly online media [35]. SW innovations and applications permit: a) Track contenders’ business advancement including associations and permitting; b) Keep up with ongoing consciousness of administrative advancements; c) Limit licensed innovation encroachment chances and related lawful expenses.

1.9.3 Banking and insurance sector With the spread of web-based banking, expanding contest has raised the requirement for giving phenomenal client assistance in the Banking and Insurance area. Computerized additionally offers safety net providers better approaches to reduce expenses and a chance to carry truly increased the value of the client experience [35]. SW innovations and applications give the accompanying advantages to Banking and Insurance clients:

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a) Backing for adequately assessing hazard profiles in financing demands b) Drastically work on the adequacy of client self-administration applications and other client care capacities automate time-concentrated manual cycles for fast and precise cases endorsement and approval

1.9.4 Petroleum sector (oil and LPG) Oil and gas are known for being an information substantial industry. Every day, a lot of information is produced day from an assortment of logical, land, and designing sources. In the oil and gas area, each progression, from disclosure to creation, is very information-concentrated. This information contains basic data to foresee disappointments or inconsistencies that can cost a huge number of dollars each day. SW innovations and applications can uphold specialists and laborers in consolidating and examining information to comprehend and anticipate occasions that sway day-by-day tasks [35, 36]. SW applications can viably deal with the accompanying exercises: a) Foresee specific occasions to all the more likely oversee tasks b) Simple and ideal admittance to information to have the option to give the perfect data at the ideal time c) Join heterogeneous information to help more viable dynamic cycles

1.9.5 Media and publishing house Advanced media offers distributers and data suppliers a thrilling chance for carrying new substance to clients and for extending their businesses [35, 36]. The cycle of putting away, arranging, and introducing content can profit from the utilization of SW advances: a) Semantically advancing substance improves the capacity of your crowd to find, explore, and share your substance. b) Effectively associate subjects across records, pages, blog entries, and so forth to offer the most applicable substance to clients. c) Supports bundling content into new items to offer extra benefit to clients.

1.10 Recent trends on SW technologies 1.10.1 Block chain technology It is an encoded information base putting away framework. In contrast to ordinary framework, it stores data in blocks, which are then joined as a chain. It offers endless

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advantages, one of which is it makes exchanges safer and mistake free. The innovation supports the computerized cash, Bitcoin. Cryptographic money utilization has expanded fundamentally somewhat recently in light of the fact that significant installment frameworks chose to acknowledge Bitcoin [36]. In 2019, excess of 34 million blockchain wallets were utilized across the globe. The significant advantages of blockchain in web improvement: a) Blockchain deals with agreement calculations, which make it beyond difficult to break into. b) Information is put away on an organization, making it effectively accessible to clients. c) The blockchain framework is decentralized and, subsequently, is less inclined to botches. d) Information can be moved across the organization without the requirement for go-between.

1.10.2 Progressive web apps A progressive web app (PWA) is a kind of use programming constructed utilizing normal web advances like HTML and JavaScript. PWA chips away at any gadget with a typical program. The innovation has acquired prevalence for its capability to bring to the table an excellent client experience. The innovation lets web designers join the abilities of sites and portable applications to: a) make a vivid client experience; b) increment client commitment and transformation rates; c) have nearly low improvement costs; d) can be utilized without relying upon application dispersion administrations like Appstore or Play Store; and e) offer quick establishment and programmed refreshing components.

1.10.3 Voice search optimization Voice search optimization (VSO) can be just characterized as the method involved with enhancing pages to show up in voice search. Gadgets that utilize voice acknowledgment are acquiring quick ubiquity on account of voice associates and IoT. The innovation has developed such a lot of that by the coming year, these gadgets will actually want to perceive the voices of various individuals and give a customized AI-based insight. In the field of web improvement, the most recent advancements are voice-enacted selfstanding gadgets and voice streamlining for applications and sites [36].

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1.10.4 Motion UI Motion UI is a front-end structure that is utilized to construct completely responsive website architectures. The innovation empowers designers to make movement inside a local application setting. It accompanies predefined movement which can be utilized for any plan project. A site ought to not just give the data a client is searching for yet must be appealing. Lovely looking sites have more opportunities to get seen by your interest group. Movement UI is another plan approach that makes computerized items more natural and easier to understand [34–36].

1.10.5 Cyber security Basically, it is characterized as the insurance of PC frameworks and organizations from data exposure, robbery, harm, or disturbance. It acquires significance in 2021 in light of the fact that, while we robotize more cycles, our information is at expanded danger of being taken. The most recent advances in this field incorporate the improvement of calculations that shield clients from phishing assaults. Another intriguing pattern is IoT communication assurance and versatile security.

1.10.6 Serverless architecture Serverless architecture (SA), otherwise called serverless processing, is a product improvement model where applications are facilitated by a third gathering, so you need not handle server programming or equipment. The innovation assists you with staying away from framework overburdening, information misfortune and lessens advancement costs. The model permits you to supplant customary servers with cloud to oversee machine asset utilization. Other than the previously mentioned benefits, SA assists with keeping the web more maintainable. In the coming years, the innovation is estimated to be utilized broadly for IoT applications and items that require complex backend demands [35, 36].

1.11 Conclusion and future scope SW is a drive that targets working on the present status of the World Wide Web. Furthermore, the key thought is the utilization of machine-processable web data. SW innovations all in all have taken enormous steps somewhat recently. Key innovations incorporate express metadata, ontologies, rationale and surmising, and astute specialists. In this manner, the advancement of the SW continues in layers. The SW norms

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like RDF, SPARQL, and OWL were just drafting in 2001; however, they have now been formalized and approved. Despite huge early corporate reception by a chosen handful leader, most organizations have not yet begun utilizing (or are even unconscious of the presence of) SW innovations. The expectation to learn and adapt for utilizing SW innovations is steep since not many instructive assets presently exist for clients new to the ideas and still less assets can be discovered that examine when and how to apply the advancements to true situations. As future work, we propose a way to deal with semantic coordination in large information that guarantees semantic interoperability between various heterogeneous assets. The item is to separate the secret information and work with their sharing to offer a help system for administrators what’s more make information interpretable (shrewd information) by web specialists to aid dynamic in a particular area. The semanticization of information shows up as a promising answer for beating this test. The combination of semantics into huge information turns into an essential need in most contemporary applications as it offers the chance of collaboration and sharing information also. The utilization of cosmology gives an incredible capacity to arise and share heterogeneous data.

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[30] Ristoski, P., Bizer, C., & Paulheim, H. (2015). Mining the web of linked data with rapidminer. Journal of Web Semantics, 35, 142–151. [31] Ranjbar–Sahraei, B., Rahmani, H., Weiss, G., & Tuyls, K. (2019). Distant supervision of relation extraction in sparse data. Intelligent Data Analysis (IDA), 23(5), 1145–1166. [32] Coneglian, C. S., Fusco, E., Segundo, J. E. S., Junior, V. A. P., & Castro Botega, L. D. (2016). Ontological semantic agent in the context of big data: A tool applied to information retrieval in scientific research. In Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds), New Advances in Information Systems and Technologies (pp. 307–316). Springer, Cham. [33] Jacksi, K., Ibrahim, R. K., Zeebaree, S. R., Zebari, R. R., & Sadeeq, M. A. (2020, December). Clustering documents based on semantic similarity using HAC and K-mean algorithms. In 2020 International Conference on Advanced Science and Engineering (ICOASE) (pp. 205–210). IEEE. [34] Jabbar, S., Ullah, F., Khalid, S., Khan, M., & Han, K. (2017). Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wireless Communications and Mobile Computing, 2017. [35] https://www.globalmediainsight.com/blog/web-development-trends/ [36] https://www.lambdatest.com/blog/top-21-web-development-trends-in-2021/

Aniket R. Singh, Pushkar P. Khadase, Somesh M. Lad, Dhanshree M. Shimpi, Mangesh M Ghonge

2 Review on role of semantic technologies in various domains Abstract: Semantic web technologies have the ability to organize and link the data over the web in a consistent and coherent way, and therefore it is used in many different fields of research. Problems of various domains can be resolved by using semantic web technologies. Here we discuss various domains in which semantic web technologies play a vital role and also have a look at a few domains which lead to the growth of semantic web technologies.

2.1 Introduction 2.2.1 What is semantic technology? A collection of methods and tools that provide a means for categorizing and processing data for the purpose of discovering relationships within varied data sets is called semantic technology. Semantic technology is built on the top of resource description framework (RDF), SPARQL (SPARQL protocol and RDF query language), and optionally Web ontology language (OWL). RDF is the semantic technology that is used to store data on semantic web or semantic graph database. SPARQL is the query language that is used to query data from various systems and databases to retrieve and process data contained in RDF format. OWL is a logic-based language that is developed to represent the data schema and also rich and complex knowledge about hierarchies of things and their relations. Aniket R. Singh, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, Maharashtra, India, e-mail: [email protected] Pushkar P. Khadase, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, Maharashtra, India, e-mail: [email protected] Somesh M. Lad, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, Maharashtra, India, e-mail: [email protected] Dhanshree M. Shimpi, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, Maharashtra, India, e-mail: [email protected] Mangesh M Ghonge, Department of Computer Engineering, Sandip Institute of Technology and Research Center, Nashik, India, e-mail: [email protected] https://doi.org/10.1515/9783110781663-002

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Semantic technologies have applications in various domains; in this review, we will discuss various domains in which semantic technologies play a vital role.

2.2 Semantic technologies Semantic web is quite similar to the existing, but varies on certain points such as, in semantic web, the data is given a distinct meaning. Semantic web conveys the structure to the meaningful content of webpages which can help the software agents to complete some sophisticated tasks for the clients [1, 2]. In the future, the power of machines that will increase the ability to add new functionalities will also be improved, hence making the machines do more than just displaying the data and comprehending them instead. Semantic web can be defined as a combination of data and related metadata. Structured data, semistructured (e.g., RDF and XML) data, and unstructured (e.g., raw text multimedia) data contain metadata depictions embedded inside the actual data. Now we will discuss the role of semantic technologies in individual domains in the subsequent sections.

2.2.1 Role of semantic technologies in IoT Nowadays, Internet of things (IoT) has been used as a tool in many diverse applications such as medical, automobile, and industrial applications. Heterogeneous networks are used to connect n number of objects having capability of remote sensing, actuation, and also sharing capacity. There are changes in their deployment contexts which result in changes in characteristics and descriptions. These objects are the origin of large amount of data with their different encoding formats. The collected data is overworked, expressed badly, and interpreted by other systems and devices. Due to this, several changes associated with security, standardization, and interoperability of IoT resources and their resources are emerged. In order to live a luxury life, a large number of people have started to integrate daily objects into networks in the domain of information and communication technologies. To avail, these sensors, actuators, and radio frequency identification are widely exploited. IoT is defined as the global infrastructure for the information society by the international telecommunication Union, which also enables advanced services to connect physical and virtual things on the basis of existing and evolving interoperable communication and information technologies. IoT is mainly based on wireless sensor networks (WSN) and it is a rapidly evolving technology currently. Semantic web technology is an ideal solution to solve all these problems.

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2.2.1.1 Semantic web of things (SWoT) One of the emerging visions in the field of information and communication is the semantic web of things. The goal of this technology is to map the data which is easily accessible and meaningful in nature, to many objects and events, by means of microdevices. Some of the examples are radio frequency identification tags and certain remote sensors [6]. For the vision of SWoT to succeed and to overcome certain pervasive issues like client and device unpredictability, platform support for heterogeneous data, limits to computing, and context dependency, a pervasive knowledge-based framework is required, which has high abilities in terms of information storage, storage management, and discovery.

2.2.1.2 RDF RDF, which stands for resource description framework, was developed by W3C in 1999 and is used for metadata encoding. RDF can prove to be very helpful in IoT applications that include exchange of data that is understandable by the machine. Propositions can be expressed by using RDF. It decomposes the information available to it into smaller parts by using some simple rules of semantics of each of the pieces. RDF is used with a general method which is simple and flexible, such that it can easily express any fact and yet be operated upon by computer applications. The key components of RDF are statement, subject, and object predicate and predicate.

2.2.1.3 Sensor networks Sensors are devices that are used to read the factors from their surroundings such as temperature and pressure. These sensors often generate a lot of data, and this data is in unstructured form. The data generated requires enhanced logical processing and interpretation by the machine. Hence interpretation of the generated data and extraction of relevant information from the data become important. Here we can use the concept of semantic sensor web, and it is an expansion of the sensor network or sensor web, in which the sensor nodes swap amongst themselves and process the data automatically without any human interference. Ontologies, query language, semantic annotation, and rule languages are some of the major components of semantic sensor web [3].

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2.2.2 Role of semantic technologies in machine learning Machine learning has gained tremendous popularity in the past few years, and machine learning techniques like artificial neural networks have also received significant attention from both research and practice. Although these techniques are at their peak in terms of their uses and practices, they do not provide any explainable outcomes required in domains like healthcare and transport. Semantic web technologies provide explainable semantical tools that provide reasons on a knowledge basis. The paper mainly focuses on combining semantic web technologies and machine learning in order to provide model explain ability based on some literature reviews [8]. The techniques of artificial intelligence and also machine learning are used in many use cases like diagnosis of medical conditions, detection of frauds in case of credit card, and recognizing human faces. These are the systems that are not that much transparent and it does not provide any explanation of their prediction which could be understood by the humans. This situation can create issues of understanding, issues of trust, and also issues of management of machine learning algorithms. All algorithms do not have to give the explainability details of their predictions. Explainability is at most important when we have to deal with incomplete problem statement in regards with safety and ethics. The concept of explainable artificial intelligence (XAI) is termed as an approach to making ML methods transparent, comprehensible, and interpretable. There were some researchers conducted on XAI which also comprised certain literature surveys of some popular techniques and methods. The authors of the renowned article argue that explainability will be highly dependent on knowledge of domain and not on analysis of data only. The incorporation of machine learning and semantic web technology can be an important factor for achieving AI systems that are achievable. Bias, the terminology is defined as variety in data that is produced by a group of people, with the help of their own actions. Due to the bias in data, there is some inaccuracy in the prediction done by the model. Algorithms having systematic errors can also lead to improper outcomes. With the help of semantic representations information of inadequate representation in data can also be included.

2.2.2.1 Background and scope Semantics are useful for tracking the inaccuracy in the AI system predictions for small and domain specific data. The quality of AI systems can be compromised due to the overfitting of the model. The terminology of XAI was not new at that tenure which ranges from 1970s to prior 1990s in the scope of the tutoring and expert systems. The mathematical study of mapping of the inputs to the outputs can be done through differentiable interpretable systems, which also helps to understand the work logic of the system. There are various topics in machine learning that explain the concept of

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interpretability and also explainability. Some of the authors of popular articles/papers also explained these concepts with the help of examples, although these researchers do not put any evidence on the merging of ML and semantic web technologies [7].

2.2.2.2 Explainable machine learning models through semantic web technologies Proper research was conducted based on keywords like machine learning, semantic web technologies, and explainability, and the researchers focused on the abstract of some papers and the full contents of a few papers. Also, the results were categorized into topics of ML and semantic technologies. Specifically talking about ML, the thesis pointed to supervised, unsupervised, and reinforcement learning while semantic technologies focused on semantic expressiveness.

2.2.2.3 Integrating semantic web technologies with machine learning Semantic web technology is primarily used for ML models such as supervised learning tasks. While the semantic web technologies when combined with neural networks behave quite diversely, the methods that are embedded usually consist of knowledge graphs. During some research on unsupervised learning, it has been noted that most of the unsupervised algorithms are used in recommendation systems. Ontologies are helpful in providing information that is not available from the sources alone which leads to limits in the actions. Semantic web technologies such as ontologies are used to get the interpretability and explainability of a model [5]. Interpretable ML systems are mostly used in the healthcare sector. Systems in the domain of healthcare offer classification models which help for diagnosis prediction with taxonomical knowledge found in medical diagnosis. The explanation form of various authors of articles is highly diverse. Some authors provide explanations in the form of texts and visuals while some provide minimalist explanations to the user. Some authors go beyond explanation to explain concepts to the user [9].

2.2.2.4 Trends for future research There are many opportunities to research more in the field of semantic technologies, and we generate this insight based on the research done in this paper. The combination of ML and semantic web technologies will be a great boon to explainable models. Some more research needs to be conducted on the topics of explainable reinforcement learning and clustering. We also noted that some works done in various areas are isolated. All the explanations by authors need to be checked that their explanations are truthful. There, further solutions should be able to represent

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how a particular output behaves to the desired presentation, and also that approach should convey its results too.

2.2.3 Role of semantic technologies in cloud computing For the past few years, there is a tremendous growth in cloud service providers and data platforms, applications, and services. With such things growing in large scale there is a need for creating new and proved solutions which deals with vast, shareable, and heterogeneous services. Due to all these circumstances, it creates challenges related to information sharing, flexibility, data security, discovery, and selection. For this semantic web technologies will pave a great way to deal with all the challenges. Cloud computing has a lot of positives and hence it is widely used, but it also has certain issues which need to be addressed. Some of those issues are storage and fault tolerance, cloud service scalability and interoperability, data management, and many more [4]. We can address the issues of interoperability by using ontologies to store the information of cloud resources and service description. Cloud computing as a technology has grown at a very large scale for providing computing resources at a pay-per-use model. Due to this vast growth, this is challenging for cloud actors and their professionals as well because of anomalous interfaces being provided in order to cope up with the components. Due to this vast growth, this is challenging for cloud actors as well as cloud practitioners since various nonstandard interfaces are provided to deal with the components. The term “semantic web technologies” coined by Tim Berners Lee has solved most of the problems in IoT related to interoperability and heterogeneity. The study in the field of semantic technology of the cloud mostly relates to cloud resources and services. Cloud services have now become richer with semantic description to increase precision and recall the discovery. Provider lock-in is one of the biggest lockins in the cloud computing domain, and this problem might be solved by an interoperable and portable cloud service [10]. In some ontologies cloud services as well as virtual appliances are classified at the Paas and SaaS levels which result in better customer support and also help in selection. Semantic discovery framework which is based in a group of technologies allows the information of resource features sponsored by cloud providers and requirements of the user application. Cloud services are being equipped with enriched number of semantic descriptions to improve result accuracy and look back on their past learning. The studies in the category of interoperable and portable cloud services describe the use of semantics that can help to achieve interoperability and flexibility across various cloud providers. As a direct consequence of cloud services and resources, semantic technologies have proved to be a great boon which points out as a means for a great future in the domain of cloud computing and its various co domains like portability, security, and interoperability [11].

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Specific attention has been given to semantic technologies in the last years in order to improve cloud computing technology.

2.2.4 Role of semantic technologies in big data Big data, as the name suggests, is a large amount of data. Data is generated from various sources, and if analyzed properly, this raw data can give us valuable piece of information; this is basically known as data analytics. In order to get the information, the data needs to be analyzed, but analysis becomes difficult if the format or structure of the data varies. And hence the W3C encourages the use of common data format, which makes the data more authentic on the web. The approach to address this problem by semantic technologies is to store the data in structured format and characterize the data sets as graphs. This approach then permits the software to query on the database, and the processing of related data makes it possible to find relevant information [12]. The field of data science is vast and growing rapidly. In this field big data plays a very essential role, and many researchers have already pointed out the analytical potential present in big data. Due to data heterogeneity, this task presents a number of challenges that may not be completely resolved with existing extract-transform-load (ETL)-based frameworks. But handling big data brings in certain problems which are required to be addressed at the earliest; these include the problem of storage of big data and the processing of big data. This happens due to the introduction of new paradigms for storage and processing of data which in turn pushes the traditional systems out of the picture, hence creating a gap between the new and the old technologies. Similar problems exist between big data and the emergence of NoSQL data stores. The lack of structure in NoSQL databases does not make them a perfect fit for analytic purposes and also for integration purposes, which lead to the idea of using semantics to improve the contents of NoSQL database in turn increasing the suitability for integration. Here we analyze different approaches for semantics which can be used to integrate NoSQL databases along with data warehouses and big data. We will be discussing the use cases and role of semantics in data warehousing’s phases of design, also in the integration scenarios based on semantics. All the work does not deal with integration of NoSQL databases. They might be potentially applicable.

2.2.4.1 Data source schema extraction When ontologies are used to describe the schemas of data sources, it makes the schemas schematically comparable; unlike relational databases, a simple query to the system is not sufficient in case of NoSQL databases to get the structure, as sections in the

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schema definition are mixed with the actual data such as identifiers in the key-value pair. Because of this unstructured nature NoSQL DBs, only extraction of the keys would not be enough. This issue could be addressed by using ontologies. Research is going on in the field of big data semantification, providing an additional layer of description. There are a variety of approaches which can be employed for data source ontology extraction. Some of the approaches include the use of OntoLT, an ontology extraction tool, and WordNet to create ontologies which are tagged. In some cases, the ontologies are extracted from the data using genetic algorithms. In one of the approaches the data sources are duplicated in the form of materialized views and then the ontology was extracted. This area is under research, and any development in this field would help in integration of NoSQL DBs with data warehouses on the basis of ontology.

2.2.4.2 Integration of data source schemas When we use semantic technologies, it is clear that ontology is used to express the data source schemas. Due to this the problem of integration becomes the problem of matching and aligning ontologies. Since it is the problem of ontology matching, it simplifies the work of data warehouse designers, as there are already a lot of tools available for this purpose. Some of the tools are COMA++, FaCT++, OLA, AROMA, and so on. Due to the availability of tools the designer does not necessarily need to have the expertise in this particular field, hence reducing the time and cost of the same.

2.2.4.3 Schema reduction As we already know that the NoSQL data sources are unstructured in nature, this unstructuredness can be the reason for resulting large schemas and having conflicting concepts. There might be few concepts which would be important but also contain few concepts which wouldn’t be that important. Due to the aforementioned reasons, there is a need to reduce the size of the schema to the concepts which are relevant to the domain of interest. This can be achieved by adopting a requirement-driven approach or a hybrid approach in the DWH design. There are a variety of approaches to include requirements in hybrid approach, but there are a few issues that arise, one of the concerns is timing: this can be addressed by starting the design process with requirement analysis and then the data and requirement-driven approaches can be executed parallelly. Another concern is expressing the requirements: this can be addressed by working with formalization frameworks; the reason behind this is the expression of requirements in a formal way, and these requirements find the route toward automizing; hence, this is a widely

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used approach. Coming to the approaches, one of the approaches is to either apply requirements on each ontology after its generation or to apply requirements to global ontology after merging the local ones. Another approach that is possible is to integrate the conceptual schemas in incremental fashion. An additional approach can be used to describe the requirements using semantic resources after normalization [14].

2.2.4.4 Outlining multidimensional concepts After completing the processes like schema extraction, integration of data sources, and schema reduction, the final product that we get is a global ontology, which contains the concepts that are relevant to the interest. Our further action is to derive multidimensional concepts such as facts, hierarchies, and dimensions, which are required for data warehouse schema definition. This can be done by handpicking the concepts, but this is one of the most error-prone approaches. Hence, we can use methods like data inspection and reasoning. Some researchers suggest a heuristic approach which is based on the measures of CWM model; here, information can be derived by examining the cardinalities of the relationship and degree of tables. In some approaches the multidimensional concepts are deducted directly from the requirements, and some of them also use graph-based representation of merged data and many more [13].

2.3 Other applications of semantic technologies 2.3.1 Semantic web technologies for digital libraries –

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For digital libraries metadata, resource description format documents can be used to identify relationships between data. Various ontologies provide vocabularies for the description of documents. Digital library provides users a large number of accesses in terms of documentations and research. All of these have been evolved from printed media to digital documents with the aim of making information more accessible. Idea behind this is to make digital libraries more efficient in terms of access. With the help of semantic web by using repositories to establish the relationship scheme in terms of ontologies. Various uses with semantic web in digital libraries are interacting with user interfaces and human machine interaction, looking over user profile and customization.

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2.3.2 Semantic web service processes –

– –

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Based on discovery of web service process the comparative analysis concludes that probabilistic latent semantic analysis model provides optimization in request and result and is better than traditional method. Hybrid approach (semantic web service) can be used, which includes ontologybased OWL and keyword-matching service. To select the approach for publishing data about web service various terms such as response time, cost, accuracy performance, speed of retrieval, simulations, agent-based framework, and pragmatic service need to be considered. RDF, Eflow tool, and multiagent-based models are older techniques used for composition. Better techniques include OWL-S and WSDL-S which are used currently in artificial intelligence. Several approaches with their advantages and disadvantages are being discussed which includes flow of work, artificial planning, and various QoS-based methods that help to provide users, the services on which their application is depended upon.

2.3.3 Using semantic web technologies for exploratory OLAP – –

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Online analytical processing (OLAP) is being used by organizations for deriving business-related critical knowledge based on the data inside the organization. Researchers suggest using the ETL approach (model) for finding data resources and describe the corresponding data transformations needed to map for data sources to the target DW concepts. Exploratory OLAP helps in analyzing new data coming from a wide area of data sources. There are several challenges present in OLAP such as schema design challenges, data provisioning challenges, semantic, and computational challenges.

2.4 Conclusion After going through various researches, we can definitely conclude that there is a huge scope of semantic technologies in domains like machine learning, IoT, cloud computing, and big data. We also came across a few applications like web services, digital libraries, and online analytical processing. We can also conclude that with some more research semantic technologies can be used to address several issues in different fields of Computer Science. We have already discussed some of these issues and also discussed their solutions, for example, the issue of interoperability in cloud computing, also the issue of different formats of data in IoT and big data, and the

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problem of explainability in machine learning. There is still some ground left which is to be covered, and this can be done through some more efforts in research and will definitely lead to solve many more problems.

References [1]

Sure, Y. & Studer, R. (2005). Semantic web technologies for digital libraries. Library Management., 26(4/5), 190–195. [2] Khamparia, A. & Pandey, B. (2013). Review on semantic web service processes, In International Elsevier conference proceedings on Computing science At: Jalandhar, Punjab, Vol. 2. [3] Alberto, A., Oscar, R., Torben, B. P., Rafael, B., Victoria, N., Aramburu, M. J., & Simitsis, A. (2015). Using semantic web technologies for exploratory OLAP: A survey. IEEE Transactions on Knowledge and Data Engineering, 27(2), 571–588. [4] Brabra, H., Mtibaa, A., Sliman, L., Gaaloul, W., & Gargouri, F., Semantic web technologies in cloud computing: A systematic literature review, In 2016 IEEE International Conference on Services Computing. [5] Reyero-Lobo, P., Daga, E., Alani, H., & Fernandez, M., Semantic Web Technologies and Bias in Artificial Intelligence: A Systematic Literature Review, 1570-0844/0-1900/$35.00 © 0 – IOS Press and the authors.1570-0844/0-1900/35.00 © 0 – IOS Press and the authors. [6] Kumar K N, P., Ravi Kumar, V., & Raghuveer, K., A survey on semantic web technologies for the internet of things, In International Conference on Current Trends in Computer, Electrical, Electronics and Communication, 978-1-5386-3243-7/17/2017 IEEE. [7] Patel, A. & Jain, S. (2021). Present and future of semantic web technologies: A research statement. International Journal of Computers and Applications, 43. [8] Seeliger, A., Pfaff, M., & Krcmar, H. Semantic Web Technologies for Explainable Machine Learning Models: A Literature Review. [9] Rhayem, A., Ben Ahmed Mhiri, M., & Gargouri, F. September 2020 Semantic Web Technologies for the Internet of Things: Systematic Literature Review, Vol. 11, ELSEVIER. [10] Menemenciolu, O. & Orak, L. M., A review on semantic web and recent trends in its applications, In 2014 IEEE, International Conference on Semantic Computing. [11] Huiping, G., Information retrieval and the semantic web, In 2010 International Conference on Educational and Informatrion Technology. [12] Knoblock, C. A. & Szekely, P. Semantics for BigData integration and analysis, In 2013 AAAI Fall Symposium Series.2013. [13] Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164. [14] Preethi, N. & Devi, T. (2012). Case and relation (CARE) based page rank algorithm for semantic web search engines. IJCSI International Journal of Computer Science, 9, 329–338.

Ujwala Bharambe, Sangita Chaudhari & Prakash Andugula

3 Geospatial semantic information modeling: concept and research issues Abstract: Representation of geospatial knowledge is vital to perform task-specific applications in geoscience domain. Traditionally spatial knowledge is represented by various vector concepts (such as lines and polygons), location information, and by spatial description of the entity. This way of organizing spatial data has limitations when dealing with multiple sources/types of information. For instance, geospatial data consists of data from various domains, namely, forest, climate, ocean, and land. In addition, variety can originate from data acquisition of multiple sensors. Furthermore, the interdisciplinary nature of data representation in various fields leads to diverse descriptions. These notions have led to data heterogeneity, which makes spatial data analysis difficult and redundant. Owing to the huge data deluge in the Earth observation data, more feasible approaches are necessary for seamless geospatial analysis. A better way of describing, representing, organizing, and visualizing big earth data will facilitate integration of and reusability of knowledge from heterogeneous sources. Geospatial semantic information consists of rich contextual description of the data/phenomenon. Specific to a certain domain, the knowledge is represented using various ontologies. Due to the more expressive nature of ontologies, domain knowledge can be exchanged and integrated across domains. With such a representation, interoperability is possible between heterogeneous domains. Also, with the rich feature descriptions by geosemantics, data retrieval by queries is also based on knowledge-based reasoning. Geospatial semantic information models conceptualize, describe, and represent spatial knowledge, which facilitates the integration of knowledge from heterogeneous sources. This work has the following objectives: 1. To discuss and understand different modeling approaches for geospatial information 2. To understand the significance of semantic modeling of geospatial information and its application

Ujwala Bharambe, Thadomal Shahani Engineering College, Bandra, Mumbai, e-mail: [email protected] Sangita Chaudhari, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, e-mail: [email protected] Prakash Andugula, Xavier Institute of Engineering Bombay, Mumbai, e-mail: [email protected] https://doi.org/10.1515/9783110781663-003

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3. 4.

Ujwala Bharambe, Sangita Chaudhari & Prakash Andugula

To present the state-of-the-art survey of the geospatial semantic information modeling approaches To highlight the current trends, research issues, and future research areas for geospatial semantic information modeling

3.1 Introduction Despite successful development of geospatial information systems (GIS) over the last 50 years, it is now recognized that the technology has lacked complete formal theoretical support that would be able to fully comprehend many geographical and temporal phenomena. In GIS software solutions, the concept of layers is often implemented as a direct result of the development of cartographic systems. Thus, raster-based or objectbased GIS applications were developed as a result. Since relational databases were so popular in the 1970s, georelational models were developed in which layers were closely related to relational tables, which provided practical, but still limited, ways to incorporate additional entity attribute properties. Therefore, most geospatial applications rely on raster or object-based models regardless of these approaches’ inadequacies in capturing real-world spatiotemporal phenomena. In this context, the key issue arises: how do we design a conceptual bridge that bridges the gap between current GIS technology and models, and the necessary theoretical GIS foundations, and how do we do so? In order to answer this question, we should investigate how humans conceptualize space and time. The question highlights the close connection between what reality is and how we interpret it. To support computer-aided world representations, conceptual models of geographic features must be developed along with appropriate abstraction paradigms. In the course of the geospatial community’s attempt to re-engineer the geospatial data models, the ontology emerged and provided canonical descriptions of knowledge domains that were “neutral and computationally tractable” [1]. The early GIS data models could not establish a close link between reality and its representation; however, ontologies have modeled the world the way it is functioning using formal and primitive entities and giving higher importance to properties and spatiotemporal relationship of the geospatial data. Ontologies for geospatial data should have formal definitions and be logically conceivable, extensible, and implementable in a specific context. Including fields, objects, processes, and qualitative spatial and temporal relationships, a geospatial ontology should define all the categorizations and abstractions needed to describe a real-world domain, beginning with a domain description [2]. Geospatial ontologies will focus on the “necessary and sufficient conditions” that identify a particular kind of entity within a given geospatial domain, rather than an abstract representation of the

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formal features that characterize all scientific areas [2]. In reality, geospatial ontology must be formulated and digitized easily so as to be used by a variety of geospatial applications. Ontologies for geospatial information are potentially useful models which can facilitate the development and sharing of geospatial data. In this chapter, we are going to give a basic understanding of semantic ontologies and their applicability to geospatial information. In Section 3.2, we describe traditional geospatial modeling, and in Section 3.3, we discuss ontologies and provide definitions of geospatial ontologies and their characteristics and elaborate how geospatial information is formalized as ontology. It also provides a definition of geospatial ontology, its characteristics, and the need for semantic modeling of geospatial information. Moreover, it will also demonstrate an ontological model for geospatial phenomena. Section 3.4 describes the application areas of geosemantic modeling.

3.2 Geospatial modeling The heart of any geographical information system is a data model which describes and represents the real-world objects and processes in a finite digital domain. Vector and raster data models are fundamental models with a set of varying methods and operations representing location, shape, and spatial relationship. Acquisition, storage, and use of spatial data are greatly affected by the model selected for its representation. Spatial(topology) relationships like connectivity, adjacency, containment, proximity, and relative direction guarantee not only rapid access and retrieval of spatial data but also reduce redundancy in spatial data. Higher level vector data models such as triangulated irregular network represent regions as higher level objects built on simple polygons and arcs. It is one of the best models to represent several disconnected/disjoint as well as overlapping constituent polygons. There are some models which are best suitable to model line coverage with linear location reference of events, routes, and sections [3]. Geospatial modeling is a physical, mathematical, or logical representation of a phenomenon or process for solving real-world problems that are spatial and distributed. It helps to determine the factors that are influencing the behavior of the phenomenon and also to predict the continuing behavior of the phenomena. There are various modeling approaches like predictive modeling, decision-making, simulation, and optimization which help in the analysis of geospatial data. Predictive modeling comprises classification, clustering, forecasting, outliers, time series, and so on, and it is best suitable to predict the likelihood of the event within specific locations or through definite events. Decision-making models like rational, bounded, intuitive, and Vroom-Yetton help in making the best choice between a set of alternatives. Simulation modeling is the process to model real-life phenomena using some preconditions or scenario generation using a set of control variables. Monto Carlo, system dynamic,

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agent–based, and discrete events are widely used simulation models. Optimization modeling like linear, nonlinear, and quadratic programming and unconstrained optimizations are used to find the best feasible solution among a number of solutions based on decision variables, objective functions, and specified constraints [4]. Geospatial modeling consists of (1) creation of model compatible spatial and nonspatial data; (2) model execution with valid inputs, basic requirement fulfillment, and model output understanding; (3) model calibration with revision of input based on accuracy assessment; (4) accuracy assessment and validation of model based on past data and comparison of output to real system behavior; (5) prediction of future scenarios by considering accuracy and validation of model output; and (6) model output assessment to derive spatial and statistical inference.

3.3 Semantic information formalization of geospatial information Geosemantics is a broad area in which it addresses the meaning attached to data element and their relation to each other. It is a key stone for understanding the implicit meaning of the geospatial data. There is an increasing requirement of geospatial information knowledge modeling, integration, knowledge management, and knowledge reuse. Also, a sizable amount of research is being undertaken within the geospatial information community to develop and deploy sharable and reusable models known as ontologies. Ontologies are used in geospatial domain primarily for semantic formalization of geospatial information. Using geospatial ontologies as a modeling alternative could facilitate the sharing and use of geographic information.

3.3.1 Geosemantic modeling and geoontologies Due to the growth in remote sensing technology, a large amount of heterogeneous multimodal data is being generated. The sharing and exploitation of these data is challenging because of the ad hoc organization of the data. The interpretation of data over heterogeneous information sources and scientific domains is indeed a big challenging task in the context of ontology. As quoted by Kokla et al. [5], ontologies are prominently used in geospatial domain for the following purposes: semantic modeling and semantic search, integration, and interoperability. This work is focused on semantic modeling of geospatial information. The advantages of using an ontology for modeling for geospatial information are [6]: – Using ontology, we can provide a rich, predefined vocabulary that can serve as a stable conceptual interface to GIS databases independent from database schemas.

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An ontology contains sufficient knowledge to enable translation of all relevant information sources. The use of ontologies facilitates efficiency in data management and the identification of inconsistent data terminologies. Ontology provides canonical descriptions of geospatial domains, which are neutral and computationally tractable descriptions of a geospatial area which is adapted and used by all the information gathering communities working in that area.

3.3.1.1 Role of ontologies From philosophy to knowledge representation, the concept of ontology has done a long journey in many fields. The origin of word ontology is from “ontos” in classical Greek which means the study of knowledge of what exists. The concept of ontology is used in various other fields to satisfy different requirements. Across the software engineering, artificial intelligence, and database communities, knowledge representation is seen as crucial for the continued development of each field [7]. In early 1980s, McCarthy [8] proposed the concept of an environment ontology. Essentially, an environmental ontology consists of a list of concepts involved with their meaning in that context, that is, what they mean in that specific context. Here, the concepts are established here for a specific domain with an application of ontologies. Then Gruber [9] has formally defined ontology as an “explicit specification of a conceptualization.” The most important word is conceptualization which represents the process of development and clarification of concepts of underlying domain. Conceptualization refers to the idea in which human mind forms. The idea is mental representation (based on human understanding) of observable and essential characteristics of the elements. And these characteristics get explicated to form domain knowledge. “Explicit” indicates explicit description of the type of concept used and the constraints on their use. “Formal” states that the ontology defined should be readable by machine and “Shared” shows the representation of consensual knowledge by ontology and it is not restricted to some individual; rather, it is accepted by a group. Basically, ontology plays a role of a facilitator to construct a domain model in the knowledge engineering process by providing a vocabulary of terms and relations to model a particular domain. Ontology is the way of knowledge representation: the subject, the object, and the predicate. In modern computers and internet, the use of ontology has preserved its original sense while giving a practical view to it. It basically provides a common understanding and representation of the objects which are referred in a different way among the community of people who know each other and who have a different cultural background. Nowadays, it is more related to the context of www and semantic web. In the context of semantic web, ontology is a common vocabulary that is sharable and transferable over the internet. Ontologies are one of the methods of representing knowledge

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and correlations between different pieces of information. To be automatically processable for machine, the knowledge has to be expressed as simply as possible in an adequate format. Even though the ontology in principle is independent of a particular language, it is necessary to choose a language to describe it. This language needs to be formal in order to share, exchange, and map ontologies. The use of natural language alone is not sufficient for above as it leaves a significant amount of interpretation for the user. This can lead to potential miss of a significant aspect of the ontology. This issue can be resolved and the knowledge can be made automatically processable by the machine by expressing it as simply as possible in an adequate format. Several syntactic and semantic languages have been developed and standardized for this purpose (RDF; resource description framework and OWL; web ontology language).

3.3.1.2 Content explication in geospatial domain Ontology is different (distinguished) from other representations as the combination of expression and formal strength that enables us to formally define the complex domain. Ontologies are considered to be a good/appropriate methodology to support geodata sharing and reusability. However, constructing ontology in geospatial domain is more difficult than in traditional domain. For example, Figure 3.1 shows the visualization of geodata. Geodata has three levels: data content, schema, and metadata. Each layer has its own characteristics. The important question that arises here is how the conceptualization (content explication) process is performed in geospatial domain.

Figure 3.1: Geospatial conceptualization process.

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3.3.2 Understanding geoontologies Geoontology provides the description of geographic entities and the relationship between them. The geographic information space is best explained by the conceptual model which further includes two sub models: object model and field model. The former represents the world as comprising different discrete objects (entities) which are represented geometrically with each entity having well-defined attributes. The objects must be identifiable, relevant, and describable. These spatial objects are subject to three types of operations: set-oriented, topological, and Euclidean. The later model represents the world as a set of continuous spatial distributions which may be formalized as mathematical function. These spatial distributions are subject to three types of operations: local, focal, and zonal. The geospatial data is represented by an ontology. As it is represented in different models and in different formats, it can be conceptualized in different forms in ontology. In the conceptualization process, the important concepts, that is, classes are identified and also the relationships between them. Using geospatial ontology makes it possible to define and reason about real-life spatial objects by combining different sources of information [10]. It forms the basis for understanding the exact meaning of the geodata. It specifies the context of meaning attached to the data elements and their interrelation [11]. A geoontology is an ontology that describes spatial entities (e.g., Road), spatial relationships (e.g., Road crosses At Junction), physical entities (e.g., River), geospatial data acquired, and the geospatial computing model (e.g., Euclidean geometry is used to represent the space) [12]. In the traditional geospatial domain, these entities have their own interpretations and representations. Typically, spatial entities (regions, objects, events) are represented using points, polygonal lines, or minimum bounding rectangles containing and enclosing objects and regions. There can be topological, orientational, distance-related, mereological, or temporal relationships among spatial entities [13]. Furthermore, spatial relationships are illustrated/exemplified as qualitative (relationship in lexical terms such as “East,” “Contained,” and “Near”) and quantitative relations (relationship represented in numerical form) [14]. As a consequence, geoontologies are defined, modeled, designed, and developed by interdisciplinary domain experts based on their interpretations and perceptions of these entities and relationships. They are influenced by specific language used for representation and inference mechanism needed by the application that uses the geoontology. Ontologies in geospatial domain are space, time, and human-dependent. A geoontology provides a description of geospatial entities. It describes objects at particular spatial location, semantic, and spatial relations between these objects [15]. Representation of geospatial entities is complex and intricate. The following are the characteristics of geospatial data which makes geospatial ontology modeling challenging. – Geospatial objects can be continuous or connected, separated or scattered, and closed or open. They are mostly complex and have constituent parts. “They are

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tied intrinsically to space and not just located in space” [16]. Every geographic concept has some properties for representation purposes. The properties could be positional, directional, geometrical, and so on. For example, a concept “Route” can be geometrically represented as a line or also as a polygon. Geospatial data has various different features like topology, size, shape, dimension and orientation, and location and regions. Many of these features are quantitative in nature. However, when they are represented in an ontology, they need to be converted in the qualitative format as ontology is text-based knowledge representation. (Numerical format needs to be converted to textual format). For example, qualitative notions of “about parallel” and “about perpendicular” and frame of reference including earth-based (north-south) [12]. Geospatial data is implicitly linked without explicit references usually via a coordinate reference system. The geo-datasets are massive as compared to alphanumeric datasets. Geospatial data is collected by different agencies at regional, national, and international and at different levels. Hence, it leads to multiple versions of same entities on the surface of earth and differs radically in terms of data, scale, model, and data generalization. Spatial relationships have predefined semantics whereas conventional relationships may vary depending on the concepts associated with them. As an example, every geographic concept has a corresponding geometry. It plays an important role in defining possible spatial relationships. For example, the concept rainfall can be represented in two geometries: as a point or as a polygon depending on the context that it is used; whether it is used for flood perspective or as a weather perspective. In view of above, the ontology thus formed is different from the conventional ontology. Geospatial data has various different features like (1) topology: it includes features like connectivity, overlaps, shapes, volume, surfaces, boundaries, and knots; (2) dimension and orientation: various parameters are projected in different manners; for example, north-south, left-right, port-starboard, and upstreamdownstream; (3) shape: it includes both 2D and 3D shapes; various qualitative descriptors like “round” and “tall;” the notions of “fits in” and “symmetry” which are linked with various quantitative representations of shape such as polygon, bounding boxes (rectangle), and functionality of shape (e.g., slope of a hill and irregularities of land surface) [12]. Two-dimensional shape can be represented by a polygon and three-dimensional shape like solid surface (e.g., toll booth on road network) can be represented by a polygon or it could be represented as a point. This also affects the way it will be modeled in ontology; (4) size: it includes length, distance, area, and volume; (5) location and regions: it includes latitudes, longitudes, elevations, and natural and geopolitical regions.) Many of these features are quantitative in nature. However, when they are represented in an ontology, they need to be converted in qualitative format as ontology is text-based knowledge representation. (Numerical format needs to be

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converted to textual format). It is possible to use these parameters as examples: qualitative notions of parallelism and perpendicularity; earth-based (northsouth), agent-based (left-right), vehicle-based (port-starboard), and force-based (upstream-downstream) frames of reference; and a transformation between coordinate system and frame of reference [12]. Implicit linking: Geospatial information generally has implicit linking without explicit references usually via coordinate reference systems. For example, bridge can be implicitly linked to river or road it crosses. Massive datasets: the volume of geodata is huge compared to general alphanumeric data. Satellite images, for example, have a large raster data volume. When this huge data is conceptualized into ontology, the resulting ontologies are also huge and detailed. Sometimes they become abstract representing only the general phenomena. There are different types of data in geospatial domain: spatial, temporal, and spatiotemporal. Ontology for stream (temporal) data is explicated either using concept of event or time series ontology representation. Multiple versions: Geospatial data is collected by different agencies (e.g., regional, national, and international) and at different levels. Hence, it leads to multiple versions of same entities on earth surface and differs radically in terms of data model, scale, and data generalization. Sometimes geo data can be stream data, for example, satellite data or sensor data. Ontology of this kind of data involves both the spatial and temporal concepts. This increases the complexity of geoontology.

Geoontology, in contrast to general ontologies, describes [10] (1) spatial factors such as location and units; (2) spatial relationships such as inside, far, and near; (3) physical facts such as physical phenomena, physical properties, and physical substances; (4) disciplines such as scientific domains and projects; (5) data collection such as collection properties such as instruments, platforms, and sensors; and (6) geospatial computing models such as input, output, and preconditions.

3.3.3 Understanding geoontology modeling The goal of ontology modeling is to accumulate the common knowledge of a given area and explain it in order to confirm the agreed vocabulary in that area. The definition of the vocabulary and their interrelationships are clearly defined according to a different layer of the formalized model, thereby facilitating implementation of the reasoning of the domain knowledge. Ontology can also be viewed from the perspective of knowledge sharing as the set of concept definitions that serve as a common language of communication between different kinds of knowledge systems. Currently, there is no standard method for ontology creation, but researchers have summed up some principles that guide the ontology modeling process.

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The modeling goal is to bridge heterogeneity across representational choices by developing a content geoontology. There is a lack of standard geoontology models which is an influencing factor in geoontology formation. There are many approaches addressing different features, all of them are relevant; however, there is a need to find the best solution for the above problem. This is because every author understands geoontology from different perspectives such as – Geoontologies modeled from a philosophical perspective: Couclelis (1992) has put forth theoretical perspective comparing object and field vision of geographical world. Camara et al. [17] present two different views for geographical world. – Modeling is typically guided by top-level foundation ontologies. Some examples include BFO (Basic Formal Ontology), DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), SUMO (Suggested Upper Merged Ontology), and SWEET (Semantic Web for Earth and Environmental Terminology). Bittner et al. [18] propose two types of spatiotemporal ontologies based on the BFO: SPAN and SNAP. SNAP is for continuants which are subject to change and SPAN for occupants which depend on continuants. – Application-specific geoontology models are formalized by Xing et al. [19], Hess et al. [20], and Huang et al. [21]. Some geoontology models have been formalized considering all spatial relations, geospatial objects, fields, processes, and their categories for a specific domain (e.g., hydrology and agriculture) or specific application (integration, web service composition, etc.). – Some knowledge-based geoontologies are developed with a pragmatic point of view and represent spatial entities and spatial relations aimed at formalizing human common sense, knowledge, and reasoning, for example, DBpedia,1 mIO! Ontology network,2 Ontology of Transportation Networks (OTN), and CityGML3. These geoontologies do not adhere to any specific philosophy. Geoontology represents geospatial knowledge in terms of concepts, semantic attributes, and semantic relations (refer Figure 3.2). In these entities semantic attributes are data properties, and semantic relations are described as IS-A and object properties. Further, IS-A and object properties describe hierarchical and spatio-temporal relation, respectively. For example, “Place IS-A SpatioTemporalRegion” and “RouteSection isPartOf RouteOfTrasportation.” Definition: GeoOntology is defined as five tuple Og = (Cg, DPg, OPg, Ig, Ag) where Cg, Ig, and Ag are extensions or specialization of, respectively, C, I, and A. P is divided in DPg and OPg the set of concepts, DP is set of data properties, OP is set of object properties, and DP and OP ∈ P. In geoontology, there are usually two types of

 http://wiki.dbpedia.org/services-resources/ontology.  http://mayor2.dia.fi.upm.es/oeg-upm/index.php/en/ontologies/82-mio-ontologies/.  https://www.citygml.org/.

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concepts: concepts that correspond to the physical phenomenon in real world (e.g., lake, mountain, and houses) and those that correspond to the features of the world that represent the social and institutional construct (e.g., 7/12 documents of land, incidence of malignancy, and obesity rate; Fonseca et al.). There are two types of properties in geospatial ontologies: feature-based properties (represented as DPg data properties) and predicate-based properties (represented OPg object properties). Feature-based properties are usually the attributes of geospatial concepts. These attributes could be bound to the data values. For example, if “town” is the concept then the “town name” and “town population” are its attributes or feature-based properties. Predicate-based or object properties are usually the links between the two geospatial concepts. For example, Town liesOn Road, liesOn will be the predicate-based property. Geospatial instances correspond to concrete objects in database system composing a concrete record of underlying database. Axioms represent geospatial knowledge and the rules or constraints of geospatial properties in geospatial domain, so as to carry through reasoning and ensure consistency and integrity of geoontology [22]. Axioms can be simple axioms or complex axioms. Simple axioms are used to ensure consistency and integrity of classes, attributes, and individuals in geoontology and are represented by restriction rules. Complex axioms are implied to embed the geospatial knowledge inside the ontology.

Figure 3.2: Semantic annotation of spatiotemporal data: data/information is observed, interpreted, conceptualized, and explicated in terms of geoontology. For example, Indian_Road geoontology presents the observation of real-world entities and humans interpret, conceptualize, and explicate this in terms of a concept that are logically structured as hierarchy/graph, relations, and attributes. The structure of ontology generally described in terms of a graph, where each geoontological concept is a node, and the semantic relationships among the terms are arcs between the nodes.

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Quantification of domain knowledge in geoontology is very challenging to measure. We have to understand it qualitatively. Domain knowledge gets implanted in geoontology using ontology modeling process shown in Figure 3.3.

Figure 3.3: Geoontology modeling process.

Ontology modeling process consists of two steps: 1. Conceptualization: In this process, all geoontological entities are identified and discovered by domain understanding. Generally, geoontological concept is defined in terms of its necessary and sufficient condition to exist in its instances. For example, River can be defined as “A River is a type of waterbody which flows into a Lake, a Sea, a Reservoir or a River.” Here, to exist an entity as the river it should be a water body and it should have property/relation which represents that it flows into other concept Lake, Sea, and Reservoir. These can also be called necessary and sufficient conditional as per Aristotelian definition [23]. 2. Formalization: These formal languages are used to construct ontologies (RDF, OWL). They allow the encoding of knowledge about specific domains and often include reasoning rules that allow that knowledge to be applied in its processing. In OWL ontology construction, classes and properties are expressed as axioms. Axioms describe these properties as well as the qualities of their identifiers. They are also used to describe characteristics of classes and properties. Generally, ontological languages have four types of class axioms: – Subclass axioms: These are conditions that must be met for subclass rules to hold. – Equivalent class axioms: These axioms reflect necessary and sufficient conditions. – Disjoint axioms: These are additional conditions that have to be met. – Domain and range axioms: An axiom that defines a property’s domain and range is what’s called a “global” axiom.

3.3.4 Geoontological engineering Ontology engineering consists of a set of tasks related to the development of ontologies for a particular domain. Ontology modeling is a process of building ontology

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model which represents the structure and integrity of the ontological elements/components/entities. In this work, ontological modeling is considered as the philosophical or intentional backbone for describing the spatial entities in geoontology. And geoontological engineering is considered as applied to develop geoontology practically. There are various methodologies documented in the literature for geoontology engineering like observation-driven geoontology engineering [24], knowledge metadata method [25], WP-onto method by Xing et al. [19], and so on. Some of them are summarized in Table 3.1. This work has considered the WP-onto method for geoontology engineering for this research as it best satisfies our requirement of embedding geospatial knowledge in the geoontologies. The general framework of WP-onto method for geoontology engineering process is depicted in Figure 3.4. Table 3.1: Comparison of existing proposals for geospatial ontology models. Author detail

Concept

Support for spatial attributes

Support for spatial Support for relationship temporal relations and context

Application

[]

Geographic objects: geodetic, administrative, manmade, and natural objects

–

Topological, Moving objects projective, distance, are considered and mereological relations

Conceptual framework for interoperability

[]

Geographic concept

Geographic features, manmade features, spatial Description

Topological, directional distance relationship based on RCC and N-intersection model

Temporal relationship represented by interval algebra theory (Allen’s)

Agriculture

[]

Top level concept based on ISO , ISO 

SchemaSchema-based based feature relations model

–

Integration

[]

Static and dynamic spatial entities

–

Topologic, orientation or distance relations, RCC- relations

D-fluents Querying, approach for reasoning representation Allen’s temporal relations

[]

Concept represents a geographic phenomenon

Positional property in terms of attributes

Spatial relation in terms of predicates

In terms of Integration spatial concepts, properties, and relations.

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Table 3.1 (continued) Author detail

Concept

Support for spatial attributes

Support for spatial Support for relationship temporal relations and context

Application

[]

Geospatial objects or events based on object-event model

Thematic, spatial and temporal features

Topological relation Temporal relations

Integration

[]

Logic-based top-level concepts: individual endurant, endurant universal, and collection

Based on top- Based on top-level level concept concept

Top-level timeindependent relations

Integration

[]

Top-level concept: field; physical and socially constructed and object type; bonafide, fiat object

Based on top- Based on top-level level concept concept

Based on toplevel concept

Interoperability

Theoretical Domain Models and Classification

Hierarchical Structure between concepts 1

Acquired Geospatial Information from various information

Building Vocabulary set 4

Redefine Vocabulary Determination the constraint rules of concept. 7

Definition of Instance

5

Human understanding and interpretation for modeling geo-ontology

Definition of property of concept

Definition of relationship between the concept

2

3

Further extension of relationship set 6

8

Coding and formalization of geo-spatial ontology using ontology tool such as Protégé, OntoEdit

Geo-ontology expressed in OWL language

Figure 3.4: Geoontology engineering process (adapted from J.42).

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2. 3.

4.

5. 6. 7.

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Geoontologies are practically formalized in the following eight steps: The first step is the identification of concepts and developing a hierarchical structure among them (the process is explained in Figure 3.4.). The hierarchical structure is formulated based on the following criteria: a. The standard theoretical classification is used for developing the hierarchical structure of the concepts in the geoontology. For example, the standard road classification for India is considered for determining the relations between the concepts and developing the hierarchical structure of the geoontology “Road.” b. Based on existing geoontological models for defining top-level concept. It is always good practice to reuse existing geoontologies. Sometimes this structure is also influenced by the existing top level or foundation ontologies. For example, for defining hydrology geoontology, the top-level concept can be from the perspective of BFO, “Endurant,” and “Perdurent” as “Hydrological Entities” and “Hydrological Process.” The next step is defining the data properties of the concepts. For example, the data properties defined for the concept FloodingLocation are levelOFWater. The next step is the extraction of the relationship between concepts represented by object properties and the relations being spatial and temporal relation. For example, concept RouteSection and RouteOfTrasportation has object property isPartOf. The next step is building a vocabulary set that relates the concept and properties. For example, the words need to be chosen to define the concepts Traffic and GeoLocation. Similarly, the vocabulary for the properties is influenced by the way they have been generally referred to in the geospatial domain. For example, property hasInstant, hasTimeUnit. The next step is redefining the vocabulary set. The spatial and temporal relations of the geospatial concept being the most important part of geodata are further extended and analyzed to represent in geoontology. The next step is to determine the constraint rules for concepts at a finer granularity. As a result, these concepts allow a more precise understanding of the relationship among features in geoontology. Constraint rule is based on a. Definition of a prerequisite for the occurrence of a concept in the geospatial domain. b. Understanding that concept according to some model or top-level geoontology such as SUMO and BFO. c. Ontological language Web Ontology Language (OWL)4 and Resource Description Framework (RDF5) define restrictions (both concept and property restrictions) that define the conditions that must hold for individuals of the given concept.

 https://www.w3.org/OWL/.  https://www.w3.org/RDF/.

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d. The ontology engineer takes advantage of the necessary and sufficient conditions theory to understand the conditions needed for the instance to belong to a particular concept in geoontology. This is necessary to validate the given geoontology so that reasoning and inference process can be applied to it. The next step is the definition of an instance where actual individual or instances are embedded in geoontology in subject-object-predicate format.

3.4 Geospatial semantic modeling and its application areas Specifically, geospatial semantic modeling is used to develop geoontologies at different levels of generality and formality tailored to meet different needs in the geospatial domain. However, elicitation is a set of processes that involve deciding what semantic information can be extracted from semistructured and unstructured geospatial resources. The use of elicitation and modeling can be synergistic for improving both: extracted information is used to enrich a geoontology, which is then used to refine geospatial information extraction results [5]. We provide below an overview of advantages and disadvantages of using ontologies for geospatial modeling. Advantages of using ontology for geospatial modeling: – Geoontology provides coherent understanding of geospatial information, and it also provides navigation to geospatial concepts. – Furthermore, ontologies are easy to extend because relationships and concept matching can easily be added to existing ones. The model can therefore grow with the unprecedented growth of geospatial data without affecting dependent processes or (acquisition techniques). – Geoontologies enable data integration of all types of data including raster and vector geospatial data, traditional unstructured, semistructured, and structured data formats, which facilitate data-driven geospatial analytics. – The main advantage of using ontologies in modeling for geospatial information is their ability to perform spatio-temporal reasoning on geospatial information of all kinds, which can be helpful for any kind of application. Disadvantages of using ontology for geospatial modeling: – Despite the fact that ontologies provide a rich set of tools for modeling geospatial data, their usability is restricted. Since spatial temporal relations are complex, describing them with properties, especially with OWL property constructs, is very limited.

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Geospatial data is typically composed of large sets of information gleaned from multiple sources in a variety of formats and may include information such as weather data, census data, satellite imagery, drawn images, mobile phone data, and social media data. Such data are difficult to geoontologize using ontology languages such as RDF and OWL. The following are some of the major challenges from an ontology perspective: (i) assessing the quality of the geoontology; (ii) improving the accuracy, consistency, and completeness of geoontology; (iii) aligning and integrating geoontology with existing databases; (iv) extracting knowledge from unstructured geographic data; (v) automatic construction and reuse of geoontology; (v) extracting and annotating semantic information of geospatial data based on geoontology automatically.

3.4.1 Comparison of geospatial modeling and geosemantic modeling Geospatial data is stored in a retrieval system using various data formats. In order to process and utilize spatial data, GIS applications are utilized to perform desired operations. Though these stand-alone systems are advantageous and powerful for geospatial data processing, issues and challenges arise when the data (or) the derived geospatial products have to be integrated, shared, and utilized across multiple operatives. In order to facilitate data sharing and to resolve data description issues, meta-data standards have been developed which have been beneficial only to a certain extent [32]. The challenges that arise in data heterogeneity are caused by characteristics of multiple sources, types, and forms of geospatial data [32]. In order to bridge the gap between understanding the differences in semantics in knowledge representation, ontologies are modeled. Various characteristics of semantics are based on space, time, spatial and temporal concepts, and relations [5]. Ontologies model spatial data and the representation models can translate data specification in a machine understandable manner and beyond that help discover the meaning implicit in the representations (Figure 3.5). These are done by representing top-level and domain-level ontologies. Having modeled and defined ontologies, various aspects of integration can be interoperable through service interfaces [33].

3.4.2 Major areas of application of geospatial semantic modeling Modeling involves elucidation or representation of geospatial knowledge. Representation is often described by formal theories and in order to represent model geospatial knowledge, various methods are adopted. Each of these representations involves the application and utility of the end user. In these sections four applications of geospatial semantic modeling are presented [34].

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3.4.2.1 Semantic interoperability Geographic information systems encapsulate various types of spatial data. When hosting locally available services on the web, it is important to have rules for seamless data exchange and smooth interaction. These rules are the semantics defined for spatial data to make services interoperable. Semantics serve as a standard for defining spatial data on the web. For example, querying spatial data information [34]. One way to promote interoperability is to use an ontology. Geospatial Modelling Framework 1

3

5

Data Collection

Model Calibration

Prediction

Model Execution

Validation

Analysis

2

4

6

Geosemantic Modelling Framework 1.1

Conceptualization

Knowledge Acquisition

1.3

2.2

Vocabulary Set Building

Hierarchical Structure Identification

Domain Understanding & Representation

Geoontological Entities Determination

1.2

2.1

Axiom/ Constraints Identification Formalization

2.3

Figure 3.5: Comparison between geospatial modeling and geosemantic modeling.

Ontology is a method of defining spatial semantic knowledge in a way that machines can understand. It is represented as a graph with nodes and edges. Nodes that represent concepts and edges define the relationships between them. Examples of spatial ontology are top-level ontology, domain ontology, and ontology design patterns [31, 35, 36]. While top-level ontology contains general terminology explanations, domain ontology is specific to each domain, for example, geoontologies. Ontology design patterns have been developed based on applications that capture interactions between multiple applications. To develop an ontology, primitive elements are used to define unique basic units for each environment [24, 34]. These are described in formal logic and textual descriptions [34]. For instance, ontologies are defined in the domains of ecosystems [34, 37], earth sciences [34, 37], and semantic sensor network [38]. As different

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representations of ontology exist within and between domains, they often need to be integrated and interacted for comparison, collation, and processing. Ontologies defined for task-specific applications require ontology alignments for data integration. To adjust ontologies for interoperability, three different alignment methods are used, namely, elementary, structure, and hybrid methods.

3.4.2.2 Digital gazetteer Descriptions of places are defined more formally or informally in everyday usage and language. The formal representation for pinning an actual geographic location is to use latitude coordinates, while in informal representation, people tend to use language to specify a particular location. Digital gazetteer is a structured dictionary of named places [34] through which informal linguistic descriptions are captured and expressed corresponding to formal geographical representations [34]. Three components in digital gazetteers, place names, place types, and spatial footprints are mainly used to describe places [34, 39]. Place entries are typically represented by a graph structure where each node represents a location and edges represent relationships between adjacent locations. Geocoding, navigation, and geographic information retrieval are some of the applications of digital gazetteers [34]. Geographic Names Information System (GNIS), GeoNames, Getty Thesaurus for Geographic Names (TGN), and Alexandria Digital Library Gazetteer (ADL) [34] are some examples of digital gazetteers. Since everyday language uses are different from formal entries in a digital gazetteer, it is vital to update the existing place entries represented by local names which are used and familiar to the general public residing in the location. This could help as it corresponds to human language use, for example, using GIS applications one can find hotels and restaurants. Representing place names in such a descriptive manner may not have clearly defined boundaries but vague spatial foot prints and approximations of geometric boundaries. Place-related titles such as generic names (city, lake, river, etc.) are used to refer to specific places. Semantic integration requires correlating or aligning data from different gazetteers. There would arise issues of multiple geographic attributes in different gazetteers that can pose bottle necks to updating the databases. For instance, a place name can be identified as a point in one description but can be identified as a geometric polygon in another. It would then necessitate resolving these inconsistencies to our advantage. Once again ontologies play a significant role in resolving these conflations by utilizing ontology alignments at various levels. Various similarity metrics are used to measure the level of correspondences among spatial foot prints, place types, and place names. Another area of work in digital gazetteers is to use reasoning capabilities to query and elicit meaningful information. There are research areas to study the evolving nature of gazetteers, as place names, structures, and boundaries tend to change over time, and reasoning capabilities will facilitate updating and extract information for analysis [34].

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3.4.2.3 Geospatial information retrieval Querying a geodatabase is one of the important aspects of geographic information retrieval [34, 40]. With the recent advances in semantic web, querying information is not only limited to structured data sets but also to unstructured data [41]. The queries posed are not meant to retrieve just the data but infer the data that is reasoned. For such a reasoned query, ontologies play a significant part by associating contextual knowledge while inferring a user query. This is particularly important because different places have the same place name and in turn each place name may have different names. To resolve this conflict, contextual querying and reasoning are necessary, where a concept of place name disambiguation is utilized [34]. To resolve the conflicts in place names, various similarity indices are used to measure the degree of similarity taking into account the surrounding words in context. The sources for this contextual information can be from gazetteers, Wikipedia, and so on. Rank-based querying is another area of research in geographic information retrieval where the candidate outcomes are rank-based on a theme. For instance, disasters are considered a theme, and the query reasoner outputs the rank based upon “< theme > < spatial relationship > < location >” [34, 40]. In such a type of reasoning domain ontologies are used which can offer contextual information for querying the required information.

3.4.2.4 Place semantics Place semantics infers meaning of places through human descriptions and their interactions at a particular place [34, 42]. Human descriptions are the details about the places and the surrounding environment. And the textual descriptions may not include the exact latitude and longitude information. Examples of these descriptions include city websites and Wikipedia information [34, 43]. A more formal approach to identify places is by using geographic coordinates from Flickr photographs, tweets, and geographic-tagged images. Given the fact that location information is also tagged with this information both the information can be used co-jointly. Place semantics can be described by thematic, spatial, and temporal descriptions. Thematic descriptions involve word clouds, that is, for instance, a user reviews a place, city, and location in the form of text. Spatial descriptions of a theme are described by locations of nearby landmarks. Examples of temporal themes are human interactions in frequenting a restaurant, number of visits to a sight-seeing spot, and so on. In addition to individual themes combining space and time interactions, space and theme, time and theme description helps in studying dynamic behaviors of place-related semantics [34].

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3.5 Conclusion Geosemantic information modeling is vital for data exchange across various systems and domains. Over the years to facilitate data description and modeling interoperability standards have been developed. For information-based retrieval mere data-retrieval systems are not sufficient. When geospatial data is described by semantics which is information rich, higher level systems can be utilized such as knowledge-based retrieval and reasoning-based queries which closely emulate human reasoning. Developing customized ontologies for specific applications can offer modularity for more complex applications. In summary, in the area of geospatial domain, the applications that benefit from ontology-based modeling are very much needed in the field of agriculture, hydrology, transportation, disaster applications, critical infrastructure, and so on.

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[36] Gangemi, A. & Presutti, V. (2009). Ontology design patterns. In Handbook on Ontologies (pp. 221–243). Springer, Berlin, Heidelberg. [37] Sorokine, A., Sorokine, R., Bittner, T., & Renschler, C. (2004). Ontological investigation of ecosystem hierarchies and formal theory for multiscale ecosystem classifications. In Proceedings of GIScience’04. [38] Compton, M., Barnaghi, P., Bermudez, L., Garcia–Castro, R., Corcho, O., Cox, S., . . . Taylor, K. (2012). The SSN ontology of the W3C semantic sensor network incubator group. Journal of Web Semantics, 17, 25–32. [39] Hill, L. L. (2000, September). Core elements of digital gazetteers: Placenames, categories, and footprints. In International Conference on Theory and Practice of Digital Libraries (pp. 280–290). Springer, Berlin, Heidelberg. [40] Jones, C. B. & Purves, R. S. (2008). Geographical information retrieval. International Journal of Geographical Information Science, 22(3), 219–228. [41] Larson, R. R. (1996). Geographic information retrieval and spatial browsing. Geographic information systems and libraries: Patrons, maps, and spatial information [papers presented at the 1995 Clinic on Library Applications of Data Processing, April 10–12, 1995]. [42] Fisher, P. & Unwin, D.(2005). Re–presenting geographical information systems. In Representing GIS (pp. 1–17). Wiley, London. [43] Tomai, E. & Kavouras, M. (2004). “Where the city sits?” Revealing Geospatial Semantics in Text Descriptions. In 7th AGILE Conference on Geographic Information Science (pp. 189–194).

Simrn Gupta✶, Rahul Patanwadia, Parth Kalkotwar, Ramchandra Mangrulkar

4 Applications of artificial intelligence for employees’ health and safety: present and future Abstract: The rapid development in Industry 4.0 era has led to an exponential increase in machines of high potency which are essential for economic growth, thereby increasing the risk for employees. An unsafe and unhealthy work environment can impede the innovation and productivity of employees, which may result in damaging the reputation of the industries. Therefore, there is a need to give a serious thought to these unaddressed issues which may be useful in the safety of our employees. Machine learning (ML) has been pervasively applied across disparate disciplines, especially in industries, and has been found to be significantly efficacious in improving the performance. The capabilities of ML and artificial intelligence (AI) can be exploited to offset the shortcomings of traditional approaches to employees’ safety. This chapter tries to address this issue. Recent advances in this domain are assessed that use ML and AI to address, resolve, and ameliorate the safety concerns of employees. Various applications using supervised learning, semisupervised learning, unsupervised learning, and reinforcement learning methodologies have been delineated. Further, detailed reasoning on why semisupervised learning methodology is preferred currently, primarily due to its high throughput, and the advantage of generating warnings with minimal requirement of historical data has been discussed. Complex deep learning models have become feasible, owing to the easy access to fast modern computers. Of the diverse implementations of neural networks used, this comes at the cost of blackbox models generated in deep learning techniques that might reduce the trust to effectuate such technologies. This treatise proposes explainable artificial intelligence (XAI) as a potential solution for this problem, which can be used to augment employees’ trust in these black-box models by providing a lucid explanation of the model’s working. In totality, this exposition maintains that these ML and deep learning techniques are effective in enhancing workplace health, safety, and environment, as their competence to automate reasoning can culminate in better-informed decisions and effective accident prevention.

✶

Corresponding author: Simrn Gupta, Dwarkadas J. Sanghvi College of Engineering, Mumbai, Maharashtra, India, e-mail: [email protected] Rahul Patanwadia, Parth Kalkotwar, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India Ramchandra Mangrulkar, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India, e-mail: [email protected] https://doi.org/10.1515/9783110781663-004

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4.1 Introduction Technology has now become an integral part of every field in existence, and only recently has the Industrial Revolution 4.0 dawned upon us. Machine learning (ML) and artificial intelligence (AI) have revamped the research sector and industrial applications in disparate fields from machinery and construction [1], healthcare [2], education [3], and business [4, 5] to chemicals [6], aviation [7], and nuclear [8] industries. These methods counter the drawbacks that conventional methods elicit, thereby upgrading the results obtained by traditional approaches. With the advent of Industrial 4.0, machines are being built with increasing complexity and potential. These machines are seen by all industrial sectors as a means of augmenting profits and producing immaculate work results. Systematic studies suggest that the imminent revolution facilitates shorter product life cycles, sustainable energy requirements, and reasonable costs to the sector [9]. Even though labor-related occupations’ automation processes have been improved, workplace safety issues for employees still exist. Relatively less attention has been given to monitoring safety issues [10] and hazardous practices that occur at the work site. As the number of intricate machines has risen sharply, so has the risk associated with the machinery [11]. Following any mishappenings at the workplace, primarily the lives of the employees are threatened, with the damaging of the company’s reputation being inevitable. Further, an insecurity in the work environment [12] may hamper the productivity of the workers and lead to dismal results. The safety and health concerns of employees at work can be addressed by leveraging the practice of ML and AI. These methods negate the limitations of prior approaches to risk analysis and accident prevention, along with providing more robust and scalable solutions [13]. This study briefs over the techniques including but not limited to supervised learning using algorithms including random forest and support vector machines, semisupervised learning using generative adversarial networks (GANs), unsupervised learning with natural language processing (NLP) and convolutional neural networks (CNNs), and reinforcement learning with Markov decision process (MDP). Rephrasing the goal of IEC/EN 62061 standard [11] states that the aim of the standard is to ensure, in the event of failure, that the system will fail in a predictable manner, with risk assessment as a fundamental metric. The ML and AI practices show promise to hold up the goals of this standard, with their faculty to perceive obscure patterns in the data and limit bias to the available data only [11]. With the advent of the industrial Internet of things (IIoT), these are being incorporated to build scalable systems that will allow timely reporting of risks for accident prevention. The rest of the chapter is organized in the following manner: Section 4.2 gives a brief outline of the related work in this field, which have employed various practices of AI and ML to realize occupational safety. Section 4.3 is an overview of the different ML and deep learning methodologies which have had a far-reaching impact in the

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Industrial 4.0 era. To give a lucid understanding, this section gives an in-depth look into supervised, semisupervised, deep learning, and reinforcement learning techniques. In addition, working and performance of supervised and semisupervised learning have been evaluated. Further, Section 4.4 delineates the several applications of ML which have been implemented to improve safety of employees in the industrial workplace. These include, but are not limited to, human error elimination and timely response, worker detection and tracking, anomaly detection, and intelligent sensors which incorporate AI. Section 4.5 outlines promising future opportunities that leverage AI, such as XAI, that can be effectively applied to enhance employee safety. Finally, the chapter concludes by maintaining that ML and AI can help make betterinformed decisions and be more effective in accident prevention.

4.2 Related works Analysis of causes of workplace mishaps as well as the risk posed to workers’ occupational safety plays a crucial role in prevention of accidents at the workplace and pertinent well-founded policies [14]. Probabilistic safety assessments involving Bayesian networks have been employed to analyse such accidents, with their perquisites of high predictive and interpretability capacities [14–17]. Neural networks have found their use in risk analysis and prediction at construction sites and have been used to derive meaningful data for risk mitigation strategies [18, 19]. NLP has been employed in several works [20–22] for data extraction. The information retrieved from these assessments can be furnished to predictive models for making opportune decisions. Structure health and damage detection can facilitate research toward automating construction processing while also focusing on safety aspects of the same. Significant works have been carried out to ascertain the safety of specific engineering structures [23] in disparate domains. These include gauging damage detection and strength of scaffoldings [16] using SVM, fault diagnosis in bearings [24], machinery components [25], and spur gears [26] using deep learning, among others. These works posit that timely detection of impairments can significantly decline the rate of accidents at the workplace. Besides this, computer vision has contributed significantly to unearthing hazardous practices [27, 28] and situations [29–31] which pose considerable threat to employee safety. Predictive modeling has paved the way for accident prediction and subsequent prevention in occupational safety and health. Application of ML models for this task has included the use of random forest [21, 32–34], stochastic gradient tree boosting [21], logistic regression [35], CART [32, 36], and support vector machines (SVM) among others. Given the steady increase in casualties at construction sites and accident rate, leading safety indicators have been developed as a means to appraise safety performance and

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control it [37]. Combining various leading indicators to forecast accident occurrence instead of a specific combination, using ML has been shown to increase the prediction accuracy as demonstrated in the works [38, 39]. Apart from leading indicators, safety outcomes extracted from prior data like sensors [11], unstructured injury reports [20, 21], and human annotations [40] have been processed and particularly been shown great prediction results by random forest and SVMs. While predictive modeling based on ML techniques yields substantial results, optimization of parameters of the algorithms used is essential for best performance [20]. Application of highly powerful optimization algorithms, namely, genetic algorithm (GA) and partial swarm optimization, outperforms the other models in terms of robustness and accuracy [20, 23, 32]. Grid search has also been used for hyperparameter tuning with the algorithms mentioned above [23]. In order to offset the limitations posed by human judgment and possible error, AI is proposed to identify the potential hazards beforehand [20, 41, 42]. In this work [42], a model-based MDP is applied with the concepts of reward and penalty, and this system based on AI raises timely alarms to prevent adverse mishaps. Another study [41] presents an AI system which identifies hazards at a preliminary stage, in addition to tendering occupational safety and hazard administration (OSHA) standards related to these hazards for alleviating them. Using risk analysis, it is possible to perform riskbased data classification on a set of process data. However, the amount of data required for supervised learning exceeds the volume of data which can be feasibly labeled with risk levels for all processes. Therefore, semisupervised learning methods have been employed to counter this limitation, incorporating GAN to develop systems that report risks in a timely manner. The architecture also uses a CNN to deal with the high-dimensional process data to generalize across warning models [43]. These ML and deep learning techniques have been integrated with IoT to pave the way for IIoT devices, with installation of sensors and alarm systems which further enhance workplace and occupational safety [33, 42].

4.3 Machine learning and deep learning techniques This section of the chapter gives a brief overview of the fundamental concepts of ML and also introduces the recent advances in ML like deep learning, reinforcement learning, and semisupervised learning. ML, in brief, can be defined as a computer program that has the ability to learn and adapt to new data without any human input or deliberate programming. ML is a branch of AI. This learning can be used to predict, analyze, and also make decisions when needed. Depending on the kind of task at hand, the amount and type of data available, different categories of ML are put to use. The three main categories of ML are (i) supervised learning; (ii) unsupervised learning; and (iii) semisupervised learning. Figure 4.1 shows the categorization of ML along

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with its subcategories diagrammatically. The following sections explain in detail about these categories. Categories

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Regression

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Figure 4.1: Categorization of machine learning.

4.3.1 Overview of machine learning Collecting mass amounts of data has become ubiquitous because of its wide variety of applications in practically every industrial field which in turn is because of the financial and competitive advantages over opponents one gets from it. This has made the process of collection of data essential in engineering sciences. Different aspects of data that are collected can be understood clearly owing to the different categories of ML. Consider, for example, a dataset with multiple numbers representing the cost of different properties and its area in square footage in a specific city. Let us say, a relationship between the cost of a property and its area has to be determined and we want to predict the cost of a property given its area. The dataset is in the form of pairs ðXi , Yi Þ, where Xi is a vector of features (area of the property) and Yi is the

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desired output (cost of the property). In the field of ML, the output Yi which we wish to predict is known as the label or response, and it is also known as the dependent variable. The input vector of features Xi is called covariates, features, or predictors. Figures 4.2 shows a general equation for ML. The feature X provides values for the label Y through the unknown function f ð X Þ. Using few words, ML technically is about finding the closest function which best predicts the label. The model or the hypothesis Y^ = ^f ð X Þ is derived from the learning carried out on the data. When the input vector or predictors X is fed into the hypothesis, Y^ is obtained as the predicted output along with some uncertainties that may be associated with the desired output. In supervised learning, the aim is to estimate ^f when the given dataset consists of paired features and labels, that is, Xi and Yi . On the other hand, in unsupervised learning, the label Y is absent and the dataset includes only the variables that act as inputs. Unsupervised learning gets its name from the fact that the program only figures out patterns and correlations among the features in the dataset without there being supervised directions. This makes unsupervised learning more difficult as compared to supervised learning. Semisupervised learning can be seen as a compromise between supervised and unsupervised learning, which means some data points can have labels and the rest of them do not have labels. Reinforcement learning does not fit into these bifurcations but it is the method of finding out missing suitable input features X * given input feature vector X and desired output label Y. The later subsections further explain the concept of reinforcement learning and the differences in semisupervised learning compared to its counterparts. Figures 4.2 and 4.3 show the basic schematic block diagram for ML.

labels inputs loss predictions

Architecture parameters update Figure 4.2: Outline of machine learning.

4.3.1.1 Supervised learning Supervised learning is used to create a mapping function which maps the input vector features to an output after processing a dataset of given observations. Supervised learning can be used to make inferences and predictions. To make inferences, supervised learning is used to figure out how the input vector is related to the output or

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Figure 4.3: Schematic diagram of machine learning.

how the input vector affects the output. In order to make predictions, the generated hypothesis is used to get expected outputs from new input features not included in the dataset. The two most common subcategories of supervised learning are regression and classification which have their uses based on the nature of the output variable, that is, whether it is quantitative or qualitative. Quantitative input features such as cost, weight, temperature, and area are predicted using regression models. Except for the basic linear regression model [44], several other ML algorithms and models are available to find the best approximate function which gives accurate predictions. Polynomial response surface, support vector regression, decision tree regression, and random forest regression are some of the more advanced algorithms that are in use [45, 46]. Other models such as Gaussian process regression and Bayesian network have an advantage over other algorithms because of their accurate uncertainty quantification in regression problems. With recent advances in deep learning, regression for higher dimensional nonlinear problems is becoming more and more commonplace. The other subcategory of supervised learning, namely classification, is used when the output variable Y is qualitative or categorical, for example, if the cancer tumor is malignant or benign. The hypothesis estimated in a classification problem does the task of finding out the probability of an input vector being in a specific category. Based on the results of the probability thus achieved, the given dataset is categorized into the category with the highest probability. Classification problems often involve no more than two categories, for example, if an email is spam or not spam and if a given image is a human or not a human. Logistic regression, K-nearest neighbor, support vector machine, decision tree, boosted tree, and random forest are examples of widely used classification algorithms [47–49]. Reference [50] presents a plethora of well-studied classification models.

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4.3.1.2 Unsupervised learning While the dataset in supervised learning consists of both the input variables and the label, the dataset in unsupervised learning consists of only input variables and the output or the labels are absent. This approach of learning at first might seem counterintuitive and perplexing as to why it helps one in studying a given dataset but unlike supervised learning, the primary objective in unsupervised learning is not to make predictions. Unsupervised learning is carried out in order to look for patterns among the data points and thus explore the distribution of given data points. The two subcategories in unsupervised learning are clustering and anomaly detection algorithms. Clustering is the process of dividing the given dataset into clusters based on the similarities present in them. Clustering is also known as unsupervised classification. Though there are some common features between clustering and supervised classification algorithms such as making groups with similarities, in supervised classification, the groups are already known and the available labels are used to define the clusters in advance. In clustering on the other hand, even the number of clusters are not known beforehand let alone the type of specific clusters. The clustering algorithm thus finds out useful patterns in the dataset which may not have been known before. Intracluster homogeneity and intercluster separability are different criteria that are considered when clustering is used. Applications of clustering range from object recognition, document retrieval, image segmentation, data mining, and e-commerce applications to genomics. Hierarchical clustering methods, K-means algorithm, densitybased clustering, and the Gaussian mixing model are different examples of clustering algorithms that are widely accepted and used. When high-dimensional noisy data is considered, the recent developments in deep neural networks such as deep autoencoder combined with clustering algorithms efficiently solves the problems at hand [51–55]. Figure 4.4 illustrates unsupervised clustering technique.

Figure 4.4: Unsupervised learning by clustering.

Anomaly detection, which is the other subcategory of unsupervised learning, helps in identifying unexpected behaviors or anomalies in a dataset. For example, the already collected data features for jet engines can be used to detect future anomalies in manufactured jet engines. Anomaly detection used in this sense will find out if a jet engine

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is faulty or normal. To define in brief, anomaly detection is finding patterns in a given dataset that are incongruous with normal or expected behavior. Anomaly detection has become very popular and has its advantages in the industry as it can figure out faulty events in a system which may be detrimental when put into use. Along with the safety of the workers in an industry, intrusion detection, banking frauds, medical conditions, and cybersecurity are other very useful examples where anomaly detection makes the field safer for the general public. Point anomalies and contextual anomalies are further bifurcations of the anomaly detection algorithm. Point anomaly being simpler is the detection of absurd deviations with respect to the already studied data points. The majority of what can be classified as normal or healthy occurs or gathers around a tight region in the feature space whereas an anomaly might occur in an isolated region away from this normal region. The complex twin contextual anomalies are rather more difficult to handle, and they appear as anomalies when studied in a context, but appear normal when studied in isolation. Figure 4.5 illustrates unsupervised anomaly detection technique. Feature B New instances Normal

Anomaly Figure 4.5: Anomaly detection using unsupervised learning.

4.3.1.3 Semisupervised learning When the cost for collecting the data is high or the dataset available itself is limited, for example, in tasks such as autonomous driving, image recognition, and photocategorization, it may not be possible to have a labeled dataset. It may be easier to get an unlabeled dataset as opposed to getting a labeled dataset, which may incur more cost and time resources. This is where both supervised and unsupervised learning techniques fail. Semisupervised learning solves such problems and it acts as the connecting link between supervised and unsupervised learning. It uses the available labeled data points to approximate the best fitting function and uses the more abundantly available unlabeled data points to make the predictions more accurate from a classification problems context. Hosting of other applications, internet content classification, bioinformatics, and speech recognition are some known areas where semisupervised

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Backpropogation Real Images

Sample

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Random Nosie Input

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Sample

Figure 4.6: GANs using semisupervised learning.

learning is used. Generative models (explained in Figure 4.6) and graph-based algorithms benefit from these models to perform better on mixed datasets having both labeled and unlabeled data [56–60].

4.3.2 Overview of reinforcement learning While the previously discussed ML algorithms all work on fixed datasets, reinforcement learning uses a distinctive set of techniques and does not operate on fixed datasets. In reinforcement learning, the model is made to interact in an environment and these interactions and experiences contribute as data points to the learning model. Figure 4.7 illustrates the block diagram of reinforcement learning. The past mistakes and experiences play a role in providing a learning experience for the model. A reward function is used as an intermediary between the experience and the learning from which the model retains the high rewarding experiences and avoids the experiences that charge a penalty or give lesser rewards. Though the trial and error mechanism provides valuable learning experiences to the model, this works against the use of such learning methods in practical applications like autonomous driving or in hazardous industries. It is mainly used in software applications where the risk of error is low and the maximization of rewards is impossible to find using brute-force. The reward can be allocated for a specific outcome or for a specific set of operations performed. One of the major challenges in reinforcement learning is striking the perfect balance between agent exploitation and exploration to obtain efficiency and stability in the learning process by tweaking the variable parameters. In case of an overexploitation scenario, the model may end up stuck in a local optima and not push for the global optima, whereas in the case of an overexploration scenario, the model may not converge to an optima at all. Online advertisement, gaming, and robotics and autonomy are among the common applications of reinforcement learning. Deep Q network,

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Q-learning, policy gradient, and actor-critic algorithm are widely known reinforcement learning algorithms [61–67].

Figure 4.7: Block diagram of reinforcement learning.

4.3.3 Overview of deep learning Deep learning can be defined as a class of ML algorithms in which multiple layers are programmed to extract out more complex and higher-level features from the raw input. The classic example of deep learning is digit recognition from handwritten text; here, the earlier layers play the role of making out the edges in the input, while the later layers identify the digit which is seen. Artificial neural networks, CNN specifically (Figure 4.8), are the most common deep learning techniques along with deep generative models such as the deep Boltzmann machines and deep belief networks [68]. At every layer of nodes in deep learning, the input data is transformed into a slightly more abstract and composite representation of the raw input data. Though with multiple learning iterations, the deep learning model without human intervention can figure out the necessary features and parameters at every layer into play, it is mandatory to make adjustments to parameters. No matter how complex and self-sustainable the machines are made to be, there is always a need for a human to control the knobs. The term “deep” in deep learning is coined considering the number of data transforming layers that make up the model. Technically, deep learning models have more credit assignment path (CAP) depth. The CAP can be said to be the path which defines the relations from the input to the output. Since the output layer in a feedforward neural network is also parameterized, the depth of the CAPs in this case is the number of hidden layers in addition to the output layer. In recurrent neural networks (RNNs), CAP is said to be possibly infinite because every layer can be accessed many times. A specific threshold depth for a neural network to be called “deep” is not defined but in most cases, a depth above two qualifies as a deep learning model [69]. A two layer deep neural network can practically emulate any function in any sense; however, neural networks having

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depths greater than two can efficiently extract out the necessary features and adding more layers may be beneficial in most cases. Input

Feature maps

Convolution

Pooling

Convolution

Fully Connected layer

Pooling Classification

Feature Extraction Output

Figure 4.8: Convolutional neural network architecture.

Deep learning models can be greedily designed layer by layer based on the results obtained. This helps in figuring out what layer is adding value or impairing the result. Deep learning techniques help in getting rid of the feature engineering process as a whole in supervised learning, thanks to principal component decomposition which makes the data compact and derives structures that eliminate the redundancy present. As deep learning techniques can be applied to unsupervised learning as well it gives a sheer advantage as there is more redundancy in unlabeled data as compared to labeled data.

4.4 Artificial intelligence applications in the safety of employees AI is a rapidly evolving technology, which has proved to be a helpful tool in a wide range of industrial sectors. Various ML algorithms have been employed that help in generating accurate results. This has encouraged industries to use such techniques for enhancing their profits and maintaining a risk-free working environment for the employees. A working environment that is prone to accidents can significantly reduce the company’s productivity. Maintaining a risk-free working environment has become mandatory as employees tend to avoid insecure, dangerous, and toxic workspaces. Employers have a responsibility to make sure that the working environment is safe and secure so that the employees are productive and innovative at their workspaces.

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Some of the main areas where AI has come into play include human error elimination, timely response, anomaly detection, and intelligent sensors. A detailed analysis of the latest trends in these areas is provided in the subsections below.

4.4.1 Human error elimination Humans form a major part of any company and are exposed to various kinds of stress-related problems, tiredness, and fatigue. The accidents caused due to the inefficiency of humans, because of such issues, are nontrivial and should be reduced to maintain a risk-free working environment. However, this can be solved using highly efficient ML and deep learning algorithms. If trained properly, these algorithms can provide accurate results as machines are not exposed to such issues of tiredness, fatigue, and stress which lead to human errors. Hence, human errors can be significantly eliminated using AI techniques. Many tools can be developed which might prove fruitful in such instances where the human error rate is unacceptable.

4.4.1.1 PPE kit detection using CNN and RNN The current COVID-19 pandemic has made use of PPE kits mandatory to reduce the transmission rate of the disease. PPE checks are typically conducted by humans and are prone to human errors. This risk can be minimized by automating the task of checking if the employee is wearing proper PPE kits. Deep learning models can detect if an employee is wearing a PPE kit using the video footage made available to the model. If the model detects improper usage of the PPE kit, then an alert message can be sent to the administrator or the person can be avoided entry into the workspace. A video consists of spatial as well as temporal data which should be evaluated to accurately classify the type of video. Deep learning models consist of neurons that can extract potential features. CNNs can detect the necessary features as there exists a huge amount of unnecessary data, which provides minimal advantages for the model when classifying the video type. Further, to detect the temporal characteristics of the video data, RNN can be used. RNNs can capture the order between the frames and enhance the classification accuracy of the model. Thus, a hybrid approach, consisting of CNN and RNN, can be used to automate the PPE kit detection process and eliminate the human error problem.

4.4.1.2 AI-based solutions for cyberattacks In cybersecurity, minute human errors can give breakthroughs to cybercriminals that would help the hacker to exploit the victim’s privacy. According to the 2021 Verizon

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Data Breach Investigations report, 36% of the breaches involved phishing attacks. Cybercriminals execute phishing attacks cardinally by manipulating human behavior. Workplaces consist of humans undergoing various kinds of stresses including financial, emotional, and social stress. These human weaknesses are exploited by the cybercriminals to execute phishing attacks, enabling the attackers with a plethora of ways to manipulate the victim. In a phishing attack, the victim is forced to give out private credentials, possibly unknowingly, and thus allowing the attacker to access bank account details or social media login details. Phishing techniques range from legitimatelooking emails to counterfeit websites. Detecting such cyberattacks is a tedious and error-prone process and must be automated to improve efficiency and eliminate human error. Various reliable AI algorithms have been developed with high accuracy in detecting various kinds of cyberattacks. In [70], Yadav et al. proposed a novel phishing URLs detection algorithm using ML techniques. The approach has been tested against several ML algorithms including k-nearest neighbor, random forest, support vector machine, and logistic regression. An accuracy of 99.57% is obtained for the ISCXURL-2016 dataset, which can be considered reliable for real-time environment usage.

4.4.2 Worker detection and tracking Worker’s safety is a matter of primary concern in industries where workers have to work in hazardous environments such as construction industries and chemical industries. AI has enabled highly skilled robots to imitate humans. AI-enabled robots can replace humans and operate themselves in dangerous environments rather than risking human lives. Recent advances in AI have helped researchers to create tools that contribute greatly to eliminate some serious threats to the workers. The Industry 4.0 era has given rise to machines that have to be continuously watched and analyzed to avoid failures and threats. Some AI-enabled tools have been developed that eliminate the threat that this machinery imposes on the operators and workers.

4.4.2.1 Worker detection and tracking for the safe operation of construction machinery The machinery used in the construction industry imposes a serious threat to the workers and operators. This risk is enhanced by the poor visibility of the surrounding environment where such constructions take place. In [71], Kim et al. propose an integrated worker detection and tracking mechanism to ensure the safe operation of construction machinery. To ensure real-time usage of the mechanism, complementary metal-oxide semiconductors image sensors have been used. These sensors were installed on the front, rear, left, and right sides of the construction machinery. Further, the real-time data obtained by these sensors were analyzed and workers were traced

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and detected using deep learning techniques. The authors use the latest version of You Only Look Once and Siamese networks to ensure that the tool operates with high efficiency. Training and testing of the mechanism were done using ImageNET and Pascal VOC 2012 datasets. The built mechanism had an accuracy of 96.04% and can be considered high enough to be used in detecting and tracking workers which operate with hazardous earthmoving machinery. An alert message can be passed to the workers if the tool detects risky positions or activities relating to the workers. Thus, a safe environment can be maintained and accidents due to construction machinery can be minimized using such tools.

4.4.2.2 Multiple workers tracking to avoid mishaps Many accidents arise due to crowded gatherings of workers and improper usage of safety gear provided to the workers. These accidents can be prevented by tracking multiple workers gathering at places prone to accidents. Angah and Chen [72] propose a gradient-based method, along with deep learning techniques, to track multiple construction workers. The study involves three stages namely detection, matching, and rematching. For object detection, the authors use region-based CNNs (R-CNNs) that help in providing better accuracy in detecting workers. The gradient-based tracking methodology detects small changes in between the image frames. Trajectories of the objects are detected in the matching stage. Unmatched detections are noted and further analysis is done to ensure maximum efficiency. Multiobject tracking accuracy (MOTA) is used as the primary performance analysis measurement. The authors have obtained state-of-the-art 56.07% MOTA when evaluated using the benchmark multiple object tracking challenge (MOT) dataset. When evaluated on construction sites, the authors obtained a MOTA of 79.0% on four testing videos. Thus, the proposed mechanism can successfully detect multiple construction workers. Many accidents that arise due to multiple workers gathering at a single place can be avoided and a risk-free environment can be maintained using such tools.

4.4.3 Anomaly detection The Industry 4.0 era has increased the number of employees working at a particular site. With the increase in the number of employees, keeping a watch on every employee has become a cumbersome task. Some automation needs to be employed to enable a practical and accurate solution for this problem. Detecting anomalies in employees’ behavior and reacting to such anomalies is important to maintain a healthy environment in the workplace. A worker who has collapsed or is injured accounts for the anomaly in the workers’ behavior. Such anomalies must be reported upon as soon

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as possible and appropriate actions must be taken. With the high rate of accuracy in detecting such anomalies, ML and deep learning techniques have contributed greatly in significantly reducing late treatments of such mishaps.

4.4.3.1 AI-enabled solution for detecting anomalous behavior in employees Using cameras and AI boxes, the worker’s movement can be tracked and any anomaly found can be reported to the supervisor. In [73], the solution monitors suspicious acts such as a worker falling on the ground, getting injured, unauthorized entry, or abnormalities in the number of workers. These suspicious acts can be monitored and action can be taken upon by the supervisor. The solution also reports posture abnormalities using the supervised learning model which is trained using in-house data. In manufacturing units or food-processing units, recognizing a person with malicious intent is a tough job as the workers mostly wear masks, caps, and uniforms. To encounter such scenarios, each employee or laborer working in the area is given an ID. If any abnormalities are detected with respect to the employee’s behavior, then the employee is identified using the ID provided to the employee. Such vision-based anomaly detection techniques can be favored over sensor-based anomaly detection techniques. Sensor-based techniques are expensive and require rigorous installation and the costs associated with them are high.

4.4.3.2 GuardRail detection using transfer learning and deep convolutional neural networks Unprotected edges are one of the major reasons behind workers falling from heights, especially in construction industries. The traditional approach to tackle this issue is manually human inspection, which is both unreliable and limited. A better approach would be to use intelligent and automated systems for guardrail detection. Luo and coworkers [74], propose a CNN and transfer learning model for high accuracy guardrail detection. The authors use augmented images with various types of background images as the training datasets. Further, the VGG16 model is used to extract the features from the augmented images. The extracted features are further passed to the CNN for guardrail detection. For testing the obtained CNN + VGG16 model, the authors use images from various construction job sites and Google. An accuracy of 96.5% was obtained and can be improved using a large dataset obtained by augmenting the images using various factors. This technology can be used to prevent accidents caused by workers falling from dangerous heights. An AI-based solution is reliable and feasible and portrays high accuracy in detecting guardrails and alerting people in real time rather than relying on manual human inspection.

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4.4.4 Robots and intelligent sensors Workers in industries carry out dangerous tasks such as drilling, painting, welding, or assembly of machinery, which can cost their lives if some unfortunate happenings take place. To eliminate the risk toward such workers dealing with hazardous tasks, robots that carry out the work with at least the same efficiency as humans have been built. Robots working in such conditions help in saving human lives as workers are kept away from such machines or activities which can be deleterious to them. Humans get bored doing the same jobs again and again leading to some errors or accidents. Robots are trained to do the same job repetitively without getting bored, which isn’t the case in humans. Nowadays, in many industries, humans work harmoniously with robots, where robots do the high-risk tasks and the humans just guide the robots to do the task. Such a collaborative environment is favored by industries where there are job vacancies for jobs that humans tend to avoid.

4.4.4.1 Safeguarding the factory floor Factories are places filled with signals, warnings, and hoardings to warn workers about hazardous environments. However, obeying the warnings relies on the workers’ willingness to comply. To overcome this issue, robots have been created which warn the operators when they are nearing a threat. Their contact with hazardous machinery is minimized and a safe distance is maintained between the worker and the machinery. For example, machinery and its surrounding area are monitored and controlled by robots. Robots can detect when there is a human worker near any dangerous machinery. Further, robots can reduce the speed of the machinery or stop the working of the machinery depending upon the proximity of the worker. Such usage of robots and sensors can significantly reduce the casualties caused by machinery.

4.4.4.2 Brainwave-driven human–robot collaboration Many industries have enforced collaborative working of humans and robots to increase efficiency as well as mitigate the risk factor. However, in some spaces, human–robot collaboration can adversely affect efficiency because of poor communication between the both. To encourage a harmonious working between the worker and robot, in [75], the authors propose a brainwave-driven methodology in which the robot learns about the worker’s tendency and workload using electroencephalograph (EEG) signals captured from the workers’ brain. The worker wears an EEG gadget, which captures his brainwaves and the signals are further accessed by the robot. The robot evaluates the obtained signals for the workload of the collaborating worker, and the performance of the robot is

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adjusted accordingly. This enforces a worker-centric approach to human–robot collaboration along with safe communication between the worker and robot.

4.5 Future opportunities The domain of ML and AI is advancing and will continue to boost the way practices are carried out in the industry, as the Industry 4.0 era proceeds. Studies indicate that the construction industry is among the few industries which is reluctant to adopt these techniques despite favorable outcomes as observed in others. One of the reasons are the abstruse and, sometimes, inexplicable ML models, which may not provide a pellucid reason for yielding a particular outcome [76]. Hence, these models are called black-box models which have a bleak prospect of being trusted by the employers. This limitation can be countered by the utilization of XAI, which overcomes the barrier of explainability [77]. The transparency provided to employers may garner assurance in these sophisticated AI-powered machines, further bolstering employee safety and accident prevention mechanisms.

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[68] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. [69] Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48. [70] Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Network Computer Communications Network, 175, 47–57. [71] Son, H. & Kim, C. (2021). Integrated worker detection and tracking for the safe operation of construction machinery. Automated Construction Technologies, 126, 103670. [72] Angah, O. & A. Y. Chen. (2020). Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy. Automated Construction Technologies, 119, 103308. [73] BroaderBiz Inc. AI-enabled monitoring of factory workers. VANTIQ. Accessed July 15, 2021. https://vantiq.com/connect/solution/ai-watching-system-for-factory-workers/. [74] Kolar, Z., Chen, H., & Luo, X. (2018). Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction, 89, 58–70. [75] Liu, Y., Habibnezhad, M., & Jebelli, H. (2021). Brainwave-driven human-robot collaboration in construction. Automation in Construction, 124, 103556. [76] Alaloul, W. S., Liew, M. S., Zawawi, N. A. W. A., & Kennedy, I. B. (2020). Industrial Revolution 4.0 in the construction industry: Challenges and opportunities for stakeholders. Ain Shams Engineering Journal, 11(1), 225–230. [77] Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

M. Revathi, D. Ramyachitra

5 Hybrid entropy-based support vector machine with genetic algorithm for classification Abstract: In a real-world database, there are a large number of traits and information. These datasets also contain missing values, which might lead to incorrect categorization predictions. Extraction of concealed predictive data is a difficult and time-consuming operation. Developing classifier model is one of the major research areas in knowledge mining using datasets. Classification is a mechanism that labels data enabling economical and effective performance in valuable analysis. Research has indicated that the quality of the feature may cause a backlash to the classification performance. In this research work, a classifier is modeled and designed using hybrid entropy-based support vector machine with genetic algorithm (HESVMGA). There are three stages in developing the classifier. The first stage handles missing values using rejection and imputation by the nearest neighbor value. The nearest neighbor is identified by Euclidean distance measure. The second stage selects relevant features from the keel dataset using entropy-based support vector machine (SVM). The process of feature selection is applied in two ways: forward feature selection and backward feature elimination. The third stage is development of classifier using hybrid SVM and genetic algorithm. The proposed research HESVMGA algorithm is applied for four datasets, such as Ecoli, Wisconsin Breast Cancer, Glass, and CHD2, and calculates the performance evaluation. Qualitative assessment of proposed HESVMGA classification mechanism has been made with classification accuracy of 94.7% and better precision time, respectively. The proposed HESVMGA compared the three types of conventional methods, such as SVM, naive Bayes, and decision tree, after the depth evaluation proposed the HESVMGA method to produce better efficiency, recall, precision, and time responsibility. Statistical analysis of accuracy values and computational time portrays that the proposed schemes provide compromising results over existent methods.

5.1 Introduction Data mining reveals an excessive volume of dataset through a thorough analysis resulting from the unmanageable growth of global data. The hidden patterns and concealed relationships between the variables have been revealed, thanks to data analytics. Data

M. Revathi, D. Ramyachitra, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, 641046 https://doi.org/10.1515/9783110781663-005

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is accumulated in every facet of life in the digital era, thanks to the growth of computerized database systems. To investigate and extract hidden knowledge from these data, knowledge extraction and representation approaches are routinely used. KDD’s data mining method deals with data analysis employing computerized approaches, techniques, and tools to extract hidden knowledge from data in databases, data warehouses, and information repositories [1]. Data mining is interdisciplinary by its nature because it integrates different techniques from database technology, pattern recognition, statistics, and machine learning [2]. Data mining involves in developing models for determining patterns from the dataset. Data mining approaches can be predictive or descriptive. Predictive data mining techniques are used to create a model that can be used for classification and regression, whereas descriptive data mining methods employ association rules and clustering to build patterns from the data [3]. UCI machine learning repository and the KEEL data repository classification process are interactive and iterative process involving a number of steps. Khan et al. [4] have discussed the steps to be followed in UCI machine learning repository dataset. Data selection, data cleaning, data integration and reduction, data mining, pattern evaluation, and knowledge representation are among the primary phases in the UCI machine learning repository dataset. Different databases and data repositories might be used to find the right dataset. Missing values, outliers, and noisy data can all be dealt with more easily if the dataset is preprocessed. The data from several repositories must be combined. Dimension reduction, feature selection, and data transformation aid in the creation of a manageable dataset with important features for the next UCI machine learning repository dataset phase. In the UCI machine learning repository dataset, data mining is a critical phase. It aids in the extraction of patterns, models, and rules from data. Using proper performance measures, the retrieved result must be analyzed. A decision tree, an IF-THEN rule, or a mathematical model can all be used to express knowledge [5]. Figure 5.1 explains the general framework for the proposed classification architecture. To deal with real-world problems in industry and commerce, knowledge management now relies primarily on KEEL data classification. It is utilized in financial data analysis, retail, telecommunications, intrusion detection and prevention in networked systems, and clinical data analysis [6]. Especially healthcare centers have facilities for generating, collecting, and storing large amount of medical data. Application of data mining techniques in clinical datasets helps to extract valuable knowledge. Clinical datasets extract interesting relationships among features to develop models and patterns that assist in decision-making process for diagnosis and treatment planning [7]. Knowledge extracted from datasets using data mining techniques becomes useful in controlling subjectivity errors due to overload work and provide indication in decision-making [8]. Predictive data mining creates models that are utilized in the diagnosis, prognosis, and treatment planning processes. Descriptive data mining aids in the discovery of interesting connections between various symptoms, diseases, survival rates, and lifestyles. Economic position, educational levels, and demographics such as

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DATASET

Handling missing values / outliers

Feature selection /entropy method

Classification

Performance Evaluation Figure 5.1: System framework.

age, sex, and ethnicity all play a role in life style [9]. Clinical data can be collected from various medical images that visualize tissues, organs, or body parts of the patient, physical checkup and sign and symptom of the patient, laboratory results, clinician observations and interpretation during diagnosis, prognosis, and treatment of the patient [10]. Clinical data mining is unique from other application areas due to heterogeneous and voluminous of the data and clinician interpretation. Since clinical data are collected on humans, there are ethical and legal practice to protect misuse and abuse of patient’s data. The knowledge mined from dataset is crucial to develop decision support systems (CDSS). CDSS is a computerized expert system that is developed using knowledge extracted from clinical datasets and expert judgment to assist the clinician in decisionmaking about patient’s condition. CDSS has three components namely the knowledge base, inference engine, and user interface [11]. The knowledge base consists of the extracted knowledge in the form of either as IF-THEN rules, mathematical functions or probabilistic association of signs, and symptoms with diagnosis or drug to drug or drug to food reaction. The inference engine uses reasoning mechanism for interpreting the patient data in consultation with the knowledge base. User interface is a communication mechanism used as a way of getting the patient data into the system and delivering the output of the system to the user. The reminder of this paper is organized as follows: Section 5.2 explains various techniques in data preprocessing, data classification, and its related work. Section 5.3 explains the proposed hybrid entropy-based support vector machine with genetic algorithm (HESVMGA. Section 5.4 presents the proposed HESVMGA and existing support

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vector machine (SVM), naive Bayes, and decision tree experimental results comparison. Finally, Section 5.5 provides the concluding remarks and future scope of the proposed chapter.

5.2 Literature review This chapter presents a review of related work on knowledge mining from dataset carried out by other researchers using one or more of the selected various datasets. The review has explored handling of missing values, selection of relevant features, classification techniques applied, and performance evaluation in each research work. This section summarizes some of the most significant existing works in these fields. Different data mining methods can be used to extract knowledge from datasets using a classification strategy [12]. The dataset to be mined and the categories of knowledge to be mined determine the data mining methods used in the KDD process. These sections cover the most common data mining algorithms for categorization. Decision tree induction is an iterative data partitioning process of generating decision tree for classification purpose [13]. A decision tree is a node-and-branch structure that looks like a tree. There is one entering node and one or more exiting nodes in the internal nodes. The class label is represented by the leaf node, which has only one incoming but no outgoing branch. For splitting purposes, each branch of the node displays the value of the appropriate feature. The root node and internal node represent the selected feature of the dataset and the leaf node represents the class label of instances. The three parameters used for generating of decision tree from dataset are feature selection method, feature value splitting criterion, and data partitioning stopping criteria [14, 15]. A Bayesian classifier uses statistical data to predict the class of an instance. The naive Bayes classifier uses the Bayes theorem to examine three probabilities: prior probability, likelihood, and evidence [16]. The occurrence of each class in the training set is called prior probability; the occurrence of the instance to each class is called likelihood; and the presence of the instance in the training set is called evidence. K-nearest neighbor (k-NN) also referred to as lazy learners, in which classification is performed without developing classifier model from the training dataset. As the class label of unknown instances is assigned by the nearest neighbor of the k-known instances, k-NN is also referred as instance-based learners (IBK) [17]. The similarity of all the training samples to each unclassified sample must be checked. If the training sample is big, the k-NN classifier is straightforward and easy to learn. As a result, when the size of the training sample grows larger, the k-NN classifier becomes prohibitively expensive and extensive storage is required. As the k-NN classifier doesn’t develop classifier model from the training set, it is extremely slow in classification process [18].

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Backpropagation is a neural network learning process that involves repeatedly changing the values of the linking weights in order to reduce the difference between the neural network’s output value and the target class [19]. The neural network that associated with backpropagation learning algorithms is referred as backpropagation neural networks [20]. If one of the following circumstances is met, the learning process will be halted: if the set number of iterations has been reached; if the specified minimal error has been reached; if the updated weight has not reduced the error. MYCIN, INTERNIST-1, QUICK, and INTERCare are a few CDSS examples. MYCIN is an early CDSS that was created in the 1970s to help clinicians detect bacteria that cause infections including bacteremia and meningitis [21]. INTERNIST-I was created between 1972 and 1973 to aid clinicians with internal medicine diagnosis [22]. Nahar et al. [23] have compared six classifiers namely Naive Bayes, sequential minimum optimization (SMO), IBK, AdaBoostM1, decision tree learner (C4.5), and rule-based classifier using partial decision tree (PART) algorithm using WEKA tool for classifying heart disease. They have used Cleveland heart disease dataset from UCI machine learning repository. They have also compared medical knowledge-driven feature selection (MFS) against computational intelligence-based feature selection (CFS) methods [24]. They have stated that Cleveland heart disease data becomes imbalanced when considering binary classification. In their feature selection and classification comparison method, first they have partitioned the dataset into five using the class label of each sample namely H-0 (healthy), Sick1 (low heart disease), Sick2 (medium heart disease), Sick3 (high heart disease), and Sick4 (serious heart disease). Feature selection for each class label of the dataset has been done using MFS and CFS. They have concluded that MFS has shown promising results for feature selection. They achieved highest accuracy of 100% for H-0 using IBK, 85.29% for Sick-1 using Naive Bayes, 88.24% for Sick-2 using SMO, 88.24% for Sick-3 using the two classifiers namely SMO and C4.5, and 97.05% using all the classifiers namely Naive Bayes, SMO, IBK, AdaBoostM1, C4.5, and PART. Bouali and Akaichi’s [25] proposed method has used binary classification for each class label of the heart disease. The hybrid algorithm produces the better results other than single machine learning approach [26]. There is no resolution method whether a single test dataset can get a class label of two more. The suggested research focuses on enhancing classification accuracy rather than placing a premium on data classification.

5.3 System design In this research we present a work that is done to design and evaluate approaches to handle missing values, attribute noise, and imbalanced class distribution in datasets to predict. In this section, a brief description of HESVMGA in knowledge discovery is presented. The goal of this step is to choose the best classification approach for a

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given dataset. Because no generalization can be made about the optimal classification approach, this step has mandated the necessity to test each and every prediction and analysis for a given dataset empirically. Our proposed model uses unsupervised learning to identify the optimal hybrid classification methods because the dataset under study is incomplete. To fill in missing values in the dataset, a hybrid method that yields more condensed sets of data is used. The entire dataset is then used to extract patterns (i.e., correctly classified examples). The proposed system framework consists of feature selection subsystem and classification subsystem. Figure 5.2 illustrates the system framework of HESVMGA.

5.3.1 Entropy Entropy was introduced by White [27]. The information is measured by the probability of the occurrence of feature values in each class labels as mentioned in eq. (3.1):     m n X X Cj Cj log2 P (3:1) Pð f i Þ P Eð F Þ = − fi fi i=1 j=1 where E(F) is entropy of feature (feature set) F with feature value of f1, f2, f3, . . ., fm, P(dj/fi) occurrence of each feature value in each class label. Cj is the class label. The number of class labels (n) in a dataset determines the maximum entropy (Emax) of a feature: Emax = log2 n

(3:2)

The UCI and KEEL datasets each have two class labels, whereas the Cleveland disease dataset contains five. The maximum entropy for hepatitis and Wisconsin breast cancer (WBC) becomes 1 using eq. (3.2), while the maximum entropy for the Cleveland dataset becomes 2.321. Entropy is used to calculate the information value of features or feature subsets based on the incidence of feature values in each class label in this study. Entropybased rough set theory is used for feature subset selection. The selected feature subset to represent the dataset is referred as reduct [28]. Entropy-based rough set theory feature selection consists of subset generation, subset evaluation, stopping criteria, and validation of the result. For feature subset generation, two heuristic approaches are used: forward feature selection and backward feature deletion [29]. Entropy of feature subset is used as a subset evaluation. Feature subset generation is stopped when the entropy value of the feature subset becomes 0. Forward feature selection [30, 31] starts from empty set as a reduct. The best feature based on the minimum value of entropy is selected and added to the reduct. The feature selection process continues until the entropy of the selected feature subset becomes 0. Backward elimination method starts from the whole feature set as a reduct.

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Clinical Dataset

Preprocessing

Pre-processed Dataset

Testing Set

Training set

SVM

Testing GA Classifier

Training with GA

GA Classifier

Classification Result

Performance Evaluation

Figure 5.2: System framework of HESVMGA.

Reject the feature with the highest entropy. Repeat the rejection of the next “worst” feature if the entropy of the remaining feature subset is larger than 0. When the entropy of the remaining feature set reaches 0, the rejection of the worst feature comes to an end. Because the feature selection technique is based on entropy, the positive region of each dataset must first be estimated using rough set theory. The positive region of the dataset is used as an input for the feature selection process. The processes for selecting forward characteristics and deleting backward features are depicted in Figure 5.3. For classification, the SVMGA is used. The SVM’s kernal is updated using a genetic algorithm (GA) [32]. The second structure uses SVM with five kernels in the output since the Cleveland heart disease dataset has five class labels. The buried layer neurons and output use the sigmoid activation function.

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Steps of forward feature selection Input: Positive region of the dataset (I).I = Fi ∪ D, ≤i≤Fmax Process Logic Step : Initialize a reduct(R) with empty set. R= { }. Step : Compute entropy (E) of each feature. Step : Select feature (F) with minimum entropy. Step : Insert the selected feature to the reduct. R = R ∪ F Step : If entropy of R =  stop. Else go to Step . Step : Generate a superset of R with one more feature. Step : Compute the entropy of each superset. Step : Select the superset with minimum entropy as a reduct. Step : Go to Step . Output: Selected features and decision class I = R ∪ D Steps of backward feature elimination Input: Positive region of the dataset (I).I = Fi ∪ D, ≤i≤Fmax Process Logic Step : Initialize a reduct(R) with all feature set. R = Fi . Step : Compute entropy (E) of each feature of the reduct. Step : Remove the feature (f) with maximum entropy R = Fi - f Step : If the entropy of reduct R = , stop, else go to Step . Output: Reduct and decision class I = R ∪ D Figure 5.3: Pseudocode for entropy feature selection approach.

GA consists of three processes: initializations of population, selection of chromosome based on the fitness value, and reproduction to produce offspring. Population is a collection of chromosome that competes for achieving better performance. Each chromosome is composed of equal number of genes. In this work, gene represents the weight that connects neurons of each layers; chromosome represents the number of weights in the neural network. The two reproduction operators in GA are crossover and mutation. Crossover operator is performed by interchanging the weight between two or more chromosomes. Mutation is performed by interchanging the position of the weight in a chromosome [33, 34]. The number of weights between each layer of mutations is the product of the genes in the corresponding layers. Equations (3.3)–(3.6) present how to calculate the number of genes: Vi, j = ni nj

(3:3)

Wj,k = nj nk

(3:4)

W = ðNi + Nk ÞNj

(3:5)

For each input layer neurons, output value of neuron (Xo s) is the same as the input value ðXi Þ:

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Xo = Xi , i = 1, 2, 3, . . . n For each output training dataset kernel point, the input (Oink ) and output (OK ) are computed as follows: Oink =

m X

(3:6)

Hj wjk

j=1

Ok =

1 1 + e−Oink

K = 1, 2, 3, . . ., p

Input: Clinical dataset with specified features (Reduct); initial crossover rate is .; mutation rate is .; Step : Generate  initial chromosomes of population with □ number of weights. Step : Allocate the number of weights to connecting links of input to hidden layer and hidden to output layer. Step : Compute the fitness value using each chromosome using Equation (.). Step : Sort the chromosomes based on the fitness value from highest to lowest Iteration =  Step : Select the first  chromosome. Step : Apply crossover to obtain  offspring. Step : Apply mutation on Step  and Step  to get  new offspring. Step : Compute the fitness value of the selected  population and  new offspring. Step : If iteration =, select chromosome that gives maximum fitness value and Stop, or else go to Step . Step : Iteration=Iteration+, Go to Step . After learning is performed on training dataset with ten-fold cross validation for  iterations, the trained neural network provides an optimized model for classification. The generated classifier model has to be evaluated using the testing set. Figure 5.4: Entropy-based support vector machine with genetic algorithm HESVMGA.

After obtaining the output value, error (er) has to be calculated using the square of the difference between the output value (ok ) and target classðTk Þ mean-squared error (mse) becomes the average of summation of errors of each instances of the training dataset. In every iteration, learning is performed by minimizing the error. Figure 5.4 shows pseudocode for proposed hybrid SVM with GA classification methods: erk = ðOk − TK Þ2  n  1 X mse = erk N k=1 Fitness =

1 mse

(3:7) (3:8) (3:9)

The learning procedure is carried out by utilizing GA to choose the chromosome in the population with the highest fitness value. The following is a diagram of the GA method for updating weights.

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5.4 Results and discussion The proposed methodology is applied by making use of PYTHON3.6IDE on Intel(R) Core (TP) i3-2410M CPU @ 3.20 GHz and 8 GB RAM. In this section it is used to compare the selected reduct with prior knowledge about the data. Prior knowledge of the dataset can be obtained from the domain expert. If no prior knowledge is available, result validation is performed in the dataset before and after feature selection using learning algorithms. If more than one reduct is selected, comparison can also be done among candidate reducts. All the comparison is done by evaluating the classification performance of the reduct. Each reduct of the clinical dataset has been partitioned to training-testing with the rate of 80–20. The training dataset has been validated using 10-fold cross-validation technique. The 10-fold cross-validation technique divides the training dataset into 10 equal subsamples. The training phase is performed in nine subsamples and the remaining one subsample is used for validation purpose. The learning and validation process repeated for 10 times in which each subsample is used exactly once as the validation data. Two different models, SVM and GA, have achieved better performance accuracy in Ecoli, WBC, Glass, and CHD2 datasets, which were developed through prediction. Also compare the performance of HESVMGA method to other standard classifiers: SVM, naive Bayes, and decision tree. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset contains 32 types of attributes with 569 real multivariate characteristics, the Ecoli dataset contains 8 attributes with 332 multivariate characteristics, the Glass dataset contains 10 attributes with 214 multivariate characteristics, and the CHD dataset contains 75 attributes with 303 categorical, integer, and real instances in multivariate characteristics. Table 5.1 explains the sensitivity comparison for Ecoli dataset, WBC dataset, Glass dataset, and CHD2 dataset in decision tree, naive Bayes, SVM, and hybrid entropybased SVM with GA (HESVMGA). Figure 5.5 shows the proposed system sensitivity achieved far better than all the four types of datasets in three existing system. In terms of sensitivity rate of proposed system Ecoli dataset is 91.1%, WBC dataset is 92.3%, Glass is 94.4, and CHD2 is 90.85. Table 5.1: HESVMGA classification results for sensitivity comparison. Algorithm

Decision tree Naive Bayes SVM HESVMGA

Sensitivity Ecoli dataset

Wisconsin Breast Cancer

Glass dataset

CHD dataset

. . . .

. . . .

. . . .

. . . .

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Comparatively other existing system archives of Ecoli dataset 6.4% of SVM, 9.6% of naive Bayes, and 12.33% of decision tree are lower than the proposed system. In WDBC dataset 4.9% of SVM, 7.8% of naive Bayes, and 9.8% of decision tree values are lesser than the proposed system. In Glass dataset 6.9% of SVM, 10.65% of naive Bayes and 12.76% of decision tree values and CHD2 dataset 12.65% of SVM, 10.96% of naive Bayes, and 9.15% of decision tree values are lesser than the proposed system. Sensitivity 100

Sensitivity in %

80 60

Decision Tree

40

Naive Bayes

20

SVM HESVMGA

0 E-coli

WBC

Glass

CHD2

Figure 5.5: HESVMGA classification results for sensitivity comparison.

Table 5.2 explains the specificity comparison for Ecoli dataset, WBC dataset, Glass dataset, and CHD2 dataset in decision tree, naive Bayes, SVM, and HESVMGA. Table 5.2: HESVMGA classification results specificity comparison. Algorithm

SVM Naive Bayes Decision tree HESVMGA

Specificity Ecoli dataset

Wisconsin Breast Cancer

Glass dataset

CHD dataset

. . . .

. . . .

. . . .

. . . .

Figure 5.6 explains the proposed system specificity achieved far better than all the four types of datasets in three existing system. In terms of specificity rate of proposed system Ecoli dataset is 90.65%, WBC is 93.5%, Glass is 91.1, and CHD2 is 87.45. Comparatively other existing system archives of Ecoli dataset 4.2% of SVM, 2.53% of naive Bayes, and 7.32% of decision tree are lower than the proposed system. In WDBC dataset 4.05% of SVM, 4.62% of naive Bayes, and 9.15% of decision tree values are lesser than the proposed system. In Glass dataset 2.2% of SVM, 6.66% of naive Bayes, and

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9.99% of decision tree values and CHD2 dataset 3% of SVM, 5.21% of naive Bayes, and 2% of decision tree values are lesser than the proposed system. Specificity

Specificity in Msec

95 90 85

Decision Tree

80

Naive Bayes SVM

75

HESVMGA 70 E-coli

WBC

Glass

CHD2

Figure 5.6: HESVMGA classification results specificity comparison.

Table 5.3 explains the time duration (ms) for desired efficiency comparison for Ecoli dataset, WBC dataset, Glass dataset, and CHD2 dataset in decision tree, naive Bayes, SVM, and HESVMGA. Table 5.3: HESVMGA classification results time duration (ms) comparison. Algorithm

SVM Naive Bayes Decision tree HESVMGA

Time duration (M.sec) Ecoli

Wisconsin Breast Cancer

Glass dataset

CHD dataset

. . . .

. . . .

. . . .

. . . .

Figure 5.7 explains the time taken for each algorithm for desired efficiency and all the four types of datasets in four classification approach. In terms of time taken in milliseconds Ecoli dataset is 11.12 ms, WBC is 19.15 ms, Glass is 6.33 ms, and CHD2 is 9.98 ms. Comparatively other existing system archives in Ecoli dataset is 3.3 ms in delay in SVM, 2.73 ms naive Bayes, and 5.73 ms decision tree of time delay to achieve the desired efficiency comparatively proposed system. In WDBC dataset 2.06 ms of SVM, 10.41 ms of naive Bayes, and 3.15 ms of decision tree time delay to achieve the desired efficiency compared to the proposed system. In Glass dataset 1.22 ms of SVM, 4.92 ms of naive Bayes, and 6.89 ms of decision tree time delay and CHD2 dataset 0.27 ms of SVM, 1.24 ms of naive Bayes, and 4.25 ms of decision tree time delay to achieve the desired efficiency compared to the proposed system.

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Time Duration 35

Time in Msec

30 25 20

Decision Tree

15

Naive Bayes

10

SVM

5 HESVMGA

0 E-coli

WBC

Glass

CHD2

Figure 5.7: HESVMGA classification results time duration (ms) comparison.

Table 5.4 explains the efficiency comparison for Ecoli dataset, WBC dataset, Glass dataset, and CHD2 dataset in decision tree, naive Bayes, SVM, and HESVMGA in terms of hybrid algorithm. Table 5.4: HESVMGA classification results accuracy comparison. Algorithm

SVM Naive Bayes Decision tree HESVMGA

Accuracy Ecoli

Wisconsin Breast Cancer

Glass dataset

CHD dataset

. . . .

. . . .

. . . .

. . . .

Figure 5.8 explains the efficiency for each classification algorithm for four types of datasets. In terms of accuracy of proposed system Ecoli dataset is 90.65%, WBC is 94.45%, Glass is 86.16, and CHD2 is 89.15. In Ecoli dataset 7% of SVM, 3.09% of naive Bayes, and 11.7% of decision tree, lesser value of efficiency is achieved compared to the proposed system. In WDBC dataset 4% of SVM, 4.56% of naive Bayes, and 6.99% of decision tree, lesser value of efficiency is achieved compared to the proposed system. In Glass dataset 2.83% of SVM, 5.05% of naive Bayes, and 6.38% of Decision tree and CHD2 dataset 1.3% of SVM, 4.5% of naive Bayes, and 7.31% of decision tree, lesser value of efficiency is achieved compared to the proposed system. The performance of HESVMGA for classification of the selected clinical datasets is presented in Table 5.4 R1 and R2 refer to the reduct obtained from forward feature selection and backward feature elimination, respectively. Higher performance result is achieved using reducts generated from forward feature selection for Ecoli, WBC, Glass dataset, and CHD2 dataset and reducts generated from backward feature elimination

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Accuracy 100

Accuracy in %

95 90 Decision Tree 85 Naive Bayes 80 SVM

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70 E-coli

WBC

Glass

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Figure 5.8: HESVMGA classification results for accuracy comparison.

method for four types of dataset. The performance result indicates that the developed classifier can be used for classifying the selected dataset to assist the clinician in decision- making. After verifying the results, it is proved that designed research can be used for real-time data classification in various environments without any error absolutely. The system is able to provide the solutions for the problems faced in real time and without delay perfect achievement is succeeded.

5.5 Conclusion In this research work, a classification model for mining knowledge from clinical datasets that assists a clinician in clinical decision-making has been developed. This research contribution presents an approach for HESVMGA for the classification of four different types of datasets. Ecoli, WBC, Glass dataset, and CHD2 dataset were all used to tailor and validate the model from the UCI machine learning repository. Using an entropy-based SVM, forward and backward elimination has been utilized to select features. The feature’s entropy value was used to select and eliminate. The GA-based single-layer feed forward neural network uses the specified feature as input. By adjusting the weights of the SVM, a GA was used to learn from the training sample dataset. Ecoli, WBC, Glass dataset, and CHD2 dataset had accuracy of 90.65%, 94.45%, 86.16%, and 89.15%, respectively. The performance of the classifier has been compared with conventional classifier techniques and other researchers’ works. The HESVMGA classifier can be used for the design and development of efficient medical diagnosis systems. The use of decision support system has revolutionized patient care, industry analysis, and treatment in the health care industry. More research can be done using the quick reduct entropy theory indiscernibility relation method; HESVMGA application of data mining advice for feature selection; and the HESVMGA application of data mining

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advice for feature selection. For data partitioning, applying oversampling techniques for a class label with small number of instances and undersampling techniques for a class labels with large number of instances to justify the imbalanced number of instances in class labels would enhance the performance of the classifier. Furthermore hybrid classification approaches using two or more classifiers may enable knowledge engineers to design efficient decision support systems in real-world scenarios. In future application of hybrid optimization techniques and bio-inspired artificial intelligence approaches would yield better classifier models that can be used for the design and development of decision support systems to improve the efficiency.

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Gaurav Singh, Anushka Kamalja, Ashutosh Karwa, Pallavi Chavan✶

6 Decision system for bone disease diagnosis using crisp rule set theory Abstract: This chapter presents an intelligent system for bone disease diagnosis developed using a rule-based system. This system simulates a patient’s consultation with a doctor. The patient is presented with a questionnaire interrogating the symptoms, which are further used to analyze the bone disease. The objective of the work is to identify a suspected bone disease according to the user’s response regarding the symptoms and to impart them with relevant information that further helps in consultation with a doctor. This interactive system is designed in such a way that it doesn’t require any medical expertise to use it. The advantage of a decision system as an aid to reducing the complexity of bone disease diagnosis is understood and an expert system has been developed accordingly. The algorithm, the information sources used to develop the knowledge base, the number and kind of diseases under consideration, the source of the specimen, and the source of the diagnosis are mentioned in this chapter. The key features of the system include assessment of responses to diagnose a suspected disease, simulating a doctor’s consultation, presenting personalized questions based on previous responses, and providing self-help measures and home remedies.

6.1 Introduction Bone diseases must be treated well on time. If timely diagnosis is not done, they can lead to many other illnesses and severely degrade well-being. Some extreme cases can also become fatal if a timely diagnosis is not done. These issues are deteriorating due to the lack of specialists, orthopedic surgeons, and medical provisions. To address these issues, the authors have designed and developed an expert system. The proposed system is developed using PROLOG. PROLOG is a fundamental language for logic programming with a role of paramount importance in other sectors. It plays a

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Corresponding author: Pallavi Chavan, Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, India, e-mail: [email protected] Gaurav Singh, Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, India, e-mail: [email protected] Anushka Kamalja, Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, India, e-mail: [email protected] Ashutosh Karwa, Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, India, e-mail: [email protected] https://doi.org/10.1515/9783110781663-006

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significant role in artificial intelligence. Logic is shown in the form of relationships called as facts and rules. At the center of PROLOG is the logic being applied or the purpose of its existence. All calculations are performed by running a query over these relations. PROLOG is worth understanding for its essence and role in solving queries about relationships in any given data. In PROLOG, we declare facts. These facts are the knowledge base of the system. We run queries against this knowledge base for our answers. We get a positive or negative output depending on whether the query can be resolved using the knowledge base. The knowledge base is like a normal tabular database. In PROLOG, facts are shown in the form of pattern. Facts have entities and these entities have relationships. Entities are portrayed within parentheses and are separated by a comma (,). Their relationship is then given at the start of the line joint before the opening parenthesis. Every fact or a rule must end with a period or a dot(.). So, a typical PROLOG fact can be expressed as follows: Format: relation (first entity, second entity, . . . nth entity). Example: likes (Jane, fruits). In this example, “Jane” and “fruits” are the entities whereas “likes” is the relation between them. A rule in PROLOG is a predicate expression that uses logical implication (:-) to express a relationship among facts. Thus, a PROLOG rule can be written as: leftHandSide: – rightHandSide. This statement is understood as follows: leftHandSide is true only if rightHandSide is valid. The left-hand side is confined to a univalued, positive, literal, which means it must comprise of a positive atomic expression. It can neither be negated nor contain any logical connectives. Example: not_sunny(X): – cloudy(X), cold(X). For all X, X is not sunny if X is cloudy, and X is cold. The following are the key features of logical programming: a. Unification: In unification, more than one variable is given to make two called terms identical. This process is called binding the variables to values. Example of unification: ? – foo(a,Y) = foo(X,b). Y = b. [Instantiation of variables may occur X = a. in either of the terms to be unified] [1] b. Backtracking: If any task doesn’t succeed, PROLOG attempts backward tracing and tries to fulfill the preceding tasks [2].

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Example of backtracking: // Facts: eats(sarah, pizza). // sarah eats pizza. eats(sarah, chicken). // sarah eats chicken //Rule: eats(george, X): – eats(sarah, X). // george eats everything that sarah eats. //Goal ? – eats(george, What). // What does george eat. “trace” command can be used in SWI-PROLOG to explain the backtracking process. Query Prompt: ? – trace. true. ? – eats(george, What). Call: (6) eats(george, _G7400) ? creep Call: (7) eats(sarah, _G7400) ? creep Exit: (7) eats(sarah, pizza) ? creep Exit: (6)eats(george, pizza) ? creep What = pizza; Redo: (7) eats(sarah, _G7400) ? creep Exit: (7) eats(sarah, chicken) ? creep Exit: (6) eats(george, chicken) ? creep What = chicken; ? – nodebug. For the given query, ? -eats(george, What), “What” is a variable. When the query is fired, PROLOG initially reads the query and tries to find a matching rule for it in the knowledge base in a hierarchical way. It detects the matching rule – eats(george, X). But from that rule we can infer eats(sarah, X). Upon finding a matching rule PROLOG will start searching backward (backtracking) and begins to look through the knowledge base from the initial line and observes “eats(sarah, pizza)” and hence responds as “What = pizza.” The variable “What” gets instantiated with the value “pizza.” Now, PROLOG notes in the database that for this query “What = pizza” has been answered and it satisfies. c.

Recursion: The basic criteria of all search algorithms in any program is Recursion. Example of Recursion:

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parent(george,paul). //george is paul’s parent parent(john,sarah). //john is sarah’s parent ancestor(P,Q): – parent(P,Q). //if someone is your parent then they are your ancestor ancestor(P,Q): – parent(P,R),ancestor(R,Q). //if someone is a parent of your ancestor then they are your ancestor This code fragment finds ancestors by relating people as mentioned in the knowledge base. The PROLOG has significant benefits such as it is an easy way to create database and doesn’t necessitate a lot of programming expertise. In PROLOG pattern matching is simple because searching for patterns is recursion-induced. Built-in handling has been provided for lists, making it simpler to experiment with algorithms that make use of lists. On the other side, some of the challenges faced with PROLOG is that it does not support graphical interfaces. PROLOG has very basic nonusable I/O features compared to other mainstream programming languages. The ordering of rules in the database greatly affects the speed of queries in PROLOG [3].

6.1.1 Background The modern development in the computer science field has given rise to the advent of intelligent systems and computational equipment designed to encapsulate and provide expert knowledge. The fundamental technology presented is derived from initial research on biomedical systems in the 1970s. Medical researchers continue to pursue advancements in such vital domains as knowledge acquisition, inductive-based reasoning, and system incorporation for scientific environments. Hence, it is significant for physicians to realize the existing shape of such research and the theoretical and logical hurdles that confront those who wish to make these systems accessible. Many valuable studies have recently been carried out with a corresponding rise of logic programming intensive trends within the field of medical science. Researchers intend to find more developed patterns in the desired field to make accurate assumptions for the newly evolved diseases and assign specific cures for them. Edward Feigenbaum, also known as the “Father of Expert systems” (ES) introduced DENDRAL and MYCIN systems which were among the first expert systems [4]. The advancement of these systems was done by the French computer language PROLOG, which was developed in 1972. PROLOG makes use of the first-order logic engine including facts and rules. It is one of the most used AI languages which is massively used for expert system production. In the 1980s with the advent of fifth-generation computers, expert systems gained recognition for solving real-world problems. Its technology was practically applied by almost two-thirds of the fortune 1,000 companies. In 1990s its growth continued further [4]. Thompson and Mooney [5] proposed a new inductive learning system – LAB (Learning for Abduction) – which acquired adductive rules from a set

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of training examples. Thompson and Mooney’s primary objective was to determine the minimum threshold for learning – the point at which there is sufficient foundation for accurate assumption generation. The proposed system contrasted with conventional systems in that it used inductive reasoning to form these predictions. Moreover, it had been experimentally examined and matched against the other learning systems, subsequently providing an expert knowledge domain of brain damage detection following stroke [5]. The XBONE hybrid system for bone disease diagnosis states that the system architecture of bone disease diagnosis consists of seven main modules. The first module is the patients database (PDB) containing the scintigrams of the patients and the demographic data. Hybrid knowledge base (HKB) states the heuristic clinical testing and domain which is shown by a hybrid representation. Working database mainly consists of data related to the case, that is, the patient’s personal information, partial diagnosis, and patient responses which are stored as facts. Next is the hybrid inference engine which determines the procedural conclusions and makes use of the existing heuristic knowledge in HKB to derive conclusions for diagnosis. The inference uses the backtracking mechanism. Explanation mechanism gives explanations when asked for. Training mechanism trains the rules. Last, user interface plays a role in making the system interactive [6].

6.2 Proposed work This section describes the bone disease diagnosis system. This expert system diagnoses five diseases in the author’s knowledge base characterized by 21 distinct symptoms. Using PROLOG’s in-built constructs, the authors have built the system to identify bone diseases. To collect information about accurate signs and symptoms the authors have made use of trusted medical website sources that host accurate information for various health concerns. The information of 21 unique symptoms has been compiled for the identification and diagnosis of five major bone illnesses. Through this information collected, an algorithm has been made that asks users to input their details and choose their symptoms and accordingly informs them of the disease they may have a risk of or could be suffering with. Along with the identification of each disease, it provides a list of home remedies common to bone illness and provides a dedicated tutorial to follow exercises. Besides, it also informs them of consulting a specialist to get themselves checked to avoid the disease in case of an elemental stage of the same. Thus, it could prove to be helpful to anyone who has bone-related symptoms and wants to get a selfdiagnosis done. This could help in the prevention of diseases and illnesses. The following are the diseases the authors have considered for experimentation:

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6.2.1 Bone cancer Bone cancer is identified when mutation of bone cells is observed. Bone cancer in the primary stage arises early in the bone and is comparatively infrequent. The symptoms of bone cancer are decreased mobility of a joint, fatigue, pain, bone fractures, and anemia [7].

6.2.2 Rickets Rickets is a disease commonly found in infants and children indicated by bone softening, leading to unusual growth of bone, and is caused by vitamin D deficiency in the body. Rickets cases in adults are known as osteomalacia. The symptoms of rickets are muscle cramps, bone fracture, tender bones, stunted growth, teeth deformities, skeletal deformities, and seizure [8].

6.2.3 Arthritis The Greek word for “joint” is arthro and itis means “inflammation.” Arthritis is the inflammation and tenderness of one or more of your joints. Osteoarthritis and rheumatoid arthritis are common types of arthritis found. The following are the symptoms of arthritis, stiffness of muscles, redness, decreased mobility, pain in the joints, and swelling of bones [9].

6.2.4 Hip fracture Hip fracture can take place at any age. It mainly happens due to drastic impact like vehicle accidents, falls, weakness of bones, or bone loss (osteoporosis). With an increase in age, the possibility of hip fracture from falls and bone loss becomes severe. The symptoms of hip fracture are swelling of the hip bone, bruising around the hip, shorter leg, and visible deformities around the hip [10].

6.2.5 Gout Gout is a disorder caused due to frequent severe swelling in single or multiple joints. Gout is caused by the settling of uric acid salts in and around the joints. These salts are in extreme quantity throughout the body in persons with this disability. Gout has the following symptoms persisting discomfort, redness around joints, joint pain, and decreased range of motion [11]. The system prompts the user with a series of questions to

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Start

Hypothesis

Bone Fracture

Yes

No

No

Other Bone Cancer Symptoms

Tender Bones

No Yes Joint pain Pain in bones

No Yes

Other Rickets Symptoms

Yes

Other Arthritis Symptoms

No

Yes

Yes Yes

Rickets

Bone Cancer

No

Other Gout Symptoms

Bruising around the hip

Other Hip Fracture Symptoms

Yes

Couldn’t Diagnose

Yes

Gout

Arthritis

Yes

Hip Fracture

Figure 6.1: Flow of execution of the bone disease diagnosis system.

evaluate the diagnosis. These questions inquire about specific symptoms related to bone diseases, and subsequent questions are determined based on the user’s previous responses. Eventually, the user receives the appropriate diagnosis decision and related suggestions based on their responses. Figure 6.1 provides a visual representation of the detailed execution flow of diagnosis by the decision support system.

6.2.6 Knowledge base This section presents the knowledge base for the bone diagnosis system. It includes facts and rules:

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go:write (‘Name of the patient: ‘), read (Patient), hypothesis (Patient,Disease),nl, write (Patient), write (‘, likely has’).write (Disease), write (‘.’),nl,nl,remedies(Disease),nl,nl,write (‘For professional treatment please visit an orthopedic doctor.’),nl. go:nl,write (‘Sorry, I am unable to diagnose the disease’),nl. symptom (Patient,bone_fracture):verify (Patient,”have a bone fracture (y/n) ?”). symptom (Patient,decreased_mobility):verify (Patient,” have decreased mobility of any joint (y/n) ?”). symptom (Patient,fever):verify (Patient,” have fever (y/n) ?”). symptom (Patient,fatigue):verify (Patient,” have fatigue (y/n) ?”). symptom (Patient,anemia):verify (Patient,” have anemia (y/n) ?”). symptom (Patient,weight_loss):verify (Patient,” have weight loss (y/n) ?”). symptom (Patient,swelling_bones):verify (Patient,” have a swelling in bones (y/n) ?”). symptom (Patient,tender_bones):verify (Patient,” have tenderness in the bones (y/n) ?”). symptom (Patient,stunted_growth):verify (Patient,” have stunted growth (y/n) ?”). symptom (Patient,muscle_cramps):verify (Patient,” have any muscle cramps (y/n) ?”). symptom (Patient,teeth_deformities):verify (Patient,” have any teeth deformities (y/n) ?”). symptom (Patient,skeletal_deformities):verify (Patient,” have any skeletal deformities (y/n) ?”). symptom (Patient,seizure):verify (Patient,” have a seizure (y/n) ?”). symptom (Patient,pain):verify (Patient,” have a pain (y/n) ?”). symptom (Patient,stiffness):verify (Patient,” have stiffness (y/n) ?”). symptom (Patient,redness):-

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verify (Patient,” have redness (y/n) ?”). symptom (Patient,bruising_around_hip):verify (Patient,” have a bruise around hip (y/n) ?”). symptom (Patient,shorter_leg):verify (Patient,” have short leg on side of injured hip (y/n) ?”). symptom (Patient,visible_deformity):verify (Patient,” have a visible deformity of lower extremity (y/n) ?”). symptom (Patient,joint_pain):verify (Patient,” have an intense joint pain (y/n) ?”). symptom (Patient,lingering_discomfort):verify (Patient,” have a lingering discomfort (y/n) ?”). ask (Patient,Question):write (Patient), write (‘, do you’), write (Question), read (N) ( (N = = yes; N = = y) – > assert (yes (Question)); assert (no (Question)), fail). : – dynamic yes/1, no/1. verify (P,S):( yes (S) – > true; ( no (S) – > fail; ask (P,S))). hypothesis (Patient,bone_cancer):symptom (Patient,bone_fracture) symptom (Patient,decreased_mobility) symptom (Patient,fever) symptom (Patient,fatigue) symptom (Patient,anemia) symptom (Patient,weight_loss) symptom (Patient,swelling_bones). hypothesis (Patient,rickets):symptom (Patient,tender_bones) symptom (Patient,stunted_growth) symptom (Patient,bone_fracture) symptom (Patient,muscle_cramps) symptom (Patient,teeth_deformities) symptom (Patient,skeletal_deformities) symptom (Patient,seizure).

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hypothesis (Patient,arthritis):symptom (Patient,pain) symptom (Patient,stiffness) symptom (Patient,swelling_bones) symptom (Patient,redness) symptom (Patient,decreased_mobility). hypothesis (Patient,hip_fracture):symptom (Patient,pain) symptom (Patient,bruising_around_hip) symptom (Patient,swelling_bones) symptom (Patient,shorter_leg) symptom (Patient,visible_deformity). hypothesis (Patient,gout):symptom (Patient,joint_pain) symptom (Patient,lingering_discomfort) symptom (Patient,redness) symptom (Patient,decreased_mobility). remedies(Disease):write (‘You can follow these remedies:’),nl,nl write (‘1. Use alternate hot and cold compresses with an interval of 15 min between each compression.’),nl write (‘2. Follow a healthy diet including vitamin D and fish oil supplements.’),nl write (‘3. Add Turmeric to dishes.’),nl write (‘4. Manage your weight.’),nl write (‘5. Get timely massages.’),nl,nl write (‘You can follow the following exercises:’),nl,nl ( Disease = = bone_cancer- > write(‘‘https://www.youtube.com/watch?v = LUlQU8glXFA&feature = you tu.be ‘‘); Disease = = rickets- > write (“https://www.youtube.com/watch?v = JnO9wL2xRo&feature = youtu.be”); Disease = = arthritis- > write (“https://www.youtube.com/watch?v = L-0juIM2aCI”); Disease = = hip_fracture- > write (https://www.youtube.com/watch?v = _-ah3d9zJWg);

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Disease = = gout- > write(“https://www.youtube.com/watch?v = c72IQl7f-nE”) ). start: – go, abolish(yes/1), abolish(no/1), dynamic(yes/1), dynamic(no/1), nl,nl,write (‘Try again ? (y/n)’), read (Response), (Response = = ‘y’- > start; nl,write (‘Bye ! Thanks for using this system’)). [12, 13–17]

6.3 Results This section demonstrates the conversation between the expert system and the patient using examples. The patient names have been mentioned for demonstration purposes.

6.3.1 Diagnosis of bone cancer The patient George is facing some bone problems and chooses to use the proposed system for bone disease diagnosis. He answers “yes” to the symptoms like bone fracture, decreased mobility of joint, fever, fatigue, anemia, weight loss, and swelling in bones which are the symptoms of bone cancer. Hence the system displays that he has a risk of bone cancer. ? – consult(bone). true. ? – start. Name of the patient: “George”. George, do you have a bone fracture (y/n) ?|: y. George, do you have decreased mobility of any joint (y/n) ?|: y. George, do you have fever (y/n) ?|: y. George, do you have fatigue (y/n) ?|: y. George, do you have anemia (y/n) ?|: y. George, do you have weight loss (y/n) ?|: y. George, do you have a swelling in bones (y/n) ?|:y. George, likely has bone_cancer. You can follow these remedies: 1. Use alternate hot and cold compresses with an interval of 15 min between each compression. 2. Follow a healthy diet including vitamin D and fish oil supplements. 3. Add Turmeric to dishes.

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Manage your weight. Get timely massages.

You can follow the following exercises: https://www.youtube.com/watch?v=LUlQU8glXFA&feature=youtu.be For professional treatment please visit an orthopedic doctor.

6.3.2 Diagnosis of rickets Patient Henry has a bone fracture but doesn’t have decreased mobility in any joint, so the system continues to diagnose with the next disease, that is, rickets. Since he answers yes to all other symptoms of rickets the system shows that he probably has rickets. Name of the patient: |: Henry. Henry, do you have a bone fracture (y/n) ?|: y. Henry, do you have decreased mobility of any joint (y/n) ?|:n. Henry, do you have tenderness in the bones (y/n) ?|: y. Henry, do you have stunted growth (y/n) ?|: y. Henry, do you have any muscle cramps (y/n) ?|: y. Henry, do you have any teeth deformities (y/n) ?|: y. Henry, do you have any skeletal deformities (y/n) ?|: y. Henry, do you have a seizure (y/n) ?|: y. Henry, likely has rickets. You can follow these remedies: 1. Use alternate hot and cold compresses with an interval of 15 min between each compression. 2. Follow a healthy diet including vitamin D and fish oil supplements. 3. Add Turmeric to dishes. 4. Manage your weight. 5. Get timely massages. You can follow the following exercises: https://www.youtube.com/watch?v=JnO9-wL2xRo&feature=youtu.be For professional treatment please visit an orthopedic doctor.

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6.3.3 Diagnosis of arthritis Here the patient William answers “no” to bone fracture which is a common symptom for both bone cancer and rickets. Hence our system starts diagnosis of the third disease which is arthritis. Try again ? (y/n)|: y. Name of the patient: |: “William”. William, do you have a bone fracture (y/n) ?|: n. William, do you have tenderness in the bones (y/n) ?|: y. William, do you have stunted growth (y/n) ?|: y. William, do you have a pain (y/n) ?|: y. William, do you have stiffness (y/n) ?|:y. William, do you have a swelling in bones (y/n) ?|: y. William, do you have redness (y/ n) ?|: y. William, do you have decreased mobility of any joint (y/n) ?|: y. William, likely has arthritis. You can follow these remedies: 1. Use alternate hot and cold compresses with an interval of 15 min between each compression. 2. Follow a healthy diet including vitamin D and fish oil supplements. 3. Add Turmeric to dishes. 4. Manage your weight. 5. Get timely massages. You can follow the following exercises: https://www.youtube.com/watch?v=L-0juIM2aCI For professional treatment please visit an orthopedic doctor.

6.3.4 Diagnosis of hip fracture Patient Sarah does not have bone fracture and tenderness in bones which are characteristic symptoms of bone cancer and rickets, respectively. Thus, the system starts diagnosing with the arthritis; here she answers “yes” for pain in bones but she doesn’t have stiffness; thus, the system diagnoses the fourth disease which is hip fracture. Try again? (y/n)|: y. Name of the patient: |: ‘Sarah’. Sarah, do you have a bone fracture (y/n) ?|: n.

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Sarah, do you have tenderness in the bones (y/n) ?|: n. Sarah, do you have a pain (y/n) ?|: y. Sarah, do you have stiffness (y/n) ?|: n. Sarah, do you have a bruise around hip (y/n) ?|: y. Sarah, do you have swelling in bones (y/n) ?|: y. Sarah, do you have short leg on side of injured hip (y/n) ?|: y. Sarah, do you have a visible deformity of lower extremity (y/n) ?|: y. Sarah, likely has hip fracture. You can follow these remedies: 1. Use alternate hot and cold compresses with an interval of 15 min between each compression. 2. Follow a healthy diet including vitamin D and fish oil supplements. 3. Add Turmeric to dishes. 4. Manage your weight. 5. Get timely massages. You can follow the following exercises: https://www.youtube.com/watch?v=_-ah3d9zJWg For professional treatment please visit an orthopedic doctor.

6.3.5 Diagnosis of gout In this scenario, the patient Mike does not have any symptoms for the previous four diseases but has symptoms like intense joint pain, lingering discomfort, redness, and decreased mobility of joint which are symptoms of gout. Hence the system displays that patient might be suffering with gout. Try again ? (y/n)|: y. Name of the patient: |: Mike. Mike, do you have a bone fracture (y/n) ?|: n. Mike, do you have tenderness in the bones (y/n) ?|: n. Mike, do you have a pain (y/n) ?|: n. Mike, do you have an intense joint pain (y/n) ?|: y. Mike, do you have a lingering discomfort (y/n) ?|: y. Mike, do you have redness (y/n) ?|: y. Mike, do you have decreased mobility of any joint (y/n) ?|: y. Mike, likely has gout. You can follow these remedies:

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Use alternate hot and cold compresses with an interval of 15 min between each compression. Follow a healthy diet including vitamin D and fish oil supplements. Add Turmeric to dishes. Manage your weight. Get timely massages.

You can follow the following exercises: https://www.youtube.com/watch?v=c72IQl7f-nE For professional treatment please visit an orthopedic doctor.

6.3.6 No disease diagnosed Smith is using the system to check if he has any bone disease, but he doesn’t have symptoms matching to any of the bone diseases fed in the system. Hence the system is not diagnosing any disease. Try again ? (y/n)|: y. Name of the patient: |: “Smith”. Smith, do you have a bone fracture (y/n) ?|: n. Smith, do you have tenderness in the bones (y/n) ?|: n. Smith, do you have a pain (y/n) ?|: n. Smith, do you have an intense joint pain (y/n) ?|: n. Sorry, I am unable to diagnose the disease Try again ? (y/n)|: n. Bye ! Thanks for using this system. true. In the prior demonstration, the system was used to illustrate all five diagnoses.

6.4 Conclusion A crisp rule-based system presented in this chapter is capable of diagnosing bone diseases. The system considers 21 symptoms for accurate diagnosis of the diseases. The bone disease diagnosis system is quite sufficient in identifying common bone diseases that a person may suffer if symptoms are provided correctly. The system contains several facts and rules using a list, recursion, and so on. This expert system for bone disease diagnosis can be used conveniently anywhere and anytime to diagnose bone

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diseases according to symptoms. The system efficiently creates a clinical suggestion like a doctor by the implementation of a logic programming algorithm. Bone disease diagnosis system does not replace a doctor’s consultation. This system should not be treated as a doctor’s suggestion or an ultimate treatment. It can be used as an aid before a meeting with the doctor for a better diagnosis.

Compliance with ethical standards Conflict of Interest: The authors Gaurav Singh, Anushka Kamalja, Ashutosh Karwa, and Pallavi Chavan declare that they have no conflict of interest. Ethical approval: This chapter does not contain any studies with human participants or animals performed by any of the authors. Informed consent: Informed consent was obtained from all individual participants included in the study.

References Unification in Prolog – Javatpoint [Internet]. [cited 2022 Aug 23]. Available from: https://www.javat point.com/unification-in-prolog [2] Backtracking in Prolog – Javatpoint [Internet]. [cited 2022 Aug 23]. Available from: https://www.javat point.com/backtracking-in-prolog [3] Prolog | An Introduction – GeeksforGeeks [Internet]. [cited 2022 Aug 23]. Available from: https://www.geeksforgeeks.org/prolog-an-introduction/ [4] Nath, P. (2013). AI & Expert System in Medical Field: A study by survey method. Aithun, 1(100), 100. [5] Thompson, C. A. & Mooney, R. J. Inductive Learning For Abductive Diagnosis*. 1994 [cited 2022 Aug 23]; Available from: www.aaai.org [6] Hatzilygeroudis, I., Europe’97 Pv- . . . I, 1997 undefined. XBONE: A hybrid expert system for supporting diagnosis of bone diseases. ebooks.iospress.nl [Internet]. 1997 [cited 2022 Aug 23]; Available from: https://ebooks.iospress.nl/volumearticle/18688 [7] David, C. Williams. bone cancer | Description, Types, Symptoms, & Treatment | Britannica [Internet]. 2020 [cited 2022 Aug 23]. Available from: https://www.britannica.com/science/bonecancer [8] The Editors of Encyclopaedia. rickets | Pathology | Britannica [Internet]. 2017 [cited 2022 Aug 23]. Available from: https://www.britannica.com/science/rickets [9] Samartzis, D., Keller,, Christian, T., Shen, F. H., etal. Arthritis | Definition, Causes, & Treatment | Britannica [Internet]. 2022 [cited 2022 Aug 23]. Available from: https://www.britannica.com/science/ arthritis [10] Anderson, J. hip fracture | Pathology | Britannica [Internet]. 2017 [cited 2022 Aug 23]. Available from: https://www.britannica.com/science/hip-fracture [11] gout | Symptoms, Treatment, & Prevention | Britannica [Internet]. [cited 2022 Aug 23]. Available from: https://www.britannica.com/science/gout [1]

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Medical-Diagnosis-system-using-Prolog/medical-diagnosis.pl at master · sjbushra/MedicalDiagnosis-system-using-Prolog [Internet]. [cited 2022 Aug 24]. Available from: https://github.com/ sjbushra/Medical-Diagnosis-system-using-Prolog/blob/master/medical-diagnosis.pl [13] Here Are 4 Exercise If You Have Gout – YouTube [Internet]. [cited 2022 Aug 24]. Available from: https://www.youtube.com/watch?v=c72IQl7f-nE [14] Exercises following Hip fracture – YouTube [Internet]. [cited 2022 Aug 24]. Available from: https://www.youtube.com/watch?v=_-ah3d9zJWg [15] At Home Exercises for Arthritis Sufferers – YouTube [Internet]. [cited 2022 Aug 24]. Available from: https://www.youtube.com/watch?v=L-0juIM2aCI [16] Bowed Legs Stretches & Exercises – YouTube [Internet]. [cited 2022 Aug 24]. Available from: https://www.youtube.com/watch?v=JnO9-wL2xRo [17] Exercise at Home for Cancer Survivors and Osteoporosis – Bone health – YouTube [Internet]. [cited 2022 Aug 24]. Available from: https://www.youtube.com/watch?v=LUlQU8glXFA

Vipul Narayan, Sritha Zith Dey Babu, Manglesh M. Ghonge, Pawan Kumar Mall, Shilpi Sharma, Swapnita Srivastava, Shashank Awasthi, L.K. Tyagi

7 Extracting business methodology: using artificial intelligence-based method Abstract: In this current journey, the technical pathway’s golden roads show the ultimate metaphor for artificial intelligence (AI) society. The clinical stores, banks, NGOs, insurance, and some government officers have integrated the circuit between AI and BI (business intelligence). Our proposed AI and genetic algorithm-based prediction methodology represent the business profit wavelength’s uttermost amplification, which may have been in longitudinal form. The vital terms of BI are gaining the accuracy-based AI and kernel ridge regression-based genetic AI algorithm-based prediction where the crossing over shows the leading figure of interest-based profit. The research occupied the responsibility of green profit-based businesses to establish the stabilizers of the green economy. The future scope of this research shows its X dimension toward the management. Y dimension denotes the notary of sustainable business technology, and the Z dimension compiles the code of soft electronic business covering E-commerce, Epayment, E-relief, and so on. The super vector machine builds the monograph of soft intelligence to create vision computing. Therefore, our research aims to clarify the span between BI and AI concerning soft computing. Using the proposed methodology prepared by our study must give the filament of transient business profit. One of the most unlocked doors is the third-party entry into the management server. However, the proposed approach technology can be very beneficial for banks that lack maintained

Vipul Narayan, School of Computing Science and Engineering, Galgotias University, Greater Noida, India, e-mail: [email protected] Sritha Zith Dey Babu, Department of Computer Science, Chandigarh University, e-mail: [email protected] Manglesh M. Ghonge, Department of Computer Engineering, Sandip Institute of Technology and Research Centre, Nashik, India, e-mail: [email protected] Pawan Kumar Mall, Department of Computer Science, Lovely Professional University, Punjab, e-mail: [email protected] Shilpi Sharma, Computer Science and Engineering Department, Amity University, e-mail: [email protected] Swapnita Srivastava, School of Computing Science and Engineering, Galgotias University, Greater Noida, India, e-mail: [email protected] Shashank Awasthi, Department of Computer Science and Engineering, GL Bajaj, Greater Noida, e-mail: [email protected] L.K. Tyagi, Department of Computer Science and Engineering, GL Bajaj, Greater Noida, e-mail: [email protected] https://doi.org/10.1515/9783110781663-007

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documentation and face risk management regularly. This approach is used for prediction of closing data.

7.1 Introduction In this emerging world of technology, artificial intelligence (AI) has its own space. AI refers to the human-made intelligence given to machines [1]. Unlike humans and animals, machines do not possess their own brains to think and make decisions on their own. Thus, the capability provided to the devices to work independently and analyze on their own is called machine learning (ML). ML and AI work basically on different mathematical algorithms, calculations, and predictions. However, we cannot compare any machine with the human brain because the latter is much more complex and heterogeneous [2]. We can calculate the multifaceted equations with the help of modern computers called supercomputers that count on ML, AI, big data, and many more complex circuits. Deep aspects pair the sustainable shell of learning the data as emerging fields grow at an exponential rate. ML has come into consideration in every sector, whether it is banking, industries, or aeronautical science. The main aim of our research is to predict the business model of a bank to save it from crashing at the wrong time. With the combination of AI, BI (business intelligence), and neural network, a model that divides all the customers of the bank into different categories has to be developed so that the bank will function accordingly. The data is also one of the key factors that distinguish a bank from another bank. All data will be encrypted with the help of a neural network and will be impossible to decrypt the data as it has outputs only, and there are infinite numbers of inputs to an outcome. With the help of our technology, the bank will be free from one of its major problems, which is risk management. The ML model in integration with a neural network will analyze all risk factors in regard to the customers and provide a solution for that. Security is one of the most crucial aspects in every field, especially in the banking sector. All data will be secured in the hidden layers whose access is given to the bank administrator. The solution to the neural network blueprint and the hash key is particular, and without that the data cannot be decrypted. In addition to that, the model will also predict the future performance of the bank and its economy. All the individual customer data will be analyzed to prepare the model, and at last, the proper and accurate model will be trained and tested, which will give all the future performance reports of the respective bank [3]. Let us dive into neural networking so that we can understand in a much better way how things work. A neural network operates on a series of algorithms and tries to mimic the human brain. The hidden layers in the neural network mimic the neurons of the brain. It tries to analyze the information and generates different and

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accurate outputs for further data. The developed model also remembers and examines the current logic and input for future reference. The basis of neural networking is AI, and it is now widely accepted in the trading industry.

7.2 Literature survey AI was first coined decades ago in 1956 by John McCarthy, defined as the science and engendering of making intelligent machines. What we today call the simulation of human intelligence is processed by machines. Cortana, Siri, and Google Assistant are the most common AI that we see in real life [4–10]. AI has made a huge upgrade since it was first introduced. In the recent past, AI has been able to accomplish this by creating robots and machines that have been used in a wide range of fields, such as robotics, space, marketing, healthcare, and business analytics. We often think of AI as a robot or machine doing our daily jobs but do not realize that it has always been in our daily lives; Google that we use is a kind of AI that gives us accurate search results even if we input something related to our desired output. AI, ML, and DL (deep learning) are often tended to be mistaken as the same since they have a common application: AI is the science of getting machines to mimic the behaviors of humans; ML is the subset of AI that makes decisions as per the needed data; and DL is the subset of ML that uses a neural network to solve difficult problems, though these three are often seen together solving algorithms and solve data-driven problems. AI has multiple positive impacts on overall business operations, and management and business investment in AI will enhance sustainability and market leadership. AI brings new risk factors to their life that has to be reduced to an acceptable level [11]. AI covers a vast division of fields like object detection, natural language processing, expert system, and robotics [12]. AI can be categorized into three sections: artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. From a business perspective, AI enables us to automate human decision-making. We can thus cut costs and waiting times as well as increase revenue and profit margins [13]. Comparing AI with the current development, we can see a major contribution of 5G in AI, which has been the biggest boom in the current world though it is accessible by very few regions. 5G has given access to a large amount of data. It gives us the bandwidth that lets AI gain access to data in a much faster way, and is 100 times faster than the 4G network. 5G being the largest network gives us access to the speed, location, and large size of data transmission that gives better access to the data transmission and massive reliability that gives a tremendous advantage to AI not just for the present but for the future too [14–16]. Getting the fastest network in collaboration with AI can be helpful, especially in prediction such as Google Maps. The smart application that we currently have found a way with the use of AI is to provide the user the best experience according to his/her preferences. AI has not been the most useful in robotics and

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healthcare sector, but in the market and business [17] sector, and no company knows what the user really wants. The real reason why business is getting so much benefit from AI is that it has been able to perceive patterns in data that humans cannot do, which helps the business to target the right audience for its products and gives customer satisfaction with its products. The potential intersection of AI and human rights. While AI has the potential to bring numerous benefits, it can also have adverse consequences that need to be carefully considered and addressed. Here are a few potential areas where AI and human rights could intersect: – Privacy: Automated cars equipped with AI systems collect vast amounts of data, including geolocation information, personal preferences, and driving patterns. Ensuring the privacy and protection of this data is essential to prevent unauthorized access, surveillance, or misuse that could infringe upon individuals' privacy rights. – Bias and Discrimination: AI algorithms used in automated cars can be prone to biases and discrimination, leading to differential treatment or profiling based on factors such as race, gender, or socioeconomic status. This can raise concerns regarding equal treatment and the right to non-discrimination. – Liability and Accountability: As automated cars operate with increasing autonomy, questions arise regarding who should be held accountable for accidents or incidents caused by AI decision-making. Determining responsibility in cases where AI is involved raises legal and ethical challenges that need to be carefully addressed. – Job Displacement: The widespread adoption of automated vehicles may result in the displacement of human drivers, affecting their livelihoods and employment rights. Adequate measures should be taken to support and retrain affected individuals to mitigate the negative impact on their rights and well-being. – Ethical Decision-Making: In situations where automated cars must make split-second decisions that may result in harm, ethical dilemmas arise. Deciding how AI systems should prioritize the safety of occupants versus pedestrians, for example, requires careful consideration to ensure that human rights and ethical values are upheld. To mitigate these potential adverse impacts, it is crucial for policymakers, researchers, and developers to work together to establish robust regulations, ethical frameworks, and transparency mechanisms that safeguard human rights in the development and deployment of AI technologies like automated cars. Ongoing dialogue and collaboration between stakeholders are necessary to address these challenges as AI technology continues to advance [18]. Doing business with AI comes with many risks. Among the most significant are the risks associated with severe accidents [19].

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The boom of AI will definitely decrease the low-level jobs because AI can do a job in a matter of seconds and gives the most accurate results [20]. But definitely more jobs would be introduced to humans for the design of AI as a programmer [21, 22], who requires a much higher education to move forward, which at the same time will be beneficial for the education sector by increasing the AI educators, and the industries that have the AI application by competing against each other for being the fastest are also created. There are still some drawbacks as AI has been misused in many ways: for example, fabricated sounds that sound real but are actually fake have been created by criminals, and hence, very dangerous [23]. Today we cannot really say AI is intelligent as no AI can have human-like intelligence. There are still some education and countermeasures to be taken into consideration when an AI application is designed [24]. There is still a lot of time when humans will introduce an AI that is much more powerful than a human. To be able to make better decisions and their implementation requires the relevant and reliable information at the right time. BI is what comes in here. The provision of tools commands data to its actionable form so that more justified services could be driven out with its help. With the appropriate knowledge of methods and plan of action to run large amounts of data, BI enables superior and informative analysis in no time. The pivotal emphasis of BI is the visualization and organization of data in a more instinctive and justifiable pattern. A known concept is that a lot of professionals are working on this very aspect and heading toward the approach to replace it from just an option to a necessity. The BI has come to evolve from a great time. Being a key tool preferably used in the twenty-first century was published way back in 1958 by the IBM researcher “Hans Peter Luhn,” later recognized as the father of BI [25]. The fluctuation in technology is intervened at different scales of time, which has led to more constructive decisions and implementations. Initially, the scientific management of data began with time studies that scrutinized production techniques and labor requirements to find greater efficiencies that boosted industrial production to a great extent. With the device of computing, the use of this technology has widened, and a boost for the organizations was that the working was also enhanced. In the 1970s, the market was in the scope of introducing the first BI providers to take part. It was the connectivity of tools like SAP, Siebel, and JD Edwards which were initially known by the name DSS (the decision support systems) [26]. These tools lead to speeding up the process of business queries. More technical minds led to the introduction of the oracle database with a true relational database management system in the market, replacing the ideas brought to use until the place was acquired by the hierarchical databases and network databases for a more robust structure, which made the search process more flexible [27, 28]. This enhanced the technology of BI and trends. Some new tools and “building blocks” were developed to increase the speed to be more. ETL was introduced to enable the communication of our ideas to the technological environment. Now OLAP is what provides

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multidimensional analysis. The broader thinking stage is acquired by the SAS vision tool offering the strategic implementation and better decision making [29, 30]. In 2000, BI solutions were widely considered a requirement to stay competitive. The perspective of solution providers was concentrated more in this stage, taking the aspect to a competitive platform. The year 2020 was a major year for the business industry, which has the capacity to last longer. Analytics and evolutionary models are enhanced for development. New trends are being brought to tell the data stories well. With the help of modern BI dashboards software, the year 2021 is the year of data security and data discovery that is to be combined with a powerful presentation. Major collaboration is set for AI and BI in the coming years, which will excite the strategies to get maximum values out [31, 32].

7.3 Methodology The algorithm that we have used is the genetic algorithm; here is the basic template of the genetic algorithm for multidetection of business data. Figure 7.1 demonstrates the workflow of genetic AI.

Population Initialization

Fitness Function Calculation

Crossover Loop Until Termination Criteria reached

Mutation

Survivor Selection

Terminate and Return Best Figure 7.1: The workflow of genetic AI.

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Algorithm 1: Kernel ridge regression (KRR)-based genetic AI 1. 2. 3. 4. 5. 6.

Start; Random_Generate(Poll[p]); PollANN=new ANN; Set i_Generation=1 Repeat For i=1,2,3,4..........,Size   + F. pollir1 − pollir2 ; a. pollimut = pollbest i mut b. Fitness[i]= polli →Compute_Error(); 7. New_P=Setect S, Crossover C, Mutaion M; 8. Set i_Generation= Set i_Generation+1 9. Poll[i_generation]=New_Poll 10. Loop till halting criteria meet; 11. End

When it comes to tuning hyper-parameters, genetic algorithms are quite effective. It may be used to improve the performance of kernel ridge regression (KRR) by optimizing the hyper-parameters. Specifically, the population is defined by vectors containing parameters with distinct values. The sample dataset is shown in Table 7.1. Table 7.1: Sample training of input data. Open

Low

High

Volume

Close

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. . . . . . . . . . . . .  . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

,, ,, ,, ,, ,, ,, , , ,, ,, ,, , , , , , , , , , ,

. . . . . . . . . . .  . .  . . . . . .

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Table 7.1 (continued) Open

Low

High

. .

. .

. .

Volume , ,

Close . .

Vectors with various combinations of parameters have varied fitness values, which may be seen. Variations in parameter values are introduced at random into the population, and vectors with a higher fitness outlast their counterparts. The value of every parameter is derived as a mutation parameter of other vectors that were randomly picked from the population for that parameter. In order to compute mutant vectors, the best1bin technique is often used. In this approach, each parameter pi of the mutant vector is determined as shown in eq. (7.1). When two vectors are randomly selected, r1 and r2, the mutant parameter is a modification of the pi parameter of the best vector (the vector with the lowest value) plus a mutation rate (F) times the pi difference of the two vectors:  r  r = pollbest + F. polli 1 − polli 2 (7:1) pollmut i i Accuracy [33–36] is the evaluation metrics used in the research work. Table 7.2: Sample testing of input data. Open

Low

High

Volume

Close

. .  . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

,, ,, ,, ,, ,, ,, ,, ,, ,, ,, , ,, , ,, , ,, ,, ,, ,,

. . . . . . . . . . . . . . . . . . .

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Table 7.2 (continued) Open

Low

High

Volume

Close

. . . . . . . .

. . . . . . . .

. . . . . . . .

,, ,, ,, ,, , ,, ,, ,,

. . . . . . . .

LIFO data

Here, green indicates open, red locates close and low, and blue occupies high and volume. In the continuity of history, after the agrarian revolution and the industrial revolution, the present world is going to face a new revolution called the information revolution [15].

7.4 Results After putting all the terms, we have obtained the output where we can see the accuracy in Figure 7.3 by pyplot. The dimension in Figure 7.2 shows the location of the data memory address asset 0 as the initial stage of 1, which is fixed. Set 1 denotes the address as risk management. Differentiating the value of set 2 locates green stabilizer and set 3 shows the uttermost cost management.

Figure 7.2: Data outcome.

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Finally, the output is shown in Figure 7.3. dflt set 0 dflt set 1 dflt set 2 dflt set 3

Figure 7.3: Accuracy chamber.

7.5 Conclusion In our proposed model, we have utilized AI, BI, and neural networks for the soothsaying of the future scope of a bank and its economy by analyzing every individual customer’s data. The proposed approach technology can be very beneficial for banks that lack maintained documentation and face risk management regularly. The KRR approach is used for prediction of closing data. Suppose that this technology comes into action in every bank. In that case, banks can know its customer much better and categorize their customers and proceed the future transactions in a preplanned manner, resulting in a profit for the bank and building a strong economy.

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Inderpal Singh, Dr Anshu Sharam

8 A blockchain-based business model to promote COVID-19 via cloud services Abstract: The pandemic’s global spread resulted in nearly 3 million deaths and millions of people’s health deterioration. In addition to curfews, other restrictions were imposed in response to the risk of COVID-19. These restrictions disrupted education and working life, especially in places where people gathered in large groups. Aside from that, workforce planning in both the public and private sectors has changed with the adoption of remote working and flexible working methods from the beginning. By outsourcing their required services, businesses can reduce their total costs using cloud computing. Outsourcing changes the data protection landscape in definitions of trustworthiness, accuracy, and privacy. Consequently, public cloud has emerged as a major differentiator or competitive advantage for cloud providers. The use of block chain in cloud services is currently one of the most common inventions that can solve security problems in cloud computing. Data integrity, security, and anonymity are all provided by blockchain, a decentralized information management system. Cloud computing security issues and blockchain solutions are examined in detail in this chapter. Finding and evaluating articles from respected journals is made easier with the help of a variety of filters and global databases. Integration, trust, or privacy are all considered when it comes to cloud security. The results show that blockchain is an effective platform for this purpose. The most pressing issue, however, is the need for increased security. An action plan for prospective research and policy is also included in the chapter.

8.1 Introduction 8.1.1 Cloud computing Cloud computing is a service that provides end users with access to computing resources, such as servers, storage, and applications, without the need to own or maintain them. This means that the public cloud provider, not the end user, owns and maintains the resources. These resources can range from client software products such as TikTok as well as Netflix through third-party data analysis for images and some other

Inderpal Singh, Department of Computer Science and Engineering, CT Group of Institutions, Jalandhar, Punjab, India, e-mail: [email protected] Dr Anshu Sharam, Department of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India, e-mail: [email protected] https://doi.org/10.1515/9783110781663-008

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types of programs and films (service cloud or Dropbox) to the third web server that sustains the database server of a marketing, research institution, or personal project. Previously, to the wide adoption of cloud technology, businesses and separate computer owners were often forced to acquire and maintain their preferred system components. With the proliferation of companies and individuals nowadays has accessibility to a wide range of being on computational resources, thanks to virtualized apps, memory, resources, and machines using World Wide Web services. Cloud clients save labor, money, and expertise involved with procuring and managing computer resources by shifting from an operating system to networked remote and distributed resources. The increasing availability of powerful computers has led to the development of new internet businesses, the transformation of IT processes across industries, and changes in how people use desktop computers. Individuals may now engage with coworkers through videoconferences and other collaboration platforms, access to machine entertaining and learning resources, interaction with household items, and hailing a taxi with a mobile phone, perhaps lease a vacation room in someone’s house, all owing toward the cloud. Definition of cloud computing: Cloud technology is the supply of supercomputers as a commodity, which implies that the internet hosting company, not the end user, owns and manages the resource. The above resources can range from personal computers to third-party repositories for photographs, cloud computing, webmail, and other digital data. They can also include third-party servers that power the data centers of companies, pilot surveys, postgraduate studies, and individual proposals. The majority of organizations and software developers were required to purchase and maintain their own technologies and tools before cloud services were widely used. Businesses and people alike may now take use of a plethora of cloud-based programs, memory, services, and desktops through World Wide Web services. Cloud clients save labor, money, the expertise involved with procuring and maintaining computing resources by shifting from being an operating system to network remote and distributed resources. This extraordinary degree of computer power availability has produced IT procedures across sectors that have been restructured and several common desktop habits that have been influenced by a new generation of online enterprises. Videoconferences and other groupware allow individuals to communicate with each other in real time, regardless of their location, connect to on-demand amusement related to education, link to household objects, and even call a taxi from their homes using their smartphones, and lease a vacation space in someone’s house, all thanks toward the cloud. Cloud computing is defined by the National Institute of Standards and Technology (NIST), an independent agency of the International Trade administration, to advance the conceptual framework of ubiquitous, easy-to-use on-demand configurable computing resource pool of customizable computer power (such as data centers and memory), which can be instantly provided and released with minimal government time and energy or involvement from providers.

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NIST identifies the following five fundamental properties of cloud computing: Self-service on-demand: Without human contact, cloud resources may be accessible or deployed. Consumers may immediately access cloud services after signing up for this approach. Additionally, organizations may develop systems that enable workers, customers, and partners to use internal cloud services based on predefined logics without involving IT services. Consumers should access the web services and infrastructure from any device and from any connected location with proper authorization. The cloud supplies resources for the benefit of several clients, but the data of each customer remains private. In contrast to the on-premise technology and infrastructure, virtual machines may be quickly increased, decreased, or altered to suit the shifting needs of cloud users. Cloud computing services are managed so that companies and other data customers only pay for the care they use during a billing cycle.

These features provide a plethora of disruptive prospects for enterprises and people alike, which we will address in full detail in Section 8.5.2.1. To provide background, let us quickly explore the evolution of cloud technology.

8.1.1.1 History of cloud computing Even as far back as the early 1950s, colleges and businesses were renting computation time on computer systems to one another. Renting computer resources were the only option available at the time since computer technologies were too vast and costly for all to possess or maintain. Computing pioneers John McCarthy at Stanford and J.C.R. Licklider at the U.S. Space Research Authority (ARPA) postulated ideas in the 1960s, which symbolized the many key advantages of computing computer science today, including the concept of information technology as a public resource and a worldwide internet connection that would enable consumers to connect data and applications from everywhere. Cloud technology, on the other hand, did not become a widely accepted fact or a household term until another years of the 20 decades. Every decade of the 2000s saw the emergence of a number of significant cloud vendors, including Amazon’s Online Computing Cloud (EC2) and Straightforward Storage Service (S3), Heroku, Cloud Platform, Alibaba Cloud, Microsoft Windows (now Cloud Platform), IBM’s SmartCloud, and DigitalOcean. Using these processes, large organizations were able to save money by moving IT roads and facilities from in-house to virtualized firms, while also providing tools for software engineers and small construction teams to design and deploy applications. We will examine software as a service (SaaS) apps more in this chapter about cloud service models that came to prominence at the same time as web-based

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programs. Even when technologies must be properly installed always on each user’s PC, SaaS enables users to access applications from any quantity of data anytime. Google’s efficiency tools such as Gmail, Drive, and plus Docs are now all cloudbased versions. Office 365 is a premium service Microsoft product that is accessible in the cloud. Netflix’s services like Netflix in 2007, Spotify’s in 2008, Dropbox’s in 2009, Zoom’s in 2012, and Slack’s in 2013 are all just a few instances of innovative SaaS businesses and entrepreneurs who have emerged as a consequence of these involved parties’ unique opportunities. Businesses and people alike are rapidly adopting cloudbased information technology architectures and cloud-based applications.

8.2 Motivation Cloud computing becomes a more popular computing model due to the flexibility it offers providers through terms of storage, processing, retrieval, or other computer resources. As cloud services become more prevalent, it is critical to evaluate them carefully so that both cloud infrastructure providers and users can benefit mutually. Despite the numerous features of cloud computing, there are several areas that must be addressed and solutions suggested so that cloud services can be delivered seamlessly and without interruption. Consider the issue of a service-level agreement (SLA), which is a document that specifies the guidelines of expected service, performance, and so on at a set cost. In this case, the customer offers to pay the public cloud provider a fee to use storage or other computational power on the cloud in accordance with the mutually agreed-upon SLA. However, if a requirement is breached due to a cloud computing provider’s inability to provide the minimum service standards, the consumer may be required to pay a penalty fee.

8.3 Contribution This chapter focuses on blockchain for the best business model in order to raise awareness about COVID with cloud services as a monopoly. To the best of our knowledge, various researchers have conducted surveys in cloud computing fields, which are taken into consideration and thoroughly discussed in the related work section of this chapter. This chapter’s main contributions are as follows: the in-depth examination of the need for and application of cloud computing; then, in the cloud computing monopoly, the design of the cloud computing monopoly has been explained. Finally, various energy-aware algorithms for the sustainable have been developed, and their future scope has been described.

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8.4 Organization of work The rest of the chapter is organized as follows: – Introduction and a brief description about cloud computing and its history – Discusses the need of cloud computing with all of the model and risk or ethics in cloud computing – Introduces blockchain in brief and the working process – Explains the case study on Covid – Surveys on the literature review – Discussions and approaches during the early stages of the coronavirus disease 2019 (COVID-19) epidemic – Implementation code

8.5 Need of cloud computing All end user objects are installed just on the bottom floor of a cloud network. Rapid time to market: By developing in the cloud, users can get their application forms from the market quickly. Data security: Because of computer network backups, hardware failures do not really result in loss of data. Equipment savings: Because cloud computing uses cloud resources, organizations save money on servers or other equipment.

8.5.1 Model of cloud delivering Cloud resources are delivered in a number of different ways, each of which provides a distinct amount of assistance and freedom to clients. Infrastructure as a service (IaaS) is a concept that refers to the provision of computer system on a pay-as-you-go basis. This developed a framework of software’s products, networks, storage, and some other subsystems. IaaS operates similarly to a virtualized version of users from having to purchase and establish healthy servers while enabling them to grow and spend on services that are provided. “Physical servers”: These are a popular alternative for companies that want to take the advantage of the cloud’s many benefits but do not have the technical expertise to monitor the deployment, setup, and administration of systems, debuggers, and other assessments in IaaS. Because of this, it is also utilized by researchers in the area to change their computer ecosystem’s essential architecture. A computer or computer technology, web hosting, and other services may all be provided through IaaS, and big dataset analytics. In platform as a service (PaaS), customers may focus their efforts on

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developing effective applications while the PaaS provider takes care of setting up, configuring, and maintaining their network technology (such as their computing device and other programs). Application programs and software developers typically employ PaaS since it facilitates the creation and maintenance of computer systems while facilitating cooperation across distant teams. PaaS may be an attractive option for programmers who do not need customization of their underlying infrastructure or who want to devote their time to creation instead of DevOps and network management. In SaaS, consumers do not have to worry about installing or maintaining the program. Git, Google Docs, Slack, and Adobe Cloud are just a few examples. Because of its ease of use, availability on a multitude of formats, and availability in free, subscriber, and commercial tiers, SaaS apps are popular with organizations and individuals alike. As with PaaS, SaaS keeps the software’s fundamental infrastructure out of the hands of end users, allowing them to simply interact with the service’s front end.

8.5.2 Cloud environments Cloud services are offered as public or private assets, with each serving a distinct set of requirements. Public cloud: This cloud service relates to cloud computing (including virtual servers, storage, or apps) that is made available to organizations and consumers by a business supplier. Public cloud services are housed on the hardware of the commercial cloud provider, which customers access over the Internet. They are not necessarily appropriate for enterprises operating in highly regulated areas, like healthcare or financing, since public cloud platforms may not adhere to industry-specific data protection standards. Private cloud: This is a phrase that relates to cloud computing which is controlled by the business that utilizes them and is accessible exclusively to its staff and consumers. Cloud systems enable businesses to exercise more management over their computer system and stored data, which may be important for businesses operating in strict regulatory fields. Cloud services are often considered to be more safe since they are directly linked to the organization’s internal network. It allows for data security to be handled by the organization. Cloud vendors periodically offer their products and services as applications that may be placed on cloud storage, enabling organizations to preserve their on-premise infrastructures and data while enjoying the public cloud’s newest innovations. Cloud computing: Hybrid and multicloud cloud computing services are used in a hybrid cloud framework by businesses to address application requirements while also complying with industry norms. The use of several cloud providers across multicloud systems is also common (e.g., combining Amazon Web Services and DigitalOcean).

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8.5.2.1 Benefits of cloud computing Public cloud benefits individuals, firms, entrepreneurs, and other organizations. These benefits vary according to the cloud user’s aims and activities. For business and industry, most enterprises and organizations prior to the widespread adoption of cloud technology had to acquire and maintain the necessary operating systems for computer operations. Increasingly, enterprises started embracing cloud computing services to store their data as they became more readily accessible, commercial software innovation, and online customer experience distribution. Numerous fully integrated cloud implementations and developments also occur. Medical professionals use cloud systems designed specifically for storing and exchanging medical data, as well as connecting with patients. Academic teachers and researchers use Internet research and education tools. However, a broad range of generic Internet solutions have been used across industries, including productivity apps, messaging, cost control, videoconferencing, programming top management, bulletins, questionnaires, customer care, identity authentication, or planning. Apps and infrastructures for businesses are rapidly expanding into a separate sector in the cloud, demonstrating that perhaps the cloud is not merely changing the corporate IT strategy. Virtualized technologies offer multiple substantial advantages to businesses. In the beginning, they may contribute to the Internet technology cost control. Renting virtual machines help business to reduce the maximum expenses associated with acquiring and maintaining IT infrastructure on-site. Moreover, cloud technology is very scalable, enabling organizations to quickly adjust to changing market conditions and expand their technological capacities (and only paying for what they use). Cost was not, however, the main factor influencing commercial cloud growth. Internet alternatives may help improve the capabilities of external IT processes by enabling employees to access data forward without having through the IT review and approval. Internet apps have the potential to improve company collaboration by enabling realtime sharing of information. Since independent developers previously restricted using a cable connection, huge corporations and organizations may now access computer technology for a fraction of their past cost. Cloud-based applications may be quickly deployed and tested in practice, thanks to this method of deployment. Additionally, Internet code sharing platforms (including GitHub) have streamlined the process of creating and working on open-source projects. Furthermore, Internet teaching platforms and dynamic coding tutorials have enhanced developer learning accessible, enabling those with no technical background to learn and to write with their own time. When combined, these cloud storage and usage have benefited in the removal of barriers to advancement and Internet software delivery. Private citizens can now research with designing and implementing apps without requesting formal education, company assist, or a significant amount of seed money, enabling a broader range of

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competition with existing market competitors, as well as the creation and sharing of new idea implementations as personal work. For researchers, the Cloud technology has become significant in a number of scientific fields, including astronomers, physics, genomics, and automated systems, as computer vision methodologies become more essential in the field research. The massive volumes of data generated and analyzed in object recognition and other communication scientific research might need computational resources that surpass the capabilities of something like the researcher’s or indeed the institution’s infrastructure. Virtualization allows investigators to access (and pay for) computers on a workload-by-workload basis and to communicate and interact with investigators situated from coast to coast. The bulk of academia classification algorithms would be restricted to those with access to classified data storage quality system resources offered by the institution. For the communities with infrastructure, certain cloud-based solutions may be used by organizations and people to meet the public standards and ideals, tailor services, protect consumer data, and gain more control over their computer system. For customers who want an alternative to show this same means, which consistently restrict the statutory authority and privacy while also limiting control over their computing devices, there are a number of open-source frameworks to choose from, including online communities like Mastodon and mobile communication applications like Jitsi. While SaaS software and social media platforms require less organizational work, numerous groups chose them owing to ethics about how popular services, particularly SaaS applications, utilize personal information and conduct transactions.

8.5.2.2 Risks, costs, and ethics in cloud computing While the cloud has many benefits, it also adds new risks, costs, and ethical considerations that must be addressed. While some of these risks apply to all cloud users, others are particularly relevant to businesses and organizations that use cloud storage data from customers.

8.5.2.3 Considerations for all cloud users Privacy: When compared to traditional on-premise computer servers, cloud solutions may provide additional security threats owing to their use of APIs, virtualization identity, and applications that facilitate unwanted access to information. This discusses the methods being used by the cloud platform to safeguard consumer data from theft and other sorts of risks, and even the techniques or additional services utilized by customers to safeguard their data.

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Loss of data: As with equipment that is actually directly controlled, public cloud may suffer permanent security breaches as a consequence of physical disasters, defects, inadvertent synchronized customer mistakes, or other unanticipated hazards. While putting cloud services and infrastructure, enquire about the supplier’s backup services and be aware that services may not be provided routinely or for free. Alternatively, you might be doing backups independently. Data persistence: Occasionally, cloud consumers might just want to confirm that the personally identifiable information supplied with cloud vendors has been removed. However, deleting data in cloud capabilities and verifying its deletion may be complicated, or even prohibitive. Before granting access to cloud businesses to your data, enquire about company’s deletion policies in case you decide to erase it later. Charges: Although the cloud may provide software applications at such a fifth of the price of equipment, cloud software costs can quickly skyrocket as demand grows. Whenever sign up for a cloud environment, go through the payment information to see how well the service is maintained and whether you should establish a payment plan. Use limits or get notifications when user exceeds your specified limits. Additionally, it is good to check the manner in which billing information is supplied, since some suppliers’ invoicing procedures may not always be obvious. Lock-in by the vendor: Professional cloud-based consumers may be more vulnerable to lock-in by the manufacturer, a situation inside which switching suppliers become hard or impossible after computerized processes are customized to work with a closed, peripheral device. Utilizing cloud-based application solutions may assist to lessen this risk, while their standardization enables computing activities to be migrated across operators. However, virtual machines should bear in mind that each move requires work, planning, and competence. Cloud computing providers may utilize customer data to get a better understanding of how their products will be used, to sell or personalize adverts, to train computational methods, or to sell customers’ information to other people. If you have reservations as to how personal or your company’s data can be used, make sure to enquire more about data usage policy of the service provider. Company ethics: Given some packages’ considerable impact over world events, cloud clients may choose to analyze the industry’s ethics. Studying a company’s rules on data gathering, advertising, inappropriate language, politics, misinformation, the environment, and labor may help cloud users choose a service provider, whose values align most closely with their own. Loss on granular rights and visibility: Once cloud customers use second computational resources, they forfeit entire control and visibility over their data centers, which may result in a range of technological and trust issues. Several of these technical challenges may be mitigated via the use of powerful analytic tools that keep cloud clients informed about the health of their infrastructure and enable them to. When difficulties develop, react swiftly. A lack of confidence in an industry’s track record

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utilization private communications may be addressed by an examination of the corporation’s consumer data management and publicly accessible data analysis.

8.6 Blockchain 8.6.1 What is blockchain? To sum it up, a blockchain is an open structure that securely maintains operational data while simultaneously ensuring its accessibility, devolution of power, and safety. Alternatively, you may conceive of it as a sequence of data stored in the form of pellets that are not subject to a single ministry’s jurisdiction. The blockchain is a digital database that is completely available to all network users. Whenever data is stored on a blockchain, editing or updating it becomes almost difficult. All operations on a blockchain are protected by a digital message that certifies its legality. Due to its use of cryptography, the information blockchain ledger is tamper-proof and cannot be changed. The word “unanimity” includes the ability among all participants in the network to reach agreement, which is made possible by blockchain. All data stored on such a blockchain is audiotaped and is available to all client computers through a shared heritage. This removes the risk of malfeasance or trade recurrence without requiring the assistance of a third party. To want a better grasp of blockchain, evaluate the two scenario: You are looking for a means to wire cash to a friend who lives in a remote location. You could generally use a banker or a fund transfer service including PayPal or Pay. This approach entails the use of third-party companies to execute the transfer, which results in the deduction of extra funds as a transfer fee. Furthermore, you cannot strengthen the security of your cash in these cases, since it is highly possible for a hacker to disrupt the connection and steal your assets. In both cases, the customer is harmed. This is the application of blockchain. Instead of relying on a bank that can transfer funds, we may use a chain in these circumstances to simplify and protect the transactions. There is really no extra fee since you receive the funds directly, eliminating the need for such a third party. Moreover, since the blockchain platform is distributed and therefore not controlled, all data and documentation saved upon that blockchain are transparent and autonomous. Also because the content is not stored in a single area, it cannot be damaged by a hacker. How does it work? A blockchain technology is a collection of blocks that hold data. Notwithstanding having previously discovered, Satoshi Nakamoto created the first well-known and important application of distributed ledger technology in 2009. He was the first to utilize

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the blockchain technology to create the world’s original digital cryptocurrency, nicknamed Bitcoin. Consider how much a blockchain actually works. Each episode in some kind of a blockchain network contains additional data related to the hash of the previous block. A hash is really a complex characteristic code that distinguishes a block consistently. If the data contained inside the block is updated, the hash of the block is likewise modified. The confidentiality of the network is assured by the connectedness of blocks through unique hash keys. Although transactions are made on a blockchain, they may be verified by network elements. Upon that blockchain network, these machines refer to here as mines, and they evaluate and validate bank transactions using the solid evidence algorithm. Each block must include a connection to the previous block’s hashes in need for the activities to be legal. Unless and until the hash function is correct, payment may occur. If a criminal tries to compromise the system and updates the data relating to a specific block, then frame’s hashes will indeed be modified as well. Due to the fact that the revised hash should be different from the old, a breach will be discovered. This ensures the unchangeability of the blockchain technologies, since any alteration to a chain of blocks will be reflected throughout the whole infrastructures but will also be immediately observable. A blockchain technology integrates public and private keys to produce a virtual signing that secures data. 1. Once authentication is achieved by using these keys, this becomes clear that permission is required. 2. Blockchain technology allows members to just do statistical analysis and reach consensus on just about any given value. 3. Whenever a transfer is initiated, the sender uses their private keys to announce the contents of the activity across network. The participant’s virtual sign, period, and public key are all stored in a block. 4. This data block is transmitted across the network, kicking off the validation process. 5. Miners all over the Internet start by solving the mathematical puzzle that is likely to have economic advantages in order to complete it. Miners must use their computing resources to resolve this puzzle. 6. The miner is rewarded with Bitcoins for finishing the task first. These are referred to as proof-of-work challenges in mathematics. 7. Once a majority of devices in the system agree on a resolution, the transaction is timestamped and appended to the current blockchain. This chunk of material or communication might be anything from currency to knowledge to communications. 8. Following the successful creation of the modern block, all terminals on the cable network that present copies of both the blockchain are changed.

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8.6.2 Features of blockchain Blockchain characteristics include the following: Decentralized: By their very nature, blockchains are decentralized, which implies that no particular individual or persons can dominate a whole community. When everyone in the organization has access to a copy of the communications link, no one should have the capacity to autonomously alter it. This unique property of blockchain offers security and accountability while also empowering customers. Peer-to-peer network: The use of blockchain technology permits direct interaction between two members throughout a peer-to-peer network, eliminating the need for such a third party. The chain technology relies on a peer-to-peer architecture that permits all participants to keep an identical replica of all operations, providing for automated validation. For illustration, if you would like to transmit payment by one part of the world to another in a couple of moments, you may do so by utilizing blockchain. Furthermore, any extra fee or inconveniences will not be deducted from the transaction. Immutable: The blockchain property of a blockchain technology refers to the inability of any data recorded to it to be changed. Consider, for instance, the integrity of the data of email. After you have sent a letter to a set of people, the action is final. To circumvent this, you will really have to request that all recipients delete your message, which is fairly tedious. This is how data integrity works. After evaluation is complete, it could be edited or altered. If you try to edit the data in one block mostly on blockchain, you would need to update a whole blockchain technology now after this transaction, since every block contains the hash of the previous block. Modifications between one hash have an effect on all succeeding passwords. It is quite difficult for somebody to alter all passwords since it requires a huge amount of computing power. Due to the absoluteness of the data that needs to be encrypted, it is immune to modification or hacker attack. Tamper-proof: Due to the permissionless property of blockchain technology, it becomes easier to detect data alteration. Due to the simplicity that any change to anything other than a solid block can be noticed and handled, blockchains are considered impregnable. Two important techniques for recognizing corruption are hashes and blocks. As noted earlier, each unit has its own cryptographic hash. Consider that it is analogous to the fingerprints of a block. Any alteration to the material will alter the cryptographic hash. Due to the fact that now the hash value of one block is coupled to the hash algorithm of another block, any alterations made by a hacking will require modifying the hashing of all consecutive blocks, which would be exceedingly hard to do.

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8.6.3 Types of blockchain Though blockchain technology has advanced to numerous levels since its inception, there are two main types into which blockchains fall, namely, formal and informal blockchains. Before we discuss the distinction between the two, it is worth noting the similarities between public and private blockchains: Both public and private blockchains rely on decentralized peer-to-peer networks. Each network participant keeps a copy of the shared ledger on their personal computer. Using consensus, the network keeps multiple versions of the ledger and synchronizes the most recent update. To avoid malicious attacks, the rules governing the ledger’s immutability and security are decided and applied on the network. Now that we have established the commonalities between these two blockchains, let us take a closer look at each of them and their distinctions. Public blockchain: As the name implies, a public blockchain is an open ledger that is accessible to anyone. Anyone with Internet access is qualified to download and use it. Additionally, one can examine the blockchain’s overall history in addition to conducting transactions. Typically, public blockchains compensate the network participants for mining and ensuring the ledger’s immutability. The Bitcoin blockchain serves as an example of a public blockchain. Public blockchains enable open and secure information exchange between communities worldwide. However, a clear drawback of this type of blockchain is that it is vulnerable to compromise if the rules governing it are not strictly followed. Additionally, the rules that are initially decided and implemented have very little room for alteration in the subsequent stages. Private blockchain: Contrary to public blockchains, private blockchains are distributed among owners only to increase consumer confidence. The owners exercise overall control over the network. Additionally, the rules governing a blockchain platform can be altered to reflect various levels of authorizations, exposed, membership, and authorization. Private blockchains can operate independently or as part of a larger network. Typically, businesses and organizations make use of these. As a result, private blockchains require a greater level of confidence among participants. Even while Bitcoin has been the most well-known one to use blockchain, it is not the sole use. Businesses and company executives all over the globe are investigating the possibilities of utilization of innovation and executing dramatic changes in a wide range of industries. Even though it has enormous potential, corporations have been over-hyping the notion of trustworthy and reliable records and giving consumers more control. When harnessed properly, the power of this breakthrough has the ability to alter whole sectors. Investigate the real-world applications of blockchain technology to separate the chaff from the wheat.

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8.6.4 Case study of Covid With over a million frequent as well as newly added customers from the Twitter blog, Twitter is progressively disseminating public healthcare details and achieving realtime health information through crowdsourcing. Numerous researchers evaluated Twitter to provide real-time updates on respiratory tract infections and other viral outbreaks. In 2009, researchers estimated the growing interest in infections and viruses by analyzing tweets and their associated keywords and predicting real-time disease activity and preventive trials. During the 2014 Ebola outbreak, Twitter users reported pertinent health information depending on the greatest level of Twitter activity and media resources on a particular day. The analysis of Twitter data from the preceding Ebola pandemic reveals Ebola-related tweets that are mostly about compassion, illness patterns, risk factors, and treatment. Similarly, Middle Eastern respiratory syndrome spreads were linked to Twitter activity during the epidemic of the illness in 2015 [1]. Twitter is an useful monitoring tool for infectious diseases. Because when Zika virus infection occurred, researchers used Twitter to track how people’s travel habits changed as a result of growing public anxiety. The World Health Organization (WHO) and the Centers for Disease Prevention and Control (CDC) have recognized the use of Facebook and Twitter because of its educational and informative potential. The global health institution’s Twitter account garnered over 2 million mentions during the beginning of the 84-day ZIKA pandemic in late 2015, suggesting its extensive influence on the distribution of health information. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new, materialized coronavirus, was discovered in December 2019 by the Chinese National Health and Family Planning Commission (Wuhan city, Hubei province). In the next days and weeks that followed, the rapidly spreading coronavirus drew considerable public interest and media exposure. Later, media attention peaked, and the WHO began producing daily status bulletins. Following this, travel restrictions and avoidance, along with widespread quarantine of Chinese people, and an increase in the number of worldwide index case scenarios, piqued the public’s interest [2]. Additionally, there is some limited perspective into the involvement in important themes mentioned and by constantly taking the emotion of the general populace. This work hypothesized that analyzing the primary content of initial stages of COVID-19 pandemic stated on Twitter would aid in the spread of the virus and provide insight into the epidemic’s effect on the public’s feelings, thinking, and beliefs. Such adoption would open up enormous possibilities for schooling as well as data dissemination about public health guidelines. Figure 8.1 illustrates the total workflow for overall stages of analysis flow.

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Twitter

Twitter Python API Tweets fetch

Tweets filteration

COWID-19 keyword

Cleaning of tweets

Data processing

Transformation and Exploration

Sentiment analysis and topic modelling

Figure 8.1: Overall flow of sentimental analysis.

8.6.5 Literature survey Survey 1: Sentiment analysis is the most helpful idea on the web clients. The current age will in general break down the items, motion pictures, medicinal services framework, and so on through client audits. Initial stages of COVID-19 pandemic were stated on Twitter by Internet customers through inspections or tweeting. The main goal of sentimental analysis is to characterize the surveys’ extremes. Deep learning (DL) employs a variety of techniques, one of which being neural networks [3]. In contrast to traditional computational approaches, neural networks adopt a distinct perspective to resolve issues. Neural computing is the research of systems of flexible nodes that store and make accessible experience information via stages of learning from challenge instances. Due to its capacity to generalize and adapt to unanticipated inputs/patterns, neural nets are commonly employed in pattern classification. Additionally, these neural

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networks may be taught to reach high accuracy when dealing with massive amounts of data, and are utilized in the modeling and control of processes. Other uses include portfolio management, identification, medical diagnosis, fraud detection, intelligent searching, voice recognition, credit scoring, and character recognition, to name a few. Sentiment analysis has been the subject of a slew of successful research. Zhao and coworkers [6] claim that consumers benefit from product surveys by helping them decide whether or not it will purchase a product. Many mining methods have been devised as a result, with judgment being a major obstacle in many cases. According to Zhao and coworkers, this problem may be solved with the help of weakly supervised deep immersion for product sentiment classification. Naive Bayes (NB), long shortterm memories (LSTM), neural network, convolutional neural network (CNN), and multilayer perceptrons are some of the numerous sentiment analysis classifiers available, and also the LSTM and CNN collaboration classifier. Survey 2: Since its inception, the Internet has become a frequently used and indispensable tool in and of itself. Disseminating information and expressing opinions may be accomplished this way. Social media has inspired a slew of assessments, comments, and publications. Social media is used by a wide range of organizations to connect with its members. The term “sentiment analysis” refers to the process organizations use to determine whether or not a customer review is positive or negative. Since its inception in 2006, Twitter has increased its popularity [4]. A study of Twitter’s moods was released in 2009. Sentiment analysis on Twitter is made easier by the 140-word limit each tweet. A bag-of-words model, such as multinomial NB or bolster vector machine (SVM) or maximum entropy (MaxEnt), is utilized in the majority of traditional methods. There are numerous tiers of perceptrons in the DL machine learning architecture, which is inspired by the human brain. DL has been utilized successfully in sentiment analysis in a number of cases. One of the two ways for learning from a sequence of data is the LSTM and the dynamic convolutional neural network (DCNN). When applied to Twitter data using bag-of-words, both approaches outperform standard methods. Furthermore, the data shows that DCNN outperforms LSTM in terms of accuracy. We want to use well-known DL techniques to analyze sentiment on Thai Twitter data. There are three main goals of our investigation: In order to compare LSTM and DCNN to other techniques that employ a bag-of-words and to assess the relevance of word sequences in Thai Twitter data, we will examine the impact of each parameter on deep neural networks. In order to get information on how people feel about a tweet, we look for regularly used emoticons inside each message. As an added bonus, we show how Thai Twitter data is prepped before being used. The discovery was confirmed by extensive testing [5]. According to the findings, DCNN exceeds LSTM in terms of efficiency, and all traditional techniques except MaxEnt are surpassed by both DL approaches. As a concluding example, we show the importance of word order in Thai. A neural network is a machine learning model that takes its cues from the functioning of the brain. This enormous network is made up of a large number of neurons. The architecture of a neural network may be changed. The number of nodes in each layer, as well as the number of

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hidden layers and weights, is customizable. When it comes to neural networks, the more layers they have, the more complicated models they are capable of learning. There are multiple hidden layers in the phrase “deep learning,” which refers to a neural network. However, a simple feed-forward neural network cannot make money by just adding more layers because of its inability to learn. Survey 3: Social media is described as a collection of Internet-based apps that are founded on the conceptual and technical underpinnings of Web 2.0 and enables the production and sharing of user-generated materials. To examine and interpret a large volume of customer evaluations, sentiment classification seems to be even more critical. Currently, this study is often employed to determine a product’s rating and the public’s choice [6]. As a result, the study and methods associated with such analysis grew more popular and mature. Recently, as social media use has increased, consumers may simply and rapidly get product reviews. However, there is little question that the customer will need some time to sift through such voluminous data in order to extract meaningful information. As a consequence, in this work, we use DL to sentiment analysis and concentrate on customer evaluations. Realm of the smartphone: Additionally, we will utilize a sentiment dictionary, a DL approach, and an opinion dictionary to assess and analyze customer evaluations in the smartphone domain, with the goal of determining the consumer’s opinion polarity. We used customer evaluations of mobile apps as the foundation for the polarity analysis in this research [7]. Additionally, by using a DL strategy, we may get a better level of accuracy. Survey 4: The high level of commitment by business and scientists to the advancement of estimation testing is a result of quality and execution concerns. Clearly, conclusion examination is a popular NLP project. For instance, there are a few worldwide rivalries and challenges that aim to determine the optimal approach for concluding the arrangement. Slant evaluation was conducted on a variety of levels, starting with the overall content and progressing to the phrase and/or state levels. Sentiment analysis is a relatively new field of research. The reference is widely regarded as the seminal [8] study on the use of machine learning techniques for text categorization in sentiment analysis. Earlier work in this subject has focused on techniques based on unsupervised learning. Bag-of-words, n-grams, and tf idf are some of the most common features used in these strategies. Nonetheless, as shown by the experimental analysis, simple models are typically superior than their more complex counterparts. The references gather sentiment data using remote learning. Survey 5: 3.3 million confirmed cases and 238,000 fatalities have been reported as of May 2, 2020, as a result of COVID-19, which is caused by SARS-CoV-2. Because of the high infectiousness of COVID-19 and the lack of available treatments and vaccines, finding the disease early is essential for halting its spread and maximizing the use of limited medical resources. When it comes to the diagnosis of COVID-19, reverse transcription polymerase chain reaction (RT-PCR) that uses nasopharyngeal and throat

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samples is the gold standard since it detects viral nucleic acid. Antigen testing is fast; however, it has a poor level of accuracy. Radiological examinations may be useful in detecting and monitoring the development of COVID-19, given that the majority of those infected were diagnosed with pneumonia. In cases where RT-PCR findings were negative or very faintly positive at first patient presentation, chest computed tomography (CT) screening shown better sensitivity to RT-PCR and even confirmed COVID-19 infection in some cases. Current COVID-19 radiological studies have concentrated on CT data because of this. Nevertheless, as the prevalence of COVID-19 rises, the need to identify COVID-19 features on chest X-rays (CXRs) is increasing since frequent CT use places a major burden on radiology departments and increases the danger of CT suite infection. It is not uncommon to find bilateral, peripheral, and ground glass opacities on CXR. According to Wong et al. [4], COVID-19 often had CXR appearances. It has been found that CXRs are only 69% as sensitive as the first round of RT-PCR testing [9]. Despite this low sensitivity, CXR abnormalities were found in 9% of those who had a negative initial RT-PCR. There has been a lot of interest in DL algorithms for COVID-19 classification based on CXRs. Open-source deep CNN platform COVIDNet was developed by Wang et al. [4] for the detection of COVID-19 in chest radiography images. They claimed that COVIDNet has an 80% sensitivity for COVID-19 cases. Survey 6: The primary objective is to gather information. We may create a model for predicting thoughts by analyzing the connections and classifying them as either positive or negative. Twitter and other browser life cycle stages are now integral to the everyday routines of many people. It is imperative that we manage the presentations on these stages, as well as assess and investigate the feelings that are expressed on these stages, in light of the emergence of artificial intelligence. An analysis approach may vary from “pure” dictionary-based analysis all the way to “serious” neural networks and DL. Machine learning and classifiers are being developed [12] to pick out relevant tweets depending on the emotional intensity of those tweets. A properly trained recurrent neural network is shown to have a substantial part in accurate classification when evaluated against a variety of other options. Network models are utilized for a variety of different kinds of messaging. As previously stated, a certain amount of communications (tweets) is analyzed, and the emotional responses to the topic are monitored. More exact and comprehensive categorization and analysis of an emotional analysis linked to a current problem will provide a more stable and proper basis for sociological and other research. The study also provides a fresh approach to pandemic research by focusing on quickly modifying human mood and attitude. People’s reactions to an epidemic of a coronavirus, for example, may be seen on social media (Twitter).

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8.7 Discussion We have documented specific spikes in aggregate. The COVID-19 epidemic commenced on January 21, 2020, and there was a flurry of hostile tweets and culturally charged stuff on Twitter to go along with it. During the early stages of the COVID-19 epidemic, the research found a correlation between the number of tweets and the number of affected people. There was a lot of anger and shock in the tweets, which were generally negative in tone and expressed the immediate alarm. In addition, despite the presence of social prejudice and misinformation, Twitter was often used to disseminate important public health information. These data assist medical specialists and public health authorities in determining how to spread and communicate in a way that minimizes emotion and also eliminates incorrect information. Emotions have been demonstrated to alter how individuals think, solve problems, and make decisions under dramatically altered epidemic situations. Patients’ iteration of the healthcare dashboard will provide information about actual reality. For leaders in the healthcare business, public health authorities, and government, knowing the public attitude and response to infectious epidemics is crucial for forecasting the future use of healthcare resources and acceptance of practices that promote public health and infection prevention. Twitter provides access to millions of subscribing users’ feelings and ideas and enables logical and real-time analysis of such attitudes about critical healthcare issues such as the ongoing COVID-19 epidemic. Programs of keeping tabs on the most serious and developing disorders are prohibitively difficult and time-consuming. It is possible to get a clear, and in this instance, startlingly realistic picture of the spread of the community health risk by following the social media posts. Though all daily tweets were in English, there was a correlation between new diagnoses and the frequency of new diagnoses, despite the fact that the majority of cases occurred in China. Infected diseases on a global scale, such as COVID-19, may create a natural breeding ground for societal ills, fragmentation, and racial hatred. As previously stated in prior epidemics such as Extracellular Vesicles (EV) [11] and more recently SARS [8], there is a distinct disparity around the transmission of these viruses to individuals depending on their identity and race. It was also noticeable that in our sample research, there were several tweets about bias. As the number of COVID-19 patients steadily increased, so did the number of these tweets. The 0.6% aggregation rate is scrutinized, owing to media allegations of prejudicial behavior toward Chinese and certain other Asian American citizens. The social media platform Twitter has been the most popular and commonly utilized medium for healthcare communications in recent years. There has been some suspicion that it has abused the subject of much description and debate, with Twitter’s critics pointing out that it is capable of processing massive amounts of data. We observed the evidence of inaccurate information and excesses in tweets and reported them to social media platforms online, as shown below:

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Numerous example tweets have examined the responsibility of functioning as servers of COVID-19-related materials eliminating incorrect material and diverting online traffic to some well and reliable sites. The person whose account was used to tweet the incorrect and misleading patient data above was later suspended [18]. Singapore Twitter optimized their search results to include connections to reputable health sites such as the WHO and the COVID-19 ministry of health outbreak. As shown in this data, although writing content is important on Twitter, it accounts for a relatively small fraction of conversation. On the other hand, it is vital to emphasize the specialists that misinformation was disseminated by both the general public and government authorities. An explanation of how data is sent in a peer-reviewed publication incorrectly stated that an asymptomatic individual who is infected with the other four coronaviruses is a coronavirus. The targeted case was not evaluated by experts, who subsequently reported that the individual is symptomatic. Since then, scholarly studies have suggested that COVID-19 includes four distinct sequence segments with genetic markers that are not seen in other coronaviruses, implying that the virus may be genetically engineered. On aggregate, the majority of tweets had the purpose of disseminating information. The majority of tweets show an amazing amount of strength and effectiveness in completing educational tasks. The crowd’s desire to utilize social media platforms such as Twitter to gather and distribute information is an opportunity to alter the current state of affairs and educate millions of members of the public. Since the outbreak began, the WHO has provided extensive knowledge to the public through a constant stream of tweets. Certain tweets are pertinent to preventative measures applicable to sick persons (self-isolation, coughing into one’s sleeve, and handwashing), but they were not widely regarded, and remain so with just 5% of tweets. In terms of public health, the ability to track tweets on real-time basis and the impact on certain demographics with important messages tailored to their data requirements and sentiments may be a more powerful instrument, and may be more powerful than other broadcasting medium. Historically, bots (a greater number of apps capable of communicating with IT systems or end users) have been often employed on Twitter for the purpose of spreading or disseminating fraudulent or unlawful content [9, 28]. Additionally, government and public health organizations such as the CDC and WHO should do research into this emerging technology. For instance, creating self-governing technologies that can recognize tweets from end users who are unaware or scared about getting COVID-19 might be used to reply to specific messages that give comfort and education about preventative measures such as handwashing. All of these factors

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together, such as targeted rapid responses to tweet attitudes and content, have the effect of including more Twitter users in public health-related discussions and increasing the usefulness and accuracy of information.

References [1]

Kasotakis, G. (2020). Faculty opinions recommendation of correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in China: A report of 1014 cases. Faculty Opinions – PostPublication Peer Review of the Biomedical Literature. [2] Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern Recognition Letters, 138, 638–643. [3] Shin, S.-Y., Seo, D.-W., An, J., Kwak, H., Kim, S.-H., Gwack, J., et al. (2016). High correlation of Middle East respiratory syndrome spread with Google search and twitter trends in Korea. Scientific Reports, 6(1). [4] Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports, 10(1). [5] Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., et al. (2020). Sensitivity of chest CT for covid-19: Comparison to RT-PCR. Radiology, 296(2). [6] Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: Relationship to negative RT-PCR testing. Radiology, 296(2). [7] Zharmagambetov, A. S. & Pak, A. A. (2015). Sentiment analysis of a document using deep learning approach and decision trees. 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO). [8] Sahni, T., Chandak, C., Chedeti, N. R., & Singh, M. (2017). Efficient twitter sentiment classification using subjective distant supervision. 2017 9th International Conference on Communication Systems and Networks (COMSNETS). [9] Lin, L., Hall, B. J., Khoe, L. C., & Bodomo, A. B. (2015). Ebola outbreak: From the perspective of African migrants in China. American Journal of Public Health, 105(5). [10] Kupferschmidt, K. (2020). Study claiming new coronavirus can be transmitted by people without symptoms was flawed. Science. [11] Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). [12] Day, M.-Y. & Lin, Y.-D. (2017). Deep learning for sentiment analysis on Google play consumer review. 2017 IEEE International Conference on Information Reuse and Integration (IRI).

R. Ganesh Babu✶, G. Glorindal, Sudhanshu Maurya, S. Yuvaraj, P. Karthika

9 Evolutionary computation and streaming analytics machine learning with IoT for urban intelligent systems Abstract: In the Internet of things (IoT) era, for a wide range of fields and applications, a vast number of detecting gadgets collect or potentially produce different tactile information after a while. These gadgets can cause large or fast/constant streams of information. Examining these information sources in order to find new data predict future bits of knowledge, and decide on control choices is a critical process that makes IoT a commendable worldview for organizations and a personal satisfaction that enhances innovation. In this chapter, we give a careful review on the use of a class of cutting-edge artificial intelligence (AI) systems to advance deep learning (DL), thus enhancing IoT space inspection and encouraging learning. We begin by articulating IoT information attributes and recognizing two essential IoT information medicines from an AI perspective: IoT massive evolutionary computation and streaming information analysis and IoT gushing information review are two expels of IoT. We also explore why DL is a good way to perfectly handle investigation in these kinds of data and applications. The capacity to use DL procedures for investigation of IoT information is then addressed, and its guarantees and difficulties are identified. On various DL systems and measurements, we pose a far-ranging base. We also research and detail major research projects that used DL in IoT space. Furthermore, elegant IoT gadgets that fused DL into their knowledge base are investigated. On the basis of IoT applications, the mist and cloud-based DL implementation approach is also overviewed. We finally shed light on some problems and possible reasons that need further research.

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Corresponding author: R. Ganesh Babu, Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India, e-mail: [email protected] G. Glorindal, Department of Information and Communication Technology, St. John the Baptist University, Lilongwe, Malawi Sudhanshu Maurya, School of Computing, Graphic Era Hill University, Uttarakhand, India S. Yuvaraj, Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India P. Karthika, Department of Computer Applications, Kalasalingam Academy of Research and Education, Tamil Nadu, India https://doi.org/10.1515/9783110781663-009

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9.1 Introduction The dream of Internet of things (IoT) is to turn traditional things into genius by using a wide variety of cutting-edge communication technologies and inventions like Internet conferences and knowledge analysis. IoT’s potential financial impacts are emergence of numerous business openings and acceleration of IoT-based administrations’ monetary growth. In light of the study, IoT’s annual monetary effect in 2025 will be between $2:7 and $6:2 trillion. The IoT refers to a network of physical objects that have integrated technology that allows them to communicate, sense, or interact with their surrounding world is shown in Figure 9.1. Social insurance accounts for a significant share: about 41% of this segment, followed by business and vitality with 33% and IoT ads with 7%. IoT Cloud in Edge Devices/ Fog Computing

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Figure 9.1: IoT information age at different stages and profound learning models.

Considering transportation, agribusiness, the urban environment, and security,, retail accounts for around 15% of IoT ads. Machine learning (ML) might well undoubtedly have an impact on jobs and the workforce because sections of numerous jobs might be “appropriate for ML applications.” This will trigger the sought-after increment for some ML items and the implied value in the errands, stages, and specialists expected to create such items. In McKinsey’s report, the financial impact of artificial intelligence (AI) is characterized by mechanization of knowledge work: the use of PCs to carry out assignments that rely on complex assessments, unobtrusive decisions, and creative critical thinking. The study specifies that advancing, for example, profound learning and neural networks in ML procedures, is the fundamental force that motivates information function [1]. For example, standard UIs, discourse, and signal recognition are specific factors that benefit exceptionally from ML advances. By 2025, the projected potential financial impact of information work automatization will reach $5:3–$6:8 trillion. Figure 9.1 shows this scale separately in different occupations. Contrasting with IoT’s financial impact, this estimation considers the extraction of significant value from knowledge and the potential effects of ML on the monetary circumstances of individuals and social

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orders to be more important. These monetary effects have real implications for individuals’ need to adapt to new ways of receiving fair pay to keep their ideal aspirations for everyday console. Low Range High Range 100

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Figure 9.2 shows that IoT, like many other technologies, has a two-way connection with minute details. IoT is a primary source that involves informing on only one side, as well as an important goal for huge information analysis to recover the processes and institutions of IoT on the other side. Indeed, large-scale IoT information analysis has demonstrated that it can provide motivation to the wider populace. Various IoT implementations have appeared in different vertical areas, that is, healthcare, transport, homemaking, smart city, farming, and training. The explanation for this heightened exposure to deep learning (DL) relates to how traditional approaches to AI do not meet the research needs of IoT framework application. Alternatively, IoT systems need a range of current information explanatory methodologies and AI techniques as demonstrated by the IoT information age pecking order and the executives as represented in Figure 9.2. IoT, too, has a two-way relationship with huge details. IoT is, on the one hand, a primary source of enormous knowledge, and, on the other hand, an important objective of large-scale information analysis is to recover the procedures of IoT [2]. In fact, large-scale knowledge analysis of IoT has shown to provide an encouragement to the general public. IoT information is not the same as huge information generally. Secret information and data from huge information is not a simple and clear errand. More testing activities that go beyond the capabilities of traditional induction

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and learning are on the horizon, as are new innovations, calculations, and foundations [3]. Thankfully, the current developments in both fast processing and advanced AI techniques open the entrances to massive information analysis and knowledge extraction that is ideal for IoT applications. Enormous IoT information specifically supports the fast and streaming analysis of IoT data. Right now, information for testing and returning the reaction to a cloud server is based on inactivity that could cause small-scale crimes or mishaps. To avoid lethal incidents, the more basic condition will distinguish these vehicles from pedestrians. Accurate identification should be accomplished in extreme constant. In the concept of streaming information task, several particular methodologies have been taken to portray the idea of streaming information. The main goal is to minimize the relationship between the preparation and test segments, or to identify the critical conditions under which model structure and speculation can occur [4]. This has been a specific issue of old-style ML. Then again, the focal point of EC has (truly) been more connected with portraying the kinds of progress under unique conditions. The last section audits the different situations to describe properties of credit task in ML model structure.

9.1.1 Statistical frameworks The free (vector sources of info) and ward discrete class names (x and y, respectively) are every now and again portrayed by utilizing a measurable model. Thus, the information stream is characterized as a constant succession of (x(t), y(t)) sets. Be that as it may, name data (the reliant variable) is normally just given by drawing in a “prophet” (e.g., the human master), in which case the stream is portrayed by x(t) alone and y(t) is accessible for the subset of t. Additionally, it is expected that “preparation” and “test” information are compared with sequential limited length groupings. Such a suspicion prompts the common appropriation supposition, that is, both preparing and test parts should be produced by a similar conveyance, p(x, y), for speculation to happen. The common dissemination suspicion suggests that the fundamental cycle answerable for making the information has the following structure: Pðx, yÞ = pðxjyÞ x pðxÞ

(9:1)

where p(x) means the conveyance of information in ascribes/highlights. p(y|x) indicates the restrictive conditions among info and mark conveyances. Contingent changes infer that the name switches for a similar contribution as the stream advances. Double changes infer that both p(x) and p(y|x) go through variety as the stream advances. The common circulation presumption comes from the imperative that preparation and test conveyances are as yet thought to be reasonably comparable or ptr(x, y) and pts(x, y) are measurably the same. It is not necessarily the case that all stream

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practices will fit this structure, in which for characterizing a reason for estimating speculation, such a requirement is embraced. Not all systems for learning under streaming information settings buy into this limitation. In fact, should two back-toback successions of information not adjust to this imperative, we have a necessity for “change” or “oddity” identification. The common dispersion system or, all the more for the most part, a Bayesian causal structure gives the premise to distinguish six purposes behind a shift among preparing and test disseminations: – Simple covariance shift: This is a change exclusively because of fleeting variety in the information dispersion, p(x). – Prior likelihood shift: For similar dispersion of information sources, p(x), the likelihood of marks, P(y), fluctuates. The ramifications of information that was all at once marked as class 0 are related to an alternate class name. – Example choice inclination: This addresses pre-preparing or estimation predisposition. For instance, client reviews as gathered from solitary medium conceivably bring about an inclination toward gathering data from a particular segment. Subsequently, any expectation dependent on review (preparation segment) would possibly not mirror the general assessment of democratic public (test segment). – Imbalanced information: This is a type of “information shift through plan.” In other words, minor/significant classes are deliberately finished/underexamined to expand/decline the affectability of a classifier to explicit classes. Nonetheless, the general recurrence of each class may shift throughout a stream. The ramifications are that any example would not really address all classes. This is likewise alluded to as slanted information. – Domain shift: This is an instance of variety because of the absence of “object invariance” in the first information credit x. Instances that can affect the lighting power and the need for the development of invariant attributes in the context of IoT data analysis. – Source segment shift: A similar idea may be portrayed from different sources (as in sensor combination). Be that as it may, the varying sources may bring about a similar idea being depicted at using multiple subsets of attributes at various points throughout the time.

9.1.2 Evolutionary computation In software engineering, developmental calculation is a group of calculations for worldwide advancement propelled by natural advancement, as well as the subfield of artificial reasoning and delicate registering, which is concerned with these calculations. In specialized terms, they are a group of populace-based experimentation issue solvers with a metaheuristic or stochastic advancement character.

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In developmental calculation, an underlying arrangement of up-and-coming arrangements is created and iteratively refreshed. Each new age is created by stochastically eliminating less wanted arrangements and presenting little arbitrary changes. In organic phrasing, a populace of arrangements is exposed to characteristic choice (or fake choice) and change. As a result, the population will gradually improve in health, based on the calculation’s chosen wellness capability in this case. Transformative calculation strategies can deliver profoundly improved arrangements in a wide scope of issue settings, making them mainstream in software engineering. Numerous variations and expansions exist, and they fit to more explicit groups of issues and information structures. In some cases, transformative calculation is additionally utilized in developmental science as an in silico exploratory methodology to contemplate normal parts of general transformative cycles. Developmental calculations structure a subset of transformative calculations in that they, by and large, just include procedures actualizing systems enlivened by organic advancement, for example, multiplication, change, recombination, normal choice, and natural selection. Up-and-coming answers for the enhancement issue assume the part of urban people in a populace, and the expense work decides the climate in which the arrangements “live.” Development of the populace at that point happens after the rehashed use of the above administrators. In this cycle, there are two principal powers that structure the premise of developmental frameworks such as recombination transformation and hybrid make the important variety and subsequently encourage oddity, while determination goes about as a power-expanding quality [5]. Developmental calculations structure a subset of transformative calculation in that they by and large just include procedures actualizing systems enlivened by organic advancement, for example, multiplication, change, recombination, normal choice, and natural selection. Up-and-coming answers for the enhancement issue assume the part of people in a populace, and the expense work decides the climate inside which the arrangements “live” (see likewise wellness work). Development of the populace at that point happens after the rehashed use of the above administrators. Numerous parts of a particularly transformative interaction are stochastic. Snippets of data changed by recombination and transformation are haphazardly picked. Then again, choice administrators can be either deterministic or stochastic. In the last case, people with a higher wellness have a higher opportunity to be chosen than people with a lower wellness, yet regularly even the powerless people get an opportunity to turn into a parent or to endure.

9.1.3 Review scope The DL models bring two significant enhancements over the customary AI approaches in the two periods of preparing and expectation. A wide range of deep neural network

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(DNN) models analyzed IoT applications that have gained from DL calculations. This chapter recognizes five fundamental IoT administrations that can be used in specific vertical areas in every space past the particular administrations. It will also cover the IoT software attributes and the manual to align them with the most suitable DL pattern [2]. This review focuses on the juncture of two developments: one in communication structures, that is, IoT, and the other in man-made reasoning, that is, DL, defining their applications for latent potential and accessible issues. The study does not cover standard AI calculations for the analysis of IoT information since there are some specific endeavors that have obtained these methodologies, referenced in region I-B. However, this summary does not address the subtleties of the IoT architecture from the point of view of communications and device management.

9.2 Related works As far as we may learn, there is no article in the writing devoted to examining the specific connection between IoT knowledge and DL just as DL techniques are used in IoT. There are hardly any works that show standard knowledge mining and AI techniques used in IoT situations. The research focused on IoT approaches to knowledge mining. This sought to provide the IoT system and administrations with distinctive organization, bunching, and visiting concept mining calculations. However, the DL technique drawing near is our study’s interest. During the thought time of a contextconscious processing system, they analyzed various classes of AI drawings and explored the possibilities of implementing these techniques in the IoT framework. This research focuses on the foundation of wireless sensor network, although our work does not rely on knowledge wellsprings (i.e., IoT foundations) and extends a wide variety of IoT applications and administrations [6]. The traditional AI strategies focus on cutting-edge and DL strategies. Eventually, the use of DL methods in traffic control systems became more common. While this work essentially revolves around a device base, it differs from our research focusing on using DL in IoT applications. Aside from IoT deals, we looked at a few typical deals of AI methods as well as some advanced procedures like DL to cope with massive amounts of data. Explicitly, they featured a combination of distinctive AI methods and signaling technologies for processing and breaking down easy, huge information applications. The variation of animals to their surroundings results from the cooperation of two cycles, specifically development and learning [7]. In contrast to development, which depends on the Darwinian model of animal types, learning depends on the connectionist model of the cerebrum. Development is a lethargic stochastic interaction at the populace level that decides the essential constructions of animal types, while learning is a cycle of step-by-step improvement of a person’s transformation capacity to its current circumstance by tuning the design of the person. Developmental

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calculations are stochastic hunt techniques propelled by the Darwinian model, while neural organizations are learning models dependent on the connectionist model. Contrasted with the connectionist model-based learning measure, fluffy frameworks are an indisputable-level reflection of human cognizance. Neural organizations, fluffy frameworks, and developmental calculations are the three significant delicate registering ideal models for computational knowledge. Neural organizations and fluffy frameworks are two significant ways to deal with the framework [8]. Adjusting neural organizations or fluffy frameworks includes the arrangement of two improvement issues: primary advancement and parametric enhancement. The underlying enhancement is the initial step that attempts to locate an ideal framework structure; it is a discrete (combinatorial) improvement issue and is exceptionally difficult to address utilizing customary analytics-based advancement procedures. Once the framework structure is resolved, parametric streamlining is applied to locate the ideal framework boundaries in a nonstop parametric space. Parametric streamlining can be settled by utilizing customary improvement strategies; nonetheless, the arrangement might be effectively caught at an awful neighborhood ideal. Developmental calculation is especially fit to the variation (learning) of neural and fluffy frameworks. Developmental calculation is a significant examination zone for variation and improvement. The methodology starts from Darwin’s standard of common choice, additionally called natural selection. In the Darwinian model, individually obtained knowledge cannot be moved into its genome, and therefore went to the future. Mix of learning and development, encapsulated by advancing neural organizations, has better flexibility to a unique climate. The collaboration of learning upon development quickens advancement, and this can appear as the Lamarckian advancement. The Lamarckian technique permits the legacy of the obtained attributes during a person’s life into the hereditary code, so the posterity can acquire its qualities. Even though the Lamarckian hypothesis is organically unwarranted, Evolutionary Algorithm (EAs) as counterfeit natural frameworks can profit by it. Furthermore, the Lamarckian hypothesis appropriately portrays the development of human societies. Then again, the Baldwin impact is organically conceivable, wherein learning causes people to adjust better to their surroundings, thus expanding their proliferation likelihood. Wellness scene is alluded to as the arrangement of every single imaginable genotype and their individual wellness esteems. The learned practices become instinctual practices in resulting ages, and there is no immediate modification of the genotype. The Baldwin impact is simply Darwinian, in spite of the fact that it has results like those of the Lamarckian development. The transformative systems’ procedures are another most well-known Expert System (EA). The Expert System (ES) was initially produced for mathematical streamlining and was subsequently stretched out to discrete improvement. The target boundaries and technique boundaries are straightforwardly encoded into the chromosome by utilizing an ordinary mathematical portrayal, and in this way, no coding or unraveling is fundamental. Rather than the Genetic Algorithm (GA), the essential hunt administrator in the ES is transformation. The rest of this chapter is organized as follows. In Section 9.3, IoT

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information characteristics that reflect a large amount of data in IoT is just as quick in disseminating data and how it is not the same as large information generally. Section 9.4 exhibits some standard and efficient DNN designs. It also provides a succinct representation of progressions toward constant and rapid DL designs as state-of-the-art calculations that paired DL. In addition, there is a brief survey of a few systems and apparatus calculations that sustain DNNs. Section 9.5 explores the efforts to bring DNN to the gadgets for asset limitation. The section clarifies the works that were analyzed to get the DNN models to the scale of cloud and mist figuring. Section 9.6 indicates the future course of study and open difficulties.

9.3 Data characteristics of IoT with requirements for analytics Gushing information refers to the information that has been created or captured within a short period of time and needs to be dissected promptly to remove quick experiences and also to make quick choices. Huge knowledge alludes to huge datasets commonly used to provide substantial bits of reliable and accurate knowledge. It featured overview articles that showcased information by combining processes that have been tried in IoT scenarios [2].

9.3.1 IoT quick and gushing information Several research efforts have suggested spilling knowledge research that can be expressed mostly on a steady planning. A big dataset is divided into a few smaller datasets by knowledge parallelism, on which equivalent research is performed all the while. There is a constant allusion of the processing to quickly prepare a little clump of knowledge in a measuring pipeline. However, these techniques minimize the time inactivity to restore a reaction from the logical structure of spilling knowledge.

9.3.2 IoT big information The perception and extraction results are higher levels for dynamic and pattern forecasting. The removal of these bits of knowledge and information from the enormous information is of outrageous importance to many organizations, as it empowers them to increase their upper hands through sociology looks individually at the impact of large-scale knowledge analysis on the creation of the telescope and magnifying space science lens.

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Figure 9.3: Structure of the investigation. Conclusion

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A few works portrayed the general highlights of large-scale information from different perspectives in volume, speed, and assortment. In any case, we embrace the general definition of large information to portray huge information about the IoT through the following “6Vs”: – Velocity: The speed of IoT generating and processing large amounts of information is sufficiently high to increasingly assist in the accessibility of huge information. This legitimizes the requirements for cutting-edge research tools and technologies to work productively despite this high pace of knowledge production. – Veracity: Veracity alludes to knowledge content, continuity, and reliability, thus prompting an accurate review. This property necessitates special consideration, particularly those with swarm information detection. – Variability: This possession alludes to changing rates of information flow. Contingent on the idea of IoT applications, there may be conflicting information streams in different information-generating parts. Additionally, having different paces of information load dependent on explicit times is workable for an information source. – Value: Quality is the transfer of enormous information to helpful details and bits of knowledge that give associations the upper hand. The understanding of information relies heavily on the basic procedures/administrations and how information is managed. For example, a particular application may need to collect all sensor information, whereas a climate estimate administration may allow pure irregular information testing from its sensors. As another example, a manufacturer of a charge card may need to hold and dispose of information for a particular time frame from that point on. – Big information may result from an advanced action, IoT transaction, or impression. The use of the most common inquiry terms used by Google to forecast daily influenza is a decent case of such advanced results. Big information frameworks should be adaptable on a level plane; that is, huge sources of information should have the option of extending them to numerous datasets. This adds to the unpredictability of large-scale information, which causes other problems such as the transfer and purification of information. Investigating persistent information sources is frequently referred to as stream preparing or planning in writing time and time again for mind-boggling occasions. A new proposed IoT information investigation program assists the volume and speed characteristics of IoT information analysis. Also considered as a feature of that work was the mix of IoT huge information and gushing information examination, an open issue that needs more examination. However, their proposed system is intended to be transmitted over cloud frameworks [9]. Figure 9.4 shows that a significant gap in this field is the lack of structures and computation that can be conveyed just on framework edge and on IoT gadgets. When DL is used in such situations, a negotiation between the DNN’s grandeur and execution is reached. What’s more, their accentuation on the board segment is based on the

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structure’s information, and they did not utilize progressed AI models like DL. Other off-the-rack things such as Apache Storm can likewise be found on the cloud for continuous investigation in Figure 9.4. The absence of structures and calculations that can be conveyed on the haze (i.e., framework edge) or even on the IoT gadgets is a major hole in this field.

9.4 Deep learning DL comprises regulated or solo learning procedures dependent on numerous layers of artificial neural networks (ANNs) equipped for learning progressive portrayals in significant structures. DL models are comprised of a few layers of handling. DL’s usefulness is emulated from human mind and neuron systems for signal handling. As of late, DL designs have picked up more noteworthy consideration contrasted with methodologies with ML. These techniques are known as DL variations of the shalloworganized learning structures (i.e., a little subset). Figure 9.4 shows the inquiry example of five famous AI calculations in examples around Google, where DL is getting more mainstream among others. While ANNs were presented in the most recent many years, the expanding pattern for DNNs started in 2006. The condition of the craftsmanship accomplishment of this innovation was then seen in different AI fields, picture recuperation, web indexes and data recuperation, and regular language handling. In addition to traditional ANNs, DL techniques were developed. Feed-forward neural networks (FNNs) were used to train systems in recent decades, but as the number of layers increases, they become difficult to train. Another factor was the small

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size of training data which resulted in overfitted models. In addition, in those days, the restriction of computational capacities hindered the introduction of powerful deeper FNNs. These specialized constraints have been defeated as of late because of advances in innovation by and large and the improvement of illustrations preparing units particularly. The hypothetical viewpoints of the DL plans unpredictability, as well as equipment advancements, DL procedures have picked up from propels in the powerful preparing of profound systems administration calculations counting: – with rectified linear units (ReLUs) as the initiation occupation; – introducing failure techniques and instatement of organization loads; – addressing the remaining learning organizations to decrease the preparing precision, the recorded DL model changes depend on experimental views on organizations dependent on the quantity of layers hidden. Neural network with at least two hidden layers coordinates the most recent calculations by and large profound models. Repeating neural organizations with veiled are also known as profound because the shrouded layer units have a circle that can be unrolled to an equivalent profound organization.

9.4.1 Architectures A DNN consists of a layer of input, multiple layers hidden, and a layer of output. Every layer contains several units called neurons. A neuron receives multiple inputs and performs over its inputs a weighted summation; then, the resulting sum goes through an activation function to produce an output. That neuron has a weight vector associated with its input size, and a bias that should be optimized during the training process. X1 W1

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Figure 9.5: Artificial neural network unit structure.

Figure 9.5 shows that a DNN is made up of an input layer, multiple neural networks, and an output layer. Each layer contains a number of units known as neurons. A neuron receives different outputs, performs a weighted concise summary over those inputs, and then passes the resulting quantity through a kernel function to lead to different results.

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9.4.2 Evolutionary computational using artificial development A counterfeit turn of events, otherwise called fake or machine insight or computational turn of events, is a subdiscipline of software engineering and design that is concerned with computational models persuaded by genotype-aggregate mappings in organic frameworks. Counterfeit improvement is regularly viewed as a subfield of transformative calculation, even though the standards of fake advancement have additionally been utilized inside independent computational models. Inside transformative calculation, the requirement for counterfeit advancement procedures was spurred by the apparent absence of versatility and evolvability of direct arrangement encodings. Counterfeit improvement involves backhanded arrangement encoding. As opposed to portraying an answer straightforwardly, a circuitous encoding depicts (either unequivocally or verifiably) the cycle by which an answer is developed. Frequently, however not generally, these circuitous encodings depend on organic standards of improvement, for example, morphogen angles, cell division and cell separation, quality administrative organizations, decadence, and syntactic development, or are practically equivalent to computational cycles, for example, recomposing, emphasis, and time. The impacts of collaboration with the climate, spatiality, and actual imperatives on separated multicell improvement have been examined all the more as of late. Fake advancement approaches have been applied to various computational and plan issues, including electronic circuit plan, mechanical regulators, and the plan of actual constructions. The information layer doles out in the planning process and transfers it to the layer below. Furthermore, its layer doles out loads to its details and generates its corresponding layer. The consistency of this assumption is determined by a misfortune function by calculating its expected qualities. The DNN is now in training and ready for deduction. The significant level component of preparation for DL models is shown in Figure 9.6. Despite that it is the most part, discriminative models offer neartargeted learning drawings, whereas generational models combine the profit of generative and unequal representation [9]. Convolutionary neural networks (CNNs): One essential reason is the property of interpreting invariance of such models. We do not get acquainted with the highlights that can alter the image (e.g., the delivery of present identification revolution). CNNs addressed this topic by supporting estimates of language equivalence. The last significant segment in CNN is ReLU, which consists of f(x) = max(0, x) neurons with the initiation function. The introduction of this initiation research in CNN leads to a faster planning time without having a fair negative influence on device speculation. The configuration of a CNN is shown in Figure 9.6. This area is considered a network of neighborhoods. For example, various IoT gadgets are fitted with cameras, such as rambles, advanced cells, and brilliant associated vehicles. The CNN design and its varieties were explored for a variety of use situations which include these gadgets. Some common applications include flood or avalanche forecasting by digital

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images, plant disease discovery using photos of plants, and traffic sign recognition using vehicle cameras. Training datasets

x1 x2 x3 x4 y1 y2

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Figure 9.6: Total training mechanism of a DL model.

Figure 9.6 shows the most important level element of DL model preparation is notwithstanding. Exclusionary models provide near-targeted required learning illustrations, whereas different generation models do not. Models consist of a collection of conceptual and unequal interpretation. Figure 9.7 shows a few preceding tests with ultimate objective of analyzing groupings of data sources while also trying to order solitary instances. In such applications, a feed-forward neural system is unimportant because it does not expect to depend on data as well as generate layers. Recurrent neural networks (RNNs): In multiple errands, forecasting is subject to a few previous tests with the ultimate goal that, while ordering singular instances, we also need to analyze groupings of sources of information. A feed-forward neural system is not important in such applications as it does not expect to rely on data and yield layers. RNNs were developed to deal with this issue at different lengths in consecutive (e.g., debate or

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0

1 Input

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Conv Layer 2

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Figure 9.7: CNN architecture.

content) or time-setting issues (sensor information). Distinguishing driver activities in shrewd cars, distinguishing the production designs of individuals, and determining the vitality usage of a family unit are just a few models used for RNNs. As such, in time step 1, the yield of an RNN determines the yield at time step t. The rising neuron has a vital circle that profits from the present yield as a contribution to the next stage. The improvement in the measurement for backpropagation, called backpropagation through time (BPTT), is used to prepare the method [9]. The utility of RNNs and the secret layers in RNNs should offer a memory rather than a separate graded portrait of highlights. Long short-term memory (LSTM): LSTM is a version of RNNs that was introduced, but the majority of them used the same first system layout. LSTM is an idea of doors for its units, each processing a 0–1 reward based on their data. Despite the fact that data is stored in an input circular, each LSTM neuron has a multiplicative input overlook, read screen, and compose information. Such doors are familiar with monitoring the entry to memory cells and preventing them from getting annoyed by superfluous sources of information. When the entrance to the overlook is complex, the neuron composes the information into itself. By sending a 0, the neuron overlooks its last material when the neglect door is destroyed. The other linked to maintain contact with neuron is set in the compose entry. On the off chance of setting the read entry to 1, the related neurons may be able to peruse the neuron material. The structure is delineated in Figure 9.8. A major difference between LSTMs and RNNs is that LSTM units use doors that bypass the entrances, whereas RNNs use sigmoid as the function for enactment. The problem evaporating tendency by backpropagation throughout the preparation time of the numerous models uses them through fact enactment capacities. Figure 9.8 shows that the use of sigmoid as a function for enactment distinguishes LSTMs from RNNs. In fact, during the preparation time of various models that use them, enactment has the capability of evaporating inclination through backpropagation. BPTT is a typical strategy with which to prepare the system to limit the error at a time when information is represented through a long-time dependency.

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output recurrent recurrent

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Figure 9.8: LSTM memory cell structure.

Autoencoders (AEs): These are an information layer that is related by at least a hidden layer. AEs have the same number of units of knowledge and yield. This system means recreating the contribution by changing contributions to yields in the least difficult way possible, with the ultimate goal that the information is undoubtedly not misrepresented. These kinds of neural systems have been used essentially as shifting learning to take care of solitary learning issues. AEs are mostly used to assess and solve problems because they produce the information on the yield sheet. This is of extraordinary enthusiasm for mechanical IoT serving numerous applications, for example, issue conclusion in equipment gadgets and machines, and discovery of peculiarities in the execution of mechanical production systems. The preparation technique in AEs involves reducing remaking error, that is, the yield and knowledge appearing to be insignificant. Figure 9.9 shows the structure of a Mill AE flight. There are several types and rises of AEs, such as denouncing AE, contractive AE, stacked AE, inadequate AE, and variational AE (VAE). Variational autoencoders: VAEs, introduced in 2013, are a well-known generative model system whose assumptions about the knowledge structure are not strong, though having a rapid cycle of preparation via backpropagation. The ideal match for IoT is set up along these lines that additionally manage a variety of data, the shortage of marked information. Such applications include recognition of disappointment in detecting or impelling levels, and recognition of interruptions in security frameworks. For each datum point x, there is a vector indicated by z to compare idle factors. A VAE’s preparation design also includes an encoder and a parameter decoder separately. A fixed appropriation of the structure q (zjx) helps the encoder to evaluate the p (zjx) back-

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dispersion. The model consists of two systems: one that produces tests and the other that performs incorrect derivations. Figure 9.10 portrays a diagram of the VAE. Encode

Decode

Input 1 Output

Input 2

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Input Hidden

Figure 9.9: Autoencoder network layout.

Recognition of frustration in detecting or propelling levels as well as recognition of disruptions in security frameworks are examples of such applications, as shown in Figure 9.9. To compare idle factors, there is indeed a vector suggested by z with each datum point x. The preparatory design of a VAE also contains an encoder, a parameter decoder, and a separator.

Input

Hidden

Output

Figure 9.10: Structure of a network of variation autoencoders.

Figure 9.10 shows that the encoder can evaluate the p(z|x) backdispersion with the help of a fixed utilization of the structure q(z|x). The model is made up of two processes: one that generates tests and another that needs to perform inaccurate derivations.

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Figure 9.11: Concept of a generative adversarial network.

A composition of two neural systems, especially the generative and discriminative systems, cooperates in creating engineered and high-quality information. The former system is responsible for creating new information after learning the distribution of information from a training dataset. Figure 9.11 shows, on the other hand, that the generative system is improved to provide the input information that is also deluding the classifier (the discriminator can only recognize whether it is phony or genuine with a considerable effort). At the end of the day, the generative method is dealing with an adversarial process of exclusionary behavior. The last system separates genuine information (originating from information preparation) from info information. The generative system has been improved to supply input information that the discriminator can only recognize with a considerable effort whether it is phony or genuine, on the other hand. The generative mechanism at the end of the day deals with an adversary system of discriminative behavior. The generative adversarial network (GAN) concept is delineated in Figure 9.11. Its generator, ready to trick the discriminator, plays in every venture of this fanciful game by generating an example of knowledge from abnormal commotion [11‒13]. Then again, alongside the generator examples, the discriminator gets some genuine models of information from the preparation set. Its purpose then is to segregate genuine and falsified information. If its groupings are right, the discriminator is considered to perform adequately. The generator also works well if the discriminator has been fooled by its templates. At

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that point, both the discriminator and the generator parameters are refreshed in order to be prepared for the next round of play. GANs may be applied in IoT applications for situations requiring the production of something new out of the accessible information in Figure 9.12. Furthermore, GANs have been extremely helpful in developing administrations for externally disadvantaged people. A large number of legitimate big name images have been evaluated in order to produce new fictitious pictures with the ultimate goal that a person cannot distinguish on the unlikely chance that they are genuine pictures or not.

Y X

Figure 9.12: Constrained Boltzmann system structure.

Furthermore, GANs are Restricted Boltzmann machine (RBM): An RBM is a stochastic ANN comprising two layers: an undeniable layer containing the data we think about, and a hidden layer containing the idle factors. The shortcoming in RBMs is even though the inventory of Boltzmann machine examined neurons, RBMs ought to create a bipartite graph with a definitive objective that each obvious neuron should correspond with all the covered neurons and the opposite way around; however, there is no relationship in a closely resembling layer between any two gatherings. Truth be told, the tendency unit is associated with entire neurons that are obvious and covered up. RBMs can be stacked for DNN outline. Information about the preparation is appointed to apparent units. Backpropagation and angle drop calculations can be used by the preparation system to upgrade system loads [10]. The aim of preparing RBM is to increase the outcome of all the units considered unmistakable. An RBM structure is shown in Figure 9.13. RBMs can likewise perform extricating data. It occurs by indicating a likelihood allotment over an assortment of wellsprings of data to which many hidden units are spoken. For model, having heaps of adored individuals’ film pictures, an RBM model can have an observable layer comprising a similar number of neurons as the quantity of open movies, and a concealed layer including three neurons to address three exceptional classes, for instance, performance, action, and parody. In this manner, the covered layer can be considered as the yield layer

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taking into account the application. Indoor regulation, wishes for usage of imperativeness, traffic impede gauges, present examination, and generally by recognizing RBMs are examples of potential applications where RBMs can be used from the usable ones. Deep belief network (DBNs): DBNs are a sort of generative ANNs comprising an observable layer (identified with the wellsprings of data) and a couple of covered layers (when contrasted with lethargic components). They can remove different representations of the planned data in the same way that their information data is redone. By including a classifier layer, for example, softmax, it tends to be utilized very well for prescient mistakes. Setting up a DBN is performed layer by layer, with a definitive objective that each layer is taken care of as an RBM arranged the same way that their information data is redone over the layer arranged in the past. This instrument makes a DBN computation in DL effective and basic. A few applications can profit by the structure of DBNs, for instance, flaw acknowledgment portrayal in current conditions, risk ID in systems prepared for security, and energetic component extraction from pictures. RBM1

RBM2

RBM3

input

Figure 9.13: RBM structure of profound network beliefs.

Figure 9.13 shows that the goal of RBM preparation is to improve the output of all units that are considered unmistakable. An RBM framework can also perform data extraction. It happens by indicating a probability distribution over a variety of data sources that many concealed units are being said. Ladder networks: Stepping storage systems for solo learning were proposed in 2015. They were therefore loosened up to work in semimanaged settings and exhibited condition of the workmanship execution for a couple of endeavors, for example, transcribed affirmation and picture request. The decoder will recreate the portrayals at each layer by using a denoising power, and it provides the comparison of tainted information. The

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contrast between reproduced information and clean information at each layer is used to record that layer’s denoising cost. The planning goal is to limit the total cost in the regulated portion and the unassisted component.

9.4.3 DL applications in IoT The DL strategies that signal in a few territories, training, normal language management, and image recognition are available. For example, in applications identified with vision, convolutional systems give better execution, while AEs perform very well with oddity identification, denouncing information, and decreasing dimensionality for perception of information. Inspect the efficient use of DL in IoT spaces. Numerous IoT-based applications use vision and image classification as their foundation can aid in view of our perception. There are various administrations, such as the human location, which are used for shrewd home applications or smart vehicle help. These administrations’ regular property is that they should be treated in a quick expository mode, as opposed to heaping up their information for later investigation. Of course, any space can have explicit advantages beyond these primary administrations. Over the fundamental administrations and IoT applications, we begin by looking at the most basic IoT administrations using DL as their insight motor in the following sections, at which point IoT applications and spaces were a mix of primary administrations that could be used just as specifically.

9.4.4 Evolutionary computation and streaming The description for evolvability be as of late projected as “the ability of framework to produce versatile phenotypic variety beneath certain ecological circumstances and to communicate it by means of a transformative cycle.” If an individual produces offspring that are probably fitter than the offspring of another individual (referred to as B), then that individual is considered more “evolvable” than the B. It suggests that evolvability is definitely not an immediate capacity of wellness (as execution) support determination, but instead an element of the genotypic-to-phenotypic planning, or the characteristic pliancy of a portrayal. It expands on perceptions and proposes to describe evolvability through: 1) the capacity to improve wellness (rather than the outright worth of wellness, or an EC point of view); and 2) the measure of genotypic variety (a natural point of view). In view of this, a three-populace model is received in which every populace accepts an alternate presentation advantage: a. absolute scalar wellness (center populace), b. offspring beating their folks (wellness change, subpopulation), with

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an “off mechanism” typically refers to the process by which offspring are produced within a population.

The creator is unequivocally compensating evolvability just as supreme wellness. Additionally, the creators show that variety is kept up through evolvability and not variety for the good of variety. It likewise utilizes Price’s condition to adjust the two auxiliary populaces, where alteration of populace size in unique conditions is a common topic. In particular, adjusting the populace size to keep a steady pace of genotypic replacement conceivably addresses a system for adjusting to a gradually changing climate in Genetic Programming (GP). It mentions the observable fact that a persistent foundation level of nonpartisanship in GP works with “explosions” of (phenotypic) changeability once the climate goes through change. Nonpartisanship can likewise be seen as a memory instrument which is already valuable or altogether exchanged all through the aggregate. To determine sources of phenotypic variety that are potentially related to the flexibility of genotype-to-aggregate mapping, a GP structure can be utilized, used an instrument in a GP structure to determine wellsprings of phenotypic variety that are conceivably identified with the pliancy of the genotype-to-aggregate planning and be described as far as the measure of inconstancy and nonpartisanship. EC can be valuable in facilitating genotype-to-aggregate mappings under specific conditions. The GE2 structure gives a plan to advancing sets of genotypes, one for determining “meta-syntax” and the second for determining “answer punctuation,” the latter describing explicit GP arrangements for the meta-language. Normally, the utilization of a meta-language presents extra ways through which a similar extreme aggregate can be found. Then, the work stretched out the arrangement syntax to give a lot more grounded portrayal to developing constants, a trademark that is sometimes overlooked in GP but has been shown to provide significant benefits in stock-trading errands. Open inquiries incorporate characterizing the best plan for coevolving metasentence structure and the people utilizing a case of the meta-language. Different specialists have additionally exhibited time with accentuation on recognizing the majority suitable guidance types. The ability to specifically address the earliest factor identified as relevant is crucial for efficient functioning in dynamic task environments. Indeed, for privacy to extend beyond a superficial level, it is crucial to consider the interdependencies between modules or achieve a state of “near decomposability.” The search measure that is capable of controlling modules productively is then suggested. Recently, the (neuro)development of a quantified trait itself was demonstrated, considering the cost of "availability" as part of the reward system. Further plans that expressly give an account of the utility of particularity in powerful conditions include quality duplication under neuroadvancement and a job for naturally characterized capacities.

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9.5 Conclusion Lately, DL and IoT have taken specialists and business verticals into consideration, as these two patterns of innovation have proven to be beneficial for our lives, urban areas, and the world as well. IoT and DL form an information creator–consumer chain in which IoT generates raw data that is analyzed by DL. In addition, the DL models generate an elevated level of thought, which is encouraged by understanding. In addition to the qualities of IoT knowledge, its difficulties with DL techniques have been surveyed now. Explicitly, we featured swift and gushing IoT information just as big IoT information as the two traditional IoT information age classifications and their investigation needs. We also exhibited some basic DL models used in IoT applications, followed by some open-source systems to improve DL models. Another piece of this review in which we distinguished 5 primary administrations alongside 11 application areas was assessing different applications in different IoT divisions that used DL. By recognizing essential administrations, as vertical IoT applications by surveying DL use cases, creators gave different analysts a reason. Recognizing the legal components of smart IoT services is associated the privacy and data Protection challenges. Another aspect of this perspective was DL, which was reliant on mist and cloud foundations to support IoT applications. It also identified the challenges and prospects for DL for IoT applications.

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Index 5G 11 accessibility 2, 16, 20 Accuracy 16, 17 accurate mode 10 Aeronautical Science 8 algorithms 8, 10, 11, 15 Artificial General Intelligence 11 artificial intelligence 1, 2, 4, 5, 6, 17, 18, 19, 23, 24 artificial intelligence 7, 8, 10, 18, 21 Artificial Narrow Intelligence 11 Artificial Super Intelligence 11 Banking 8 8 Big data 7, 10, 13 Block chain 22 business data 14 business intelligence 7, 8, 12–13 Cloud computing 6, 10 clustering 11 conceptualization 6 consistent data terminology 5 contraptions 6, 10, 12–13 convolutional neural networks 3, 15, 18, 21, 22 coordinate reference system 9 credit assignment path 16 critical Infrastructure 23 customer's data 18 customers 8, 10, 18 Cyber Security 24 data properties 12, 16 data transmission 11 decision support systems 13 deep learning 1, 2, 3, 4, 6, 10, 15, 16, 17, 18, 20, 21 digital gazetteers 21 domain ontology 20 Domain 1, 2, 4, 5, 6, 8, 10 E-commerce 7 economy 7, 10, 18 E-payment 7, 8 E-relief 7 explainable artificial intelligence 2, 4, 25 https://doi.org/10.1515/9783110781663-010

exponential rate 8 extensible markup language 8 EyeOS- 6 6 formalization 4, 5 gadgets 2, 13, 25 generative adversarial networks 3 genetic algorithm 5 genetic algorithm 7, 14, 18, 20 Genetic Algorithm 8, 20 geographic information retrieval 21 geo-ontological Engineering 15 geo-semantic information modelling 1, 3 geo-Semantic 1–5, 19, 23 geospatial applications 1, 3 geospatial computing models 8, 9, 10 geospatial concepts 12, 17, 189, 191, 194, 198, 206, 221, 223, 227 geospatial data 3, 20 geospatial knowledge 12, 14, 19 geospatial Modelling 2–4, 18–19 geospatial ontology model 8, 15 geo-spatial semantic information 1 GIS technology 2 GIS, geospatial information systems 2, 5 GlideOS 6 Guardrail 22, 23 hash key 10 heterogeneous information sources 5 heterogeneous multimodal data 5 heterogeneous sources hidden layers 10 HTML 3–4, 7, 19, 25 human brain 8, 10 industrial internet of things 3 Industries 8 industry 4.0 1, 20, 21, 24 information 2, 4–18, 20, 22–24, 26 Integrated AI 8 Interoperability 1–5, 20–23 IoT 12–13, 25–26 IoT 2, 3, 6 Kernel Ridge Regression 7, 14, 15, 18

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logistic regression 5 machine learning 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14 Machine learning 1, 3, 4, 5, 10 machine learning 8, 10, 19 markov decision process 3, 5 metadata-retrieval systems 23 modelling interoperability standards 23 multidimensional analysis 13 Multiontology 17 Natural Language Processing 15 natural language processing 3, 4 network 2, 16, 22 neural network 8, 10, 11 NoSQL 7, 8 object model and field model 1 object properties 12, 16 object-based GIS applications 2 OLAP 10 ontologies - 1–2, 8, 11, 14–15, 17–21, 27 Ontology 8, 16 ontology design patterns 20, 21, 24 ontology 2, 20 OWL 1, 2, 3, 4, 5, 9, 10, 11 OWL, Web Ontology Language 6, 14 place names 3, 12 PPE kit 18 predictive modeling 4, 159, 204–205, 207

Semantic Interoperability 19 semantic relations 12 Semantic Web 1, 2, 4, 5, 6, 7, 9, 10, 11 semantic web 6, 22 semantic web (SW) 1–2, 8, 11, 14–15, 17–21, 27 Semantic Web Architecture 7 semi-supervised learning 1, 3, 6, 8, 13, 14 Serverless Architecture 24 SPARQL 1 spatial entities 8, 11, 15 spatial foot prints 21 spatial knowledge 1 spatial relations 1, 10 stochastic gradient tree boosting 5 supervised learning 9, 10, 11, 12, 13, 14, 17, 22 Supply chain management 21 SVM 4, 5 SWoT 3 syntactic and semantic languages 7 text-based knowledge representation 9 top-level ontology 20 traditional geospatial modelling 9 Triangulated Irregular Network 4 unsupervised learning 1, 3, 6, 8, 10, 11, 12, 13, 17 Vector and raster data models 3 visionary web 6 Visualization 19, 46

Query 9 random forest 3, 5, 10, 19 raster data model 3 RDF 1, 2, 3, 9 RDF (resource description framework) 8 RDF schema (RDFS) 8 RDF, Resource Description Framework 6, 14, 17, 18 reasoning-based queries 23 recurrent neural networks 16, 18 reinforcement learning 1, 3, 6, 8, 14, 15 RFID 2, 3

web 1.0 1, 2, 3, 4 web 2.0 1, 2, 3, 4, 5, 8 web 3.0 3, 4, 5 web 4.0 3, 4, 6 web 5.0 3–4, 6 Web Architecture 6 Wireless Sensor Networks 15 WSDL 9 WSN 30

De Gruyter Frontiers in Computational Intelligence Already published in the series Volume 15: Computer Vision. Applications of Visual AI and Image Processing Pancham Shukla, Rajanikanth Aluvalu, Shilpa Giteand Uma Maheswari V (Eds.) ISBN 978-3-11-075667-8, e-ISBN (PDF) 978-3-11-075672-2, e-ISBN (EPUB) 978-3-11-075682-1 Volume 14: Artificial Intelligence for Virtual Reality Jude Hemanth, Madhulika Bhatia, Isabel De La Torre Diez (Eds.) ISBN 978-3-11-071374-9, e-ISBN (PDF) 978-3-11-071381-7, e-ISBN (EPUB) 978-3-11-071385-5 Volume 13: Computational Intelligence in Software Modeling Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal, Utku Kose, Abha Jain (Eds.) ISBN 978-3-11-070543-0, e-ISBN (PDF) 978-3-11-070924-7, e-ISBN (EPUB) 978-3-11-070934-6 Volume 12: Artificial Intelligence and Internet of Things for Renewable Energy Systems Neeraj Priyadarshi, Sanjeevikumar Padmanaban, Kamal Kant Hiran, Jens Bo Holm-Nielson, Ramesh C. Bansal (Eds.) ISBN 978-3-11-071379-4, e-ISBN (PDF) 978-3-11-071404-3, e-ISBN (EPUB) 978-3-11-071415-9 Volume 11: Cyber Crime and Forensic Computing. Modern Principles, Practices, and Algorithms Gulshan Shrivastava, Deepak Gupta, Kavita Sharma (Eds.) ISBN 978-3-11-067737-9, e-ISBN (PDF) 978-3-11-067747-8, e-ISBN (EPUB) 978-3-11-067754-6 Volume 10: Blockchain 3.0 for Sustainable Development Deepak Khazanchi, Ajay Kumar Vyas, Kamal Kant Hiran, Sanjeevikumar Padmanaban (Eds.) ISBN 978-3-11-070245-3, e-ISBN (PDF) 978-3-11-070250-7, e-ISBN (EPUB) 978-3-11-070257-6 Volume 9: Machine Learning for Sustainable Development Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas, Sanjeevikumar Padmanaban (Eds.) ISBN 978-3-11-070248-4, e-ISBN (PDF) 978-3-11-070251-4, e-ISBN (EPUB) 978-3-11-070258-3 Volume 8: Internet of Things and Machine Learning in Agriculture. Technological Impacts and Challenges Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Singh Rathore, Vishal Jain (Eds.) ISBN 978-3-11-069122-1, e-ISBN (PDF) 978-3-11-069127-6, e-ISBN (EPUB) 978-3-11-069128-3

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Volume 7: Deep Learning. Research and Applications Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy (Eds.) ISBN 978-3-11-067079-0, e-ISBN (PDF) 978-3-11-067090-5, e-ISBN (EPUB) 978-3-11-067092-9 Volume 6: Quantum Machine Learning Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, Susanta Chakraborti (Eds.) ISBN 978-3-11-067064-6, e-ISBN (PDF) 978-3-11-067070-7, e-ISBN (EPUB) 978-3-11-067072-1 Volume 5: Machine Learning Applications. Emerging Trends Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy (Eds.) ISBN 978-3-11-060853-3, e-ISBN (PDF) 978-3-11-061098-7, e-ISBN (EPUB) 978-3-11-060866-3 Volume 4: Intelligent Decision Support Systems. Applications in Signal Processing Surekha Borra, Nilanjan Dey, Siddhartha Bhattacharyya, Mohamed Salim Bouhlel (Eds.) ISBN 978-3-11-061868-6, e-ISBN (PDF) 978-3-11-062110-5, e-ISBN (EPUB) 978-3-11-061871-6 Volume 3: Big Data Security Shibakali Gupta, Indradip Banerjee, Siddhartha Bhattacharyya (Eds.) ISBN 978-3-11-060588-4, e-ISBN (PDF) 978-3-11-060605-8, e-ISBN (EPUB) 978-3-11-060596-9 Volume 2: Intelligent Multimedia Data Analysis Siddhartha Bhattacharyya, Indrajit Pan, Abhijit Das, Shibakali Gupta (Eds.) ISBN 978-3-11-055031-3, e-ISBN (PDF) 978-3-11-055207-2, e-ISBN (EPUB) 978-3-11-055033-7 Volume 1: Machine Learning for Big Data Analysis Siddhartha Bhattacharyya, Hrishikesh Bhaumik, Anirban Mukherjee, Sourav De (Eds.) ISBN 978-3-11-055032-0, e-ISBN (PDF) 978-3-11-055143-3, e-ISBN (EPUB) 978-3-11-055077-1