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hassanien artificial intelligence industry 4 0 2022

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Textbook in PDF format Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT. Artificial Intelligence and Industry 4.0 Copyright Contributors Influence and implementation of Industry 4.0 in health care Introduction Industry 1.0 to 4.0 Healthcare 1.0 to 4.0 Literature review Big data in health care Internet of Things in the healthcare sector Blockchain technology in the healthcare sector Applications in healthcare 4.0 Observing physiological and pathological signals Self-management, wellness monitoring, and prevention Smart pharmaceuticals and monitoring of medication intake Personalized healthcare system Cloud-based health information systems Telepathology, telemedicine, and disease monitoring Assisted living Rehabilitation Case studies Conclusion The recent innovations and research solutions of Industry 4.0 in healthcare sector References Impact of artificial intelligence in the healthcare sector Introduction Literature review AI in health care Utilization of AI in health care in India Hospitals Pharmaceuticals Diagnostics Medical equipment and supplies Health insurance Telemedicine Research framework and development of hypotheses Technological perspective (TP) Cost-effectiveness (CE) Relative advantage (RA) Security and privacy concerns (SPC) Complexity (COMP) Organizational perspective (OP) HR readiness (HRR) Top management support (TMS) Organizational readiness (ORR) Environmental perspective (EP) Competitive pressure (CP) Support from technology vendors (STV) Environmental uncertainty (EU) Research methodology Demographics of the respondents Data analysis Reliability and validity Cronbachs alpha Composite reliability Harman test Exploratory factor analysis (EFA) Construct validity (CV) Structural equation modeling Discussion Technological perspective (TP) Organizational perspectives (OP) Environmental perspectives (EP) Conclusion Limitations and future scope of the study Appendix A. Measurement items Appendix B. Questionnaire References Embedded system for model characterization developing intelligent controllers in industry 4.0 Introduction Theoretical framework Processing unit Raspberry PI Analogic digital converter ADS1115 Genetic algorithms Population Selection Crossover operation Mutation Transfer function First-order systems Second-order systems Methods General description of the identification process Time analysis for the transfer functions Fitness function for characterizing TFs First-order systems fitness function Second-order systems fitness function Results and discussion Design of experiment Results Configuration of parameters in the optimization algorithm First-order transfer function Second-order transfer function Discussion Integration of the proposed algorithm into industry 4.0 Conclusions Future work References Industry 4.0 multiagent system-based knowledge representation through blockchain Introduction Literature survey What is industry 4.0 Virtual Sensors in Intelligent industry Intelligent model virtual sensor description Clustering Opinion analysis Trends Industry 4.0 vertical integration What is blockchain Blockchain as the industry engine 4.0 Blockchain-based conventional networks Decision making concept What is ambient intelligence Industry as a smart environment Sensors Decision-making in ambient intelligence Better decision-making in industry Multiagent systems Blockchain for the representation of knowledge from multiagent systems Multiagent systems in industry Model presentation Comparison between the existing model and our proposed model Country-wise comparison of multiagent-based industry 4.0 Conclusion and future work References Artificial Intelligence: A tool to resolve thermal behavior issues in disc braking systems Introduction Artificial Intelligence in the automobile sector AI in automobile disc braking systems Notable brake complaints observed at servicing centers Literature survey Application of AI to resolve thermal problems during long braking Results and conclusion Future scope References Proposal of a smart framework for a transportation system in a smart city Introduction Literary review Spider monkey optimization Structure of fission-fusion culture Spider monkey conduct Social conduct Spider monkey optimization process Application methodology SMO SMO parameters Practical application Initial conditions SMO control parameters Discussion of results Conclusions References Society 5.0: Effective technology for a smart society Introduction Literature review Industrial revolutions Industry 1.0 Industry 2.0 Industry 3.0 Industry 4.0 The concept of Society 5.0 and artificial intelligence The transition process from Industry 4.0 to Society 5.0 The concept of Society 5.0 The factors that reveal Society 5.0 The goals of Society 5.0 The innovations provided by Society 5.0 The relationship between Society 5.0 and artificial intelligence The obstacles to Society 5.0 Conclusions Recommendations and future scope References Big data analytics for strategic and operational decisions Introduction Overview Business analytics Type of analytics Big data analytics Enterprise Big data sources Opportunities Challenges Proposed solution Case study Results and discussion Conclusion References Person-based automation with artificial intelligence Chatbots: A driving force of Industry 4.0 Introduction Chatbots Role of Chatbots in Industry 4.0 Importance of Chatbots Key concepts Pattern matching Artificial Intelligence Markup Language (AIML) Latent semantic analysis (LSA) Chat script Natural language unit (NLU) Popular Chatbots NBC Chatbot A.L.I.C.E Chapter organization Related work History of Chatbots Machine learning and artificial intelligence algorithms in Chatbots Models of Chatbots The selective model The generative model Selective vs. generative models Challenges in Chatbot implementation Machine learning in Chatbots for sentiment analysis Proposed work The general architecture of Chatbots Analyzing user request Proposed model Applications of Chatbots Weaknesses of Chatbots and scope for improvement Results and discussion Conclusion and future work References Index

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hassanien artificial intelligence industry 4 0 2022