Kooperativer Bibliotheksverbund

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Format: 1 online resource (264 pages)

ISBN: 0-323-90639-7

Series Statement: Intelligent data centric systems

Content: 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.

Note: Intro -- Artificial Intelligence and Industry 4.0 -- Copyright -- Contents -- Contributors -- Part 1: Artificial intelligence and Industry 4.0 with healthcare -- Chapter 1: Influence and implementation of Industry 4.0 in health care -- 1.1. Introduction -- 1.2. Industry 1.0 to 4.0 -- 1.3. Healthcare 1.0 to 4.0 -- 1.4. Literature review -- 1.5. Big data in health care -- 1.6. Internet of Things in the healthcare sector -- 1.7. Blockchain technology in the healthcare sector -- 1.8. Applications in healthcare 4.0 -- 1.8.1. Observing physiological and pathological signals -- 1.8.2. Self-management, wellness monitoring, and prevention -- 1.8.3. Smart pharmaceuticals and monitoring of medication intake -- 1.8.4. Personalized healthcare system -- 1.8.5. Cloud-based health information systems -- 1.8.6. Telepathology, telemedicine, and disease monitoring -- 1.8.7. Assisted living -- 1.8.8. Rehabilitation -- 1.9. Case studies -- 1.10. Conclusion -- 1.11. The recent innovations and research solutions of Industry 4.0 in healthcare sector -- References -- Chapter 2: Impact of artificial intelligence in the healthcare sector -- 2.1. Introduction -- 2.2. Literature review -- 2.2.1. AI in health care -- 2.2.2. Utilization of AI in health care in India -- 2.2.2.1. Hospitals -- 2.2.2.2. Pharmaceuticals -- 2.2.2.3. Diagnostics -- 2.2.2.4. Medical equipment and supplies -- 2.2.2.5. Health insurance -- 2.2.2.6. Telemedicine -- 2.3. Research framework and development of hypotheses -- 2.3.1. Technological perspective (TP) -- 2.3.1.1. Cost-effectiveness (CE) -- 2.3.1.2. Relative advantage (RA) -- 2.3.1.3. Security and privacy concerns (SPC) -- 2.3.1.4. Complexity (COMP) -- 2.3.2. Organizational perspective (OP) -- 2.3.2.1. HR readiness (HRR) -- 2.3.2.2. Top management support (TMS) -- 2.3.2.3. Organizational readiness (ORR) -- 2.3.3. Environmental perspective (EP). , 2.3.3.1. Competitive pressure (CP) -- 2.3.3.2. Support from technology vendors (STV) -- 2.3.3.3. Environmental uncertainty (EU) -- 2.4. Research methodology -- 2.4.1. Demographics of the respondents -- 2.5. Data analysis -- 2.5.1. Reliability and validity -- 2.5.1.1. Cronbachs alpha -- 2.5.1.2. Composite reliability -- 2.5.2. Harman test -- 2.5.3. Exploratory factor analysis (EFA) -- 2.5.4. Construct validity (CV) -- 2.5.5. Structural equation modeling -- 2.6. Discussion -- 2.6.1. Technological perspective (TP) -- 2.6.2. Organizational perspectives (OP) -- 2.6.3. Environmental perspectives (EP) -- 2.7. Conclusion -- 2.8. Limitations and future scope of the study -- Appendix A. Measurement items -- Appendix B. Questionnaire -- References -- Part 2: Role of artificial intelligence in industry 4.0 -- Chapter 3: Embedded system for model characterization developing intelligent controllers in industry 4.0 -- 3.1. Introduction -- 3.2. Theoretical framework -- 3.2.1. Processing unit Raspberry PI -- 3.2.2. Analogic digital converter ADS1115 -- 3.2.3. Genetic algorithms -- 3.2.3.1. Population -- 3.2.3.2. Selection -- 3.2.3.3. Crossover operation -- 3.2.3.4. Mutation -- 3.2.4. Transfer function -- 3.2.4.1. First-order systems -- 3.2.4.2. Second-order systems -- 3.3. Methods -- 3.3.1. General description of the identification process -- 3.3.2. Time analysis for the transfer functions -- 3.3.3. Fitness function for characterizing TFs -- 3.3.3.1. First-order systems fitness function -- 3.3.3.2. Second-order systems fitness function -- 3.4. Results and discussion -- 3.4.1. Design of experiment -- 3.4.2. Results -- 3.4.2.1. Configuration of parameters in the optimization algorithm -- 3.4.2.2. First-order transfer function -- 3.4.2.3. Second-order transfer function -- 3.4.3. Discussion -- 3.4.3.1. Integration of the proposed algorithm into industry 4.0. , 3.5. Conclusions -- 3.5.1. Future work -- References -- Chapter 4: Industry 4.0 multiagent system-based knowledge representation through blockchain -- 4.1. Introduction -- 4.2. Literature survey -- 4.3. What is industry 4.0 -- 4.3.1. Virtual Sensors in Intelligent industry -- 4.3.2. Intelligent model virtual sensor description -- 4.3.2.1. Clustering -- 4.3.2.2. Opinion analysis -- 4.3.2.3. Trends -- 4.3.3. Industry 4.0 vertical integration -- 4.4. What is blockchain -- 4.5. Blockchain as the industry engine 4.0 -- 4.6. Blockchain-based conventional networks -- 4.7. Decision making concept -- 4.8. What is ambient intelligence -- 4.8.1. Industry as a smart environment -- 4.8.2. Sensors -- 4.8.3. Decision-making in ambient intelligence -- 4.8.4. Better decision-making in industry -- 4.9. Multiagent systems -- 4.10. Blockchain for the representation of knowledge from multiagent systems -- 4.10.1. Multiagent systems in industry -- 4.10.2. Model presentation -- 4.11. Comparison between the existing model and our proposed model -- 4.12. Country-wise comparison of multiagent-based industry 4.0 -- 4.13. Conclusion and future work -- References -- Chapter 5: Artificial Intelligence: A tool to resolve thermal behavior issues in disc braking systems -- 5.1. Introduction -- 5.2. Artificial Intelligence in the automobile sector -- 5.3. AI in automobile disc braking systems -- 5.4. Notable brake complaints observed at servicing centers -- 5.5. Literature survey -- 5.6. Application of AI to resolve thermal problems during long braking -- 5.7. Results and conclusion -- 5.8. Future scope -- References -- Chapter 6: Proposal of a smart framework for a transportation system in a smart city -- 6.1. Introduction -- 6.2. Literary review -- 6.3. Spider monkey optimization -- 6.3.1. Structure of fission-fusion culture -- 6.3.2. Spider monkey conduct -- 6.3.3. Social conduct. , 6.3.4. Spider monkey optimization process -- 6.3.5. Application methodology SMO -- 6.3.5.1. SMO parameters -- 6.4. Practical application -- 6.4.1. Initial conditions -- 6.4.2. SMO control parameters -- 6.5. Discussion of results -- 6.6. Conclusions -- References -- Chapter 7: Society 5.0: Effective technology for a smart society -- 7.1. Introduction -- 7.2. Literature review -- 7.3. Industrial revolutions -- 7.3.1. Industry 1.0 -- 7.3.2. Industry 2.0 -- 7.3.3. Industry 3.0 -- 7.3.4. Industry 4.0 -- 7.4. The concept of Society 5.0 and artificial intelligence -- 7.4.1. The transition process from Industry 4.0 to Society 5.0 -- 7.4.2. The concept of Society 5.0 -- 7.4.3. The factors that reveal Society 5.0 -- 7.4.4. The goals of Society 5.0 -- 7.4.5. The innovations provided by Society 5.0 -- 7.4.6. The relationship between Society 5.0 and artificial intelligence -- 7.4.7. The obstacles to Society 5.0 -- 7.5. Conclusions -- 7.6. Recommendations and future scope -- References -- Chapter 8: Big data analytics for strategic and operational decisions -- 8.1. Introduction -- 8.2. Overview -- 8.2.1. Business analytics -- 8.2.2. Type of analytics -- 8.2.3. Big data analytics -- 8.2.4. Enterprise Big data sources -- 8.3. Opportunities -- 8.4. Challenges -- 8.5. Proposed solution -- 8.6. Case study -- 8.7. Results and discussion -- 8.8. Conclusion -- References -- Chapter 9: Person-based automation with artificial intelligence Chatbots: A driving force of Industry 4.0 -- 9.1. Introduction -- 9.1.1. Chatbots -- 9.1.2. Role of Chatbots in Industry 4.0 -- 9.1.3. Importance of Chatbots -- 9.1.4. Key concepts -- 9.1.4.1. Pattern matching -- 9.1.4.2. Artificial Intelligence Markup Language (AIML) -- 9.1.4.3. Latent semantic analysis (LSA) -- 9.1.4.4. Chat script -- 9.1.4.5. Natural language unit (NLU) -- 9.1.5. Popular Chatbots -- 9.2. NBC Chatbot -- 9.3. A.L.I.C.E. , 9.3.1. Chapter organization -- 9.4. Related work -- 9.4.1. History of Chatbots -- 9.4.2. Machine learning and artificial intelligence algorithms in Chatbots -- 9.4.3. Models of Chatbots -- 9.4.3.1. The selective model -- 9.4.3.2. The generative model -- 9.4.4. Selective vs. generative models -- 9.4.5. Challenges in Chatbot implementation -- 9.4.6. Machine learning in Chatbots for sentiment analysis -- 9.5. Proposed work -- 9.5.1. The general architecture of Chatbots -- 9.5.1.1. Analyzing user request -- 9.5.2. Proposed model -- 9.6. Applications of Chatbots -- 9.7. Weaknesses of Chatbots and scope for improvement -- 9.8. Results and discussion -- 9.9. Conclusion and future work -- References -- Index.

Additional Edition: Print version: Hassanien, Aboul Ella Artificial Intelligence and Industry 4. 0 San Diego : Elsevier Science & Technology,c2022 ISBN 9780323884686

Language: English