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In Industry 4.0, intelligent analytics has a broader scope in terms of descriptive, predictive, and prescriptive sub-domains. To this end, the book will aim to review and highlight the challenges faced by Intelligent Analytics in Industry 4.0 and present the recent developments done to address those challenges.
List of contents
1. Analytics Approach for Intelligent Cyber-Physical System Integration in Industrial Internet of Things (Industry 4.0). 2. Digital twins - a state of the art from Industry 4.0 perspective. 3. Human-Centered Approach to Intelligent Analytics in Industry 4.0. 4. ADVANCE IN ROBOTICS INDUSTRY 4.0. 5. A Cloud-based Real-Time Healthcare Monitoring System for CVD Patients. 6. Assessment of fuzzy logic assessed recommender system: A critical critique. 7. Intelligent Analytics in Big Data and Cloud. 8. Various Audio Classification Models for Automatic Speaker Verification System in Industry 4.0. 9. Trending IoT Platforms on Middleware Layer. 10. Healthcare IoT: A Factual and Feasible Application of Industrial IoT. 11. IoT based Spacecraft Anti-Collision HUD Design Formulation. 12. Coverage of LoRaWAN in Vijayawada: A Practical Approach. 13. Intelligent Health Care Industry for Disease Detection. 14. Challenges with Industry 4.0 security. 15. Dodging Security Attacks and Data Leakage Prevention for Cloud and IoT Environments. 16. Role of Blockchain in Industry 4.0. 17. Blockchain and Bitcoin Security in Industry 4.0. 18. Technology in Industry 4.0. 19. Intelligent Analytics in Cyber Physical Systems. 20. An Overlook on Security Challenges in Industry 4.0.
About the author
Avinash Chandra Pandey, Munesh Singh
Summary
The advancements in intelligent decision-making techniques have elevated the efficiency of manufacturing industries and led to the start of the Industry 4.0 era. Industry 4.0 is revolutionizing the way companies manufacture, improve, and distribute their products. Manufacturers are integrating new technologies, including the Internet of Things (IoT), cloud computing and analytics, and artificial intelligence and machine learning, into their production facilities throughout their operations. In the past few years, intelligent analytics has emerged as a solution that examines both historical and real-time data to uncover performance insights. Because the amount of data that needs analysis is growing daily, advanced technologies are necessary to collect, arrange, and analyze incoming data. This approach enables businesses to detect valuable connections and trends and make decisions that boost overall performance. In Industry 4.0, intelligent analytics has a broader scope in terms of descriptive, predictive, and prescriptive subdomains. To this end, the book will aim to review and highlight the challenges faced by intelligent analytics in Industry 4.0 and present the recent developments done to address those challenges.
