Industry 6. 0 : Technology, Practices, Challenges, and Applications

Industry 6. 0 Technology, Practices, Challenges, and Applications

The proposed book entitled Industry 6.0: Technology, Practices, Challenges and Applications determine the ways to create a paradigm shift from conventional to intelligent companies by integrating Industry 6.0 technology with data, identifies limitations, pitfalls, and open research questions in indu...

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Bibliographic Details
Other Authors: Reddy, C. Kishor Kumar, editor (editor)
Format: eBook
Language:Inglés
Published: Boca Raton, FL : CRC Press [2025]
Edition:First edition
Series:Future generation information systems.
Subjects:
Industry 5.0.
Artificial intelligence > Industrial applications.
Internet of things.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869111806719
Table of Contents:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the editors
  • List of Contributors
  • Chapter 1 Exploring the synergy of IIoT, AI, and data analytics in Industry 6.0
  • Chapter 2 Artificial intelligence and machine learning in Industry 6.0
  • Chapter 3 Big data fusion with GEN AI Tools: Driving Industry 6.0 advancements
  • Chapter 4 Aero metamorphosis in Industry 6.0: Pioneering structural adaptability in contemporary aviation using deep learning
  • Chapter 5 Industry 6.0 in transportation systems: Exploring the Simulated futures of automated bus fleet integration with alternative fuel infrastructures in closed sociotechnical environments
  • Chapter 6 Enhanced deep learning networks for advanced intrusion detection and prevention systems
  • Chapter 7 Using the fuzzy analytic hierarchy process for selecting a closed sociotechnical environment for autonomous vehicle testing in the world of Industry 6.0
  • Chapter 8 ScatterNet-based IPOA for predicting violent individuals using real-time drone surveillance system
  • Chapter 9 An optimal framework for intelligent machine learning-based early diagnosis of pre-diabetes and type 2 diabetes using genomic data
  • Chapter 10 Diabalance ML: Enhancing diabetes detection performance through dataset balancing strategies
  • Chapter 11 Identification of earthquake patterns for predictive modeling using decision tree classifiers to maintain sustainability
  • Index.

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