Data and Model Centric Artificial Intelligence for Industry 5.0

1. Auflage 2026
ISBN: 978-1-032-83332-3
Verlag: Taylor & Francis

Buch, Englisch, 368 Seiten, Format (B × H): 156 mm x 234 mm

This book explores the application of Data and Model Centric AI for Industry 5.0 Solutions, vital in the evolving manufacturing landscape. It delves into data-centric AI, focusing on data collection, processing, and analysis for informed decision-making and operational optimisation. Additionally, it examines model-centric AI, showcasing advanced machine learning models for predictive maintenance and demand forecasting, adapting to dynamic conditions. Through practical insights, it illustrates AI's role in enhancing Industry 5.0 processes, offering valuable knowledge for professionals and scholars alike.

This book:

- Emphasis on compact and robust solutions, addressing challenges in resource-constrained environments.

- Explains how the most recent advances in artificial technology have opened the way for new breakthroughs in the supply chain industry, cloud computing, IoT based system etc.

- Discusses demand Forecasting AI models analyze historical sales data and external factors to predict future product demand, optimizing inventory management and production planning.

- Present models for collecting, processing, and analyzing large volumes of data from various sources, such as sensors, machines, production processes, and the supply chain.

- Explain developing and deploying sophisticated machine learning models to predict outcomes, optimize processes, and adapt to changing conditions in Industry 5.0 environments.

The reference book is a valuable resource for researchers and scholars interested in AI for Industry 5.0 Solutions.

Zielgruppe

Academic

Autoren/Hrsg.

Issar, Neha

Balusamy, Balamurugan

Anand, Vijay

Kadry, Seifedine

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering Softwaretests & Prüfsoftware
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Webprogrammierung

Inhaltsverzeichnis

1. AI-Driven Exploration in Industry 5.0. 2. Fundamentals of Industry 5.0: Human-Centric Intelligence and Sustainable Industrial Transformation. 3. Data-Centric AI. 4. Intervening Effects of Data Preprocessing and Feature Engineering for Industry 5.0 Transformation. 5. Compact AI Models for Industry 5.0. 6. Robustness of AI Models for Industry 5.0. 7. Industry 5.0 Machine Learning Algorithms: Using Human-Centric Automation to Revolutionize Manufacturing- Applying Unsupervised Learning Algorithms to Industrial Scenarios. 8. Deep Learning as an Impetus for Industry 5.0: Innovations in Managing Energy, Materials, and Manufacturing Processes. 9. Handling Noisy and Incomplete Data for Predictive Maintenance in industry 5.0. 10. Case Studies in Industry 5.0 for Detecting Faults. 11. Synergizing AI and IoT for Human-Centric and Sustainable Industry 5.0. 12. Emerging Horizons in Advancing Robotics and AI in the Era of Industry 5.0. 13. Industry 5.0: Bridging Human and Machine Intelligence Through AI and IoT. 14. Future Directions in Industry 5.0 for Robotics and AI. 15.Gaining Insights into the Latest Disruptive Technologies and Their Impact on Traditional Business Models

Über Autor(innen)

Neha Issar is working as Associate Professor at Lloyd Business School. Her subject areas are Database and Query Language, Data Science using R, Management Information System and Enterprise Resource Planning. She is a commerce graduate from Dr. B.R. Ambedkar University and holds a post graduate diploma in Finance and Marketing. Currently, she is pursuing his Ph.D. in Management from Amity University. She has more than 14 years of experience in academics and industry. She has worked as a Data Researcher at S&P Global Market Intelligence Pvt Ltd. She has also worked on Projects at Aditya Birla Group, Times Internet in et.com. She was a Senior Manager Investor Relations at Opera Gratia Pvt Ltd. She is an IBM-certified professional in Spark, Deep Learning, Guardium, and Design Thinking. She has completed Predictive Analytics Program at IIM Bangalore

Balamurugan Balusamy has served up to the position of Associate Professor in his stint of 14 years of experience with VIT University, Vellore. He had completed his Bachelors, Masters, and PhD Degrees from top premier institutions from India. His passion is teaching and adapts different design thinking principles while delivering his lectures. He has published 30+ books on various technologies and visited 15+ countries for his technical course. He has several top-notch conferences in his resume and has published over 150 of quality journal, conference and book chapters combined. He serves in the advisory committee for several startups and forums and does consultancy work for industry on Industrial IOT. He has given over 175 talks in various events and symposiums. He is currently working as a professor at Shiv Nadar University teaches students, does research on Blockchain and IoT.

Vijay Anand R. is currently working as Associate Professor in School of Information Technology & Engineering, VIT University, South India. He has completed his Ph.D. in Agile Software Testing from VIT University, Vellore, Tamil Nadu, India. Previously he was appointed as Lead Engineer and Technical Lead at HCL-CISCO, Chennai. Dr. Vijay has published various articles in various international conferences and journals. He has received honours including Faculty Publication Award 2015-17-VIT University, and in HCL-CISCO, this award is given for the best performance, during the third quarter of 2009.

Seifedine Kadry has a Bachelor degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present his research focuses on Data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.