Big Data Analytics in Smart Manufacturing

Principles and Practices
Herausgeber: Balamurugan, B.; Poongodi, T.; Suresh, P.; Sharma, Meenakshi

• Gebundenes Buch

Produktbeschreibung

The ultra-secure and immutable ledgers, strong, consensus mechanisms, decentralization, and self-sovereign identity of AI and IoT technologies have tremendous potential to rebalance and improve machine learning algorithms. This book discusses the possibility of using AI, IoT and machine learning for the enhancement of healthcare systems.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 192
• Erscheinungstermin: 14. Dezember 2022
• Englisch
• Abmessung: 261mm x 185mm x 19mm
• Gewicht: 524g
• ISBN-13: 9781032065519
• ISBN-10: 1032065516
• Artikelnr.: 65614009

Autorenporträt

P Suresh; Meenaskshi Sharma

Inhaltsangabe

1. Machine Learning Techniques and Big Data Analytics for Smart
Manufacturing. 2.Data-Driven Paradigm for Smart Manufacturing In The
Context of Big Data Analytics. 3.Data-Driven Models in Machine Learning- An
Enabler of Smart Manufacturing. 4. Local Time Invariant Learning from
Industrial Big Data for Predictive Maintenance in Smart Manufacturing. 5.
Integration of Industrial IoT and Big data Analytics for Smart
Manufacturing Industries: Perspectives and Challenges. 6.Multimodal
Architecture for Emotion Prediction in Videos using Ensemble Learning. 7.
Deep PHM: IOT based Deep Learning approach on Prediction of Prognostics and
Health Management of an Aircraft Engine. 8.A Comprehensive Study on
Accelerating Smart Manufacturers using Ubiquitous Robotic Technology. 9.
Machine Learning Techniques and Big Data Tools in Design and Manufacturing.
10.Principles of Comprehension of IoT and Smart Manufacturing System.