Big Data Analytics in Smart Manufacturing - Principles and Practices

List of contents

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.

Summary

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.

Product details

• Authors: P (Galgotias Uni.) Poongodi Suresh
• Assisted by: B Balamurugan (Editor), T Poongodi (Editor), Meenakshi Sharma (Editor), Sharma Meenakshi (Editor), P Suresh (Editor)
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 14.12.2022
• EAN: 9781032065519
• ISBN: 978-1-0-3206551-9
• No. of pages: 192
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; Data Mining, TECHNOLOGY & ENGINEERING / Manufacturing, COMPUTERS / Programming / Games, computer science, Electrical Engineering, Engineering: general, Electronics engineering, COMPUTERS / Data Science / Data Analytics