Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things

Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments.

The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency.

A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.

Preface

Chapter 1 : Challenges, Opportunities, and the Future of Industrial Engineering with IoT and AI
Chapter 2 : Evolution and Future of Industrial Engineering with the IOT and AI
Chapter 3 : Applications of Artificial Intelligence and Internet of Things IoT in Marketing
Chapter 4 : An Introduction to Multi-Objective Decision Programming with Fuzzy Parameters
Chapter 5 : Data Analytics
Chapter 6 : Recent Advances on Deep Learning Based Thermal Infrared Object Tracking in Videos : A Survey
Chapter 7 : Heuristics to Secure IoT-based Edge Driven UAV
Chapter 8 : Phased.js : Automated Software Deployment & Resource Provisioning and Management for AI
Chapter 9 : Robust Image Enhancement Technique to Automatically Enrich the Visibility of Satellite Captured Snaps
Chapter 10 : Implementation of FIR Filter and Creation of Custom IP Blocks
Chapter 11 : Use Cases of Blockchain in Post-Covid Healthcare
Chapter 12 : A prediction of Telecom Customer Churn Analysis uses the I-GBDT Algorithm
Chapter 13 : Deployment of Machine Learning and Deep Learning Algorithms in Industrial Engineering
Chapter 14 : Simulation Analysis of AODV and DSDV Routing Protocols for Secure and Reliable Service in Mobile Adhoc Networks (MANETs)
Chapter 15 : Landmine Detection and Classification Based on Machine Learning Algorithms
Chapter 16 : Application of Queuing Technique in an Educational Institute Canteen : A Case Study
Chapter 17 : IoT based Driver Drowsiness Detection and Alerting System using Haar Cascade and Eye Aspect Ratio Algorithms
Chapter 18 : Force/position Control of Constrained Reconfigurable Manipulators using Hybrid Backstepping Neural Networks Based Control Approach

Index