Buch, Englisch, 296 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 594 g
Buch, Englisch, 296 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 594 g
Reihe: Intelligent Manufacturing and Industrial Engineering
ISBN: 978-1-032-46601-9
Verlag: CRC Press
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.
Zielgruppe
Professional Reference and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
- Technische Wissenschaften Technik Allgemein Industrial Engineering
- Technische Wissenschaften Technik Allgemein Technik: Allgemeines
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
Weitere Infos & Material
1. Challenges, Opportunities, and the Future of Industrial Engineering with IoT and AI. 2. Evolution and Future of Industrial Engineering with IOT and AI. 3. Applications of Artificial intelligence and Internet of Things IoT in Marketing. 4. An Introduction to Multi-Objective Decision Programming with Fuzzy Parameters. 5. Data Analytics. 6. Recent advances on deep learning based thermal infrared object tracking in videos: a survey. 7. Heuristics to Secure IoT-based Edge Driven UAV. 8. Phased.js: Automated Software Deployment & Resource Provisioning and Management for AI. 9. Robust Image Enhancement Technique to Automatically Enrich the Visibility of Satellite Captured Snaps. 10. Implementation of FIR Filter and Creation of Custom IP Blocks. 11. Use Cases of Blockchain in Post-Covid Healthcare. 12. A prediction of Telecom Customer Churn Analysis uses the I-GBDT algorithm. 13. Deployment of Machine Learning and Deep Learning Algorithms in Industrial Engineering. 14. Simulation Analysis of AODV and DSDV Routing Protocols for Secure and Reliable Service in Mobile Adhoc Networks (MANETs). 15. Landmine Detection and Classification Based on Machine Learning Algorithms. 16. Application of Queuing Technique in an Educational Institute Canteen- A Case Study. 17. IoT based Driver Drowsiness Detection and Alerting System using Haar Cascade and Eye Aspect Ratio Algorithms. 18. Force/position control of constrained reconfigurable manipulators using hybrid backstepping neural networks based control approach.