AI and Machine Learning for Mechanical and Electrical Engineering [Hardcover]

The book examines issues involved in the transition from traditional mechanical and electrical engineering and their management systems to the new engineering paradigms created by the application of smart systems. It covers applications, methods to transition to smart engineering and management, and associated ethical implications.

1. Development of a Smart Algorithm to Integrate Fault Detection and Classification of End-to-End Monitoring of Autonomous Transfer Vehicles 2. Data Science and ML Algorithms to Investigate Different Testing Scenarios for Various Anomalies in Driven Electric Motor 3. A Data Fusion Technique to Detect and Assess Electromechanical Damage 4. AI: Classifications and Protection of Smart Grid Systems 5. An Artificial Intelligence-Based Solar Radiation Prophesy Model for Green Energy Utilization in Energy Management System 6. Two-Channel Convolutional Neural Networks for Rolling Bearing Fault Diagnosis in Unbalanced Datasets 7. The Implementation of Artificial Intelligence for Auto Gearbox Failure Detection 8. Evolutionary Algorithms to Optimise Deep Learning Models for Water Industry Forecasts 9. Artificial Intelligence Anomaly Detection and Root-Cause Analysis 10. Artificial Intelligence and Internet of Things-Based Intelligent Scheduling for Load Distribution in Power Grids 11. Coordinated Response Strategies: Swarm Robotics for Crisis Management 12. Smart Farming and Human-Bioinformatics Systems Based on IoT and Sensor Devices 13. Machine Learning Techniques Applied to Predictive Maintenance: A Review 14. Optimization of Parameters During Tribological Investigations on Azadirachta Indica Based Bio-Composites 15. ANFIS Modelling Study on Surface Water Analysis 16. WSN-Based Optimal Crude Oil Storage Health Monitoring Framework 17. Cybersecurity Education Gamification: A Current Review and Research Agenda 18. Artificial Intelligence and Cybersecurity in 6G Wireless Networks

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