Artificial Intelligence and Machine Learning for Intelligent Engineering Systems

The book series editors are inviting edited, reference and text book proposals for consideration in the book series. This book series “Artificial Intelligence and Machine Learning for Intelligent Engineering Systems” highlights into the profound impact of AI and ML across various engineering disciplines, focusing on mechanical, materials, electrical, computer, and electronics engineering. Each volume explores deep into specific applications tailored to each field. For instance, the series explores how AI and ML algorithms can predict equipment failures and optimize maintenance schedules, leading to significant benefits for both mechanical and electrical systems. In materials science, the series investigates how machine learning can be harnessed to discover innovative materials with targeted properties, accelerating the pace of discovery. Similarly, the application of AI for intelligent control of complex systems in electrical and mechanical engineering is addressed, paving the way for enhanced performance and efficiency. Additionally, the series explores how machine learning can be employed to optimize designs across mechanical, electrical, and computer systems, taking into account factors like functionality, ease of manufacturing, and energy consumption. Furthermore, the series sheds light on the utilization of machine learning algorithms to analyze sensor data from diverse engineering systems, enabling real-time diagnostics and proactive fault detection. Finally, the integration of AI and ML into robotics is explored, highlighting its potential for enhanced decision-making, path planning, and fostering a more collaborative human-machine interface. This comprehensive series serves as an invaluable resource for engineers, researchers, and students across various engineering disciplines. It provides a thorough overview of the latest advancements in AI and ML, along with practical applications for designing and developing intelligent engineering systems that will shape the future.

The book series invite book proposals for submission on all topics mentioned below:

AI, Ml powered Manufacturing Systems for Industry 5.0 / Predictive Maintenance: Prolonging the Life of Machines with AI and ML/ Optimizing Design: Harnessing Machine Learning for Efficient Engineering Solutions/ From Sensor Data to Insights: AI-Powered Analytics for Intelligent Systems / The Rise of Collaborative Robots: How AI and ML are Revolutionizing Human-Machine Interaction / Safety by Design: Using Machine Learning to Prevent Engineering Failures / Controlling the Uncontrollable: AI and Machine Learning for Complex Systems Optimization / Building the Future: AI and Machine Learning for Sustainable Engineering Practices/AI-Powered Diagnostics: Revolutionizing Inspection and Fault Detection in Engineering /Beyond the Physical: Machine Learning for Virtual Prototyping and Simulation/ The Human Touch in the Age of AI: Leveraging Machine Learning for Enhanced Engineering Expertise/Mathematical and Computational Modeling Techniques in Digital Twin/ Interoperability Design principles for manufacturing systems in Industry 5.0/ Discrete event simulation techniques for manufacturing systems/ Modeling and Simulation in Engineering Systems/ Computational Approaches in Aerospace Engineering/ Mathematical Modelling of Agriculture Equipments/ AI and ML Approaches in Chemical Engineering/ AI and ML for Sustainable Engineering/ AI and ML for Renewable Energy Systems/ Advanced Signal Processing Techniques in Engineering/ Multiscale Modeling and Simulation in Materials Characterization/ Topology Optimization in Structural Design/ AI and ML Models for Cognitive Robotics/ Computational Biology and Bioinformatics in Engineering/ Mathematical Models for Population Dynamics/ Nonlinear Dynamics and Chaos in Engineering Systems/ Computational Fluid Dynamics for Engineering Applications/ Advanced Control Systems for Complex Engineering Systems/ Quantum Computing for Solving Engineering Problems/ Machine Learning Approaches in Engineering Optimization/ Optimization Techniques in Engineering Design/Computational Electro-magnetic for Innovative Technologies/Advanced Robotics and Kinematics/ AI and ML for Environmental Engineering/ AI and ML Approaches for Urban Planning/ AI and ML for Intelligent Transportation Systems.