Artificial Intelligence And Machine Learning Algorithms For Engineering Applications
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Artificial Intelligence and Machine Learning Algorithms for Engineering Applications
The text showcases the use of artificial intelligence and machine learning-based algorithms with advanced tools and methodologies for diverse engineering applications. It further discusses topics such as data preprocessing, cross-validation techniques, support vector machines in manufacturing, and reinforcement learning for control systems.
Artificial Intelligence and Machine Learning Algorithms for Engineering Applications
This book comprehensively covers core algorithms and techniques used in artificial intelligence (AI) and machine learning (ML) for engineering applications. It further explores the use of AI in civil and structural engineering, quality control, and product design. Features: Presents autonomous robots using onboard computing and artificial intelligence (AI) algorithms to process the data from their sensors and make real-time decisions. Discusses nature-based optimization-based computing techniques to enhance the computational speed for solving engineering problems. Provides conceptual and practical knowledge about the design of modern computation techniques with advanced tools and methodologies. Highlights the importance of using smart techniques including AI and ML in product design and development. Covers time series analysis and forecasting in engineering, robotic process automation, and autonomous robots in manufacturing. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, manufacturing engineering, and environmental engineering.
Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications
This book presents various machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: biomedical engineering, signal processing, and computer science. Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications explores recent advancements in the use of AI/ML in practical engineering applications by inviting top experts to share the outcomes of their most recent work. Among the topics explored are detection, measurement, and monitoring of signals (biosensors and biomedical devices) and the use of diagnostic interpretations of bioelectric data using signal-processing techniques. The authors also address several machine learning tasks, such as classification (supervised learning) and clustering (unsupervised learning), in the context of engineering. Finally, the book also describes the development of new biomaterials for use in the body. The book will be a great help to researchers and academics working in the fields of biomedical signaling and/or human-machine interface.