{"product_id":"recent-advances-in-machine-learning-and-deep-learning-theories-towards-intelligent-fault-diagnosis-hardcover","title":"Recent Advances in Machine learning and Deep Learning Theories: Towards Intelligent Fault Diagnosis - Hardcover","description":"\u003cp\u003eby \u003cb\u003eHeung Soo Kim\u003c\/b\u003e (Guest Editor), \u003cb\u003eSalman Khalid\u003c\/b\u003e (Guest Editor), \u003cb\u003eAnanda Shankar\u003c\/b\u003e (Guest Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis Reprint presents a collection of cutting-edge research and review articles focusing on the integration of machine learning and deep learning theories for intelligent fault diagnosis in industrial and engineering systems. With the rapid advancement of computational intelligence, data-driven fault diagnosis has become a cornerstone of modern Prognostics and Health Management (PHM), enabling early detection, prediction, and mitigation of system failures. The contributions in this Reprint highlight innovative applications of artificial neural networks, convolutional and recurrent neural architectures, transfer learning, and hybrid intelligent systems for diagnosing faults in rotating machinery, robotic systems, power plants, and manufacturing processes. In addition, the included studies explore explainable AI models, data augmentation, and sensor fusion methods that enhance model interpretability and robustness under real-world operating conditions. By bringing together theoretical insights and practical implementations, this Reprint aims to serve as a valuable reference for researchers, engineers, and practitioners engaged in machine learning-based condition monitoring and intelligent fault diagnosis. It reflects recent trends shaping the future of autonomous and resilient industrial systems within the framework of Industry 4.0.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 240\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.81 x 9.61 x 6.69 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 09, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":71744031555837,"sku":"9783725859511","price":116.17,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/3414\/0157\/files\/JCsIvfZffV9783725859511.webp?v=1793299937","url":"https:\/\/booktolia.com\/products\/recent-advances-in-machine-learning-and-deep-learning-theories-towards-intelligent-fault-diagnosis-hardcover","provider":"booktolia","version":"1.0","type":"link"}