دانلود کتاب مدلسازی تحلیل در قابلیت اطمینان و یادگیری ماشین و کاربردهای آن

Today, billions of people are connected through their mobile devices, and data are being collected and analyzed like never before. The era of big data, driven by machine learning algorithms, statistical analytics, and reliability computing, has experienced a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. On the one hand, it helps organizations achieve better results and make fact-based decisions in all aspects of life; on the other hand, business today is more competitive and challenging due to global competition and the complexity of the business itself. This book consists of 17 chapters that present a selection of research and applications on related topics in reliability, applied statistics, big data, and machine learning. The emphasis of this book is on the analytical models in various disciplines and their practical applications in reliability, data science, manufacturing, healthcare, and industry using machine learning, AI, optimization, and other computational methods. All chapters are written by experts and active researchers in their respective fields, with the hope of bridging the gap between theory and practical applications. The book covers a broad range of current research on analytics modeling and techniques and their applications in industry, including inventory management, imperfect data, human factors, risk assessment, unsupervised learning, IoT networks, deep learning, human-machine integration, big data, fraud detection, classification, biomedical imaging, software testing, safety-critical devices, machine learning algorithms, software quality assurance, stress analysis, time series forecasting, trustability, and digital twin. I acknowledge Springer for this opportunity and professional support. Importantly, I would like to thank all the chapter authors and reviewers for their contributions to this work.