Machine Learning In Python For Visual And Acoustic Data Based Process Monitoring
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Machine Learning in Python for Visual and Acoustic Data-based Process Monitoring
This book is designed to help readers gain quick familiarity with deep learning-based computer vision and abnormal equipment sound detection techniques. The book helps you take your first step towards learning how to use convolutional neural networks (the ANN architecture that is behind the modern revolution in computer vision) and build image sensor-based manufacturing defect detection solutions. A quick introduction is also provided to how modern predictive maintenance solutions can be built for process critical equipment by analyzing the sound generated by the equipment. Overall, this short eBook sets the foundation with which budding process data scientists can confidently navigate the world of modern computer vision and acoustic monitoring.
Recent Advances in Manufacturing Engineering and Processes
This book comprises state-of-the-art papers in manufacturing engineering & processes including computer-aided design and manufacturing, environmentally sustainable manufacturing processes, modelling, analysis, and simulation of manufacturing processes, composite materials manufacturing, nanomaterials and nano-manufacturing, semiconductor materials manufacturing, rapid manufacturing technologies, 3D printing and non-traditional manufacturing engineering and processes. In particular, the papers in the book cover latest advances especially in 3D printing and additive manufacturing techniques and processes for sustainable materials including ceramic and polymer-matrix composite where there is paucity of good papers in the literature. The contents of this volume will be useful to researchers and practicing engineers alike.
Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals
Achieving the United Nations' Sustainable Development Goals (SDGs) requires innovative solutions that address global challenges such as climate change, poverty, and social inequality. Artificial intelligence (AI), machine learning, and data-driven technologies offer transformative potential by optimizing resource management, improving healthcare outcomes, and enhancing decision-making processes. However, integrating AI into sustainable development efforts presents ethical, technical, and policy-related challenges that must be carefully navigated. A multidisciplinary approach is essential to ensure these technologies are applied inclusively and responsibly, maximizing their positive societal impact. Machine and Deep Learning Solutions for Achieving the Sustainable Development Goals enhances understanding and application of machine learning, deep learning, data mining and AI technologies in the context of the SDGs. It fills the gap by linking theory and practice and addresses both the opportunities and challenges inherent in this intersection. Covering topics such as demand side management, agricultural productivity, and smart manufacturing, this book is an excellent resource for engineers, computer scientists, practitioners, policymakers, professionals, researchers, scholars, academicians, and more.