Machine Learning Applications - by Indranath Chatterjee & Sheetal Zalte (Hardcover)

About the Book

"Machine learning (ML), and deep learning (DL) technologies are changing virtually every industry around the world. These technologies are increasingly being used in robotics and vehicle automation, and in businesses such as financial services, retail, manufacturing, healthcare, and life sciences. This book details the advances in these technologies and presents case studies on how they can be applied to different domains such as image processing, computer vision, robotics, and more. Comprised of 13 chapters, this book introduces real-world applications of machine and deep learning to healthcare, blockchain technology, cyber security, and climate change completed with case studies, solutions, and use cases for the reader's active learning. With a team of expert contributors, applications to image processing are examined, including medical imaging, pattern recognition, object detection, image segmentation, image transformation, morphological processing and more. An explanation of AI and robotic applications in mechanical design is also discussed including robot-assisted surgeries, security, and space exploration. Aimed at professionals and researchers alike, this book covers a range of important machine and deep learning applications. The editors and contributors describe the importance of each subject area and detail why they are so important to us"--

Book Synopsis

Machine Learning Applications

Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations

Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning.

Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective.

Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on:

• Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
• Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
• AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
• Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records

With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

From the Back Cover

Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations

Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader's active learning.

Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective.

Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on:

• Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
• Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
• AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
• Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records

With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

About the Author

Indranath Chatterjee is a Professor in the Department of Computer Engineering, at Tongmyong University, South Korea. He received his PhD from University of Delhi, India and has authored several books and numerous, research papers. His areas of research are AI, computer vision, computation neuroscience and medical imaging.

Sheetal Zalte is an Assistant Professor in the Department of Computer Science at Shivaji University, India. She earned her PhD from Shivaji University, India, and has published many research papers. Her research area is mobile adhoc networks.