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Machine Learning Applications: From Computer Vision to Robotics

Machine Learning Applications: From Computer Vision to Robotics

Machine Learning Applications: From Computer Vision to Robotics
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Machine Learning Applications: From Computer Vision to Robotics Hardback - 2023

by Indranath Chatterjee

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Details

  • Title Machine Learning Applications: From Computer Vision to Robotics
  • Author Indranath Chatterjee
  • Binding Hardback
  • Condition New
  • Pages 240
  • Volumes 1
  • Language ENG
  • Publisher Wiley-IEEE Press
  • Publication date 2023-12-27
  • Features Bibliography, Index
  • Bookseller's Inventory # 45301055-n
  • ISBN 9781394173327 / 1394173326
  • Weight 1.07 lbs (0.49 kg)
  • Dimensions 9 x 6 x 0.56 in (22.86 x 15.24 x 1.42 cm)
  • Category Technology & Industrial Arts
  • Library of Congress subjects Machine learning - Industrial applications, Machine learning - Scientific applications
  • Library of Congress Catalogue Number 2023043411
  • Dewey Decimal Code 006.31
  • Quantity available 5

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Reader reviews for Machine Learning Applications: From Computer Vision to Robotics

From the publisher

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 rear 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.

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