Deep Learning Applications for Cyber-Physical Systems
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Deep Learning Applications for Cyber-Physical Systems

Monica R. Mundada (M S Ramaiah Institute of Technology, India), S. Seema (Ramaiah Institute of Technology, India), Srinivasa K G (National Institute of Technical Teachers Training and Research, Chandigarh, India) and M. Shilpa (Ramaiah Institute of Technology, India)
Projected Release Date: October, 2021|Copyright: © 2022 |Pages: 330
DOI: 10.4018/978-1-7998-8161-2
ISBN13: 9781799881612|ISBN10: 179988161X|EISBN13: 9781799881636|ISBN13 Softcover: 9781799881629
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Description & Coverage
Description:

Big data generates around us constantly from daily business, custom use, engineering, and science activities. Sensory data is collected from the internet of things (IoT) and cyber-physical systems (CPS). Merely storing such a massive amount of data is meaningless, as the key point is to identify, locate, and extract valuable knowledge from big data to forecast and support services. Such extracted valuable knowledge is usually referred to as smart data. It is vital to providing suitable decisions in business, science, and engineering applications.

Deep Learning Applications for Cyber-Physical Systems provides researchers a platform to present state-of-the-art innovations, research, and designs while implementing methodological and algorithmic solutions to data processing problems and designing and analyzing evolving trends in health informatics and computer-aided diagnosis in deep learning techniques in context with cyber physical systems. Covering topics such as smart medical systems, intrusion detection systems, and predictive analytics, this text is essential for computer scientists, engineers, practitioners, researchers, students, and academicians, especially those interested in the areas of internet of things, machine learning, deep learning, and cyber-physical systems.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence
  • Cloud of Things
  • Cyber Physical System
  • Deep Learning
  • Deep Neural Networks
  • Energy-Efficient Resource Allocation
  • Fog Computing
  • Image Denoising
  • Intrusion Detection System
  • Predictive Analytics
  • Real-Time Task-Scheduling Systems
  • Sentiment Analysis
  • Smart Medical Systems
  • Task Scheduling
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