Deep Learning Applications for Cyber-Physical Systems

Monica R. Mundada

ISBN 10: 1799881628 / ISBN 13: 9781799881629

Edité par Engineering Science Reference, 2021

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9781799881629

Détails bibliographiques

• Titre: Deep Learning Applications for Cyber-Physical Systems
• Éditeur: Engineering Science Reference
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 1799881628
• ISBN à 13 chiffres: 9781799881629
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 254 L x 178 H x 17 P (en millimètres) 254 millimètres de largeur par 178 millimètres de hauteur par 17 millimètres de profondeur
• Poids: 605 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Srinivasa K. G. is currently working as a Professor at NITTTR, Chandigarh. Earlier he was an associate professor at CBP Government Engineering College, New Delhi. Earlier, he was a faculty in the Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore. He is the recipient of AICTE - Career Award for Young Teachers, Indian Society of Technical Education – ISGITS National Award for Best Research Work Done by Young Teachers, Institution of Engineers (India) – IEI Young Engineer Award in Computer Engineering, Rajarambapu Patil National Award for Promising Engineering Teacher Award from ISTE - 2012. He has published more than hundred research papers in International Conferences and Journals. He has visited many Universities abroad as a visiting researcher – He has visited University of Oklahoma, USA, Iowa State University, USA, Hong Kong University, Korean University, National University of Singapore are few prominent visits. He has authored two books namely File Structures using C++ by TMH and Soft Computer for Data Mining Applications LNAI Series – Springer. He has been awarded BOYSCAST Fellowship by DST, for visiting University of Melbourne.