Produkt
KlappentextMACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS
As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue.
Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.
Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store 'contextualized marketing', and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.
Audience
Scholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists.
Shalli Rani, PhD is an associate professor in the Department of CSE, Chitkara University, Punjab, India.
R. Maheswar, PhD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India.
G. R. Kanagachidambaresan, PhD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India.
Sachin Ahuja, PhD is a professor in the Department of CSE, Chitkara University, Punjab, India.
Deepali Gupta, PhD is a professor, Department of CSE, Chitkara University, Punjab, India.
As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue.
Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.
Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store 'contextualized marketing', and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.
Audience
Scholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists.
Shalli Rani, PhD is an associate professor in the Department of CSE, Chitkara University, Punjab, India.
R. Maheswar, PhD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India.
G. R. Kanagachidambaresan, PhD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India.
Sachin Ahuja, PhD is a professor in the Department of CSE, Chitkara University, Punjab, India.
Deepali Gupta, PhD is a professor, Department of CSE, Chitkara University, Punjab, India.
Details
Weitere ISBN/GTIN9781119763482
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis2 - DRM Adobe / Adobe Ebook Reader
FormatFormat mit automatischem Seitenumbruch (reflowable)
Verlag
Erscheinungsjahr2022
Erscheinungsdatum11.02.2022
Auflage1. Auflage
Seiten304 Seiten
SpracheEnglisch
Dateigrösse28483 Kbytes
Artikel-Nr.8917542
Rubriken
Genre9201