Real-Time Applications of Machine Learning in Cyber-Physical Systems

$152.00

Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live. The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians. Additional ISBNs 1799893081, 1799893103, 9781799893080, 9781799893103Real-Time Applications of Machine Learning in Cyber-Physical Systems and published by Engineering'science Reference. ISBNs for Real-Time Applications of Machine Learning in Cyber-Physical Systems are 9781799893110, 1799893111 and the print ISBNs are 9781799893080, 1799893081. Additional ISBNs include 1799893081, 1799893103, 9781799893080, 9781799893103.

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We accept returns and exchanges within 30 days from the date your order is delivered. Items must be unused, in the original condition, and accompanied by the original packaging and receipt or proof of purchase.

How to Return or Exchange an Item

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Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

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Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

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Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live.

The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians. Additional ISBNs 1799893081, 1799893103, 9781799893080, 9781799893103Real-Time Applications of Machine Learning in Cyber-Physical Systems and published by Engineering’science Reference. ISBNs for Real-Time Applications of Machine Learning in Cyber-Physical Systems are 9781799893110, 1799893111 and the print ISBNs are 9781799893080, 1799893081. Additional ISBNs include 1799893081, 1799893103, 9781799893080, 9781799893103.