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Fadi (Near East Uni., Nicosia) Al-Turjman & Meenu (Chandigarh University, India) Gupta 
Cancer Prediction for Industrial IoT 4.0 
A Machine Learning Perspective

Ondersteuning

Cancer Prediction for Industrial Io T 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines.


Features


• Covers the fundamentals, history, reality and challenges of cancer


• Presents concepts and analysis of different cancers in humans


• Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer


• Offers real-world examples of cancer prediction


• Reviews strategies and tools used in cancer prediction


• Explores the future prospects in cancer prediction and treatment



Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions.



This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.

€61.02
Betalingsmethoden
Formaat PDF ● Pagina’s 217 ● ISBN 9781000508581 ● Editor Fadi (Near East Uni., Nicosia) Al-Turjman & Meenu (Chandigarh University, India) Gupta ● Uitgeverij CRC Press ● Gepubliceerd 2021 ● Downloadbare 3 keer ● Valuta EUR ● ID 8213205 ● Kopieerbeveiliging Adobe DRM
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