Cancer Prediction for Industrial IoT 4.0

Cancer Prediction for Industrial IoT 4.0

EnglishHardbackPrint on demand
Taylor & Francis Ltd
EAN: 9781032028781
Print on demand
Delivery on Monday, 1. of July 2024
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Detailed information

Cancer Prediction for Industrial IoT 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.

EAN 9781032028781
ISBN 1032028785
Binding Hardback
Publisher Taylor & Francis Ltd
Publication date December 31, 2021
Pages 203
Language English
Dimensions 254 x 178
Country United Kingdom
Illustrations 24 Tables, black and white; 46 Line drawings, black and white; 18 Halftones, black and white; 64 Illustrations, black and white
Editors Al-Turjman, Fadi; Gupta, Meenu; Jain Rachna; Solanki, Arun
Series Chapman & Hall/CRC Internet of Things