Name: Cancer Prediction for Industrial iot 4. 0: A Machine Learning Perspective (Chapman & Hall (en Inglés)
Price: 439.49 PEN
Availability: InStock
Author: Rachna Jain, Arun Solanki, Fadi Al-Turjman, Meenu Gupta
ISBN: 9781032028781

portada Cancer Prediction for Industrial iot 4. 0: A Machine Learning Perspective (Chapman & Hall (en Inglés)

Cancer Prediction for Industrial iot 4. 0: A Machine Learning Perspective (Chapman & Hall (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Rachna Jain, Arun Solanki, Fadi Al-Turjman, Meenu Gupta

Idioma

Inglés

Encuadernación

Tapa Dura

ISBN13

9781032028781

Cancer Prediction for Industrial iot 4. 0: A Machine Learning Perspective (Chapman & Hall (en Inglés)

Rachna Jain, Arun Solanki, Fadi Al-Turjman, Meenu Gupta (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Cancer Prediction for Industrial iot 4. 0: A Machine Learning Perspective (Chapman & Hall (en Inglés)"

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 treatmentReaders 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.