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Machine Learning For Critical Internet Of Medical Things Applications And Use Cases Fadi Al Turjman

  • SKU: BELL-42279386
Machine Learning For Critical Internet Of Medical Things Applications And Use Cases Fadi Al Turjman
$ 31.00 $ 45.00 (-31%)

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Machine Learning For Critical Internet Of Medical Things Applications And Use Cases Fadi Al Turjman instant download after payment.

Publisher: Springer
File Extension: RTF
File size: 6.69 MB
Pages: 376
Author: Fadi Al Turjman, Anand Nayyar
ISBN: 9783030809287, 3030809285
Language: English
Year: 2022

Product desciption

Machine Learning For Critical Internet Of Medical Things Applications And Use Cases Fadi Al Turjman by Fadi Al Turjman, Anand Nayyar 9783030809287, 3030809285 instant download after payment.

This book discusses the applications, challenges, and future trends of machine learning in medical domain, including both basic and advanced topics. The book presents how machine learning is helpful in smooth conduction of administrative processes in hospitals, in treating infectious diseases, and in personalized medical treatments. The authors show how machine learning can also help make fast and more accurate disease diagnoses, easily identify patients, help in new types of therapies or treatments, model small-molecule drugs in pharmaceutical sector, and help with innovations via integrated technologies such as artificial intelligence as well as deep learning. The authors show how machine learning also improves the physician’s and doctor’s medical capabilities to better diagnosis their patients. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning that will enhance the efficiency and effectiveness of the healthcare system. Provides researchers in machine and deep learning with a conceptual understanding of various methodologies of implementing the technologies in medical areas; Discusses the role machine learning and IoT play into locating different virus and diseases across the globe, such as COVID-19, Ebola, and cervical cancer; Includes fundamentals and advances in machine learning in the medical field, supported by significant case studies and practical applications.

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