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Machine learning for critical internet of medical thingsapplications and use cases /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for critical internet of medical thingsedited by Fadi Al-Turjman, Anand Nayyar.
Reminder of title:	applications and use cases /
other author:	Al-Turjman, Fadi.
Published:	Cham :Springer International Publishing :2022.
Description:	x, 261 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-80928-7
ISBN:	9783030809287$q(electronic bk.)

Machine learning for critical internet of medical thingsapplications and use cases /
Machine learning for critical internet of medical thingsapplications and use cases /[electronic resource] :edited by Fadi Al-Turjman, Anand Nayyar. - Cham :Springer International Publishing :2022. - x, 261 p. :ill. (chiefly col.), digital ;24 cm.

Introduction -- An Introduction to Basic Concepts on Machine Learning, its architecture and framework -- Machine Learning Models and techniques -- Diseases diagnosis and prediction using Machine Learning -- Machine learning for Mobile/e-health, Tele-medical and Remote healthcare networks -- Machine learning in biomedical, Neuro-critical and medical image processing field -- AI, Deep learning and machine learning enabled connected health informatics -- Machine learning enabled smart healthcare system -- Machine learning based efficient health monitoring systems -- Machine learning case study for virus disease Ebola, COVID-19 consequences -- CASE Study: Machine Learning in Medical domain for Cervical Cancer -- Use cases and applications of machine learning in medical domain -- Conclusion.

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.

ISBN: 9783030809287$q(electronic bk.)

Standard No.: 10.1007/978-3-030-80928-7doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: R859.7.A78 / M33 2022

Dewey Class. No.: 610.28563

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505 0 $a Introduction -- An Introduction to Basic Concepts on Machine Learning, its architecture and framework -- Machine Learning Models and techniques -- Diseases diagnosis and prediction using Machine Learning -- Machine learning for Mobile/e-health, Tele-medical and Remote healthcare networks -- Machine learning in biomedical, Neuro-critical and medical image processing field -- AI, Deep learning and machine learning enabled connected health informatics -- Machine learning enabled smart healthcare system -- Machine learning based efficient health monitoring systems -- Machine learning case study for virus disease Ebola, COVID-19 consequences -- CASE Study: Machine Learning in Medical domain for Cervical Cancer -- Use cases and applications of machine learning in medical domain -- Conclusion.
520 $a 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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650 0 $a Artificial intelligence $x Medical applications. $3 237917
650 0 $a Internet of things. $3 670954
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650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Health Informatics. $3 274212
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650 2 4 $a Biomedical Engineering and Bioengineering. $3 826326
700 1 $a Al-Turjman, Fadi. $3 830682
700 1 $a Nayyar, Anand. $3 861579
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856 4 0 $u https://doi.org/10.1007/978-3-030-80928-7
950 $a Computer Science (SpringerNature-11645)