國立虎尾科技大學 |

Machine Learning for Critical Internet of Medical Things = Applications and Use Cases /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning for Critical Internet of Medical Things/ edited by Fadi Al-Turjman, Anand Nayyar.
Reminder of title:	Applications and Use Cases /
other author:	Nayyar, Anand.
Description:	X, 261 p. 89 illus., 79 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Biomedical Engineering and Bioengineering. -
Online resource:	https://doi.org/10.1007/978-3-030-80928-7
ISBN:	9783030809287

Machine Learning for Critical Internet of Medical Things = Applications and Use Cases /
Machine Learning for Critical Internet of Medical ThingsApplications and Use Cases /[electronic resource] :edited by Fadi Al-Turjman, Anand Nayyar. - 1st ed. 2022. - X, 261 p. 89 illus., 79 illus. in color.online resource.

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

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

1211019
Biomedical Engineering and Bioengineering.

LC Class. No.: TK7895.E42

Dewey Class. No.: 621.38

Machine Learning for Critical Internet of Medical Things = Applications and Use Cases /
LDR:03739nam a22004335i 4500 001 1093909
003 DE-He213
005 20220203103323.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030809287 $9 978-3-030-80928-7
024 7 $a 10.1007/978-3-030-80928-7 $2 doi
035 $a 978-3-030-80928-7
050 4 $a TK7895.E42
050 4 $a TK5105.8857
072 7 $a TJF $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC007000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a GPFC $2 thema
082 0 4 $a 621.38 $2 23
245 1 0 $a Machine Learning for Critical Internet of Medical Things $h [electronic resource] : $b Applications and Use Cases / $c edited by Fadi Al-Turjman, Anand Nayyar.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a X, 261 p. 89 illus., 79 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 2 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Cyber-Physical Systems. $3 1387591
650 0 $a Biomedical engineering. $3 588770
650 0 $a Telecommunication. $3 568341
650 0 $a Medical informatics. $3 583858
650 0 $a Artificial intelligence. $3 559380
650 0 $a Cooperating objects (Computer systems). $3 1387590
700 1 $a Nayyar, Anand. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1320157
700 1 $a Al-Turjman, Fadi. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1168152
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030809270
776 0 8 $i Printed edition: $z 9783030809294
776 0 8 $i Printed edition: $z 9783030809300
856 4 0 $u https://doi.org/10.1007/978-3-030-80928-7
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)