國立虎尾科技大學 |

Advanced Bioscience and Biosystems for Detection and Management of Diabetes

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advanced Bioscience and Biosystems for Detection and Management of Diabetes/ edited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik.
其他作者:	Malik, Rayaz A.
面頁冊數:	VIII, 313 p. 150 illus., 148 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-99728-1
ISBN:	9783030997281

Advanced Bioscience and Biosystems for Detection and Management of Diabetes
Advanced Bioscience and Biosystems for Detection and Management of Diabetes[electronic resource] /edited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik. - 1st ed. 2022. - VIII, 313 p. 150 illus., 148 illus. in color.online resource. - Springer Series on Bio- and Neurosystems,132520-8543 ;. - Springer Series on Bio- and Neurosystems,7.

Introduction -- Review of Emerging Approaches Utilizing Alternative Physiological Human Body Fluids in Non- or Minimally Invasive Glucose Monitoring -- Current Status of Non-invasive Diabetics Monitoring -- A New Solution for Non-invasive Glucose Measurement Based on Heart Rate Variability -- Optics Based Techniques for Monitoring Diabetics -- SPR Assisted Diabetics Detection -- Infrared and Raman Spectroscopy Assisted Diagnosis of Diabetics -- Photoacoustic Spectroscopy Mediated Non-Invasive Detection of Diabetics -- Electrical Bioimpedance Based Estimation of Diabetics -- Millimeter and Microwave Sensing Technique for Diagnosis of Diabetics -- Different Machine Learning Algorithm involved in Glucose Monitoring to Prevent Diabetes Complications and Enhanced Diabetes Mellitus Management -- The role of Artificial Intelligence in Diabetes management -- Artificial Intelligence and Machine learning for Diabetes Decision Support -- Commercial Non-Invasive Glucose Sensor Devices for Monitoring Diabetics -- Future Developments in Invasive and Non-Invasive Diabetics Monitoring.

This book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes.

ISBN: 9783030997281

Standard No.: 10.1007/978-3-030-99728-1doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: R856-857

Dewey Class. No.: 610.28

Advanced Bioscience and Biosystems for Detection and Management of Diabetes
LDR:03465nam a22004095i 4500 001 1088046
003 DE-He213
005 20220701103701.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030997281 $9 978-3-030-99728-1
024 7 $a 10.1007/978-3-030-99728-1 $2 doi
035 $a 978-3-030-99728-1
050 4 $a R856-857
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
245 1 0 $a Advanced Bioscience and Biosystems for Detection and Management of Diabetes $h [electronic resource] / $c edited by Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A M Al-Ali, Rayaz A. Malik.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a VIII, 313 p. 150 illus., 148 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
490 1 $a Springer Series on Bio- and Neurosystems, $x 2520-8543 ; $v 13
505 0 $a Introduction -- Review of Emerging Approaches Utilizing Alternative Physiological Human Body Fluids in Non- or Minimally Invasive Glucose Monitoring -- Current Status of Non-invasive Diabetics Monitoring -- A New Solution for Non-invasive Glucose Measurement Based on Heart Rate Variability -- Optics Based Techniques for Monitoring Diabetics -- SPR Assisted Diabetics Detection -- Infrared and Raman Spectroscopy Assisted Diagnosis of Diabetics -- Photoacoustic Spectroscopy Mediated Non-Invasive Detection of Diabetics -- Electrical Bioimpedance Based Estimation of Diabetics -- Millimeter and Microwave Sensing Technique for Diagnosis of Diabetics -- Different Machine Learning Algorithm involved in Glucose Monitoring to Prevent Diabetes Complications and Enhanced Diabetes Mellitus Management -- The role of Artificial Intelligence in Diabetes management -- Artificial Intelligence and Machine learning for Diabetes Decision Support -- Commercial Non-Invasive Glucose Sensor Devices for Monitoring Diabetics -- Future Developments in Invasive and Non-Invasive Diabetics Monitoring.
520 $a This book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes.
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 0 $a Machine learning. $3 561253
650 0 $a Biomedical engineering. $3 588770
700 1 $a Malik, Rayaz A. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1028257
700 1 $a A M Al-Ali, Abdulaziz Khalid. $e editor. $1 https://orcid.org/0000-0003-0006-2642 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1395179
700 1 $a Cabibihan, John-John. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1273569
700 1 $a Sadasivuni, Kishor Kumar. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1065621
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030997274
776 0 8 $i Printed edition: $z 9783030997298
776 0 8 $i Printed edition: $z 9783030997304
830 0 $a Springer Series on Bio- and Neurosystems, $x 2520-8535 ; $v 7 $3 1302615
856 4 0 $u https://doi.org/10.1007/978-3-030-99728-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)