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on1336590528 |
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OCoLC |
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20240623213015.0 |
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m o d |
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|b eng
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|d OCLCQ
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| 019 |
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|a 1334527793
|a 1334890351
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|a 9783030997281
|q (electronic bk.)
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|q (electronic bk.)
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|z 9783030997274
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|z 3030997278
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7 |
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|a 10.1007/978-3-030-99728-1
|2 doi
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|a (OCoLC)1336590528
|z (OCoLC)1334527793
|z (OCoLC)1334890351
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|a RC660
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|a TEC059000
|2 bisacsh
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| 049 |
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|a HCDD
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| 245 |
0 |
0 |
|a Advanced bioscience and biosystems for detection and management of diabetes /
|c Kishor Kumar Sadasivuni, John-John Cabibihan, Abdulaziz Khalid A.M. Al-Ali, Rayaz A. Malik, editors.
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| 264 |
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1 |
|a Cham :
|b Springer,
|c 2022.
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| 300 |
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|a 1 online resource :
|b illustrations (black and white, and color).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Springer series on bio- and neurosystems ;
|v volume 13
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| 505 |
0 |
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|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.
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| 520 |
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|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.
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| 588 |
0 |
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|a Print version record.
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| 650 |
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0 |
|a Diabetes
|x Diagnosis.
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| 650 |
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0 |
|a Diabetes
|x Diagnosis
|x Technological innovations.
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| 650 |
|
7 |
|a Diabetes
|x Diagnosis
|2 fast
|
| 700 |
1 |
|
|a Sadasivuni, Kishor Kumar,
|d 1986-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjyDfWdyK6XBycBqPb9jQ3
|1 https://isni.org/isni/000000038466595X
|
| 700 |
1 |
|
|a Cabibihan, J.-J.
|q (John-John),
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjtQcJvW7DKDKdgxhJGVwd
|1 https://isni.org/isni/000000049789234X
|
| 700 |
1 |
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|a Al-Ali, Abdulaziz Khalid A. M.,
|e editor.
|
| 700 |
1 |
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|a Malik, Rayaz A.,
|e editor.
|1 https://isni.org/isni/0000000040465508
|
| 776 |
0 |
8 |
|i Print version:
|t Advanced bioscience and biosystems for detection and management of diabetes.
|d Cham : Springer, 2022
|z 9783030997274
|w (OCoLC)1314331996
|
| 830 |
|
0 |
|a Springer series on bio- and neurosystems ;
|v v. 13.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-99728-1
|y Click for online access
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| 903 |
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|a SPRING-ENGINE2022
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| 994 |
|
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|a 92
|b HCD
|