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Image based computing for food and health analyticsrequirements, challenges, solutions and practices : IBCFHA /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Image based computing for food and health analyticsedited by Rajeev Tiwari, Deepika Koundal, Shuchi Upadhyay.
Reminder of title:	requirements, challenges, solutions and practices : IBCFHA /
other author:	Tiwari, Rajeev.
Published:	Cham :Springer International Publishing :2023.
Description:	viii, 246 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Medical informatics.
Online resource:	https://doi.org/10.1007/978-3-031-22959-6
ISBN:	9783031229596$q(electronic bk.)

Image based computing for food and health analyticsrequirements, challenges, solutions and practices : IBCFHA /
Image based computing for food and health analyticsrequirements, challenges, solutions and practices : IBCFHA /[electronic resource] :edited by Rajeev Tiwari, Deepika Koundal, Shuchi Upadhyay. - Cham :Springer International Publishing :2023. - viii, 246 p. :ill. (chiefly col.), digital ;24 cm.

1. Food Computing Research opportunities using AI and M -- Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering -- Digital Twins for Food Nutrition and Health Based on Cloud Communication -- Smart Healthcare Systems: An IoT with Fog Computing based Solution for Healthcare,- An Intelligent and Secure Real-time Environment Monitoring System for healthcare using IoT and Cloud Computing with the Mobile Application Support -- Efficient BREV Ensemble Framework: A Case Study of Breast Cancer Prediction,- Current and Future Trends of Cloud-based solutions for Healthcare,- Secure Authentication in IoT based healthcare management environment using integrated Fog computing enabled blockchain system -- SENTIMENT ANALYSIS OF COVID-19 TWEETS USING VOTING ENSEMBLE-BASED MODEL -- Cloud and machine learning based solutions for healthcare and preventio -- Interoperable Cloud-Fog architecture in IoT-enabled Health Sector -- COVID-19 Wireless Self-Assessment Software for Rural Areas in Nigeria -- Efficient Fog-to-Cloud Internet-of-Medical-Things System.

Image Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing based solutions to enhance and improve the accuracy of current measurements of dietary intake. Many solutions are presented to improve the accuracy of assessment by analyzing health images, data and food industry based images captured by mobile devices. Key technique innovations based on Artificial Intelligence and deep learning-based food image recognition algorithms are also discussed.

ISBN: 9783031229596$q(electronic bk.)

Standard No.: 10.1007/978-3-031-22959-6doiSubjects--Topical Terms:

196487
Medical informatics.

LC Class. No.: R858

Dewey Class. No.: 610.285

Image based computing for food and health analyticsrequirements, challenges, solutions and practices : IBCFHA /
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505 0 $a 1. Food Computing Research opportunities using AI and M -- Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering -- Digital Twins for Food Nutrition and Health Based on Cloud Communication -- Smart Healthcare Systems: An IoT with Fog Computing based Solution for Healthcare,- An Intelligent and Secure Real-time Environment Monitoring System for healthcare using IoT and Cloud Computing with the Mobile Application Support -- Efficient BREV Ensemble Framework: A Case Study of Breast Cancer Prediction,- Current and Future Trends of Cloud-based solutions for Healthcare,- Secure Authentication in IoT based healthcare management environment using integrated Fog computing enabled blockchain system -- SENTIMENT ANALYSIS OF COVID-19 TWEETS USING VOTING ENSEMBLE-BASED MODEL -- Cloud and machine learning based solutions for healthcare and preventio -- Interoperable Cloud-Fog architecture in IoT-enabled Health Sector -- COVID-19 Wireless Self-Assessment Software for Rural Areas in Nigeria -- Efficient Fog-to-Cloud Internet-of-Medical-Things System.
520 $a Image Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing based solutions to enhance and improve the accuracy of current measurements of dietary intake. Many solutions are presented to improve the accuracy of assessment by analyzing health images, data and food industry based images captured by mobile devices. Key technique innovations based on Artificial Intelligence and deep learning-based food image recognition algorithms are also discussed.
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