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Titlebook: Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practic; IBCFHA Rajeev Tiwari,Deepika Koundal,

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书目名称Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practic
副标题IBCFHA
编辑Rajeev Tiwari,Deepika Koundal,Shuchi Upadhyay
视频video
概述Provides a comprehensive understanding of computer vision and intelligence methodologies.Focuses on the underlying idea of employing sustainable 4 IR technologies.Allows readers to understand healthca
图书封面Titlebook: Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practic; IBCFHA Rajeev Tiwari,Deepika Koundal,
描述.Increase in consumer awareness of nutritional habits has placed automatic food analysis in the spotlight in recent years. However, food-logging is cumbersome and requires sufficient knowledge of the food item consumed. Additionally, keeping track of every meal can become a tedious task. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. Food understanding from digital media has become a challenge with important applications in many different domains. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information effectively and efficiently remains a challenging and open research problem. The provided recommendations could be based on calorie counting, healthy food and specific nutritional composition. In addition, if we also consider a system able to log the food consumed by every individual along time, it could provide health-related recommendations in the long-term...Computer Visi
出版日期Book 2023
关键词Food Computing; Nutritional Analysis; Image Processing; Ai and ML; Industry 4; 0 frameworks
版次1
doihttps://doi.org/10.1007/978-3-031-22959-6
isbn_softcover978-3-031-22961-9
isbn_ebook978-3-031-22959-6
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Efficient BREV Ensemble Framework: A Case Study of Breast Cancer Prediction,malignant breast cancer after blood and adipose tissue samples are obtained. The concentrations of chemicals in females with breast cancer malignancy is contrasted with benign patients and a prediction framework is developed applying an efficacious framework of ensemble machine learning. The finding
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Sentiment Analysis of COVID-19 Tweets Using Voting Ensemble-Based Model,ond one is a machine learning classifier. TFIDF vectorizer and classifier model are used in the third stage for making a single classifier model. In the fourth stage, we have applied a hard voting Ensemble classifier technique for the overall average accuracy of machine learning models.
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Interoperable Cloud-Fog Architecture in IoT-Enabled Health Sector,composition; hence the result can form the maximum use of dispersed assets beyond jeopardizing security, &service measures (H. Yan, L. D. Xu, Z. Bi, Z. Pang, J. Zhang and Y. Chen, Journal of Management Analytics 2:121-137, 2015). Here, a Fog-based Internet of Things-Healthcare is presented for suita
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COVID-19 Wireless Self-Assessment Software for Rural Areas in Nigeria,y the public. Therefore, it is critical to develop a self-assessment tool that not only allows users to assess whether they are at risk and informs them of the steps they need to take to protect their own safety and the safety of others, but that is also easily accessible and understandable to the g
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