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Deep Learning for Classifying Food Waste
One third of food produced in the world for human consumption – approxim...
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Honey Bee Dance Modeling in Real-time using Machine Learning
The waggle dance that honeybees perform is an astonishing way of communi...
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Honey Authentication with Machine Learning Augmented Bright-Field Microscopy
Honey has been collected and used by humankind as both a food and medici...
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KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks
As a vast number of ingredients exist in the culinary world, there are c...
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Comparison of Self-monitoring Feedback Data from Electronic Food and Nutrition Tracking Tools
Changing dietary habits and keeping food diary encourages fewer calorie ...
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ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network
Food recognition has received more and more attention in the multimedia ...
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A decision support system for addressing food security in the UK
This paper presents an integrating decision support system to model food...
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The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics
The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally high as compared to the rest of the world. In fact, the prevalence of diabetes in the Middle East is 17-20 Research has shown that food intake has strong connections with the blood glucose levels of a patient. In this regard, there is a need to build automatic tools to monitor the blood glucose levels of diabetics and their daily food intake. This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning, respectively. Our system not only helps diabetics to track their daily food intake but also assists doctors to analyze the impact of the food in-take on blood glucose in real-time. For food recognition, we collected a large-scale Middle-Eastern food dataset and proposed a fusion-based framework incorporating several existing pre-trained deep models for Middle-Eastern food recognition.
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