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Food

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Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily because most machine learning models struggle with problems characterized ...

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

Sensors (Basel, Switzerland)
The work aims to leverage computer vision and artificial intelligence technologies to quantify key components in food distribution services. Specifically, it focuses on dish counting, content identification, and portion size estimation in a dining ha...

Development and validation of an AI-driven tool to evaluate chewing function: a proof of concept.

Journal of dentistry
BACKGROUND: Masticatory function is an important determinant of oral health and a contributing factor in the maintenance of general health. Currently, objective assessment of chewing function is a clinical challenge. Previously, several methods have ...

Improving fine-grained food classification using deep residual learning and selective state space models.

PloS one
BACKGROUND: Food classification is the foundation for developing food vision tasks and plays a key role in the burgeoning field of computational nutrition. Due to the complexity of food requiring fine-grained classification, the Convolutional Neural ...

An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.

Nutrients
BACKGROUND: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical fea...

Sensory-biased autoencoder enables prediction of texture perception from food rheology.

Food research international (Ottawa, Ont.)
Understanding how the physical properties of food affect sensory perception remains a critical challenge for food design. Here, we present an innovative machine learning strategy to decode the complex relationships between non-Newtonian rheological a...

AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.

Sensors (Basel, Switzerland)
Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven approaches, to address various challenges in the food sector. It encompasses a wide range of technol...

Reducing food waste in the HORECA sector using AI-based waste-tracking devices.

Waste management (New York, N.Y.)
This study assesses the effectiveness of an intervention employing an AI-based, fully automatic waste-tracking system for food waste reduction in HORECA establishments. Waste-tracking devices were installed in a restaurant within a holiday resort and...

Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene.

Scientific reports
The burden of diet-related diseases is high in Central Asia. In recent years, the field of food computing has gained prominence due to advancements in computer vision (CV) and the increasing use of smartphones and social media. These technologies pro...

LeFood-set: Baseline performance of predicting level of leftovers food dataset in a hospital using MT learning.

PloS one
Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' dietary intake. However, estimating food leftovers through visual observation is time-consuming and bi...