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Domain Heuristic Fusion of Multi-Word Embeddings for Nutrient Value Prediction

https://doi.org/10.3390/math9161941

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/jfca.1997.0527openalex-snapshot

Procedures for Estimating Nutrient Values for Food Composition Databases

inferredcites →https://doi.org/10.1007/978-3-030-49795-8_10openalex-snapshot

Analysis and Prediction of Soil Nutrients pH,N,P,K for Crop Using Machine Learning Classifier: A Review

inferredcites →https://doi.org/10.1007/978-981-16-1048-6_43openalex-snapshot

Predicting and Estimating the Major Nutrients of Soil Using Machine Learning Techniques

inferredcites →https://doi.org/10.1016/0169-7439(87)80084-9openalex-snapshot

Principal component analysis

inferredcites →https://doi.org/10.1016/j.foodchem.2017.03.103openalex-snapshot

EuroFIR Guideline on calculation of nutrient content of foods for food business operators

inferredcites →https://doi.org/10.1038/s41746-021-00455-yopenalex-snapshot

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

inferredcites →https://doi.org/10.1109/access.2018.2879117openalex-snapshot

A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

inferredcites →https://doi.org/10.1109/access.2020.2973625openalex-snapshot

DeepFood: Food Image Analysis and Dietary Assessment via Deep Model

inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378151openalex-snapshot

BuTTER: BidirecTional LSTM for Food Named-Entity Recognition

inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378159openalex-snapshot

Exploring Knowledge Domain Bias on a Prediction Task for Food and Nutrition Data

inferredcites →https://doi.org/10.1109/tim.2014.2303533openalex-snapshot

Measuring Calorie and Nutrition From Food Image

inferredcites →https://doi.org/10.1109/tpami.2019.2927476openalex-snapshot

Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

inferredcites →https://doi.org/10.2196/14850openalex-snapshot

Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

inferredcites →https://doi.org/10.2196/19848openalex-snapshot

Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot

Glove: Global Vectors for Word Representation

inferredcites →https://doi.org/10.3390/math8101811openalex-snapshot

P-NUT: Predicting NUTrient Content from Short Text Descriptions

inferredcites →https://doi.org/10.3390/s20102809openalex-snapshot

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1310.4546openalex-snapshot

Distributed Representations of Words and Phrases and their Compositionality

inferredcites →https://doi.org/10.48550/arxiv.1405.4053openalex-snapshot

Distributed Representations of Sentences and Documents

inferredcites →https://doi.org/10.5220/0006504302030210openalex-snapshot

Mapping Food Composition Data from Various Data Sources to a Domain-Specific Ontology

inferredcites →https://doi.org/10.5220/0007686309150922openalex-snapshot

FoodIE: A Rule-based Named-entity Recognition Method for Food Information Extraction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s00530-025-02151-3openalex-snapshot

A multimodal deep learning framework for nutritional estimation and health-oriented recipe analysis

inferred← citeshttps://doi.org/10.1007/s44187-024-00253-xopenalex-snapshot

Leveraging machine learning techniques to analyze nutritional content in processed foods

inferred← citeshttps://doi.org/10.1016/b978-0-443-40344-6.00116-2openalex-snapshot

Artificial Intelligence in Food, Nutrition, and Health

inferred← citeshttps://doi.org/10.1016/j.crfs.2023.100500openalex-snapshot

Machine learning models to predict micronutrient profile in food after processing

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121507openalex-snapshot

MsGEN: Measuring generalization of nutrient value prediction across different recipe datasets

inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020698openalex-snapshot

Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets

inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11401478openalex-snapshot

Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation

inferred← citeshttps://doi.org/10.1109/iccpct65132.2025.11176605openalex-snapshot

Ingredient-Based Nutrition Label Generator: An Automated Approach for Food Transparency

inferred← citeshttps://doi.org/10.31276/vjst.66(6).01-08openalex-snapshot

Using deep learning model to predict micronutrients in food after processing

inferred← citeshttps://doi.org/10.3389/fnut.2024.1503389openalex-snapshot

NutriBase – management system for the integration and interoperability of food- and nutrition-related data and knowledge

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