arXiv (All)
open access
A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management
Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an exploratory machine learning framework for estimating daily restaurant food waste quantities from operational and contextual features. A structured datase
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Methodological challenges in machine learning and deep learning applied to food analysis: A critical review#297023
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.#67444
- Dataset on the application of machine learning to assess the quality of food products. Case study: coffee bean#647367
- Machine Learning-Based Delivery Time Prediction for Low-Carbon Food Delivery#1062019
- Wine Quality Prediction using Machine Learning#111598
Record · ID 985470
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive