REPOSITORIO INSTITUCIONAL DA UFOP
metadata only
CC BY-NC-SA 3.0
Towards explainable and reliable ferrous scrap classification with deep learning.
Programa de Pós-Graduação em Instrumentação, Controle e Automação de Processos de Mineração. Departamento de Engenharia de Controle e Automação, Escola de Minas, Universidade Federal de Ouro Preto.
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
- Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset#972897
- MWaste: A Deep Learning Approach to Manage Household Waste#972842
- Automated quality control of recycled aggregates via deep learning: A unified framework for instance segmentation and mass estimation#213118
- Deep learning-driven intelligent mesoscopic model (DeepMeso): a case study on ferroelectrics.#362143
- Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0#1037266
Record · ID 1368230
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