A novel system for the classification of zinc-plated components by benchmarking deep neural networks-Reference-Cited by-同舟云学术

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A novel system for the classification of zinc-plated components by benchmarking deep neural networks

Published:2024-12 Issue: Volume:255 Page:124866
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ramos-Acosta Edgar Rene^ORCID,García-Guerrero Enrique Efrén^ORCID,López-Bonilla Oscar Roberto^ORCID,Tamayo-Pérez Ulises Jesús^ORCID,Aguirre-Castro Oscar Adrián^ORCID,Ramírez-Rios Lidia Yolanda^ORCID,Inzunza-Gonzalez Everardo^ORCID

Funder

Universidad Autonoma de Baja California

Publisher

Elsevier BV

Reference55 articles.

1. Deep learning in industry 4.0: Transforming manufacturing through data-driven innovation;Agrawal;Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2024

2. Wasserstein gan;Arjovsky,2017

3. Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai;Arrieta;Information Fusion,2020

4. Automated defect classification of aluminium 5083 tig welding using hdr camera and neural networks;Bacioiu;Journal of Manufacturing Processes,2019

5. Machine learning for industrial applications: A comprehensive literature review;Bertolini;Expert Systems with Applications,2021

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