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Vol.15, No.3, August 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Convolutional Neural Networks for the Binary Classification of Textile Materials
Cesar Luis Ramos Cabrera, Edward Alberto Vega Gavidia, Segundo Edwin Cieza Mostacero
© 2026 Cesar Luis Ramos Cabrera, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 3, Pages 2252-2260, ISSN 2217-8309, DOI: 10.18421/TEM153-19, August 2026.
Received: 12 August 2025.
Abstract:
This study aims to improve the binary classification of textile materials-specifically distinguishing between acceptable items and those with defects using convolutional neural networks (CNNs). A quantitative, applied approach with a pure experimental design was employed, incorporating the Mobile-D methodology. Data collection was conducted through direct observation, and two groups were compared: The experimental group (EG), used the mobile application, and the control group (CG), did not. Results, validated through the Mann–Whitney U test, showed that the EG outperformed the CG with a 35-second reduction in classification time, a 10% increase in accuracy, and a 17% improvement in the timely detection of non-conformities. In conclusion, the use of convolutional neural networks enhances the effectiveness and efficiency of binary classification in textile inspection processes.
Keywords – Convolutional neural networks, deep learning, binary classification, textile inspection. |
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