International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 11, Issue 6 (June 2024), Pages: 229-236

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 Original Research Paper

Utilizing convolutional neural networks for the classification and preservation of Kalinga textile patterns

 Author(s): 

 Hancy D. Campos 1, 2, *, Meo Vincent Caya 1

 Affiliation(s):

 1School of Electrical, Electronics, and Computer Engineering, Mapua University, Manila, Philippines
 2Department of Computer Engineering, Kalinga State University, Bulanao, Tabuk City, Kalinga, Philippines

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0009-4094-2231

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.06.024

 Abstract

This study introduces a system that utilizes Convolutional Neural Networks (CNN) to categorize Kalinga textiles in a structured manner. The main objective is to systematically identify and name the patterns found in these textiles. The research uses a dataset that includes ten different categories of Kalinga textiles. Metrics such as accuracy, precision, recall, and F1 Score are used to assess the performance of the system. The outcomes demonstrate high precision values between 0.8 and 1.00, showcasing the model's proficiency in precisely classifying and labeling the patterns of Kalinga textiles. Similarly, the recall values, which vary from 0.75 to 1.00 for each category, underscore the model's effectiveness in categorization. These results highlight the system's capability to recognize and categorize Kalinga textiles, with recall values providing strong evidence of its reliability. F1 scores, which consider both precision and recall, range from 0.86 to 0.97 across the categories, indicating the model's accuracy in classification. The introduced technique for image identification shows promise for identifying and categorizing Kalinga textiles, thereby contributing to the preservation and promotion of this cultural heritage. It offers a valuable tool for researchers, enthusiasts, and cultural institutions. Future research could focus on expanding the dataset to improve the model's robustness and exploring its application to other areas of textiles. Continuous enhancements to the model, based on user feedback and technological advancements, will ensure its ongoing effectiveness and relevance.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Convolutional neural networks, Kalinga textiles, Image processing, Raspberry Pi, Image classification

 Article history

 Received 13 November 2023, Received in revised form 1 April 2024, Accepted 12 June 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Campos HD and Caya MV (2024). Utilizing convolutional neural networks for the classification and preservation of Kalinga textile patterns. International Journal of Advanced and Applied Sciences, 11(6): 229-236

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1 Table 2 

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