Optimizing feature extraction in ai-based cocoon classification: a hybrid approach for enhanced silk quality

Sharifbayev, Raximjon

Наманган муҳандислик­технология институти илмий­техника журнали · 2025-yil

Annotatsiya

Abstract. Silk production is a crucial industry that relies heavily on the classification of silk cocoons to ensure the highest quality output. Traditional classification methods are labor-intensive, inconsistent, and subject to human error. Artificial Intelligence (AI)-based systems, particularly those using deep learning, have significantly improved classification accuracy. However, optimizing feature extraction techniques remains a challenge. This paper explores a hybrid approach that combines deep learning with traditional machine learning feature extraction methods to enhance classification accuracy and silk quality assessment [1][2]. This study examines various feature extraction techniques, including texture, shape, and color analysis, alongside CNN-based automatic feature selection. By integrating handcrafted features with AI-driven feature learning, we propose a robust classification system that improves efficiency and accuracy. The proposed approach is validated using real-world datasets, and its implications for large-scale silk production are discussed [3].

Maqola ma’lumotlari
MualliflarSharifbayev, Raximjon
JurnalНаманган муҳандислик­технология институти илмий­техника журнали
NashriyotНаманган муҳандислик-технология институти
ISSN2181-8622, 1682-8658
Nashr sanasi2025-03-31
Jild10
Son1
Betlar20-23
TilIngliz
DOI10.61151/stjniet.v10i1.700
Indeksga qo‘shilgan2026-08-26

DOI: 10.61151/stjniet.v10i1.700 · Maqolaning asl sahifasi

Iqtibos keltirish

APASharifbayev, Raximjon (2025). OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY. Наманган муҳандислик­технология институти илмий­техника журнали, 10(1), 20-23. https://doi.org/10.61151/stjniet.v10i1.700
GOST (OAK)Sharifbayev, Raximjon OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY // Наманган муҳандислик­технология институти илмий­техника журнали. – 2025. – T. 10, № 1. – P. 20-23. – DOI: 10.61151/stjniet.v10i1.700.
MLASharifbayev, Raximjon. “OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY.” Наманган муҳандислик­технология институти илмий­техника журнали, 10(1), 20-23, 2025. https://doi.org/10.61151/stjniet.v10i1.700
ChicagoSharifbayev, Raximjon. “OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY.” Наманган муҳандислик­технология институти илмий­техника журнали, 10(1), 20-23 (2025). https://doi.org/10.61151/stjniet.v10i1.700
HarvardSharifbayev, Raximjon (2025) ‘OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY’, Наманган муҳандислик­технология институти илмий­техника журнали, 10(1), 20-23. https://doi.org/10.61151/stjniet.v10i1.700
BibTeX
@article{sharifbayevraximjon2025,
  title = {OPTIMIZING FEATURE EXTRACTION IN AI-BASED COCOON CLASSIFICATION: A HYBRID APPROACH FOR ENHANCED SILK QUALITY},
  author = {Sharifbayev, Raximjon},
  journal = {Наманган муҳандислик­технология институти илмий­техника журнали},
  year = {2025},
  volume = {10},
  number = {1},
  pages = {20-23},
  doi = {10.61151/stjniet.v10i1.700},
  url = {https://doi.org/10.61151/stjniet.v10i1.700}
}

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