Selected work

Representative Papers

LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning

Jul 05, 2026

This study addresses the limitations of conventional manual white blood cell (WBC) counting and classification, which suffer from low efficiency and susceptibility to human error, thereby compromising diagnostic accuracy. To overcome these challenges, this work proposes an automated deep learning–based approach that innovatively integrates YOLOv5 for object detection, a fine-tuned MobileNetV2 for feature extraction, and a logistic regression classifier to achieve precise WBC detection, enumeration, and four-class classification. Experimental evaluation on the BCCD dataset demonstrates that the proposed method attains a WBC detection accuracy of 98%, a classification accuracy of 99.04%, and an F1 score of 99.73% for red blood cell detection, significantly outperforming existing baseline methods.

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HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English

Dec 03, 2025

This paper addresses the end-to-end automatic translation of ancient Egyptian hieroglyphic images into English—a challenging task due to polysemy (one glyph, multiple meanings) and complex spatial layouts. We propose a three-stage framework: (1) robust glyph segmentation leveraging contour detection and Detectron2; (2) standardized semantic mapping using Gardiner codes as an intermediate linguistic representation; and (3) CNN-based sequence-to-sequence translation. Crucially, we embed symbol-level structural priors—namely, the Gardiner classification system—into the translation pipeline to mitigate semantic ambiguity. The method is trained and evaluated on the Morris Franken and EgyptianTranslation datasets, achieving a BLEU score of 42.2—substantially outperforming prior approaches. To our knowledge, this is the first work to achieve both high-accuracy and interpretable direct image-to-text translation of hieroglyphs, enabling faithful, linguistically grounded reconstructions without manual transcription intermediaries.

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High-Dimensional BWDM: A Robust Nonparametric Clustering Validation Index for Large-Scale Data

Oct 15, 2025

Determining the optimal number of clusters in high-dimensional, noisy data remains a fundamental challenge in unsupervised learning. To address this, we propose HD-BWDM, a robust nonparametric evaluation framework. HD-BWDM generalizes the Between-Within Distance Metric (BWDM) criterion to high-dimensional spaces by integrating random projection and PCA-based dimensionality reduction to mitigate the curse of dimensionality, and incorporates cluster trimming and center-point distance metrics to enhance robustness against outliers. Theoretically, it provides embedding consistency guarantees grounded in the Johnson–Lindenstrauss lemma. Extensive experiments on multiple high-dimensional and contaminated benchmark datasets demonstrate that HD-BWDM significantly outperforms conventional centroid-based indices—including Calinski–Harabasz and Silhouette—achieving superior stability, interpretability, and reliability. As a theoretically justified, efficient, and robust stopping criterion, HD-BWDM advances practical unsupervised clustering in challenging real-world scenarios.

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Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review

May 22, 2025

This study addresses the limited integration of generative artificial intelligence (GAI) and the metaverse in the fashion industry. Following the PRISMA framework, we systematically reviewed 118 peer-reviewed publications from 2014 to 2023, identifying four key application domains: design, manufacturing, sales, and user experience. We propose the first integrated GAI–metaverse framework for fashion, featuring multimodal content generation, virtual try-on, digital twin–enabled production, and immersive retail. A SWOT analysis distills core opportunities—including accelerated personalization and reduced carbon footprint—and challenges—such as data privacy concerns and computational bottlenecks. Empirical validation confirms the feasibility of cross-technology synergy in driving end-to-end industry transformation. We further articulate a phased implementation roadmap and a scalable collaborative application paradigm. This work provides both theoretical foundations and actionable guidelines for the intelligent transformation of the global fashion industry.

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Recent publications

Latest Papers

LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning

Jul 05, 2026

This study addresses the limitations of conventional manual white blood cell (WBC) counting and classification, which suffer from low efficiency and susceptibility to human error, thereby compromising diagnostic accuracy. To overcome these challenges, this work proposes an automated deep learning–based approach that innovatively integrates YOLOv5 for object detection, a fine-tuned MobileNetV2 for feature extraction, and a logistic regression classifier to achieve precise WBC detection, enumeration, and four-class classification. Experimental evaluation on the BCCD dataset demonstrates that the proposed method attains a WBC detection accuracy of 98%, a classification accuracy of 99.04%, and an F1 score of 99.73% for red blood cell detection, significantly outperforming existing baseline methods.

0 citationsRead paper

HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English

Dec 03, 2025

This paper addresses the end-to-end automatic translation of ancient Egyptian hieroglyphic images into English—a challenging task due to polysemy (one glyph, multiple meanings) and complex spatial layouts. We propose a three-stage framework: (1) robust glyph segmentation leveraging contour detection and Detectron2; (2) standardized semantic mapping using Gardiner codes as an intermediate linguistic representation; and (3) CNN-based sequence-to-sequence translation. Crucially, we embed symbol-level structural priors—namely, the Gardiner classification system—into the translation pipeline to mitigate semantic ambiguity. The method is trained and evaluated on the Morris Franken and EgyptianTranslation datasets, achieving a BLEU score of 42.2—substantially outperforming prior approaches. To our knowledge, this is the first work to achieve both high-accuracy and interpretable direct image-to-text translation of hieroglyphs, enabling faithful, linguistically grounded reconstructions without manual transcription intermediaries.

0 citationsRead paper

High-Dimensional BWDM: A Robust Nonparametric Clustering Validation Index for Large-Scale Data

Oct 15, 2025

Determining the optimal number of clusters in high-dimensional, noisy data remains a fundamental challenge in unsupervised learning. To address this, we propose HD-BWDM, a robust nonparametric evaluation framework. HD-BWDM generalizes the Between-Within Distance Metric (BWDM) criterion to high-dimensional spaces by integrating random projection and PCA-based dimensionality reduction to mitigate the curse of dimensionality, and incorporates cluster trimming and center-point distance metrics to enhance robustness against outliers. Theoretically, it provides embedding consistency guarantees grounded in the Johnson–Lindenstrauss lemma. Extensive experiments on multiple high-dimensional and contaminated benchmark datasets demonstrate that HD-BWDM significantly outperforms conventional centroid-based indices—including Calinski–Harabasz and Silhouette—achieving superior stability, interpretability, and reliability. As a theoretically justified, efficient, and robust stopping criterion, HD-BWDM advances practical unsupervised clustering in challenging real-world scenarios.

0 citationsRead paper

Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review

May 22, 2025

This study addresses the limited integration of generative artificial intelligence (GAI) and the metaverse in the fashion industry. Following the PRISMA framework, we systematically reviewed 118 peer-reviewed publications from 2014 to 2023, identifying four key application domains: design, manufacturing, sales, and user experience. We propose the first integrated GAI–metaverse framework for fashion, featuring multimodal content generation, virtual try-on, digital twin–enabled production, and immersive retail. A SWOT analysis distills core opportunities—including accelerated personalization and reduced carbon footprint—and challenges—such as data privacy concerns and computational bottlenecks. Empirical validation confirms the feasibility of cross-technology synergy in driving end-to-end industry transformation. We further articulate a phased implementation roadmap and a scalable collaborative application paradigm. This work provides both theoretical foundations and actionable guidelines for the intelligent transformation of the global fashion industry.

0 citationsRead paper