Selected work

Representative Papers

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

Oct 01, 2026

This study addresses the issues of information leakage and optimistic bias in performance evaluation arising from improper data splitting during machine learning model validation. Focusing on biomedical contexts, it systematically reviews validation strategies and contrasts flawed versus leakage-free designs through eight controlled experiments. Employing techniques such as nested grouped cross-validation for reproducible simulations, this work proposes deployment-oriented, scenario-specific validation guidelines. Its primary contributions include practical tools—namely decision trees, checklists, and code templates—that operationalize the core principle of aligning independent units with deployment objectives within an auditable evaluation framework. Ultimately, these contributions significantly enhance the reliability and standardization of machine learning model validation practices.

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Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Models

Aug 10, 2026

This study addresses the limitation in existing sign language recognition research, which predominantly relies on RGB images and lacks access to real depth data, thereby hindering the application of point cloud–based methods. For the first time, it systematically compares the performance of point clouds derived from real versus synthetic depth maps for sign language recognition. Specifically, synthetic depth maps are generated from RGB images using Depth Anything V2, and point clouds are constructed from both synthetic and real depth data. These point clouds are then evaluated using representative models including PointNet variants, LSTM, and Point Gesture Map. Experimental results demonstrate that point clouds from synthetic depth consistently achieve recognition accuracy on par with or even surpassing those from real depth across multiple architectures, confirming their feasibility and practical utility as a viable alternative in scenarios where genuine depth sensors are unavailable.

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A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

May 01, 2025

This paper addresses the problem of finding zeros of the sum of a co-coercive operator and a maximally monotone operator in real Hilbert spaces—a formulation that unifies various regression and classification tasks. To this end, we propose a novel doubly inertial forward–backward splitting algorithm, the first to incorporate two independent, tunable inertia parameters. Crucially, this design accelerates convergence and enhances numerical stability without incurring additional computational cost. Under standard assumptions of monotonicity and co-coercivity, we establish rigorous weak convergence of the generated iterates. Our theoretical analysis integrates tools from operator splitting, inertial acceleration, and monotone operator theory. Extensive experiments on benchmark regression and classification tasks demonstrate that the proposed method achieves faster convergence and higher accuracy than classical and recent forward–backward-type algorithms, delivering consistent state-of-the-art performance.

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Recent publications

Latest Papers

Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

Oct 01, 2026

This study addresses the issues of information leakage and optimistic bias in performance evaluation arising from improper data splitting during machine learning model validation. Focusing on biomedical contexts, it systematically reviews validation strategies and contrasts flawed versus leakage-free designs through eight controlled experiments. Employing techniques such as nested grouped cross-validation for reproducible simulations, this work proposes deployment-oriented, scenario-specific validation guidelines. Its primary contributions include practical tools—namely decision trees, checklists, and code templates—that operationalize the core principle of aligning independent units with deployment objectives within an auditable evaluation framework. Ultimately, these contributions significantly enhance the reliability and standardization of machine learning model validation practices.

0 citationsRead paper

Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Models

Aug 10, 2026

This study addresses the limitation in existing sign language recognition research, which predominantly relies on RGB images and lacks access to real depth data, thereby hindering the application of point cloud–based methods. For the first time, it systematically compares the performance of point clouds derived from real versus synthetic depth maps for sign language recognition. Specifically, synthetic depth maps are generated from RGB images using Depth Anything V2, and point clouds are constructed from both synthetic and real depth data. These point clouds are then evaluated using representative models including PointNet variants, LSTM, and Point Gesture Map. Experimental results demonstrate that point clouds from synthetic depth consistently achieve recognition accuracy on par with or even surpassing those from real depth across multiple architectures, confirming their feasibility and practical utility as a viable alternative in scenarios where genuine depth sensors are unavailable.

0 citationsRead paper

A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems

May 01, 2025

This paper addresses the problem of finding zeros of the sum of a co-coercive operator and a maximally monotone operator in real Hilbert spaces—a formulation that unifies various regression and classification tasks. To this end, we propose a novel doubly inertial forward–backward splitting algorithm, the first to incorporate two independent, tunable inertia parameters. Crucially, this design accelerates convergence and enhances numerical stability without incurring additional computational cost. Under standard assumptions of monotonicity and co-coercivity, we establish rigorous weak convergence of the generated iterates. Our theoretical analysis integrates tools from operator splitting, inertial acceleration, and monotone operator theory. Extensive experiments on benchmark regression and classification tasks demonstrate that the proposed method achieves faster convergence and higher accuracy than classical and recent forward–backward-type algorithms, delivering consistent state-of-the-art performance.

0 citationsRead paper