Browsing by Author "Watcharaporn Cholamjiak"

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    A double inertial embedded modified S-iteration algorithm for nonexpansive mappings: A classification approach for lung cancer detection
    (Elsevier B.V., 2025) Watcharaporn Yajai; Kunrada Kankam; Jen-Chih Yao; Watcharaporn Cholamjiak; W. Cholamjiak; Department of Mathematics, School of Science, University of Phayao, Phayao, 56000, Thailand; email: watcharaporn.ch@up.ac.th
    This paper introduces a double inertial embedded modified S-iteration algorithm for finding a common fixed point of nonexpansive mappings in a real Hilbert space. A weak convergence theorem is established under suitable conditions involving control parameters. Three algorithms are directly obtained for addressing split equilibrium problems through the equivalence of nonexpansive mappings. An illustrative example in an infinite-dimensional space is provided to substantiate the proposed main algorithm. Furthermore, we highlight the practical application of these algorithms in lung cancer screening, where they are employed to optimize three different machine learning models, thereby potentially improving patient outcomes. The efficiency of the proposed algorithms is validated through comparative analysis with existing algorithms. © 2025
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    An inertial projective forward-backward-forward algorithm for constrained convex minimization problems and application to cardiovascular disease prediction
    (International Scientific Research Publications, 2024) Prasit Cholamjiak; Watcharaporn Cholamjiak; Kunrada Kankam; P. Cholamjiak; School of Science, University of Phayao, Phayao, 56000, Thailand; email: prasit.ch@up.ac.th; W. Cholamjiak; School of Science, University of Phayao, Phayao, 56000, Thailand; email: watcharaporn.ch@up.ac.th; K. Kankam; Elementary Education Program, Faculty of Education, Suan Dusit University Lampang Center, Lampang, 52100, Thailand; email: kunradazzz@gmail.com
    In this paper, we introduce a novel machine learning algorithm designed for the classification of cardiovascular diseases. The proposed inertial projected forward-backward-forward algorithm is developed to address constrained minimization in Hilbert spaces, with a specific focus on improving the accuracy of disease classification. Utilizing inertial techniques, the algorithm employs a projected forward-backward-forward strategy, demonstrating convergence under mild conditions. Evaluation of the algorithm employs four essential performance metrics-accuracy, F1-score, recall, and precision to gauge its effectiveness compared to alternative classification models. Results indicate significant performance gains, achieving peak metrics of 77.50% accuracy, 71.57% precision, 91.27% recall, and 80.23% F1-score, thereby surpassing established benchmarks in machine learning models for cardiovascular disease classification. © 2025 All rights reserved.