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Two-Step Inertial Algorithms for Nonexpansive Mappings: An Application to Osteoporosis

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  • Watcharapon Yajai
  • Wongthawat Liawrungrueang
  • Pronpat Peeyada
  • Watcharaporn Cholamjiak

Abstract

This paper introduces a novel approach incorporating a double inertial technique with Mann’s algorithm to find common fixed points of two nonexpansive mappings. We rigorously prove the weak convergence of this algorithm under suitable conditions. The applications section explores the connection among nonexpansive mappings, split equilibrium problems, and metric projection. We make use of these concepts to devise three algorithms tailored for extreme machine learning applications. Utilizing the osteoporosis dataset from Kaggle for data classification, our algorithms demonstrate impressive performance metrics: an accuracy of 88.66%, precision of 92.13%, recall of 84.54%, and an F1-score of 88.17%. These results show the effectiveness of our proposed methods in handling complex classification tasks.

Suggested Citation

  • Watcharapon Yajai & Wongthawat Liawrungrueang & Pronpat Peeyada & Watcharaporn Cholamjiak, 2025. "Two-Step Inertial Algorithms for Nonexpansive Mappings: An Application to Osteoporosis," Journal of Mathematics, Hindawi, vol. 2025, pages 1-13, October.
    • Handle: RePEc:hin:jjmath:6867595
    DOI: 10.1155/jom/6867595
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