A double inertial embedded modified S-iteration algorithm for nonexpansive mappings: A classification approach for lung cancer detection
Date
2025
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Article
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Elsevier B.V.
Journal Title
A double inertial embedded modified S-iteration algorithm for nonexpansive mappings: A classification approach for lung cancer detection
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Abstract
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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Communications in Nonlinear Science and Numerical Simulation