Phumin Sumalai

In machine learning, feature selection is crucial for reducing computing costs, increasing generalization, reducing dimensionality, and improving model interpretability. Due to multicollinearity and redundancy, traditional approaches often encounter difficulties when dealing with high-dimensional data. We propose a hybrid framework called Fuzzy Feature Selection using Fuzzy C-Means Clustering and Recursive Feature Elimination (FCM-RFE), which combines fuzzy logic, filter, and wrapper approaches, to address these problems. In order to capture complex relationships, fuzzy C-Means clustering first partitions related features into soft clusters. Then, within each cluster, less significant features are repeatedly eliminated using Recursive Feature Elimination with Random Forest (RFE-RF). For more precise selection, features are ranked according to the strength of their cluster link using a fuzzy membership-based scoring system. Experiments on 18 benchmark datasets using KNN and SVM classifiers evaluated metrics including accuracy, precision, recall, F1-score, specificity, and AUC-ROC. The proposed approach maintained or enhanced performance while significantly decreasing dimensionality, selecting, on average, only 4.1% of the original features. The maximum accuracy was 92.75% for SVM with FCM-RFE and 89% for KNN. The proposed method demonstrated effectiveness and scalability for high-dimensional data analysis, outperforming eight state-of-the-art techniques and demonstrating computing efficiency. This framework is suitable for high-dimensional data analysis in various disciplines because it not only increases classification performance but also improves interpretability and scalability.

The aim of this paper is to prove the existence and stability of a solution to the time fractional parameters of Atangana–Baleanu fractional ordered 2019‐nCoV model. For the existence of the solution, we derive Darbo‐type fixed‐point theorems and its corollaries for ‐admissible and ‐condensing operator in the setting of modular space. Moreover, the stability analysis and behavior of solution are analyzed by the numerical technique governed by Toufik and Atangana [1].

In this article, we propose an iterative scheme for solving a generalized split common null point problem. The iterative method uses self‐adaptive stepsizes that do not depend on the Lipschitz constants of the underlying operators. Instead, the stepsizes are updated using a simple updating formula. We prove the strong convergence theorems of the sequence generated by the iterative scheme under some mild assumptions. Furthermore, we derive iterative methods for solving split generalized variational inequality, convex feasibility, and minimization problems. In addition, some numerical illustrations are provided to support the main result.

The aim of this manuscript is to improve certain results established in a recent paper of Imdad et al on the existence of a common fixed point for contractive mappings in fuzzy metric spaces. Further, we generalize and extend some common fixed point theorems due to Ali and Imdad and some fixed point theorems due to Mihet to the case of fuzzy metric‐like spaces. We also furnish an illustrative example to demonstrate the validity of the hypotheses and degree of generality of our main result.