Browsing by Author "Aghazadeh, Nasser"

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Citation - Scopus: 1
A Finite Difference Approach To Solve the Nonlinear Model of Electro-Osmotic Flow in Nano-Channels
(University of Tabriz, 2025) Aghazadeh, Nasser; Rabbani, Kianoosh; Otaghsara, Seyyed Hemmatollah Taheri; Rabbani, Mohsen; 01. Izmir Institute of Technology; 04. Faculty of Science; 04.02. Department of Mathematics
This article considers a system of coupled equations constructed by the nonlinear model of electro-osmotic flow through a one-dimensional nano-channel. Functions that belong to this system include distributions of mole fraction of cation and anion, electrical potential, and velocity. We try to find an accurate closed-form solution. To this end, some mathematical approaches are concurrently used to convert the equations to a nonlinear differential equation in terms of the mole fraction of anion. The latter nonlinear differential equation is transformed into a nonlinear algebraic system by the finite difference method, and the system’s solution is obtained using Newton’s iterative algorithm. Furthermore, equations for the mole fraction of cation, electrical potential, and velocity in terms of the mole fraction of anion are obtained. We calculate errors by substituting the proposed solution into the equations to validate the results. Comparing the results with some other numerical research works demonstrates an acceptable accuracy. © 2025 Elsevier B.V., All rights reserved.
FW-S3KIFCM: Feature Weighted Safe-Semi Kernel-Based Intuitionistic Fuzzy C-Means Clustering Method
(Tsinghua Univ Press, 2025) Khezri, Shirin; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Oskouei, Amin Golzari
Semi-supervised clustering (SSC) methods have emerged as a notable research area in machine learning. These methods integrate prior knowledge of class distribution into their clustering process. Despite their efficiency and straightforwardness, SSCs encounter some fundamental issues. Generally, the proportion of unlabeled data surpasses that of labeled data. Consequently, handling the uncertainty of unlabeled data becomes difficult. This issue is frequently related to numerous real-world problems. On the other hand, existing SSC techniques fail to differentiate between the varied attributes within the feature space. When forming clusters, they presume uniform significance for all attributes, disregarding potential variations in feature importance. This presumption hinders the creation of optimal clusters. Furthermore, all existing approaches employ the Euclidean distance metric, susceptible to noise and outliers. This paper proposes a robust safe-semi-supervised clustering algorithm to mitigate these shortcomings. For the first time, this approach combines two concepts of Intuitionistic Fuzzy C-Means (IFCM) clustering and Safe-Semi-Supervised Fuzzy C-Means (S3FCM) clustering to address the uncertainty problem in unlabeled data. Also, it uses a kernel function as a distance metric to tackle noise and outliers. Additionally, incorporating a feature weighting parameter in the objective function highlights the importance of significant features in creating optimal clusters. The effectiveness of the proposed method is thoroughly evaluated on various benchmark datasets, and its performance is compared with state-of-the-art methods. The results show the superiority of the proposed method over its competitors.
A Robust Possibilistic Semi-Supervised Fuzzy Clustering Algorithm With Neighborhood-Aware Feature Weighting
(Springer Heidelberg, 2025) Moghaddam, Arezou Najafi; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Oskouei, Amin Golzari
The Semi-Supervised Fuzzy C-Means (SSFCM) method integrates class distribution information with fuzzy logic to overcome the challenges of semi-supervised clustering methods. While the inclusion of label information in the objective function improves the quality of the clustering method, semi-supervised fuzzy techniques still encounter important limitations, including (1) sensitivity to noise and outliers, (2) uniform feature importance, (3) neglecting the influences of neighborhood in the clustering process. In this paper, an improved semi-supervised clustering algorithm is presented to address these challenges. First, the algorithm reduces the sensitivity to noise and outliers by integrating the possibilistic fuzzy C-means algorithm into the SSFCM method. Second, a dynamic feature weighting method assigns different weights to the features in each cluster, which improves the performance of the algorithm in imbalanced datasets. Third, the proposed algorithm introduces a neighborhood mechanism that incorporates the neighbor's trade-off weighting and feature weighting strategy considering a strong metric. Finally, a robust kernel metric is used to further improve the performance on complex and nonlinear datasets. Extensive experiments are conducted on several benchmark datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms the current state-of-the-art techniques. The implementation source codes of the proposed method are publicly available at https://github.com/Amin-Golzari-Oskouei/RPSSFC-NAFW.