Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article FW-S3PFCM: Feature-Weighted Safe-Semi Possibilistic Fuzzy C-Means Clustering(Springer, 2026) Khezri, Shirin; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Golzari Oskouei, AminThe safe semi-supervised fuzzy c-means clustering (S3FCM) method is a well-known clustering method that can produce successful results by incorporating prior knowledge of the class distribution. Its process is fast and simple but still has two limitations. The first issue is that it gives equal weight to all data features, while in real-world applications, different features usually have different importance. Secondly, S3FCM is very sensitive to noise and outliers. This paper proposes an extension of the S3FCM, entitled FW-S3PFCM, to mitigate these shortcomings. The proposed method uses a local feature weighting scheme to consider the different feature weights in the clustering process. Additionally, a possibilistic version of the S3FCM is designed to reduce the sensitivity to noise and outliers. The effectiveness of the proposed method is comprehensively evaluated on various benchmark datasets, and its performance is compared with the state-of-the-arts methods. To practically asses the FW-S3FCM, a real-world dataset of brain MRI images and its segmentation performance are analyzed as well. The average Accuracy, F1-score, Sensitivity, and Precision measures obtained by FW-S3FCM are 0.9682, 0.9826, 0.9743, and 0.9925, respectively, which are better than the competitors' performance.Article Determining Area Affected by Corona in Lung Computed Tomography Images by Three-Phase Level Set and Shearlet Transform(Wolters Kluwer Medknow Publications, 2025) Aghazadeh, Nasser; Noras, Parisa; Moghaddasighamchi, SevdaBackground:The COVID-19 pandemic has created a critical global situation, causing widespread challenges and numerous fatalities due to severe respiratory complications. Since lung involvement is a key factor in COVID-19 diagnosis and treatment, accurate identification of infected regions in lung images is essential.Methods:We propose a multiphase segmentation method based on the level set framework to determine lunginvolved areas. The shearlet transform, a high-precision directional multiresolution transform, is employed to guide the gradient flow in the level set formulation. Additionally, the phase stretch transform (PST) is applied to enhance the contrast between infected and healthy regions, improving convergence speed during segmentation.Results:The proposed algorithm was tested on 500 lung images. The method accurately identified infected areas, enabling precise calculation of the percentage of lung involvement. The use of the shearlet transform also allowed clear delineation of ground-glass opacity boundaries.Conclusion:The proposed multiphase level set method, enhanced with shearlet and phase stretch transforms, effectively segments COVID-19-infected lung regions. This approach improves segmentation accuracy and computational efficiency, offering a reliable tool for quantitative lung involvement assessment.Article A Chebyshev Wavelet Approach to the Generalized Time-Fractional Burgers-Fisher Equation(Univ Tabriz, 2025) Aghazadeh, NasserThis work proposes a new method for obtaining the approximate solution of the time-fractional generalized BurgersFisher equation. The method's main idea is based on converting the nonlinear partial differential equation to a linear partial differential equation using the Picard iteration method. Then, the second kind Chebyshev wavelet collocation method is used to solve the linear equation obtained in the previous step. The technique is called the Chebyshev Wavelet Picard Method (CWPM). The proposed method successfully solves the time fractional generalized Burgers-Fisher equation. The obtained numerical results are compared with the exact solutions and with the solutions obtained using the Haar wavelet Picard method and the homotopy perturbation method.Article 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 GolzariThe 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.Article 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 GolzariSemi-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.Article An Efficient Chebyshev Wavelet Collocation Technique for the Time-Fractional Camassa-Holm Equation(World Scientific Publ Co Pte Ltd, 2025) Aghazadeh, NasserBy employing the third-order Chebyshev collocation technique along with relevant wavelets, we tackle a third-order singular fractional partial differential equation (PDE). We directly build the Chebyshev operation matrix of the third kind, avoiding the use of the block-pulse function or any approximations. To reduce the order of equation in this approach, we transform the higher-order PDEs into a system of PDEs. Next, we utilize the third-kind Chebyshev wavelet collocation method to convert the resulting system from the prior step into a set of algebraic equations. To demonstrate the method's effectiveness, we apply it to the time-fractional Camassa-Holm equation and a third-order time-singular PDE. The outcomes are compared with those from several established methods to illustrate the method's efficiency and practicality.