Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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 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 Citation - WoS: 25Citation - Scopus: 25Topology Degree Results on a G-Abc Implicit Fractional Differential Equation Under Three-Point Boundary Conditions(Public Library Science, 2024) Rezapour, Shahram; Thabet, Sabri T. M.; Rafeeq, Ava Sh.; Kedim, Imed; Vivas-Cortez, Miguel; Aghazadeh, NasserThis research manuscript aims to study a novel implicit differential equation in the non-singular fractional derivatives sense, namely Atangana-Baleanu-Caputo (A B C) of arbitrary orders belonging to the interval (2, 3] with respect to another positive and increasing function. The major results of the existence and uniqueness are investigated by utilizing the Banach and topology degree theorems. The stability of the Ulam-Hyers (U H) type is analyzed by employing the topics of nonlinear analysis. Finally, two examples are constructed and enhanced with some special cases as well as illustrative graphics for checking the influence of major outcomes.Article Citation - WoS: 5Citation - Scopus: 6Lung Parenchyma Segmentation From Ct Images With a Fully Automatic Method(Springer, 2023) Mousavi Moghaddam, Reza; Aghazadeh, NasserFor the last three years, the world has been facing an infectious disease that primarily affects the human breathing organ. The disease has caused many deaths worldwide so far and has imposed high economic costs on all countries. Therefore, attention to computer-aided detection/diagnosis (CAD) systems to help diagnose and treat diseases related to the human respiratory system should be given more attention so that countries’ health systems can treat patients in epidemics. Considering the importance of CAD systems, we proposed a two-step automatic algorithm. In the first step, we obtain the primary boundary of the lobes in CT lung scan images with the help of some conventional image processing tools. In the second stage, we obtained a more precise boundary of the lung lobes by correcting the unusual dimples and valleys (which are sometimes caused by the presence of juxtapleural nodules). This proposed method has low implementation time. Given that a precise boundary of the pulmonary lobes is essential in the more accurate diagnosis of lung-related diseases, an attempt has been made to ensure that the final segmentation of the lung parenchyma has an acceptable score in terms of evaluation criteria so that the proposed algorithm can be used in the diagnosis procedure. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.