Aghazadeh, Nasser
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Aghazadeh, N
Aghazadeh, N.
Aghazadeh, N.
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nasseraghazadeh@iyte.edu.tr
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04.02. Department of Mathematics
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Current Staff
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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Documents
47
Citations
529
h-index
11
Documents
47
Citations
465
Scholarly Output
16
Articles
16
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7086/548
Supervised MSc Theses
0
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0
WoS Citation Count
59
Scopus Citation Count
65
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0
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0
WoS Citations per Publication
3.69
Scopus Citations per Publication
4.06
Open Access Source
2
Supervised Theses
0
| Journal | Count |
|---|---|
| Computational Methods for Differential Equations | 3 |
| Mathematical Sciences | 2 |
| Numerical Algorithms | 2 |
| International Journal of Wavelets, Multiresolution and Information Processing | 1 |
| Journal of Integral Equations and Applications | 1 |
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16 results
Scholarly Output Search Results
Now showing 1 - 10 of 16
Article Citation - WoS: 9Citation - Scopus: 8Taylor Wavelets Collocation Technique for Solving Fractional Nonlinear Singular Pdes(Springer, 2022) Aghazadeh, Nasser; Mohammadi, Amir; Tanoğlu, GamzeA novel technique has been introduced to solve the Emden-Fowler equations. It has been derived from the Taylor wavelets collocation method. The proposed scheme has been successfully implemented in order to solve the singular equations. The singular problem converts to a system of algebraic equations that can be solved numerically. Moreover, the technique is very effective to remove the strong singularity point at x = 0. The numerical experiments have been checked out with the exact and approximate solutions that have been achieved by others including the Adomian decomposition method (Wazwaz in Appl Math Comput 166:638-651, 2005), Modified Homotopy Perturbation Method (Singh et al. J Math Chem 54(4):918-931, 2016). Also, the error analysis of the technique has been considered.Article An Effective Legendre Wavelet Technique for the Time-Fractional Fisher Equation(Univ Tabriz, 2026) Idiz, Fatih; Tanoglu, Gamze; Aghazadeh, Nasser; Mohammadi, AmirThis study modifies the time-fractional Fisher equation by adding a source term and generalizing the non-linear power to an arbitrary order. A numerical technique is proposed for the modified time-fractional Fisher equation using Legendre wavelets and the quasilinearization technique. The non-linear term is iteratively linearized using the quasilinearization technique. The convergence analysis and error estimates of the proposed method are studied. Several test problems are solved using the proposed technique, and numerical outcomes are contrasted with those obtained using some other approaches existing in the literature.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 - Scopus: 1A Finite Difference Approach To Solve the Nonlinear Model of Electro-Osmotic Flow in Nano-Channels(University of Tabriz, 2025) Aghazadeh, N.; Rabbani, K.; Otaghsara, S.H.T.; Rabbani, M.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.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 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 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 Citation - WoS: 9Citation - Scopus: 8A Numerical Method Based on Legendre Wavelet and Quasilinearization Technique for Fractional Lane-Emden Type Equations(Springer, 2023) İdiz, Fatih; Tanoğlu, Gamze; Aghazadeh, NasserIn this research, we study the numerical solution of fractional Lane-Emden type equations, which emerge mainly in astrophysics applications. We propose a numerical approach making use of Legendre wavelets and the quasilinearization technique. The nonlinear term in fractional Lane-Emden type equations is iteratively linearized using the quasilinearization technique. The linearized equations are then solved using the Legendre wavelet collocation method. The proposed method is quite effective to overcome the singularity in fractional Lane-Emden type equations. Convergence and error analysis of the proposed method are given. We solve some test problems to compare the effectiveness of the proposed method with some other numerical methods in the literature.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 Citation - WoS: 1Citation - Scopus: 1An Iris Segmentation Scheme Based on Bendlets(Springer London Ltd, 2023) Aghazadeh, Nasser; Abbasi, Mandana; Noras, ParisaDue to the effect of agents such as ambiance, transition channel, and other agents, images are polluted by noise during collection, transition, and compaction, leading to decrease image quality. Noise can decrease the accuracy of the next stages of image processing systems. Therefore, one of the vital stages in the novel processing systems is denoising. This article offers a novel image denoising approach using bendlets. Other multi-scale transformations (such as wavelets, curvelets, and shearlets) cannot recognize properties such as location, direction, and curvature of discontinuities well in piecewise stable images. To solve this problem, bendlets are suggested in this article. Bendlets differ from other multi-scale transformations in that an additional bending parameter is utilized for recognizing the curvature of discontinuities. Bendlets need a fewer number of coefficients to identify curvatures than other multi-scale transformations. Furthermore, they help to make the edges more obvious. The suggested approach is utilized on the UBIRIS.V2 database. It earns better accuracy and stability than other multi-scale transformations.