Shahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48594120250401A new rational Legendre neural network for solving the Blasius equation1910561110.48308/CMCMA.4.1.1ENMohammadMohammadiDepartment of Computer and Data Sciences, Shahid Beheshti University, Tehran, IranYeganehGhaderiDepartment of Computer and Data Sciences, Shahid Beheshti University, Tehran, IranHassanDana MazraehDepartment of Computer and Data Sciences, Shahid Beheshti University, Tehran, IranKouroshParandDepartment of Computer and Data Sciences, Shahid Beheshti University, Tehran, Iran0000-0001-5946-0771Journal Article20250303In this paper, we present a novel artificial neural network framework for solving the Blasius equation, a nonlinear ordinary differential equation defined on a semi-infinite domain. Our experiments revealed that traditional activation functions, such as Tanh and ReLU, did not produce satisfactory results. To address this, we employed custom activation functions based on Rational Legendre polynomials, which demonstrated superior performance in approximating the solution. The results highlight the effectiveness and potential of this approach, offering a valuable contribution to the scientific community for addressing similar nonlinear differential equations.https://cmcma.sbu.ac.ir/article_105611_053332729b63dfd21a8943628607e84b.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48594120250401On oscillation of solutions of nonlinear neutral hyperbolic equations with delay102010564810.48308/CMCMA.4.1.10ENYutakaShoukakuFaculty of Engineering, Kanazawa University, Kanazawa 920-1192, Japan0000-0002-3608-8783Journal Article20241111This paper establishes the existence of some sufficient conditions for oscillation of nonlinear neutral hyperbolic equations with delay. Our results generalize and extend those reported in the literature. Examples are included to illustrate the importance of the results obtained.https://cmcma.sbu.ac.ir/article_105648_ddaefa21215e3a711a6d41f628371b66.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48594120250401A mathematical modeling evaluating the role of booster vaccine and Quarantine strategy as interventions for cholera control214210599710.48308/CMCMA.4.1.21ENMutairu KayodeKolawoleDepartment of Mathematical Sciences, Osun State University, Osogbo, Nigeria0000-0003-1500-2060S.R.AdebayoDepartment of Mathematical Sciences, Osun State University, Osogbo, NigeriaJournal Article20250226Cholera remains a global health challenge, especially in regions with inadequate water and healthy sanitation scheme . This research investigates the effectiveness of booster vaccination and quarantine strategies in controlling cholera outbreaks using a mathematical model that incorporates key epidemiological factors such as infection rates, recovery rates and waning immunity. The model also integrates booster vaccination to prolong immunity and quarantine measures to reduce contact between susceptible and infected individuals. Qualitative analysis of the model in lieu of sensitivity testing, demonstrates that the combined use of booster vaccination and quarantine significantly lower the basic reproduction number $(R_{0})$, effectively for controlling cholera transmission. The Laplace Adomian Decomposition Method (LADM) was used to solve the system and numerical simulation which confirm that booster vaccination enhances long-term immunity, while quarantine measures reduce transmission by limiting contact between infected and susceptible populations. Results provide valuable a valuable insights that can guide policymakers in developing more effective cholera prevention strategies to reduce disease incidence and mortality.https://cmcma.sbu.ac.ir/article_105997_47ce32f7fd56681028cd856d55c6cf5f.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48594120250401Predicting football player features through hierarchical clustering and representative selection435510603710.48308/CMCMA.4.1.43ENMahdiNouraieDepartment of Statistics, Shahid Beheshti University, Tehran, Iran0000-0002-4792-4994MeysamAgahDepartment of Mathematics, Shahid Beheshti University, Tehran, IranChangizEslahchiDepartment of Computer and Data Sciences, Shahid Beheshti University, Tehran, Iran0000-0002-8913-3904ArnoldBacaCentre for Sport Science and University Sports, University of Vienna, Vienna, AustriaJournal Article20250306This study offers a comprehensive analysis of both classic and advanced features of football players, aiming to enhance player evaluation and feature prediction. We applied a three-step methodology: first, hierarchical clustering was used to reveal the group structure of features in each position. Second, representative features were identified within clusters leveraging the correlation matrix of features, reducing the dimensionality of the dataset while retaining critical information. Third, several regression models were employed to predict other player features using the representatives. This approach was applied across the distinct positions of goalkeeper, defender, midfielder, and forward. Bootstrap resampling confirms the robustness of the results obtained, revealing consistent clusters against random data variations. The findings indicate that representative features effectively encapsulate the entire feature space for each position, allowing other features to be predicted accurately with minimal errors. This study contributes to football analytics by providing a robust method for feature selection and prediction, ultimately improving the accuracy and efficiency of player performance analysis.https://cmcma.sbu.ac.ir/article_106037_bd1a61abfb66d7d3269acf0d1108ae98.pdf