CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES

E-School Information Management System (E-SIMS)

2025-04-06T16:17:07+00:00

In order to appropriately and effectively address all of the long-standing school information problems in Iraqi schools, this study aims to build and use a requirement model as a foundation to develop an electronic school information management system (e-SIMS). Online learning and related activities were the subject of this case study, which looked at elementary schools in Iraq. It is my sincere wish that the system developers will find this model useful in gaining a better grasp of the needs for the e-school activity management system's concepts, processes, and procedures. In this study, the requirements were defined using fact-finding techniques such as observation, interviews, and requirement model analysis. This requirement model, which includes specific model diagrams, was built using Unified Modeling Language (UML). The requirement model for e-SIMS was built using a combination of visual aids like use case diagrams, class diagrams, activity diagrams, and interaction diagrams (sequence diagrams and collaboration diagrams). The model was backed up by textual information such as a use case specification and a requirements list with 51 functional and non-functional requirements. Nevertheless, the scope of this investigation is limited to capturing functional requirements. The test script technique and the prototype system were used to validate this concept. e-SIMS is an online platform that aims to improve communication between users, regardless of their location or the time of day. Findings from this research suggest an improved approach to creating e-SIMS that relevant education organizations in Iraq can use.

Predicting The Number of Traffic Accidents Using Box-Jenkins Models to Contribute to Reducing These Accidents (An Applied Study in Baghdad Governorate)

2025-04-07T00:54:50+00:00

This research aims to analyze the time series of the number of recorded accidents in Baghdad during the period using Box-Jenkins models to find the best and most efficient predictive model for the number of accidents during the period. The results، based on comparison criteria (AIC، SCH، HQC) for the significant models and the comparison between the proposed parameters and models، showed that the suitable model for estimating the number of accidents is the ARIMA(1،1،2) model. The predictive values have shown consistency with their counterparts in the time series with the actual values in the trend، indicating the efficiency of the model.

Error Analysis and Stability of Numerical Methods for Solving Fractional Differential Equations in Biophysical Modeling

2025-04-09T02:00:19+00:00

Fractional differential equations (FDEs) have eCombined as a powerful tool for Representationing Complicated biophysical phenomena such as anomalous diffusion and viscoelastic behavior due to their ability to capture memory effects and hereditary properties. notwithstanding reAnswer fdes numerically presents important challenges including Problems of truth constancy and computational Productivity. This paper addresses these challenges by proposing and analyzing a novel numerical method tailored for solving FDEs in biophysical contexts. the wise employs amp limited limited Disagreement access with accommodative time-stepping ensuring both great truth and constancy spell maintaining computational feasibleness. A rigorous theoretical analysis is conducted to establish error estimates and stability conditions demonstrating the method consistency and convergence properties. quantitative experiments are performed along pragmatic biophysical problems such as arsenic abnormal dissemination inch tProblems and elastic matter distortion to corroborate the method operation. The results show that the proposed scheme achieves first-order temporal Precision and second-order spatial Precision outperforming standard techniques like the Grünwald-Letnikov method in terms of both precision and Productivity. Furthermore, the wise exhibits iron constancy low variable down orders and measure sizes devising it good for long Imitations of biophysical systems. These findings underscore the potential of the proposed approach to advance our understanding of Complicated biological Methods and Improve Foretelling Representation Ing in biophysics. away addressing name limitations of present methods this read Adds to the evolution of true and prompt quantitative tools for reAnswer fdes inch pragmatic Uses.

Modeling the Evolution of Population Dynamics Using Ordinary Differential Equations: Mathematical Analysis and Modern Applications

2025-04-11T02:29:51+00:00

Population dynamics modeling plays a critical role in understanding ecological stability, epidemiological spread, and sustainable resource management. Classical models such as the logistic growth and Lotka-Volterra equations offer foundational insights but often overlook environmental stochasticity and multispecies complexity. Existing frameworks frequently simplify nonlinear feedbacks or exclude real-time ecological interactions, limiting predictive capacity in dynamic systems. This study advances population modeling by integrating ordinary differential equations (ODEs) with modern computational tools, including machine learning-enhanced simulations and neural ODE frameworks. Analytical techniques such as linear stability, Lyapunov methods, and bifurcation analysis revealed equilibrium classifications and transitions in logistic, predator-prey, and epidemiological models. Numerical simulations validated theoretical findings, showing that hybrid AI-augmented models achieved higher accuracy (relative error 2.1%) and computational efficiency (98%) than traditional models. A novel contribution lies in embedding neural networks within classical ODE systems to dynamically adjust model parameters using heterogeneous data streams. The developed framework enhances the realism and adaptability of population models, with direct applications in conservation planning, disease control, and ecological forecasting, thus offering a versatile and interdisciplinary tool for addressing real-world biological challenges.

A Review on Development of a Client-Server Chat Application Using Python for Real-Time Communication and Networking

2025-04-13T04:55:08+00:00

This paper focuses on developing a client-server chat application using Python to explore key concepts in computer networking and real-time communication. The client-server architecture acts as the foundation, with the server managing message routing and connections between multiple clients. Using TCP/IP protocols, the system ensures reliable data transfer, error checking, and segmentation. Python's socket programming facilitates connections, where sockets serve as endpoints for data exchange. The server listens for incoming connections and employs multi-threading to handle multiple users concurrently without affecting performance. The chat application emphasizes network security by considering encryption mechanisms like SSL/TLS to safeguard communication. Additional security measures such as error handling, timeout mechanisms, and user authentication can further enhance its robustness. The paper demonstrates essential networking concepts like packet transmission, client-server interactions, and data flow management. Its real-time nature ensures seamless information exchange, improving user experience. Future enhancements include message broadcasting, private messaging, and a graphical user interface (GUI) for better usability. The paper serves as a foundation for understanding modern messaging systems and can evolve with advanced security protocols and additional functionalities like file sharing. Overall, it provides a practical learning experience in network-based application development and security implementation.

Developing an Inclusive and Efficient Extractive Text Summarization System Using NLP Algorithms

2025-04-15T04:29:18+00:00

From brainstorming to code coding and beyond, the research covers it all in its comprehensive analysis of text summarisation system development. Improving accessibility and user experience in the field of extractive text summarisation is its key objective. The study's overarching goal is to create a novel summarisation model that can employ natural language processing (NLP) to efficiently condense information while also meeting the varied requirements of its users. This involves using cluster, graph-based ranking algorithms for Natural Language processing, and TextRank to facilitate the extraction of important information and the development of short summaries. To ensure that people with different abilities can make full use of the summarisation system, the research also takes a user-centric approach, which emphasises accessibility. Therefore, we work to meet a variety of accessibility needs, such as providing alternative formats for visually impaired users, including voice interfaces for motor impairment users, and introducing future features like adjustable reading speeds and screen reader compatibility to allow for user preference-based customisation. We have utilised ROUGE-L for testing and evaluation. An inclusive and powerful text summarisation system that caters to users' demands and improves their experience is the ultimate goal of this research.

Development of a Hybrid Optimization Algorithm for Efficient Neural Network Training

2025-04-17T11:00:39+00:00

In this study, a hybrid optimization algorithm in mathematical combining the Hestenes-Stiefel (HS) and Polak-Ribiere (PR) methods was developed using an adaptive approach to optimize the training of Feed-Forward Neural Networks (FFNNs). This approach aims to leverage the global convergence power of the HS method and the ability of PR to avoid local minimum. The hybrid algorithm was tested on three real world datasets (Iris, Glass, and Wine), and compared to the results obtained using the original two methods (HS and PR). The hybrid algorithm showed shorter training times across all datasets and hybrid algorithm significantly reduced the mean square error (MSE) compared to the two separate methods, resulting in faster convergence with training accuracies fir the Iris datasets, Glass dataset, and Wine dataset being 98.50%, 98.39%, and 65.98%, respectively, enhancing efficiency. The algorithm also proved to be able to handle data with high variance or nonlinearity more effectively, making it suitable for training neural networks in machine learning applications.