CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES

E-School Information Management System (E-SIMS)

Ekhlas Ghaleb Abdulkadhim, Zaman Mahdi Abbas, Muqdad Abdulraheem Hayder — Sun, 06 Apr 2025 16:17:07 +0000

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)

Ahmed Ali Salman — Thu, 03 Apr 2025 00:00:00 +0000

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

Saif Abdulkareem Mari Al-Qaraghuli — Wed, 09 Apr 2025 02:00:19 +0000

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

Basheer Abd Al Rida Sadiq — Fri, 11 Apr 2025 02:29:51 +0000

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

S. Namachivayam, A.T. Ashmi Christus, J. Rahila, A. Prabha — Sun, 13 Apr 2025 04:55:08 +0000

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

M. Shagar Banu, Shajini G. Inbakani, T.R. Suchithra, K. Kandan — Tue, 15 Apr 2025 04:29:18 +0000

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

Mohammed Dawood Salman, Ghufran K. Joad, Saif Ahmed Hussein — Thu, 17 Apr 2025 00:00:00 +0000

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.

Real-Time DDoS Attack Detection using Random Forest and Streamlit: A Machine Learning-Based Web Solution

V. Maria Christy, R. Srinivasan, E. Kousalya, P. Reena, M.T. Beevi Fathima — Sun, 20 Apr 2025 17:41:35 +0000

DDoS assaults threaten network availability and security in today's linked world. We propose a real-time DDoS assault detection method using machine learning, specifically the Random Forest algorithm. Our technology fits smoothly into web settings using the renowned Streamlit architecture, giving an easy and interactive platform for threat monitoring and mitigation. We collect a large dataset of network traffic features from benign and harmful operations. After careful preprocessing and feature engineering, we prepare data for training and evaluation. We use the resilient and scalable Random Forest method to create a predictive model that can distinguish typical traffic patterns from DDoS attacks. The model is rigorously tested using performance indicators to detect and categorize DDoS assaults with low false positives. The model is effortlessly integrated into a Streamlit-based online application, improving end-user accessibility and usability. Our experiments show that the model can identify in real time with excellent accuracy and efficiency. Our technology strengthens network resilience and protects dynamic online environments from disruptive cyber threats by empowering stakeholders with proactive DDoS mitigation capabilities.

Utilizing Fuzzy Linear Regression in Medical Data Analysis: A Comparative Study of FLR and FLSLR Models for Enhancing Regional Health Planning

Sufian Munther Salih, Ghufran Khalil Joad — Wed, 23 Apr 2025 21:20:16 +0000

This research deals with the planning study in using fuzzy linear regression in the cases of fuzzy; non-fuzzy data, where the analysis was conducted using several methods, including fuzzy linear regression (FLR) and modified fuzzy linear regression, in counting up to the (FLR) method using least squares (FLSLR), in which linear programming (LP) was employed in the analysis process.The fuzzy parameters were estimated based on fuzzy and non-fuzzy data, and the regression idol was determined based on the concepts of fuzzy set theory. These methods were also applied in the medical field to accurate data on osteoporosis in a statistical study as part of the requirements for achieving regional planning, which is concerned with health population studies as an integral part of sustainable development, in which individual health is one of the most important variables affecting planning levels in general, where the researchers used this method, by measuring bone density using a DEXA device for thirty patients (10 males and 20 females). The study was conducted in the laboratories of the Department of Biomedical Engineering - College of Engineering - Al-Nahrain University.The results showed that the modified Tanaka model is more efficient than the FLR model, as it helps to elude the emergence of non-fuzzy given parameters. The FLSLR method also proved superior to FLR based on the degree of model affiliation, which enhances the accuracy and effectiveness of the estimation.

The Weighted Time-Domain Laplace-Fourier Transform: A Comprehensive Study

Khalid Farhan Fazea — Thu, 24 Apr 2025 17:48:04 +0000

Traditional integral transforms like Fourier and Laplace have been foundational tools for signal and system analysis in physics, engineering, and mathematics. However, these classical methods are often inadequate for analyzing nonlinear, non-stationary, and time-varying signals that are increasingly prevalent in real-world systems. In response to these limitations, this study introduces the Weighted Time-Domain Laplace-Fourier Transform (WTLFT), which integrates the benefits of Fourier and Laplace transforms with a time-domain weighting function to enhance analytical flexibility. Prior methods lack robustness in capturing transient behaviors and localized features in dynamic systems. There is limited development of transform techniques that address both fractional dynamics and time-varying structures within a single framework. This research aims to define the WTLFT, prove its core properties, and demonstrate its effectiveness in solving complex differential equations involving non-integer derivatives and dynamic systems. The WTLFT is analytically validated through properties such as linearity, time-shifting, convolution, and differentiation. It is successfully applied to exponential and Mittag-Leffler functions, and solves Caputo-type fractional models, diffusion equations, and differential-algebraic systems. Unlike conventional transforms, the WTLFT allows for flexible weighting strategies that adapt to specific application domains, enabling enhanced signal analysis and accurate reconstruction of solutions. The WTLFT establishes a powerful new direction for signal processing, control theory, and fractional calculus applications, especially in engineering, biomedical, and physical science fields.

Gathering of Unit Disk Robots

Rusul J. Alsaedi — Fri, 25 Apr 2025 15:22:01 +0000

The gathering problem in distributed robotics involves coordinating autonomous mobile robots to converge at a single location, a task complicated by physical constraints and limited sensing. Prior research predominantly utilizes point robots or assumes full visibility, which neglects real-world conditions where robots have physical dimensions and obstruct each other's vision. No existing algorithm in the classical model addresses gathering of unit-disk robots under obstructed visibility using small, constant memory. This study proposes a novel algorithm for collision-free gathering of opaque unit-disk robots with limited memory, operating under a fully synchronous scheduler. The algorithm proceeds in two phases: a visibility phase to reposition robots into a convex hull ensuring mutual visibility, and a gathering phase to move all robots toward the centroid. It achieves collision-free convergence in O(n) rounds using only O(1) memory per robot, without knowledge of the total number of robots. Unlike prior models that rely on lights, transparency, or global knowledge, this work uniquely solves the gathering problem using opaque robots with geometric reasoning and no communication. This result advances the feasibility of memory-efficient, scalable coordination strategies for real-world swarm robotics where physical and visibility limitations are critical.

Expectile Regression with Reciprocal Lassopenalty

Rammah Oday Hassan — Sat, 26 Apr 2025 00:00:00 +0000

Expectile regression (ER) has emerged as a valuable alternative to quantile regression, offering robust modeling of the conditional distribution of response variables across diverse fields such as economics, medicine, and ecology. While traditional penalized ER models like SCAD, elastic-net, and adaptive Lasso improve variable selection, they often struggle with achieving optimal sparsity and precision. Existing penalties typically exhibit continuity and symmetry, which may not sufficiently penalize near-zero coefficients in high-dimensional data, limiting their effectiveness in variable selection. This study proposes a novel penalized expectile regression method using the reciprocal Lasso (rLasso) penalty, which introduces discontinuity at the origin and infinite shrinkage as coefficients approach zero. Simulation studies across various settings and error distributions demonstrate that ER-rLasso consistently achieves the lowest root mean square error (RMSE) and false positive rate (FPR), outperforming established ER models. In real-data analysis involving prostate cancer, ER-rLasso showed superior predictive accuracy across all tested expectile levels. The distinct penalization structure of rLasso, being non-symmetric and non-convex, introduces a more aggressive variable selection mechanism that enhances model sparsity and interpretability.

Poisson Distribution and its Relationship to The Normal and Binomial Distributions: Review Article

Adel Abbood Najm, Bashar Khalid Ali — Sat, 26 Apr 2025 04:27:07 +0000

The Poisson Distribution (PD) is a foundational statistical model widely utilized in probability theory to represent the frequency of discrete, independent events within a fixed interval of time or space. Its analytical structure, based on the parameter λ, allows it to effectively model rare occurrences in diverse fields such as telecommunications, health, commerce, and environmental science. It also serves as a mathematical bridge between the Binomial distribution under Bernoulli trials and the Normal distribution under large-sample conditions. Despite the PD’s established applications, a comprehensive synthesis of its convergence behavior and comparative properties with the Binomial and Normal distributions remains underexplored in the literature. This article aims to review the mathematical relationships and convergence properties of the PD, particularly in the context of approximations to Binomial and Normal distributions, and to reaffirm its applicability in modeling real-world phenomena. The analysis confirms that the PD approximates the Binomial distribution when the number of trials is large and the success probability is small, and it converges to the Normal distribution as λ increases. These findings are substantiated by theoretical derivations and supported with examples from current scientific applications. The study unifies theoretical derivations and practical illustrations, highlighting the central role of PD in statistical modeling and its versatility across various domains. The findings reinforce the importance of PD as a core tool in applied statistics and suggest potential for its enhanced application in complex systems through hybrid models and extensions involving fuzzy logic.

Advanced Image Processing for Breast Cancer Detection Using CNN-Based Transfer Learning on Mammograms

Azhar Amer Alsoufi — Mon, 28 Apr 2025 12:47:17 +0000

Breast cancer remains the most commonly diagnosed disease and the second leading cause of death among females. Statistically speaking, roughly one out of every eight American women was diagnosed with breast cancer last year. The precise identification of breast cancer also largely relies upon careful analysis of medical images. Though several Deep Learning (DL) algorithms have been employed to analyses such images, therefore, this study focuses on using a Convolutional Neural Network (CNN) to differentiate between different types of mammograms. The use of CNN in image recognition and visual processing has quickly drawn the attention of scholars. Therefore, in this current research, an approach is presented to extract patches from mammograms and utilize them to train the CNN, whereby the order of the section’s feeds into the classification process. In addition, a transfer learning approach is utilized, in which a model created in the initial phase is later utilized as an initial model. Besides using single and multi-CNN and Artificial Neural Network (ANN) layers, two more approaches—Auto-Encoder and VGG16—are used to evaluate and compare the effectiveness of the models on different datasets.

Exploring Numerical Methods: Solving Lane-Emden Type Equations with Padé Approximations

Nazhan Al-Din Ahmed Jasim Obeid — Wed, 07 May 2025 16:40:27 +0000

This research explores the effectiveness of using Padé approximations to enhance the accuracy of numerical solutions for Lane-Emden type differential equations. By applying the Adomian decomposition method to series solutions derived from previous studies, Padé techniques are integrated to obtain more precise approximate solutions. The supplied examples demonstrate that Padé approximations extensively outperform conventional strategies, yielding numerical results with smaller mistakes and nearer proximity to genuine solutions. Additionally, those approximations make a contribution to a higher information of the behavior of the studied structures by providing more stable and comprehensive answers. When in comparison to conventional answers, Padé approximations show off advanced performance throughout a number of situations, highlighting the importance of choosing the right numerical approach based on the nature of the hassle. This approach plays a crucial role in scientific and engineering fields that require high precision in modeling and analysis. Overall, the research emphasizes that Padé approximations represent an advanced and reliable option for addressing complex differential equations, opening new avenues for understanding mathematical and physical phenomena more effectively.

A Modified Conjugate Gradient Method with Elastic Properties for Unconstrained Problem

Hayder Ali Mohsin Al-Baidhani — Sun, 11 May 2025 14:49:05 +0000

In this paper, we suggest a new version of the conjugate gradient (CG) method that adds elastic features to improve its ability to solve problems where there are no limits on the solutions. Traditional Conjugate Gradient (CG) methods are effective but can become unstable and slow to find solutions when dealing with difficult problems that aren't well-structured or that don't fit a simple curve. Our suggested method includes a flexible adjustment system that changes how we search and the size of our steps. This helps keep things stable and makes the process faster. The method meets the basic requirements for going downhill and has been shown to work well overall based on common expectations. We tested a new method on a group of standard optimization problems without limits, comparing it to traditional CG methods. The results show that our method works better than others because it requires fewer steps and less time to compute. This proves that it is strong and effective for many different tests.

The Role of Statistics in Enhancing Data Quality in Social Research

Zainab A Khudhair — Wed, 14 May 2025 19:37:24 +0000

This study discusses the core function of statistics in improving data quality in social research. The more empirical data is at the core of social science, the more critical become issues of truth and accuracy. This study will use statistical methodologies to pinpoint and resolve various data quality problems common in general studies. The result may be a framework for researchers to ensure that the data is indeed valid. It kicks off with an in-depth review of the literature to try and get a handle on what is being done currently and how the phenomenon of data quality is construed in its dimensions. At this point, the methodology will be able to point to the gaps. Yes, statistics will be able to fill them. The study adopts a descriptive research design with both survey and interview methods for collecting information from informants composed of social researchers. Advanced statistical techniques such as regression analysis, cluster analysis, and factor analysis will be employed to critically assess the efficiency of these methods in enhancing data quality. Illustration through the analysis of case studies on practical application and challenges is what provides evidence. Indeed, statistical tools make social research data within reach-in terms of clarity, consistency, and dependability. Results of this study lead to practical recommendations for the implementation of statistical techniques by researchers and a future study on setting the use of artificial intelligence on another level with statistical methods toward further improvement in the quality of the data

Predicting User Engagement in Mobile Applications Using Machine Learning: Insights for Optimizing App Design and Retention

Sat, 17 May 2025 09:20:23 +0000

The use of machine learning methods to foretell how users will interact with mobile applications is the focus of this research. Predictive models are created to foretell engagement levels by assessing a variety of features, including user demographics, app usage habits, and user feedback. In order to help app developers optimise user experiences and retention methods, our research seeks to shed light on the elements that influence user engagement and to construct accurate prediction models. Machine learning algorithms may be able to predict how users will interact with a mobile app, according to the results. This might lead to better app designs and more downloads. User engagement dynamics are illuminated by this ground-breaking investigation on the complex relationship between user behaviour and app functionality. Results from extensive testing and analysis show that machine learning has great promise as a method for understanding user actions and preferences. These findings demonstrate the impact of predictive analytics on app development and herald a new age of personalised and interactive user experiences. The findings from this study could significantly impact how mobile app optimisation strategies are developed in the future.

Nanostructural and Plasmonic Features of KDB-3 Silicon Modified by High-Temperature Manganese Diffusion

S.O. Saidov, F.A. Hakimboyev — Sun, 18 May 2025 04:08:35 +0000

This study explores the nanostructural, morphological, and plasmonic modifications induced by high-temperature manganese diffusion into monocrystalline silicon of the KDB-3 grade. To determine structural, phase, and plasmonic changes arising from manganese diffusion in silicon. The study employed a comprehensive suite of modern analysis techniques, including X-ray diffraction (XRD), Raman spectroscopy, impedance spectroscopy, scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and nitrogen adsorption-based BET/DFT porosimetry. Results revealed significant amorphization, formation of Mn₅Si₃ and SiB₆ phases, a rise in specific resistivity, and a decline in carrier mobility. Raman and impedance analyses identified localized surface plasmon resonance (LSPR) manifestations within 40–70 kHz and at 570–600 cm⁻¹. BET/DFT analysis confirmed mesoporosity with pore sizes between 2–5 nm and a specific surface area reaching 23 m²/g. The study contributes novel insights into the formation of plasmonic-active regions within transition-metal-modified silicon, linking plasmonic effects to specific nanostructural and phase alterations. Findings suggest potential applications of Mn-diffused KDB-3 silicon in sensor platforms, nanophotonics, and spintronic devices. Further investigations are required to explore broader temperature ranges, long-term stability, and in-depth LSPR characterization via advanced photonic correlation techniques.

Comparative Study of Padé Approximation Methods in Solving Lane-Emden Type Differential Equations

Hayder Ali Abdulsada Alkinani — Mon, 19 May 2025 03:24:01 +0000

This study examines how effective Padé approximation methods are for solving Lane-Emden type differential equations. These are special types of equations that are often used in astronomy, like when understanding the structure of some stars and how gases act in round shapes. These equations are hard because there’s an issue at the beginning point and they get more complicated due to the polytropic index. Traditional power series methods usually don't work well beyond a small range because they are only effective within specific limits. Padé approximants are a kind of math tool that can give more accurate estimates. They are effective because they can be used in many different situations and show what happens accurately at places where functions act strangely. In this study, we apply different Padé approximations of different levels to the Lane-Emden equation. They are evaluated based on how correct they are, how fast they find a solution, and how well they use computer resources. Computer simulations are performed, and the results are compared with older techniques, such as the Runge-Kutta method and standard series solutions. The comparison shows that Padé approximants are better because they give accurate results faster over larger areas, especially when the polytropic index goes up. But there are still issues with picking the best orders for the estimates and handling more complicated nonlinear problems. The results show that Padé approximation is a helpful way to understand things while being efficient with computers. This makes it a helpful tool in both ideas and real life, especially when regular methods don’t work well due to unusual situations or high computer expenses.

Analysis of Modern Digital Solutions for Implementing Feedback

Nazokat Isaevna Adullaeva , Davlatjon Farrukhovich Abdurakhimov — Sun, 18 May 2025 00:00:00 +0000

The purpose of the article is a comprehensive analysis of modern digital solutions used to organize and improve the effectiveness of feedback in the educational and corporate spheres. Using the methods of comparative analysis, expert surveys and content analysis of digital platforms, a study was conducted on the effectiveness of such systems as Google Forms, MS Teams, Zoom, Moodle, Telegram bots and intelligent solutions based on artificial intelligence. Based on the results of the study, key technological and functional parameters that determine the successful implementation of feedback were identified. The article provides statistical data, examples of successful practice and recommendations for the implementation of digital solutions at the institutional level.

Design and Simulation of Fiber Wireless Communication Network

Dhafer Sabah Yaseen, Fahad Ayad Khaleel Albazaz, Omar S. Almolaa — Sat, 24 May 2025 07:27:20 +0000

The exponential growth of mobile data demand and the need for high-capacity backhaul networks have led to the integration of optical and wireless technologies. Existing passive optical networks (PONs) offer high-speed connectivity, yet limitations remain in addressing increased data rates and dense small-cell deployments in 4G, 4.5G, and emerging 5G networks. Despite advancements in radio-over-fiber (RoF) and WDM-PON architectures, comprehensive simulation-based evaluations of their joint performance for large-scale deployments are limited. This study aims to design and simulate a WDM-PON based Radio and Fiber (R&F) communication system using 16 wavelengths to assess its feasibility in supporting dense cell site architectures and reliable transmission. Simulation results reveal that a 10 Gbps per wavelength configuration over 20 km can support 32,768 base stations with a cell radius of ~110 m, while 40 Gbps configurations over 1 km achieve a 1:65536 splitting ratio. The RoF system optimized at 24 dBm RF input provides maximum Q-factor (26) and highest receiver sensitivity (–30 dBm) over 5 km fiber. The integration of R&F and RoF architectures using WDM-PON is simulated with varying input RF power, frequency bands, and fiber lengths to determine optimal configurations for reliable, scalable communication. The findings demonstrate the viability of WDM-PON based R&F networks for next-generation mobile backhaul, offering a scalable solution with high data rates, extended reach, and optimized performance suitable for dense, low-latency wireless access environments.

A New Method for Solving The Fractional Spectral Collocation Equation

Maryam Aad Alwan, Hayder Hassoon Mohammed — Sun, 25 May 2025 02:16:25 +0000

This research presents a fractional discrete collision spectral method (FSCM) for solving fractional-order partial differential equations (FPDEs) such as the Borgers equation and the Fokker-Planck equation. The method is based on constructing exact fractional numerical derivatives using a fractional Lagrange interpolator that satisfies the Kronecker delta property at collision points. Fractional PDEs are developed based on several proposed points, including the roots of fractional Jacobian polynomials, achieving exponential convergence in solutions. The study includes an analysis of numerical matrices of fractional derivatives and a comparison of applications in multiple time-invariant and constant-time problems. The method is characterized by ease of implementation and reduced computational cost compared to traditional methods.

The Properties Of z-Essential (z-Closed) Fuzzy Submodules

Hassan K. Marhon — Mon, 26 May 2025 10:33:29 +0000

The theory of fuzzy submodules, rooted in the broader domain of fuzzy set theory, provides a nuanced framework for studying algebraic structures under uncertainty. Traditional studies on essential and closed fuzzy submodules have offered important foundations, yet they fall short in addressing deeper generalizations required for more complex fuzzy module interactions. While essential and closed fuzzy submodules are well-established, their extensions namely, -essential and -closed fuzzy submodules have received limited attention, particularly concerning their characterizations and behavior under module homomorphisms. This study aims to define and investigate the properties of -essential and -closed fuzzy submodules within -fuzzy modules, exploring their structural implications and interrelations with classical fuzzy submodule concepts. The research establishes that -essential fuzzy submodules generalize essential ones and introduces conditions under which a -essential submodule becomes essential. Similarly, it proves that every -closed submodule is closed, though not conversely, and provides conditions for closed submodules to be -closed. Moreover, the behavior of -closed submodules under homomorphisms and their preservation through module operations is rigorously examined. This study pioneers the formalization of -essential and -closed fuzzy submodules, offering novel characterizations and multiple propositions that extend classical fuzzy module theory. These results enhance the understanding of structural properties in fuzzy algebra and provide a basis for further exploration in module theory under fuzzified environments, with potential applications in computational algebra and systems modeling where partial membership and graded structures are relevant.

Applicability and Assessment of Diverse FPGA Architectures and Algorithms for the Internet of Things Applications

Anitha Velu, Raghu Ramamoorth — Mon, 26 May 2025 16:19:30 +0000

The "Web of things" (IoT) is turning into an interesting issue in the work environment and outside. IoT is an idea that possibly influences the way of human’s life and work area. The IoT can coordinate the actual humankind straightforwardly into PC based frameworks. It additionally permits objects to be detected or controlled from a distance across the current organization framework. IoT further develops proficiency, precision, and monetary advantage of the habit framework to diminish human creation. The test of planning the design in IoT applications is to contract with a copious amount of finding the information produced from the connected appliances that are limited to resources and likely to misplaced information due to the distress in network connectivity. Applications of IoT in the FPGA (Field Programmable Gate Array) framework definitely said to be a challenging one to reach diversity in the upcoming years with the local area networks. FPGA configuration enables a sturdy, less cost architecture that are easy to understand supporting the way of 24 hours of frequent checking with remote detection framework. This paper proposes a review of carrying out the FPGA innovation for IoT applications by investigating different designs. The main aim of this review work is to facilitate the way in which the users can make understand the FPGA-oriented design and architectures embedded with IoT for variety of applications.

Existence, Uniqueness, and Numerical Solutions for 2D Volterra-Fredholm Equations with Singular Kernels

Mustafa Ali Hussein Al-Mohammadi — Wed, 28 May 2025 00:00:00 +0000

Vaultra-Fredhom Integrated integrated integrated equations and non-linear cores have extensive applications in modelling complex physical problems such as heat transfer, fluid mobility and image processing. Because of their special complications, these equations are difficult to solve analytically and require effective and stable numerical methods to get an accurate solution. The main objective of this research is to investigate the existence and specificity properties of the two-dimensional Volterra peace-to-life solution. In this connection, the taum method was used as an accurate numerical technique to solve both linear and non-linear equations. In this study, integrated equations were converted to algebraic systems, and their numerical solutions were achieved effectively using base functions and a series of appropriate mats. The results suggest that the taum method is able to show high stability in producing accurate solutions for complex equations under different border conditions and solving equations with a cynical core. This research is used especially in engineering and physics in non -linear and complex problems.

Volumetric DDoS Attacks: Types, Operation Mechanism, And Analysis Of Protection Methods

Furqat A. Rakhmatov, Muxammadi Sh. Toshtemirov — Wed, 28 May 2025 00:00:00 +0000

With the rapid advancement of digital infrastructure, safeguarding network systems against cyber threats has become increasingly vital. Among these threats, Distributed Denial of Service (DDoS) attacks—particularly volumetric variants—pose a serious risk by overwhelming network bandwidth and disrupting essential services. Volumetric DDoS attacks such as UDP Flood, ICMP Flood, and DNS Amplification are designed to consume system resources at scale, often leading to significant financial and reputational damage. These attack vectors exploit open network protocols and reflection mechanisms to maximize disruption. Despite the proliferation of mitigation techniques, there remains a lack of comprehensive analysis addressing the specific operational mechanisms and practical defense strategies for different volumetric attack types in contemporary environments. This study aims to examine the classification, technical execution, and associated risks of major volumetric DDoS attacks and to evaluate current protection methods, highlighting their strengths and limitations. The research identifies key characteristics and vulnerabilities exploited in UDP, ICMP, and DNS-based attacks. Analysis of countermeasures—such as traffic filtering, rate limiting, and deep packet inspection—demonstrates variable effectiveness depending on attack type. DNS amplification, in particular, poses severe challenges due to its high traffic amplification ratio. This article provides an integrated assessment of attack vectors and defense techniques through both technical analysis and graphical representation of traffic behavior, offering insight into real-time anomaly detection. The findings contribute to the development of more adaptive, algorithm-driven protection systems and offer a methodological basis for future research in cyber defense and secure network architecture.

A Hybrid Deep Learning and Bagging Method for Automatic Modulation Recognition Utilizing Time-Frequency Data

Intisar K. Saleh — Mon, 02 Jun 2025 00:00:00 +0000

In satellite communications systems, submarine communications, and military communications, determining out the type of modulation is a crucial issue. A newly developed digital modulation classification model is introduced in this study to identify many types of modulated signals. At the first step, the density of specter for the frequencies accompanied with the modulation signals at the scalogram image is visually represented using continuous wavelet transform (CWT). Then, a deep convolutional neural network (CNN) is utilized to extract features from the scalogram pictures. The MRMR method is then used to get the best features. By decreasing the size of the features, the MRMR method improves classification speed and model interpretation. Using the group learning technique, the modulations are categorized in the fourth stage. Modulated signals with various levels of noise and SNRs ranging from 0 to 25 dB are taken into consideration in the simulations. The simulations' result shows that the suggested model outperforms other earlier research and functions effectively related to various noise levels.

An Enhanced 3D Haar Wavelet Approach for Parabolic Equations Involving Coupled Nonlinear Source Terms

Nadwah Najm Hamzah — Wed, 21 May 2025 00:00:00 +0000

The paper presents a new hybrid cryptographic system for image encryption algorithm combining the lightweight Ascon-AEAD with neural networks and chaotic systems. The CNAIE system uses Mish activation functions for neural diffusion and employs Q-learning-based reinforcement learning for the adaptability of key scheduling. Our solution caters to the current pressing demand of lightweight secure encryption methods with minimum computational overhead for IoT embedded systems. Results of encryption on some test images show the system testing near-optimal encryption entropy (≈7.99) and negligible adjacent pixel correlation (<0.01) compared to that of plaintext images (>0.90). The uniform histogram distribution and lack of meaningful pixel relations within encrypted images bear witness to the system's strength against statistical attacks. Furthermore, patch analysis establishes that the algorithm is quite sensitive to minor changes in key bits and cavity output variations caused by changes in even a single bit of the key. Performance evaluation establishes the system as feasible with security as per NIST for resource-constrained environments of the IoT.

Beta Principal Component Regression Model with Application

Rammah Oday Hassan — Wed, 18 Jun 2025 11:44:27 +0000

The main goal of regression analysis is to estimate the effect relationship between independent variables and the dependent variable. Consequently, the type of response variable data determines the type of regression model to be used. If the dependent variable data is continuous and represents proportions confined between (0, 1), the Beta regression model is considered a good choice for representing such a relationship. However, at times, the Beta regression model encounters issues that make the estimated relationship unstable. This instability is due to the presence of some econometric problems that cause the estimators to be inaccurate. One such issue is multicollinearity. This problem arises when there is a strong correlation between the explanatory variables, which particularly affects the estimators of the parameters (β) by reducing their accuracy. Multicollinearity leads to an unusual inflation of parameter variances, making the estimators less reliable. In this paper, we will present a new method that integrates the Beta regression model with Principal Component Regression to develop a hybrid regression model. This hybrid model will be more suitable for estimation in the presence of multicollinearity issues. Simulation examples and real data are used to evaluate the performance of the proposed method in comparison with existing methods.

Prices of Building Materials and Their Impact on Residential Growth Trends in Iraq

Ali G. Nori — Thu, 19 Jun 2025 11:27:09 +0000

This research discusses and applies a study on the impact of changes in the prices of building materials on residential expansion and the construction or renovation of new units in Iraq. The aim was to determine the extent of the impact of price fluctuations in building materials on urban expansion and to identify the most important materials affected by this change. The researcher relied on a descriptive analytical approach, collecting information on the study variables based on publications by the Central Statistical Organisation of Iraq, the International Monetary Fund. Some variables were adopted, represented by the dependent variable, which is the number of housing units (number of building permits granted for construction or renovation), in addition to four independent variables represented by three building materials considered essential for construction, namely bricks, cement and reinforcing steel, and a fourth variable, which is the (estimated) per capita income NGDPOPC, based on the annual publication of the International Monetary Fund. The time period ranges from 2015 to 2023. The data was analysed using a descriptive analytical approach and some statistical methods, the most important of which was the variance inflation factor (VIF) to measure the absence of linear multicollinearity . and the Pearson criterion was used to measure the correlation between the dependent variable and other variables . Finally, a regression equation was derived to predict the number of housing units according to price changes. The results indicate a strong correlation between the number of units and the prices of building materials, and that the relationship between the variables is statistically significant. Therefore, the hypothesis that there is a statistically significant level and that the four independent variables all affect the model to varying degrees is accepted, noting that the most influential material is the price of bricks, as the higher the price of bricks, the lower the number of units, and that per capita income has a direct effect on the increase in the number of units, as an increase in per capita income leads to an increase in the number of units. brick prices rise, the number of units decreases, and per capita income has a direct effect on the increase in the number of units, as an increase in per capita income leads to an increase in the number of units.

Reducing The Influence of High Leverage Points in Beta Regression Using The Gm6 Robust Estimator

Hamza Lateef Katea Al-Ayashy, Taha Alshaybawee — Sun, 22 Jun 2025 21:37:59 +0000

The problem of outliers and high leverage points is one of the most prominent challenges facing the design of statistical models, especially in regression models, as they have a significant impact on distorting the results of statistical estimation. This research aims to address the impact of high leverage points in a beta regression model using robustness estimation methods, specifically the GM6 multistage estimator (GM6-BR). A comparison of four estimation methods was studied: traditional estimation (BR), least discrete squares estimator (LTS-BR), generalized estimation (GM-BR), and GM6-BR. A simulation study was conducted on two models: one linear and the other nonlinear, with data contamination of 10% and 20% introduced to test the robustness of the different methods. The results demonstrated a clear superiority of the GM6-BR method in terms of reducing error (RMSE, MAE) and skewness (BIAS), while maintaining stability in the presence of contamination. Practical application on real data also showed that GM6-BR was least affected by outliers compared to other methods, while the traditional BR method exhibited the highest level of distortion. Therefore, the study recommends adopting GM6-BR as an effective and accurate option for analyzing data containing high leverage points or outliers, especially within beta regression models.

Robust Variable Selection for Quantile Regression: Application to Daily Demand Forecasting Orders Data

Zainab S Alsaadi — Mon, 23 Jun 2025 00:29:47 +0000

This paper proposes two robust variable selection methods within the quantile regression framework: Robust Elastic Net Quantile Regression (REN-QR) and Robust MCP Quantile Regression (R-MCP-QR). These approaches integrate adaptive penalization with GM-type weighting schemes to improve estimation accuracy and feature selection under high-dimensional and contaminated conditions. Through extensive simulation studies, the proposed methods demonstrate superior performance in terms of mean squared error (MSE), true positive rate (TPR), and false positive rate (FPR) compared to classical penalized quantile regression techniques. Furthermore, the application to a real-world dataset on daily demand forecasting orders confirms their effectiveness in capturing relevant predictors while maintaining robustness against outliers. The results highlight the utility of robust penalized quantile regression for accurate and interpretable modeling in complex data environments.