International Journal of Computer (IJC)

Innovative Solutions for Enhancing System Performance on the .net Platform

Mykhailo Karpenko — 2025-04-07

This study examines architectural solutions and scaling approaches on the .NET platform in the development of distributed web applications. The impact of microservices and ASP.NET Core MVC on response speed, system resilience, and resource efficiency is analyzed. Methods of automation aimed at monitoring metrics and dynamically managing computing power are discussed. A review of research detailing codebase organization, testing, and cloud service deployment is presented. Special attention is given to DevOps practices that facilitate seamless functionality updates and reduce the risk of overloads during peak traffic periods. It is noted that collaboration among specialists from different fields accelerates product releases and simplifies task management of varying complexity. Additionally, conclusions are drawn on containerization strategies that streamline component integration and optimize communication in scalable projects. Approaches to service orchestration and automated quality control are also explored, extending CI/CD capabilities and increasing process transparency in software development. The systematization of collected data contributes to a deeper understanding of the principles behind building high-load solutions on the .NET platform. Future research perspectives are also considered. This study will be valuable for web development specialists, DevOps engineers, and IT team leaders.

Evolution of User Interface in Mobile Applications

Oleg Sukhorukov — 2025-05-05

The evolution of user interfaces (UI) in mobile applications is a crucial aspect of modern digital interaction, influencing both user experience and functionality. This paper explores the historical development of mobile UI, from early monochrome displays to augmented reality (AR) and artificial intelligence (AI)-driven interfaces. A comparative analysis of iOS and Android UI paradigms highlights key differences and commonalities. The study provides a systematic overview of UI advancements, emphasizing the transition from button-based navigation to gesture-based and voice-controlled interfaces. The findings offer insights into emerging trends that will shape the future of mobile UI design.

Analyse of Weather Forecasting Models Using LSTM with Noise Removal Methods

Nge — 2025-04-22

Weather forecasting is one of the most important fields for all sectors such as transportation (air traffic, marine), industry, agriculture, forestry and even public health sector. The aim of this study is to analyse the weather forecasting models using filtering techniques which can remove the noise involved in the time series weather data. We utilize ten years weather data (2013 to 2022) in Hmawbi region, Yangon, Myanmar. The weather dataset includes maximum and minimum temperatures, humidity, wind speed, cloud amount and weather type. It is taken from the Department of Meteorology and Hydrology, Myanmar. The original data includes noise data. Thus, two noise removal methods, Simple Moving Average and Weighted Moving Average, are used for data cleaning. In this paper, three prediction models are developed by using Long Short-Term Memory (LSTM) with different datasets: one with the original data (without noise removal), one with data cleaned using the Simple Moving Average method, and another with data cleaned using the Weighted Moving Average method. The research goal is to compare and evaluate the performance of these three weather prediction models to determine which one gives the better results. To evaluate these models, Root Mean Square Error (RMSE) and Mean Square Error (MSE) are calculated for each model. LSTM achieves RMSE of 5.398 and MSE of 29.020, SMA achieves RMSE of 3.767 and MSE of 14.841, WMA achieves RMSE of 0.798 and MSE of 0.534. According to the experimental results, it is found that model with data cleaned using the Weighted Moving Average method is lower error rates than other two models and gives the better predicting results than other two models.

The Evolution of Test Automation: From Selenium to Playwright. A Comparison of Automation Tools: Selenium vs. Playwright vs. Cypress

Anatolii Tymoshchuk — 2025-04-22

This study examines the evolution of web application test automation tools, focusing on a comparative analysis of Selenium, Playwright, and Cypress. The research justifies the relevance of transitioning from the traditional Selenium-based approach to modern frameworks that offer higher performance and stability in the rapidly evolving landscape of web applications. The study follows a methodology that involves sequential execution of test scenarios of varying complexity, including a simple static site test, end-to-end testing in a production environment, and a comprehensive test suite evaluation. Key performance metrics such as average execution time, standard deviation, and coefficient of variation are assessed. The findings indicate that Playwright demonstrates the best performance for testing dynamic web applications, while Cypress, despite an initial slowdown in simple scenarios, becomes competitive when executing local test suites. The article provides practical recommendations for selecting an automation tool based on the characteristics of the tested applications and outlines future development prospects, including the integration of artificial intelligence technologies and the optimization of CI/CD processes. This research addresses an existing gap in the field and offers practical solutions to enhance the quality and efficiency of modern web application testing. The findings will be of interest to researchers in software engineering, quality assurance professionals, test system architects, and academic professionals seeking to integrate advanced methodologies into software development and testing processes.

Best Practices for Personal Data Protection in Scalable Enterprise Applications

Serhii Onishchenko — 2025-04-22

This article examines existing methods for protecting personal data in corporate applications, considering modern challenges in dynamic and distributed cloud environments. The study includes an extensive analysis of encryption techniques, such as proxy encryption, DNA encryption, dual encryption with fragmentation, and homomorphic encryption, as well as mechanisms for ensuring data integrity and access control. These mechanisms include cryptographic hash functions, digital signatures, message authentication codes (MAC), role-based and attribute-based access control, federated authentication, and multi-factor authentication. The research also reviews publicly available studies found on the Internet. Particular attention is given to the specifics of data protection in cloud infrastructures, where high intensity, data fragmentation, and the lack of physical security control necessitate architectural solutions such as Trusted Virtual Data Centre (TVDc) and Tera Architecture, along with the integration of security measures into the software development lifecycle (SDLC). The materials presented in this study are relevant to researchers, system architects, and corporate IT infrastructure practitioners seeking to synthesize theoretical and empirical approaches to achieve a high level of information security in the face of rapidly evolving threats.

Modern Approaches to Risk Management in the Use of Big Data in the Financial Sector

Di Feng — 2025-05-10

This study analyzes modern approaches to financial risk management using Big Data, artificial intelligence (AI/ML), and generative AI. A comprehensive literature review has been conducted, examining the advantages of traditional methods such as MCMC, as well as the potential of machine learning algorithms and generative models for data synthesis and stress testing. A scientific gap has been identified, highlighting the lack of integrated methodologies that combine statistical modeling, AI/ML, and generative AI into a unified risk management system. The objective of this study is to explore the specific features of contemporary approaches used in risk management processes involving Big Data in the financial sector. The study's scientific novelty lies in analyzing the feasibility of forming a unified integrative system capable of synthesizing synthetic data for extreme scenario modeling, as well as automating risk monitoring and analysis processes through cloud computing platforms and RegTech solutions. The findings presented in this study may be of interest to researchers, postgraduate students, and professionals in finance and risk management. Additionally, the data outlined in this research may be valuable for specialists seeking interdisciplinary synergy between financial engineering, information technology, and statistical methods to optimize managerial decision-making in the era of digital transformation.

Analyzing the Performance of ECLAT Algorithm for Large Datasets by Comparing K-means and Gaussian Mixture Model

Nandar Lin — 2025-05-06

Frequent Itemset Mining (FIM) is a technique that transforms historical data into useful information by identifying beneficial patterns. The ECLAT method uses depth-first search to intersect the transaction ID sets with the corresponding k^thitem sets in order to calculate the support items. While searching for the best-selling products, ECLAT uses a lot of memory and processing time due to the enormous number of transaction ID sets. To overcome these problems, the clustering method combines with the ECLAT algorithm to retrieve the support items. Description elements 100,000 to 400,000 were used to retrieve the support items of the most popular selling goods. For the K-means clustering approach, the optimal value of k is 8 clusters according to the 0.59 silhouette value. For the Gaussian Mixture Model, the ideal value of k is 14 clusters based on a 0.59 silhouette score value between 100,000 and 400,000 data items. After clustering the same product items, the ECLAT algorithm retrieves the support items by applying a minimum support value of 0.00001 in this investigation. According to the experimental results, the Gaussian Mixture Model not only offers more flexibility for clustering the same items but also reduces the memory usage and execution times. The outcomes of this investigation indicate that the Gaussian Mixture Model provides more efficient enhancement of the performance of the ECLAT algorithm than the K-means algorithm.

Defensive Cybersecurity Preparedness Assessment Model for Universities

William Kipkoech Too — 2025-04-07

Broadband and internet access has become readily available to citizens across the globe as a result the recent uptake of fiber connectivity. General Cyber Security threats like malware attacks, social engineering scams and financial frauds have increased. Though numerous security models have been advanced by NIST and ISO standards, but the frightening truth is that escalating cyber-attacks are still on the rise. This is because most existing security analysis tools focus mainly on detecting attacks. Despite the steady flow of security updates and patches, this scenario has led to a continued rise of attack surface in institutions of higher learning where students and staff sensitive information and valuable assets is of high stake. Therefore, the purpose of this study is to establish the factors for effective defensive cyber security in Universities. The study utilized a survey method to collect data from cyber security experts of the sampled universities. The study targeted 27 respondents (ICT experts) from 5 universities both public and private that were purposively sampled in Kenya. 23 questionnaires were returned translating to 85% response rate. This was very sufficient for the study. Correlation analysis was carried and the findings indicated a statistically significant relationship for human factors (87.7%), technology factors (83.5%), and policy factors (83.2%) on defensive cyber security preparedness. Multiple linear regression was also done to predict the extent of the effect of each independent variable on defensive cyber security preparedness. In conclusion, the study noted that, all the three cyber security factors were significant hence there was a need to enhance them so as to improve security against the advancing threat landscape across all sectors especially institutions of higher learning like universities.