The International Journal of Computer (IJC) is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. IJC plays its role as a refereed international journal to publish research results conducted by researchers. This journal accepts scientific papers for publication after passing the journal's double peer review process (within 4 weeks). For detailed information about the journal kindly check About the Journal page. All IJC published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.
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Interrogating the preparedness of Uganda’s Higher Education and the Role of Information and Communication Technology in the era of COVID-19 Pandemic and its Aftermath
This article is based on a qualitative study conducted to establish the preparedness of Uganda’s Higher Education and the role of Information and Communication Technology (ICT) during COVID-19 pandemic and its aftermath. Coincidentally, the dynamics of COVID-19 pandemic predicated the need for urgent adoption and scale up of ICT in education. However, universities in Uganda were strategically ill-prepared to adopt and sustain to digital and online methods of engagement, sighting problems ranging from policy and curriculum deficit, to lack of staff and student preparedness. Ideally, the inability for universities to adopt and sustain to digital and virtual mode of operations demonstrated a strategic irregularity between ICT and higher education systems. In response, this study was intended to address the salient gap by identifying the key pedagogical challenges encountered and recommend appropriate strategic options based on the global best practice in ICT and higher education. Procedurally, the study adopted qualitative study approach, using scoping literature review techniques and content analysis methods of extracting evidence. By way of reflective analysis and scrutiny, the study sought to establish how various universities coped with COVID-19 lockdown, while identifying the relevant information in the domain of ICT and higher education systems. Hence, data analysis followed a descriptive approach, linking the identified gaps with the global best practices. Subsequently, the general implication of the study finding is that there was a strategic mismatch between ICT and higher education systems. Thus, the key challenges identified include lack of pedagogical flexibility and inefficient social interaction among learners and instructors, lack of self-directed and independent learning, restricted mode of assessment, and lack of staff competence in ICT and pedagogical approaches. Hence, this study provides a strategic guidelines to higher education planners to formally integrate ICT functions and education systems.
Copyright (c) 2025 Joe Mutebi, Margaret Kareyo, Paul Akampurira, Brian Mugisha, Watson Kabyesiza, Amos Kahara https://creativecommons.org/licenses/by-nc-nd/4.0
The Impact of Cross-industry Collaboration on the Effectiveness of Devops Practices
This article examines the influence of interdepartmental and cross-industry collaboration on the effectiveness of DevOps practices. As the demand for faster release cycles increases, organizations face the challenge of simultaneously enhancing system stability and reducing error rates. The study is based on materials from DORA, reports by Deloitte, Atlassian, Brainhub, DevMio, and publications by Ahmad et al., Dryka et al., Offerman, and Smith. A comparative analysis is conducted on internal collaboration models involving development, operations, testing, and information security teams, as well as on knowledge exchange between IT organizations and representatives of the banking, telecommunications, and manufacturing sectors. The goal is to assess the impact of collaboration formats on release frequency, time to incident recovery, and the proportion of failed changes. The research methodology includes literature systematization, statistical analysis of performance metrics, and synthesis of empirical evidence. The results show that integrating professionals from different domains accelerates delivery cycles, lowers failure rates, and improves team satisfaction. The article is relevant to project managers, DevOps team leads, digital transformation experts, and consultants responsible for optimizing the development and operations of software solutions.
Copyright (c) 2025 Nikita Romm https://creativecommons.org/licenses/by-nc-nd/4.0
Machine learning applications for event routing in streaming systems
The paper provides a broad overview and classification of machine learning methods used to optimize routing in distributed streaming architectures. The aim of the study is to provide a detailed analysis of existing approaches: from classical reinforcement learning algorithms to modern deep neural networks, with an assessment of their potential in various operational scenarios and identification of key limitations. The methodological basis was a systematic review of publications dealing with intelligent routing, real-time data processing, and integration of ML solutions into system pipelines. Three main classes of algorithms were identified and considered: reinforcement learning methods (including DQN and actor-critic), deep networks (CNN, RNN and their hybrids), as well as ensemble and evolutionary techniques. The advantages and disadvantages of each class are analyzed in terms of key criteria — response time to flow changes, scalability in the number of nodes, and the ability to dynamically adapt. Special attention was paid to hybrid strategies that combine several models to increase the reliability and accuracy of recommendations on event transmission routes. In conclusion, the main conclusions about the current state of research are formulated and promising areas are outlined: the development of more robust architectures with explicable decision-making logic, as well as the integration of graph neural networks for modeling complex topologies of distributed systems. The presented results will be useful for engineers developing streaming platforms, big data analysis specialists, and research groups working on information channel optimization tasks.
Copyright (c) 2025 Vladyslav Vodopianov https://creativecommons.org/licenses/by-nc-nd/4.0
Polycrystalline Organic Semiconductors for Low-Power AI-Integrated Devices: Fabrication and Characterization
Polycrystalline organic semiconductors constitute a promising class of functional materials, combining architectural flexibility, compatibility with low-temperature processing, and tunable electronic properties. Owing to their ability to form ordered morphologies with defined crystallinity, these materials can be seamlessly integrated into energy-efficient intelligent systems—such as neuromorphic components, sensing nodes, and photo-analytical elements. In the context of rapidly growing interest in edge computing and Internet-of-Things infrastructures, they offer advantages over silicon counterparts, including mechanical robustness, spectral sensitivity, and the capacity for on-site primary signal processing. This study reviews methods for producing polycrystalline films, examines how fabrication parameters influence morphological and electrical structure, and surveys quality-control techniques at both micro- and structural levels. Organic semiconductors thus fulfil a strategic role in the design of adaptive, miniaturized, and self-learning devices operating under unstable conditions, forming the basis for subsequent advances in intelligent organic electronics.
Copyright (c) 2025 Sidra A. Shaikh https://creativecommons.org/licenses/by-nc-nd/4.0
Lean MVP Development Via SQL Prototypes: Fast ETL Pipelines, Temporary DWHs, and Accelerated Validation of Product Hypotheses
This paper discusses the use of lightweight SQL prototyping for rapid ETL pipeline construction and MVPs in terms of enabling temporary data warehouses and accelerating product hypothesis validation. It lays out, formalizes, and tests aspects of a Lean Analytical Circuit Building approach based on declarative SQL language such that verifiable metrics may be available to a team without waiting for long procurement and infrastructure approval cycles. Relevance comes with high uncertainty at the beginning of both a startup and product-project development, when classic corporate data warehousing takes from six months up to two years to deploy, thus injecting great schedule and budget risk on top of this, reducing the speed of validated learning through loss of the value of data due to lack of operational feedback. It is new in the mixture of three-tier architecture (Staging, Transform, Data Mart), usage of any current DBMS or cloud engines (in temporary cluster mode, DuckDB, ClickHouse, BigQuery Sandbox), content analysis regarding availability of SQL skills and economic risk assessment together with a systematic comparative and instrumental analysis of performances of prototypes. The main finding is that the cycle from event arrival to target table update can fit within fifteen minutes which means fulfillment of a requirement that needs fast reaction for changes in user behavior and marketing campaigns while keeping the flexibility on the structure level among SQL prototypes preserving transparency and reproducibility plus automatic policies deleting obsolete data and serverless sandbox mode controlling costs. A smooth transition from the temporary solution to stable platforms, according to the Infrastructure as Code principle, minimizes operational risks and ensures continuity of metrics. The article will be helpful to startups and product teams, data engineers, and business analysts seeking to combine the speed of Lean methodology with data reliability.
Copyright (c) 2025 Daria Bogun https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence in Design Systems: Advancing Scalable and Adaptive User Interface Frameworks
Artificial intelligence has fundamentally reshaped design systems, establishing a new standard for modern interface development and digital product engineering. AI-powered frameworks drive efficiency by automating component creation, identifying design patterns, and monitoring accessibility compliance at scale. Through the application of neural networks, deep learning, and computer vision, these systems interpret user interactions and adjust interfaces dynamically. Evidence from testing healthcare and enterprise applications confirms that such systems consistently shorten development cycles, improve user satisfaction, and lower operational costs. Automation further enables design and engineering teams to shift focus toward higher-value innovation while ensuring uniformity across platforms. Machine learning enhances personalization, strengthens validation through automated testing, and ensures more reliable user experiences. Beyond measurable outcomes, AI integration improves accessibility compliance, decreases technical debt, and facilitates stronger collaboration across teams. Collectively, this evolution represents a decisive step forward in how organizations design, optimize, and sustain user interfaces in the digital era. This article will be especially useful for software engineers, UX designers, product managers, and academic researchers seeking to understand and implement AI-enabled design systems for scalable, adaptive, and efficient digital interfaces.
Copyright (c) 2025 Tamerlan Mammadzada https://creativecommons.org/licenses/by-nc-nd/4.0
Leveraging Big Data Analytics for Combating Fake News: A Supervised Learning Approach to Identifying Misinformation on Social Media
The rapid rise of social media has transformed how people consume and share information but has also accelerated the spread of misinformation that undermines public trust, public health, and democratic stability. Manual fact-checking and platform moderation often lag behind the speed of misinformation, highlighting the need for scalable, automated solutions. This study develops a supervised machine learning framework supported by Big Data analytics for fake news detection. Using the ISOT Fake News Dataset of 44,898 labeled articles, we implemented a structured pipeline that included text normalization, tokenization, stopword removal, stemming, and TF-IDF vectorization, followed by training four classifiers: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Evaluation was conducted using a stratified 80/20 train-test split with 10-fold cross-validation, applying Accuracy, Precision, Recall, and F1-score as performance metrics. Results show that ensemble models, particularly XGBoost and Random Forest, consistently outperformed LR and SVM, achieving accuracies near 99% with strong precision and recall across both classes. These findings demonstrate the strength of optimized ensemble methods in detecting misinformation and their scalability for real-world application. Beyond model performance, this work proposes a distributed architecture leveraging Apache Spark for real-time deployment, providing a foundation for practical and scalable misinformation detection systems.
Copyright (c) 2025 Kehinde Racheal Ilugbiyin, Damilola Nnamaka Ajobiewe https://creativecommons.org/licenses/by-nc-nd/4.0
Infrastructure Deployment Patterns for AI Services on the Claude Platform
This article questions how to select and use infrastructure patterns in the deployment of industrial AI services on Claude due to the increased enterprise demand and broadened capabilities. This study proves relevant since, within a very short span, generative AI work has transformed from novelty experiments to mission-critical business workloads where effectiveness directly depends on architectural decisions ensuring not only low latency but also cost predictability together with the compliance of corporate security requirements. Therefore, the purpose of this paper is to identify recurring architectural patterns and their operational mechanisms that may be used in balancing response time, context size, cost, and reliability when integrating Claude into enterprise IT systems. The scientific novelty of the research is a detailed taxonomy of infrastructural solutions (serverless gateways, containers in isolated VPCs, batch queues) and how their combination begins to meet the very special challenges of Claude—challenges such as context windows up to one million tokens and loads that seem to increase sharply above 25 billion requests per month. The main findings show that the best setup comes from using small APIs with autoscaling and local cache for interactive work; containers with private links and Batch interfaces when dealing with deep analytics and big processing. The quota system and context thinning, breaking up queues by priority and budget, as well as token control and telemetry, are pieces to help give teams a p95 latency promise while staying inside financial and regulatory fences. The article will be helpful to researchers in cloud architectures, enterprise IT system architects, and practitioners deploying AI services based on Claude models.
Copyright (c) 2025 Yevhen Mykhailenko https://creativecommons.org/licenses/by-nc-nd/4.0
Cloud Security Architecture for the Automotive Industry: A Framework for Secure Multi-Cloud Deployment
This paper examines the automotive industry since it fundamentally transforms toward Software-Defined Vehicles (SDV) and exponentially increases the reliance on cloud and multi-cloud infrastructures. The attack surface expands in a dramatic way because of a shift to this model. Today cyber risks target centralized cloud backends managing entire fleets not individual vehicles. A comprehensive security architecture should be developed proactively and without delay, as potential threats may escalate, and stringent regulatory frameworks such as UNECE R155 and ISO/SAE 21434 are anticipated to come into effect. In this paper, the Automotive Multi-Cloud Security Framework (AMCSF), which is a conceptual cloud security architecture model for the automotive industry, is proposed based on a systematic literature review as well as comparative analysis of industry practices. ISO/SAE 21434 describes vehicle cybersecurity lifecycle processes the five-layer model would integrate. It works also with modern cloud-oriented technologies like Cloud Security Posture Management (CSPM) and Cloud Workload Protection Platforms (CWPP). The study primarily concludes that risk can be effectively managed in modern automotive ecosystems only if protections are applied synergistically and include both the vehicle itself and its cloud infrastructure. This paper will be helpful to cybersecurity and cloud solution engineers and architects in automotive (OEM, Tier-1/Tier-2), vSOC/DevSecOps teams, cybersecurity and compliance management specialists (CSMS/UNECE R155, ISO/SAE 21434), as well as consultants and executives responsible for designing and operating secure multi-cloud backends.
Copyright (c) 2025 Geol Kang https://creativecommons.org/licenses/by-nc-nd/4.0
Federated Learning Approaches for Privacy-Preserving Conversational AI in Dental Informatics
This study investigates how federated learning can enable privacy-preserving conversational AI for dental clinics while complying with GDPR and HIPAA. The main objective is to determine whether clinics can automate patient communications, call handling, reminders, and triage, without transferring raw patient data beyond local systems. The methodology combines a narrative review and comparative analysis of recent healthcare federated-learning studies with a practical deployment blueprint tailored to dental workflows. Three training paradigms are contrasted, local, centralized, and federated, to summarize evidence on model accuracy and computational cost. A layered privacy stack is presented, including differential privacy, secure aggregation, and homomorphic encryption, with guidance on when each technique is most appropriate. An end-to-end workflow is described in which clinics train lightweight local adapters on anonymized speech and text, share only encrypted model updates, and receive an aggregated global model that improves across participating sites. Findings indicate that federated models achieve accuracy comparable to centralized baselines in representative tasks (e.g., segmentation and risk prediction) while keeping patient data local. Cryptographic protections introduce overhead but remain practical when applied selectively. This study demonstrates that federated learning enables privacy-preserving conversational AI in dental informatics without compromising model performance. In practical terms, such systems can reduce missed appointments through targeted reminders, accelerate routing of urgent complaints, and support intern training with simulated dialogues, delivering the benefits of shared learning while maintaining strong confidentiality and regulatory compliance.
Copyright (c) 2025 Usman Tariq https://creativecommons.org/licenses/by-nc-nd/4.0
Paradigms of ELT Data Pipeline Architectures for LLM Training
This article presents a systematic analysis of ELT pipeline architectures used in the training of large language models. The study is based on an interdisciplinary approach that integrates engineering principles of data infrastructure design, theoretical foundations of transformer architectures, and data flow automation practices under conditions of high source variability. Particular attention is given to the content analysis of scientific and applied publications addressing the role of LLMs in transformation loops, the implementation of agent-oriented solutions, and the support of multimodal adaptive pipelines. Key ELT architecture types are identified, including prompt-driven, agent-based, high-throughput, and cognitively enhanced solutions, reflecting varying levels of model involvement in data processing. The analysis shows that architectural shifts toward feedback integration and dynamic routing enable the creation of robust and adaptive solutions suited to contemporary training scenarios. Special emphasis is placed on issues related to data stream instability, the lack of benchmarks for agent-based systems, and insufficient integration of pipelines with model evaluation mechanisms. The paper proposes a conceptual classification of ELT paradigms and an outline of their adaptive evolution toward building scalable and logically coherent infrastructures. The article will be of interest to researchers in machine learning systems, LLM infrastructure developers, data platform architects, and professionals in digitalization and automation of AI training workflows.
Copyright (c) 2025 Olusesan Ogundulu https://creativecommons.org/licenses/by-nc-nd/4.0
Edge-Computing Assisted Robotic Vision Systems: Test Automation, Fault Prediction & Recovery
This dissertation investigates the integration of edge-computing technologies into robotic vision systems, focusing on enhancing test automation, fault prediction, and recovery processes. The research articulates the critical gap in operational efficiency and reliability within existing robotic vision systems due to delayed data processing and insufficient fault management strategies. Through a comprehensive analysis of real-time performance metrics, fault occurrence logs, and corresponding recovery times, the findings demonstrate a significant reduction in system downtime and an increase in fault detection accuracy, thereby optimizing the functionality of robotic vision applications. The key results reveal that implementing edge-computing not only facilitates immediate data analysis and decision-making but also substantially improves the predictive capabilities for system failures, leading to more resilient automation strategies. These advancements hold considerable significance in the healthcare sector, where robotic vision systems are increasingly deployed for surgical assistance and diagnostics, enhancing patient safety and operational workflow. The broader implications of this study suggest that by fostering robust edge-computing frameworks, healthcare institutions can leverage improved robotic systems to enhance clinical outcomes, reduce costs associated with system failures, and ultimately support the transition towards more intelligent and responsive healthcare environments. This research contributes to the ongoing dialogue regarding the adoption of innovative technologies in healthcare, positing edge-computing as a pivotal element in the future development of reliable and efficient robotic solutions.
Copyright (c) 2025 Himani Singhai https://creativecommons.org/licenses/by-nc-nd/4.0
A Review of Event-Driven Architecture Patterns Using Message Brokers in .NET
This article describes different event-driven architecture (EDA) patterns that use message brokers to implement distributed computing in .NET. This article also describes data consistency and state management techniques in a distributed system architecture. In response to the industry trend of moving from monolithic architecture to microservice architecture for scalability, agility, and resilience, this project seeks to provide a thorough basis for decision-making for the adoption of EDA patterns and message brokers (like Apache Kafka, RabbitMQ, Azure Service Bus) based on non-functional requirements and organizational maturity levels. The methodological foundation is based on a Systematic Literature Review (SLR), ensuring reproducibility, completeness, and methodological rigor of the analysis. The scientific novelty of this work lies in integrating three analytical dimensions: theoretical architectural patterns, the technological implementation of message brokers, and the practical aspects of applying them within .NET systems. The main findings emphasize that the choice of EDA patterns and broker technologies is not a search for an optimal, universal solution but a deliberate balancing of technical and organizational factors. Successful use of EDA requires maturity and experience in DevOps and observability, as well as strict adherence to idempotency principles. Therefore, the target audience for this article is software architects, developers, and researchers concerned with microservices and event-driven systems of all kinds built on .NET, as well as technical decision-makers responsible for enterprise adoption of EDA.
Copyright (c) 2025 Serhii Yakhin https://creativecommons.org/licenses/by-nc-nd/4.0
AI Approaches to Software Quality Assessment: From Defect Prediction to Test Coverage Optimization
The article addresses the problem of growing inefficiency in traditional approaches to software quality assurance (QA) under accelerated cycles of continuous integration and delivery (CI/CD). The aim of the study is to present and evaluate a new integrated platform that leverages large language models (LLMs) to automate and optimize key QA processes. The methodology is based on an industrial case study of a system utilizing Claude 4 and Amazon Q CLI for semantic log analysis and predictive test prioritization. The paper presents key quantitative results, including an increase in nightly build stability from ~70% to over 90%, a one-third reduction in regression testing time, and a halving of pull request verification time. Additionally, it is emphasized that the adoption of such solutions enhances testing transparency, improves collaboration between development and QA teams, and accelerates release cycles while ensuring higher product quality. The main contribution of the article is the provision of empirical evidence from a large corporate environment, confirming that modern AI-driven approaches can significantly improve the efficiency, accuracy, and strategic value of software testing. The article will be useful for researchers, quality engineers, CI/CD architects, and DevOps practitioners interested in applying LLMs to optimize testing processes.
Copyright (c) 2025 Taras Buriak https://creativecommons.org/licenses/by-nc-nd/4.0
Principles of Designing Monetization Systems in Free Mobile Games
The article presents a comprehensive analysis of the principles underlying the design of monetization systems in free-to-play mobile games as a distinctive phenomenon of the digital economy, where behavioral psychology, game design, and user interaction ethics converge. The relevance of the topic lies in the fact that monetization in the free-to-play format has evolved beyond a purely technical function; it has become an independent design domain that shapes the longevity and reputation of gaming ecosystems. Despite a considerable body of research, there remain substantial discrepancies in the assessment of the ethical acceptability of microtransactions, the role of artificial intelligence in offer personalization, and the balance between commercial benefit and player autonomy. The purpose of this study is to identify the key patterns behind the creation of sustainable monetization systems that maintain harmony between the developer’s economic interests and the player’s psychological comfort. Drawing on academic literature, statistical data, and the author’s own experience in designing F2P projects, a set of principles is formulated to support the development of transparent and non-intrusive monetization models. The article demonstrates that the effectiveness of such systems depends not on the number of transactions but on the quality of the trust-based relationship established between the player and the game. The author’s contribution lies in the systematization of interdisciplinary approaches—economic, cognitive, and design-oriented—and in emphasizing the importance of long-term audience retention and the integration of commercial mechanisms into the game narrative. The conclusions are of particular interest to game designers, user behavior analysts, and researchers of digital markets.
Copyright (c) 2025 Oleh Riazanov https://creativecommons.org/licenses/by-nc-nd/4.0