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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Applying Machine Learning for Network Security Monitoring in Kubernetes Environments
This article presents a systematic analysis of methodological approaches to applying machine learning for network security monitoring in Kubernetes environments, where the dynamic nature of containerized workloads, microservice architectures, and distributed observability significantly complicate attack detection and interpretation. The study is conducted as a review-and-analytical synthesis of peer-reviewed publications, summarizing architectural models, telemetry types, algorithmic approaches, and operational constraints without quantitative aggregation of results due to methodological heterogeneity of the sources. Particular attention is paid to the impact of Kubernetes network infrastructure on the properties of observed data, the role of non-identical and imbalanced distributions in distributed environments, and the limitations of classical centralized training schemes. The analysis shows that the prevailing practice of using ML-based intrusion detection systems is oriented toward isolated event detection and does not account for systemic telemetry distortions introduced by container network interfaces and the control plane. It is established that the greatest practical robustness is demonstrated by architectures based on hybrid data sources and federated learning algorithms, as well as two-tier schemes that separate anomaly detection from semantic interpretation. It is shown that the effectiveness of ML monitoring in Kubernetes is determined not so much by model complexity as by the degree of architectural integration into a managed security control loop encompassing observation, analysis, interpretation, and controlled response. The article will be useful for cybersecurity researchers, cloud platform architects, container security engineers, and specialists in operating distributed systems.
Copyright (c) 2026 Matvii Horskyi https://creativecommons.org/licenses/by-nc-nd/4.0
Methodological Aspects of Integrating Security Processes into Continuous Development Pipelines (CI/CD Security)
The article presents a theoretical and methodological analysis of integrating security processes into continuous integration and continuous delivery pipelines. The study consolidates DevSecOps models, software supply chain protection approaches, and multi-layer container security into a unified CI/CD Security framework. The pipeline is structured by stages of source code management, build, artifact storage, test automation, and deployment, with corresponding typical vulnerabilities and integrated security processes. This allows the pipeline to be interpreted not as a set of isolated practices but as a coherent trust chain. A multi-layer protection model is proposed, combining image control, orchestrator security, host operating system hardening, runtime protection, and artifact provenance. Within this model, the container platform and traceability mechanisms are viewed as complementary components of a single methodological foundation. The key methodological challenges of integrating security into CI/CD are identified, and it is shown that ignoring them leads to underestimated risk assessments and overstated expectations regarding adopted practices. Promising directions for advancing CI/CD Security are substantiated, including the use of intelligent telemetry analysis, self-healing mechanisms, cryptographically reinforced trust boundaries, and formalized maturity metrics. The article may be useful for architects, engineers, and researchers involved in designing secure continuous delivery pipelines in highly automated and regulated environments.
Copyright (c) 2026 Praveen Ravula https://creativecommons.org/licenses/by-nc-nd/4.0
Distributed Caching and Real-Time Sync in Active-Active Environments for Critical Cloud Native Systems
Active–active cloud-native architectures are widely adopted in mission-critical domains such as railway operations, healthcare platforms, and large-scale enterprise systems to achieve near-zero downtime, high availability, and fault tolerance. Distributed caching and real-time synchronization are foundational enablers of these architectures, ensuring low-latency data access and consistency across geographically distributed deployments. However, the combination of active–active execution, event-driven microservices, and distributed state introduces substantial challenges related to consistency, observability, and operational complexity. This paper presents a comprehensive architectural and empirical study of distributed caching and real-time synchronization in active–active cloud-native systems. We propose a reference architecture integrating event-driven communication, distributed in-memory data grids, and AI-driven observability. Experimental results demonstrate significant improvements in latency, availability, and mean time to recovery, providing practical guidance for designing resilient mission-critical platforms.
Copyright (c) 2026 Rahul Ganta, Rajkumar shevagani, Suchitra Masiragani https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Driven Predictive Maintenance in Retail IT Systems Using DevOps: A Review
Retail IT infrastructure needs to be extremely reliable and highly available to provide seamless operations, best customer experience, and effective resource utilization. Predictive maintenance driven by Artificial Intelligence (AI) and natively integrated with DevOps practices has become a game-changing solution in this regard. This paper provides an extensive review of 40 recent research articles on AI-DevOps convergence for predictive maintenance in retail IT infrastructures. A systematic literature review methodology was adopted, involving structured search, selection, and thematic analysis of peer-reviewed studies from 2019 to 2026. Some of the prime subjects discussed include common DevOps practices in retail IT, using AI for predictive analytics assistance, and merging machine learning models with failure prediction. Techniques for anomaly detection and pattern recognition in favour of early detection of possible problems are given special attention. Also, merging AI insights with DevOps pipelines is discussed in terms of automated feedback loops, CI/CD optimization, and real-time monitoring of system health. The review also addresses open issues like data quality, model drift, and integration complexity, and explores growing trends in self-healing systems and AIOps. This research aims to offer researchers and practitioners an extensive review of state-of-the-art techniques and their probable contribution to increased operational resilience and cost-effectiveness in retail IT systems through smart, automated maintenance practices.
Copyright (c) 2026 Suresh Gangula, Satyanarayana Gudimetla https://creativecommons.org/licenses/by-nc-nd/4.0
Powering the Future of AI – Leveraging Mukkudam SHP's Clean Energy for Sustainable Computational Intelligence
The present work elucidates the engineering advantages of run-of-river generation schemes, which are characterized by predictability of the generation regime and lower dependence on large reservoirs, and analyzes architectural solutions that ensure co-location of computing capacity with hydrogeneration facilities. A particular emphasis is placed on ensuring base-load stability, which is fundamentally important for continuous computing processes, and on application of the Circular Energy Hub concept, oriented toward beneficial utilization of secondary heat from compute nodes within coupled local energy-consumption circuits. The objective of the study is to substantiate the effectiveness of small hydropower plants as a dedicated energy-supply source for the Green AI paradigm. To achieve the stated objective, methods of system analysis and synthesis of relevant international literature are employed; publications related to sustainable information-technology infrastructure and hydropower are examined. In the concluding part, the potential of the Mukkudam project is defined as a reproducible model capable of supporting implementation of ESG-oriented goals at a global scale, and the practical significance of the results for the domains of artificial intelligence, energy, and environmental management is documented. The article will be of interest to engineers in artificial intelligence and data center infrastructure, specialists in energy and renewable energy sources (especially small/run-of-river hydropower), ESG/sustainability and climate-policy managers, as well as stakeholders in regional development seeking practical models for implementation of green AI through co-location of edge computing capacity and reuse of waste heat within a closed-loop cycle.
Copyright (c) 2026 Unni Siva Sankar https://creativecommons.org/licenses/by-nc-nd/4.0
Development of Quality Assurance Standards for the Industrialization of Software Applications Prototyped Using Intelligent Assistants
The article is dedicated to the analysis of quality assurance transformation in the context of industrializing software applications prototyped using intelligent assistants. The relevance of the study is determined by the rapid diffusion of large language models into software development workflows and the growing mismatch between accelerated prototyping and delayed quality degradation in production environments. Scientific novelty lies in the analytical reinterpretation of quality assurance as a layered, distributed regulatory system rather than a terminal verification phase. The work describes structural shifts in assurance logic, including constraint-driven generation, embedded testing, knowledge grounding, and orchestration-level governance. Special attention is paid to the temporal displacement of quality signals and the limitations of metric-centered evaluation in AI-assisted development. The work sets itself the goal of conceptualizing quality assurance standards suitable for the industrialization of AI-prototyped software. To achieve this goal, analytical synthesis and comparative analysis are used. A corpus of recent studies on generative AI, program repair, knowledge-enhanced systems, autonomous agents, and software quality tools is examined. The conclusions demonstrate that no single assurance paradigm stabilizes quality under AI augmentation, and layered standards emerge as a structural necessity. The article will be useful for researchers, software architects, quality engineers, and technology managers.
Copyright (c) 2026 Andrii Shaliev https://creativecommons.org/licenses/by-nc-nd/4.0
Personnel Strategies for the Formation of Engineering Teams for High-Load Projects
The article is devoted to the study and development of personnel strategies for the formation and management of engineering teams working on projects with high loads. The relevance is determined by the increasing complexity of IT products and the importance of system stability and performance. The scientific novelty lies in the proposed integrated model that combines predictive analysis of staffing needs, a competency matrix for high-load systems, and adaptive methodologies for team management. The work describes traditional and modern approaches to recruiting and developing engineers and examines cases of successful team scaling in technology companies. Particular attention within the study is paid to the role of technical leadership and the creation of a culture conducive to innovation and stress resilience. The aim of the work is to develop a comprehensive strategy that enables the formation of effective and resilient engineering teams. To achieve this, methods of scientific literature analysis, synthesis, modeling, and the study of practical experience are used. Sources devoted to talent management, organizational psychology, and agile development methodologies are examined. The conclusion describes the proposed model and provides practical recommendations for technical directors and HR specialists. The information presented in the article will be of interest to heads of IT departments, project managers, and human resource management specialists in the technology sector.
Copyright (c) 2026 Shatukha Ivan https://creativecommons.org/licenses/by-nc-nd/4.0
Comparative Analysis of Leader Election Algorithms In Distributed Systems
Leader election is a fundamental problem in distributed computing, requiring processes to agree on a single coordinator to manage critical operations such as mutual exclusion, transaction coordination, and state machine replication. This paper presents a comprehensive comparative analysis of leader election algorithms across multiple dimensions: message complexity, time complexity, fault tolerance, and practical applicability. We examine classical ring-based algorithms (Chang-Roberts, Hirschberg-Sinclair), complete network algorithms (Bully), and modern consensus-based approaches (Paxos, Raft, ZAB). Through systematic evaluation using both theoretical analysis and empirical experimental simulation, we identify trade-offs between algorithm simplicity, efficiency, and robustness. Our results indicate that while ring-based algorithms offer optimal message complexity of O(N log N ), consensus-based algorithms such as Raft provide superior fault tolerance and practical implementation characteristics for modern distributed systems. We synthesize these findings into a decision framework for practitioners selecting leader election mechanisms based on system requirements and operational constraints.
Copyright (c) 2026 Yevhen Piotrovskyi https://creativecommons.org/licenses/by-nc-nd/4.0
Strategies for Handling Barge-in Interruptions in Conversational AI Interfaces
The article addresses the problem of handling interruptions (barge-in) in voice-based ordering interfaces operating on top of large language models. The aim of the study is, based on a review of solutions for streaming speech recognition, voice activity detection, turn-taking, and end-to-end dialogue architectures, to synthesise an integrated set of barge-in handling strategies for high-load QSR scenarios. The relevance of the research is determined by the growing share of voice orders in noisy environments and their relationship to transaction accuracy, service speed, and customer satisfaction. The novelty of the article lies in treating interruption as an end-to-end intention marker that imposes coherent requirements on stack architecture, dialogue policy, utterance design, and the transactional logic of the cart. It is demonstrated that effective barge-in handling relies on prioritising user speech over system synthesis, streaming recognition with interim hypotheses, separating stable dialogue state from the instantaneous response plan inside the language model, and phase-dependent policies for interpreting identical acoustic patterns at the stages of greeting, order configuration, confirmation, and payment. It is argued that short, interruptible system utterances, explicit invitations to interrupt, and conservative interpretation of interventions at payment steps transform interruption from a source of errors into a controlled mechanism for order correction and failure mitigation. The article is intended for researchers in dialogue systems, voice interface engineers, and product teams developing voice solutions for large-scale ordering scenarios.
Copyright (c) 2026 Dhaval Hemant Shah https://creativecommons.org/licenses/by-nc-nd/4.0
An Econometric Framework for Estimating the Joint Elasticity of Advertising and Promotions on Retail Sales
Contemporary media fragmentation and heightened marketing accountability necessitate quantifying the contribution of each marketing dollar to sales, including the joint effects of advertising and price promotions. Standard Marketing Mix Models (MMM), estimated on observational time series, remain correlational and leave a causal gap, especially when campaigns and discounts are executed concurrently. The intended audience for this framework includes both academic researchers and industry practitioners, with particular emphasis on data scientists and analytics professionals working in e-commerce, retail media, and digital marketing. For these readers, the model is designed to be readily implementable within contemporary data science pipelines, leveraging familiar tools such as regression-based modeling, regularization, hyperparameter search, and out-of-sample validation to support scalable, reproducible, and decision-relevant MMM applications. A log-log model is specified in which weekly log sales are regressed on channel-specific adstocked spends (Koyck formulation) and promotions, augmented with multiplicative interactions Adstock×Promo. Decay parameters are constrained by funnel theory (Video: 0.6–0.8; Display: 0.4–0.6; SP/SB/SD: 0.3–0.5) and selected via grid search using AIC/BIC and out-of-sample RMSE under a 70/30 chronological split. Controls capture seasonality and trading peaks. Diagnostics include VIF (<10), White’s test, and DW/ACF with AR(1) errors when indicated; robustness is assessed via Ridge/Lasso. Elasticities are computed at sample means. The empirical setting is a retailer case study spanning five channels (Video, Display, Sponsored Products, Sponsored Brands, Sponsored Display) plus total promotional discounts. Elasticities along the funnel for each of the owned channels are also large and meaningful: Promo 0.28-0.36, SP 0.22-0.28, SB 0.20-0.26, Display 0.15-0.20, Video 0.10-0.14, SD 0.08-0.12. All interaction point estimates are meaningful showing interference SP×Promo ?0.05 to ?0.08 SB×Promo ?0.04 to ?0.07 Display×Promo ?0.03 to ?0.05 SD×Promo ?0.01 to ?0.03 Video×Promo ? ?0.02 to 0.00. Budget Simulation: Allocate 60-68% of budget to advertising and 32-40% to promotions. For the advertising budget, allocate 30-35% to SP, 20-25% to SB, 18-22% to display advertising, and 12-16% to video advertising. Operationally, stacking deep discounts with lower-funnel bursts depresses marginal ROI, whereas staggering upper/mid-funnel activity 1–2 weeks before promotions improves outcomes. The framework quantifies negative joint elasticities between promotions and most ad channels in retail e-commerce, challenging maximal pressure strategies. Sequenced execution, rather than synchronous peaks, maximizes incremental sales and ROI. Limitations include observational endogeneity and linear response assumptions; future research should integrate hierarchical Bayesian adstock, causal identification (e.g., IV, RDD, geo-experiments), nonlinear saturation functions, and uncertainty-aware budget optimization.
Copyright (c) 2026 Shruti Dash https://creativecommons.org/licenses/by-nc-nd/4.0
Reducing “Unknown Certificate” Risk at Scale: A Comparative Evaluation of TLS Certificate Discovery Architectures
TLS/SSL certificates are a foundational trust primitive in modern digital infrastructures, yet certificate-related outages persist despite the widespread adoption of monitoring tools and enterprise certificate lifecycle management platforms. This paper addresses this puzzle by (1) classifying and analyzing existing certificate monitoring and management approaches to identify their systematic architectural limitations; (2) designing an alternative automated monitoring method that targets these gaps by rethinking certificate discovery; and (3) evaluating its effectiveness relative to conventional approaches using controlled cyber-threat modeling in a virtualized environment. We develop a human-error-aware simulation that applies identical, time-indexed infrastructure change events to multiple monitoring architectures across legacy and cloud scenarios, enabling direct comparison of inventory completeness, omission risk, and discovery latency. The results show that configuration-bound monitoring approaches degrade with infrastructure scale and dynamism, whereas an auto-discovery model grounded in L7 load balancer and front-end state maintains substantially higher visibility and lower omission rates, particularly in cloud environments. These findings suggest that reducing “unknown certificate” risk requires architectural innovation in discovery mechanisms rather than incremental improvements to configuration discipline, with important implications for organizational risk management and national cybersecurity resilience.
Copyright (c) 2026 Vasilii Turuntaev https://creativecommons.org/licenses/by-nc-nd/4.0
Implementation of Zero-Trust Architecture in Mobile Applications of the Financial Sector
This article examines the rethinking of zero-trust architecture for mobile applications in the financial sector as a crystallization of the contemporary model of cyber-resilience amid explosive growth in mobile banking and the rise of specialized smartphone attacks. It is demonstrated that traditional perimeter-based security models do not provide the required level of protection in the context of a high density of vulnerabilities in client applications and the exponential increase in malicious activity, which predetermines the relevance of a transition to an approach in which every request and every action is treated as potentially untrusted. The purpose of the study is to conceptualize and specify the principles of zero-trust architecture at the mobile client level. The scientific novelty lies in shifting the center of gravity from an abstract trusted perimeter to a set of explicitly defined decision points across the interface, domain logic, and infrastructural library layers, as well as in introducing an application trust map, standardized interface components as carriers of security policy, and a unified software kit for authentication, session management, and authorization checks. The proposed roadmap, from constructing a trust map to the phased implementation of multi-step, risk-oriented authentication, enables transforming the zero-trust architecture from a one-time initiative into a continuous process embedded in the life cycle of mobile financial product development. The article is intended for researchers, architects, and practitioners engaged in the design and evolution of mobile solutions in the banking and fintech sectors.
Copyright (c) 2026 Pankiv Oleg https://creativecommons.org/licenses/by-nc-nd/4.0
Evolution of Cloud LMS Architecture: From Local Spreadsheet Workflows to a Distributed White-Label System
The article examines the architectural evolution of a commercial cloud-based learning management system that originated as a set of Google Sheets used to coordinate operations in a Ukrainian IT school and later evolved into the white-label platform DrivEd, adopted by networks of schools in the USA, Australia, and Europe. The study focuses on the transition from fragile spreadsheet-centred workflows to a dedicated LMS, and then to a multi-tenant, cloud-native solution that supports branded deployments for hundreds of tenant schools without code forks. The objective is to reconstruct the main architectural decisions that enabled this trajectory and correlate them with current research on cloud LMS and multi-tenant SaaS. The analysis relies on published work on LMS design, SaaS multi-tenancy, and serverless platforms, and employs an analytical case-study method. The conclusions highlight architectural patterns that are reproducible in other education-technology products facing international scaling demands.
Copyright (c) 2026 Burmistrov Aleksandr https://creativecommons.org/licenses/by-nc-nd/4.0
Conceptual Approaches to Organizing Feature Stores in High-Load Machine Learning Systems
The paper examines conceptual approaches to organizing feature stores in high-load machine learning systems. The growth of production ML has exposed persistent problems, including duplicated feature logic, training–serving skew, and brittle pipelines, particularly under low-latency inference and heavy batch training workloads. This paper proposes an original analytical framework for organizing feature stores under high-load conditions. The framework links internal architectural decisions (registry design, transformation semantics, offline/online materialization, synchronization, geo-distribution, and maintenance control) to operational objectives (latency, point-in-time correctness, reproducibility, governance, and cost discipline), enabling structured design selection rather than a descriptive survey of existing systems. The study characterizes functional blocks of a feature store (feature registry, transformation layer, offline and online materializations, and serving interfaces), then analyzes consistency guarantees, point-in-time correctness, refresh strategies, and geo-distribution patterns. Methods rely on a comparative analysis of recent systems and surveys, with a focus on scalability bottlenecks in joins, cache design, and maintenance scheduling. The conclusion formulates design recommendations for selecting storage layouts, synchronization policies, and operational controls across the ML lifecycle. The paper targets engineers and researchers working on production MLOps and data infrastructure. Implications for feature reuse and compliance are discussed.
Copyright (c) 2026 Artem Korkhov https://creativecommons.org/licenses/by-nc-nd/4.0
Vehicle Feature VQA: Visual Question Answering for Vehicle Feature
Visual Question Answering (VQA) can automatically produce the predict answers for questions and real-world images. In this paper, we propose the VQA dataset for Vehicle Feature to know the knowledge of Vehicle. We develop the VQA model using RestNet50 in Convolutional Neural Networks (CNN) for feature extraction of images and Long Short-Term Memory (LSTM) for question feature extraction and answer generation. The experimental result describes the training loss, evaluation loss, Blue Score, and VQA accuracy for epochs 20 and epochs 30. In epochs 20, after VQA model generated the training loss 1.1949 , evaluation loss 1.7953, Blue Score 0.6180, and VQA accuracy 0.0493, this model predicted the one correct answer for question and image. In epochs 30, the VQA model predicted the five correct answers in fifteen test data for vehicle feature questions and image according to generate the training loss 0.8780, evaluation loss 1.6634, Blue Score 0.6775 and VQA accuracy 0.0627.
Copyright (c) 2026 Pa Pa Tun, Khin Mar Soe https://creativecommons.org/licenses/by-nc-nd/4.0