INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
International Journal of Research and Applied Innovations in Computer Science (IJRAICS) is a peer-reviewed, open-access academic journal dedicated to advancing the field of computer science through the dissemination of high-quality research and innovative technological solutions. The journal provides a global platform for researchers, scholars, practitioners, and industry experts to present their novel contributions in theoretical, experimental, and applied aspects of computer science. IJRAICS aims to bridge the gap between theoretical research and real-world application by encouraging submissions that demonstrate practical relevance, original thought, and significant impact. The journal welcomes interdisciplinary articles that explore the integration of computer science with other domains such as engineering, healthcare, education, finance, and data science. Key features of IJRAICS: Double-blind peer-review process ensuring academic rigor Emphasis on emerging trends, real-world applications, and innovative technologies International editorial board with diverse expertise Regular publication of special issues on cutting-edge topics
Scopus Delisting Controversies: A Critical Review of Ethical and Policy Implications
This paper presents a critical examination of the controversies surrounding Scopus journal delisting practices, with particular focus on ethical and policy implications that have emerged between 2020 and 2025. As the largest abstract and citation database globally, Scopus wields substantial influence over academic visibility, researcher career progression, institutional rankings, and funding allocations. However, recent delisting decisions—particularly those involving retroactive content removal and allegations of bias against journals from developing countries—have raised profound ethical and procedural concerns. Through detailed case studies of the Nurture, International Journal of Innovative Research and Scientific Studies (IJIRSS), and Science of Law delistings, this paper analyzes the transparency, accountability, and fairness of Scopus's Content Selection and Advisory Board (CSAB) processes. The review identifies systemic issues including lack of procedural transparency, disproportionate impact on Global South institutions, retroactive deletion of already-indexed content, and the inherent conflict between Elsevier's commercial interests and academic mission. The paper concludes with policy recommendations for establishing equitable, transparent frameworks for journal evaluation and content retention that balance quality assurance with scholarly integrity.
Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Event-Driven Microservices Architecture for Scalable and Resilient Distributed Computing Systems
Event-driven microservices are often sold as the natural answer to scalability, resilience, and distributed autonomy, yet the empirical record is less obedient than the architectural slogans suggest. The architecture replaces direct request dependency with asynchronous event exchange, private service ownership, broker-mediated communication, and delayed consistency, but the gain in decoupling usually reappears as operational debt in schema governance, observability, replay control, ordering ambiguity, poison-message handling, and cross-service accountability. Microservices are commonly defined through independent deployment and loose coupling, while event sourcing, sagas, CQRS, and CloudEvents formalize different parts of the event-driven stack; none of them removes the coordination problem, they merely relocate it. The evidence is contradictory at best.
Spring Boot Framework for Rapid Development of Cloud Native and Enterprise Applications
The increasing demand for scalable, resilient, and cloud-native applications has accelerated the adoption of modern application development frameworks. Spring Boot has emerged as one of the most widely used frameworks for building enterprise and cloud-native applications due to its convention-over-configuration approach, embedded server support, microservices compatibility, and extensive ecosystem integration. This paper examines the role of Spring Boot in enabling rapid application development, discusses its architectural features, deployment capabilities, and enterprise benefits, and reviews existing literature related to its adoption and performance. The study highlights how Spring Boot simplifies application development, enhances developer productivity, and supports modern cloud platforms. Furthermore, a comparative analysis and deployment trend chart are presented to demonstrate its relevance in enterprise software engineering.
Intelligent Cloud Native DevOps Frameworks for Autonomous Infrastructure Management and AI Driven Service Orchestration in Hybrid AWS and Azure Ecosystems
Cloud native DevOps systems operating across AWS and Azure environments have been marketed as self-healing computational fabrics capable of autonomous orchestration, elastic remediation, and predictive infrastructure governance. The evidence is contradictory at best. Most enterprise deployments still depend on fragmented observability stacks, manually tuned Kubernetes policies, and brittle CI/CD chains that collapse under state inconsistency, service drift, or cross-cloud latency amplification. Autonomous infrastructure management remains trapped between algorithmic optimism and operational entropy, since AI-driven orchestration engines frequently inherit the same institutional failures embedded within legacy governance structures. Short-term elasticity appears impressive during benchmark simulations. Production systems behave differently. Hybrid cloud ecosystems expose hidden synchronization delays, opaque cost propagation, identity sprawl, and feedback-loop instability that conventional DevOps literature continues to understate. The proposed framework integrates AIOps telemetry, GitOps reconciliation models, Kubernetes-native orchestration, and reinforcement-based remediation logic into a distributed hybrid control plane spanning AWS and Azure infrastructures. The friction lies in coordination failure rather than computational scarcity, since orchestration intelligence degrades rapidly once heterogeneous APIs, conflicting security abstractions, and asynchronous infrastructure states begin interacting under load volatility. This paper evaluates those tensions through a comparative architectural methodology grounded in scholarship, simulated orchestration metrics, and systemic failure analysis.
Explainable Artificial Intelligence Driven Fraud Detection and Risk Prediction Framework for Secure Financial Services in Cloud Computing Enabled Banking Ecosystems
Digital banking infrastructures have drifted into a paradoxical condition where transactional intelligence grows exponentially while institutional interpretability collapses under algorithmic opacity, fragmented cloud dependencies, and increasingly synthetic fraud behaviors generated through automated adversarial systems. Traditional fraud detection architectures, despite aggressive claims regarding predictive superiority, remain structurally brittle because they optimize for classification accuracy under historically static assumptions while real financial ecosystems mutate under volatile behavioral shifts, API-driven shadow integrations, cross-border transaction asymmetries, and latency-sensitive cloud orchestration failures. The friction lies in the inability of black-box Artificial Intelligence systems to justify risk attribution decisions during real-time banking operations where regulatory scrutiny, customer trust erosion, and systemic audit instability intersect simultaneously. Existing fraud analytics engines routinely demonstrate statistically impressive benchmark performance under sanitized datasets while collapsing under dynamic transaction bursts, adversarial data poisoning, and infrastructure-level synchronization delays. The reality is simpler. Banking AI systems often fail silently. This research proposes an Explainable Artificial Intelligence driven fraud detection and risk prediction framework engineered for cloud computing enabled banking ecosystems where interpretability, anomaly resilience, and infrastructural transparency are treated as operational dependencies rather than cosmetic compliance features. The framework integrates graph-based transaction modeling, SHAP-guided interpretability scoring, adaptive federated learning, cloud-native stream analytics, and multi-layer behavioral drift detection into a distributed risk architecture capable of exposing hidden fraud propagation pathways while preserving operational continuity. Experimental simulation across heterogeneous banking datasets demonstrated improved fraud prediction stability, reduced false-positive escalation, and stronger anomaly traceability under adversarial conditions compared with conventional ensemble systems. Yet the evidence is contradictory at best because explainability itself introduces computational overhead, interpretive inconsistency, and decision latency during high-volume transaction storms. Efficiency deteriorates quickly.
Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Event Driven Microservices Based Distributed Data Intelligence for Real Time High Throughput Enterprise Applications
Modern enterprise applications demand real-time data intelligence to process massive, continuous streams of information with sub-second latency. Traditional monolithic architectures and request-response paradigms often introduce bottlenecks, failing to meet the scalability and throughput requirements of contemporary digital business. This paper proposes an architectural framework that integrates Event-Driven Microservices (EDM) with distributed data intelligence pipelines to address these challenges. By leveraging asynchronous communication, persistent event logs (e.g., Apache Kafka), and stream processing engines (e.g., Apache Flink), the framework enables decoupled, resilient, and highly responsive services. We demonstrate how embedding intelligence—such as pattern detection, anomaly scoring, and predictive analytics—directly within the event streaming layer facilitates high-throughput processing. A case study in financial fraud detection illustrates the architecture’s effectiveness in achieving sub-50ms latency for over 1 million events per second. The paper concludes with performance benchmarks and a discussion on trade-offs between consistency, fault tolerance, and event ordering.
Designing Resilient Cloud Computing Infrastructures through Advanced Zero Trust Architecture and Continuous Verification Mechanisms
Zero Trust Architecture in cloud computing is often framed as a deterministic security upgrade, yet operational data suggests a more unstable reality where continuous verification introduces computational overheads, identity fragmentation, and policy drift that erode system resilience over time. The proposed investigation interrogates this contradiction by constructing a layered cloud infrastructure model where identity, device posture, and behavioral telemetry are continuously re-evaluated under adversarial simulation, exposing nonlinear degradation patterns rather than linear improvements. The results suggest resilience is not a property of enforcement intensity but of adaptive equilibrium between verification frequency and system tolerance, a balance rarely achieved in production deployments. The argument is uncomfortable. It challenges assumptions. The study positions resilience not as a fixed outcome but as a fluctuating condition shaped by latency thresholds, identity entropy, and orchestration inefficiencies, revealing that excessive verification can paradoxically expand attack surfaces through synchronization failures and policy misalignment. Continuous verification becomes both safeguard and destabilizer, depending on architectural discipline and feedback-loop governance. This forces a choice. The system either stabilizes or collapses under its own safeguards.
Energy-Efficient Resource Allocation in Edge-Integrated Cloud Infrastructures
As demand for low-latency and high-performance computing increases, edge-integrated cloud infrastructures have emerged as a hybrid solution to meet the needs of diverse applications, from IoT to AI workloads. However, ensuring energy efficiency while maintaining performance in such distributed environments remains a significant challenge. This paper investigates techniques for resource allocation that optimize energy consumption while preserving service-level agreements (SLAs). We analyze existing approaches, propose architectural considerations, and explore energy-saving strategies that balance edge-cloud workload distribution
Copyright (c) 2021 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Building Sustainable AI Microservices in Heterogeneous Digital Infrastructure Environments Using Hybrid Machine Learning Approaches and Scalable Service Design
The emergence of microservices as a dominant architectural paradigm has transformed the way software systems are designed, deployed, and scaled. However, integrating artificial intelligence (AI) into microservices presents challenges related to sustainability, interoperability, and scalability, particularly within heterogeneous digital infrastructures. This paper explores a hybrid machine learning approach to enhance AI-driven microservices in varied environments. We propose a design framework that ensures adaptability, energy efficiency, and distributed intelligence. Through scalable service design patterns and hybrid models combining supervised and reinforcement learning, this research addresses the technological and architectural barriers that limited earlier deployments. Practical implications are demonstrated through comparative performance benchmarks and a proposed sustainability-aware architecture for AI microservices.
Copyright (c) 2020 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Integrating Artificial Intelligence Techniques into Machine Learning Models: Enhancing Predictive Accuracy, Adaptability, and Decision Making in Complex Data Environments
Content of the Abstract Artificial Intelligence (AI) integration into machine learning (ML) has accelerated advancements in predictive analytics, adaptive learning, and strategic decision-making across complex data environments. This paper explores the synergy between AI techniques and traditional ML models, emphasizing improvements in accuracy, adaptability, and real-world applicability. Purpose The purpose of this study is to investigate how AI methods enhance standard machine learning frameworks, addressing challenges in dynamic data processing, model adaptability, and decision support. Design/methodology/approach This research employs a mixed-method approach combining theoretical analysis, comparative evaluation of hybrid AI-ML architectures, and empirical assessment on benchmark datasets. Findings The study finds that integrating AI techniques—such as deep learning optimization, reinforcement learning components, and adaptive ensemble strategies—significantly improves model performance metrics, reducing error rates while enhancing adaptability in complex environments. Practical implications Results offer insight for practitioners seeking to deploy AI-enhanced ML systems in domains like healthcare diagnostics, financial forecasting, and autonomous systems, suggesting practical frameworks and performance benchmarks. Originality/value This paper synthesizes cross-disciplinary advances in AI and ML before 2015 and connects them with modern integrative techniques, presenting novel insights into hybrid model design and evaluation.
Copyright (c) 2024 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Developing Robust Testing Pipelines for Artificial Intelligence Algorithms in Clinical Decision-Making Systems
Artificial Intelligence (AI) is increasingly being deployed in clinical decision-making (CDM) systems, where reliability and accuracy are paramount. However, the lack of standardized and rigorous testing pipelines remains a significant barrier to safe and effective implementation. This paper proposes a structured and adaptable testing pipeline tailored to AI models in healthcare, emphasizing risk mitigation, interpretability, and clinical relevance. By synthesizing insights from prior studies and incorporating modular testing stages, the proposed framework enhances both pre-deployment validation and post-deployment monitoring. We also review existing AI validation techniques in clinical contexts and present a process-driven architecture using visual diagrams and performance evaluation metrics.
Scalable Privacy Preservation in Distributed Healthcare AI Models Using Secure Multi-Party Computation and Federated Aggregation
The rapid digitization of healthcare systems and the integration of AI-driven diagnostics have amplified concerns around patient data privacy. Traditional centralized training of AI models exposes sensitive health records to privacy breaches, prompting the exploration of decentralized, privacy-preserving approaches. This paper proposes a scalable framework that integrates Secure Multi-Party Computation (SMPC) with Federated Learning Aggregation (FLA) to preserve privacy in distributed healthcare AI systems. By evaluating real-world healthcare data across simulated institutions, the framework demonstrates high privacy resilience and model performance without compromising scalability. The proposed solution enables collaborative AI development across healthcare providers while adhering to ethical standards.
Secure Container Deployment Using Runtime Threat Detection and Image Hardening Techniques in Kubernetes Environments
Containerized workloads orchestrated by Kubernetes have become foundational to modern cloud-native systems, yet they introduce complex security challenges across the software supply chain and runtime layers. This research paper investigates secure container deployment by integrating image hardening techniques with runtime threat detection mechanisms in Kubernetes environments. The study synthesizes research and proposes a layered security workflow combining static image analysis, policy enforcement, and behavioral monitoring. Conceptual diagrams, charts, and tables are used to illustrate architectural flows and risk distributions. The paper highlights how defense-in-depth strategies significantly reduce attack surfaces and improve incident response readiness.
Edge-to-Cloud AI Pipelines for Industrial IoT Using SAP Leonardo and Blockchain
The convergence of Industrial Internet of Things (IIoT), edge computing, artificial intelligence (AI), and blockchain technology is reshaping the future of digital manufacturing and industrial automation. This paper explores how SAP Leonardo serves as an integrative digital innovation system enabling edge-to-cloud AI pipelines. We investigate blockchain’s role in enhancing data security and trust in IIoT environments. Through a review of foundational research and architectural models, this paper proposes an optimized AI pipeline architecture leveraging SAP Leonardo and blockchain. Two diagrams and two tables are presented to illustrate system architecture and comparative evaluations.
Dynamic Knowledge Representation Strategies for Autonomous Learning in Large-Scale Computational Networks
The increasing complexity and dynamism of large-scale computational networks necessitate innovative approaches for knowledge representation that support real-time autonomous learning. Traditional static models are insufficient for addressing the fluidity and scale of data interactions, especially within decentralized and self-evolving systems. This paper explores the development and integration of dynamic knowledge representation strategies that enhance learning capabilities in distributed computational environments. Emphasis is placed on adaptive graph structures, ontology-based frameworks, and neural-symbolic systems capable of handling real-time data updates and feedback mechanisms. We present a conceptual model combining these strategies and evaluate its potential for enabling efficient, scalable, and resilient autonomous learning. Our findings suggest that such dynamic approaches not only improve learning accuracy and adaptability but also offer significant advantages in fault tolerance, interoperability, and self-optimization.
Molecular Adaptations of Pathogenic Bacteria to Oxidative Stress During Host Immune Evasion
Pathogenic bacteria face significant oxidative stress upon invasion of host organisms, primarily due to the generation of reactive oxygen species (ROS) by immune cells. To survive, these microbes have evolved intricate molecular strategies that facilitate resistance to oxidative damage while enabling evasion of immune surveillance. This review synthesizes current understanding of bacterial oxidative stress response mechanisms and highlights their roles in modulating virulence and host-pathogen interactions. We discuss transcriptional regulators, antioxidant systems, and enzymatic detoxification pathways that contribute to redox homeostasis.
Microbial Community Shifts in Response to Long-Term Heavy Metal Contamination in Agricultural Soils
Long-term exposure to heavy metals has profound impacts on microbial community dynamics in agricultural soils, potentially influencing soil fertility, crop productivity, and ecosystem sustainability. This study investigates the structural and functional shifts in microbial communities subjected to prolonged contamination by heavy metals such as cadmium, lead, zinc, and copper. Using high-throughput sequencing and bioinformatic tools, microbial diversity, abundance, and ecological function were analyzed in soils exposed to heavy metals under controlled and field conditions. Results revealed a marked decline in microbial diversity and a shift toward metal-resistant taxa, including Actinobacteria and Proteobacteria. Functional predictions suggest a stress-adaptive microbial response involving metal resistance genes and metabolic reprogramming. These findings provide crucial insights into microbial resilience and soil health under persistent anthropogenic stressors.
Copyright (c) 2023 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Characterization of Environmental Microbial Consortia Involved in Biodegradation of Industrial Organic Pollutants
The persistence of industrial organic pollutants in the environment presents a critical challenge to ecological and public health. Biodegradation using indigenous or engineered microbial consortia offers a sustainable and cost-effective alternative to conventional physicochemical remediation methods. This study provides a comprehensive characterization of environmental microbial consortia involved in the degradation of organic pollutants commonly associated with industrial waste, including polycyclic aromatic hydrocarbons, chlorinated compounds, dyes, and phenolics. Employing metagenomic and functional profiling approaches, this work identifies taxonomic structures, metabolic capabilities, and synergistic interactions within microbial communities sampled from contaminated soils and effluents.
Copyright (c) 2022 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
Thermotolerance and Genetic Stability of Extremophilic Microorganisms Isolated from Geothermal Environments
Extremophilic microorganisms, particularly thermophiles and hyperthermophiles, demonstrate remarkable resilience under high-temperature environments, making them key models for understanding molecular adaptations to thermal stress. This study investigates the thermotolerance and genetic stability of extremophilic strains isolated from geothermal sites, focusing on both physiological resilience and genomic integrity under sustained thermal exposure. By employing phenotypic assays, whole-genome sequencing, and comparative genomics, we characterize the growth kinetics, mutation rates, and adaptive mechanisms of selected isolates. Results indicate the presence of unique chaperonins, DNA repair systems, and heat-shock regulatory networks that collectively ensure survival and genome stability at elevated temperatures. These findings hold significant implications for biotechnological applications in industrial processes requiring robust microbial performance under thermophilic conditions.
Integrating Cloud and Edge Computing Architectures for Scalable, Low-Latency, and Intelligent Service Delivery in Distributed Systems
With the explosive growth of latency-sensitive and data-intensive applications—such as autonomous vehicles, remote surgery, and IoT-based monitoring—traditional cloud computing architectures are increasingly insufficient in meeting performance requirements. Edge computing, by processing data closer to the data source, has emerged as a complement to the cloud, enabling low-latency and context-aware services. This paper proposes a hybrid cloud–edge computing integration model that combines the scalability of cloud platforms with the low-latency benefits of edge computing. We analyze architectural considerations, integration challenges, and deployment frameworks, and we present a comparative analysis of system performance across hybrid, cloud-only, and edge-only models. The paper also outlines strategies for workload distribution and AI integration in distributed environments
Design and Implementation of Context-Aware Systems for Mobile and Ubiquitous Computing: Enhancing Real-Time Adaptability and Intelligent User Interaction in Dynamic Environments
In an increasingly connected world, mobile and ubiquitous computing environments demand systems that are not only intelligent but also adaptive to rapidly changing contexts. This paper explores the design and implementation of context-aware systems that leverage environmental data, user behavior, and device status to improve real-time responsiveness and user interaction. A prototype architecture is proposed, combining sensor fusion, edge processing, and machine learning to dynamically adjust system behavior. The study highlights the benefits of context-awareness in enhancing user experience, efficiency, and system personalization in mobile computing environments.