INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
type: channel
description: 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
language: en
publication name: INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
items: Privacy Preserving Analytics for Large Scale CRM Data Using Federated Learning and Secure Machine Learning Frameworks
items: Scopus Delisting Controversies: A Critical Review of Ethical and Policy Implications
items: Event-Driven Microservices Architecture for Scalable and Resilient Distributed Computing Systems
items: Spring Boot Framework for Rapid Development of Cloud Native and Enterprise Applications
items: Intelligent Cloud Native DevOps Frameworks for Autonomous Infrastructure Management and AI Driven Service Orchestration in Hybrid AWS and Azure Ecosystems
items: Explainable Artificial Intelligence Driven Fraud Detection and Risk Prediction Framework for Secure Financial Services in Cloud Computing Enabled Banking Ecosystems
items: Event Driven Microservices Based Distributed Data Intelligence for Real Time High Throughput Enterprise Applications
items: Designing Resilient Cloud Computing Infrastructures through Advanced Zero Trust Architecture and Continuous Verification Mechanisms
items: Energy-Efficient Resource Allocation in Edge-Integrated Cloud Infrastructures
items: Building Sustainable AI Microservices in Heterogeneous Digital Infrastructure Environments Using Hybrid Machine Learning Approaches and Scalable Service Design
items: Integrating Artificial Intelligence Techniques into Machine Learning Models: Enhancing Predictive Accuracy, Adaptability, and Decision Making in Complex Data Environments
items: Developing Robust Testing Pipelines for Artificial Intelligence Algorithms in Clinical Decision-Making Systems
items: Scalable Privacy Preservation in Distributed Healthcare AI Models Using Secure Multi-Party Computation and Federated Aggregation
items: Secure Container Deployment Using Runtime Threat Detection and Image Hardening Techniques in Kubernetes Environments
items: Edge-to-Cloud AI Pipelines for Industrial IoT Using SAP Leonardo and Blockchain
items: Dynamic Knowledge Representation Strategies for Autonomous Learning in Large-Scale Computational Networks
items: Molecular Adaptations of Pathogenic Bacteria to Oxidative Stress During Host Immune Evasion
items: Microbial Community Shifts in Response to Long-Term Heavy Metal Contamination in Agricultural Soils
items: Characterization of Environmental Microbial Consortia Involved in Biodegradation of Industrial Organic Pollutants
items: Thermotolerance and Genetic Stability of Extremophilic Microorganisms Isolated from Geothermal Environments
items: Integrating Cloud and Edge Computing Architectures for Scalable, Low-Latency, and Intelligent Service Delivery in Distributed Systems
items: Design and Implementation of Context-Aware Systems for Mobile and Ubiquitous Computing: Enhancing Real-Time Adaptability and Intelligent User Interaction in Dynamic Environments

Privacy Preserving Analytics for Large Scale CRM Data Using Federated Learning and Secure Machine Learning Frameworks
type: item
description: Customer Relationship Management systems increasingly depend on large volumes of customer information collected from sales platforms, digital channels, customer service applications, transactional systems, mobile applications, and enterprise databases. Conventional analytics generally requires customer information to be transferred into centralized repositories, creating privacy, security, regulatory, and data-governance concerns. This paper presents a privacy-preserving analytics framework for large-scale CRM environments using federated learning and secure machine learning techniques. The proposed approach enables multiple organizational units or distributed CRM platforms to collaboratively develop analytical models while retaining sensitive customer records within their original environments. Local models are trained using customer interactions, purchase histories, campaign responses, service records, and behavioral characteristics, while only protected model parameters are exchanged with a federated aggregation server. Secure aggregation, differential privacy, encryption, access control, and privacy-aware model governance are incorporated to reduce information leakage during collaborative learning. The framework supports analytical functions including customer segmentation, churn prediction, recommendation, campaign targeting, customer lifetime value analysis, and service optimization. The study demonstrates how federated analytical architectures can reduce centralized exposure of personally identifiable customer information while maintaining the advantages of enterprise-scale machine learning. The proposed framework provides a practical foundation for developing scalable, secure, and privacy-conscious CRM intelligence across distributed organizational environments.
subject: Articles
  topic: section
subject: Customer Relationship Management
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Privacy Preserving Analytics
  topic: keywords
subject: Secure Machine Learning
  topic: keywords
subject: Customer Data Privacy
  topic: keywords
subject: Secure Aggregation
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Distributed Analytics
  topic: keywords
creator: Amit Muruganantham V
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-08-23
publication date: 2026-08-23
starting page: 1
ending page: 11

Scopus Delisting Controversies: A Critical Review of Ethical and Policy Implications
type: item
description: 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.
subject: Articles
  topic: section
subject: Scopus
  topic: keywords
subject: journal delisting
  topic: keywords
subject: academic publishing ethics
  topic: keywords
subject: research integrity
  topic: keywords
subject: bibliometric databases
  topic: keywords
subject: Global South
  topic: keywords
subject: content removal
  topic: keywords
subject: scholarly communication
  topic: keywords
creator: Prof. Dr. S. Balasubramanian
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-06-29
publication date: 2026-06-29
starting page: 42
ending page: 61

Event-Driven Microservices Architecture for Scalable and Resilient Distributed Computing Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: event-driven architecture
  topic: keywords
subject: microservices
  topic: keywords
subject: distributed systems
  topic: keywords
subject: scalability
  topic: keywords
subject: resilience
  topic: keywords
subject: event sourcing
  topic: keywords
subject: CQRS
  topic: keywords
subject: saga coordination
  topic: keywords
subject: message brokers
  topic: keywords
subject: eventual consistency
  topic: keywords
subject: fault isolation
  topic: keywords
subject: backpressure
  topic: keywords
subject: schema evolution
  topic: keywords
subject: system leakage
  topic: keywords
subject: cloud-native computing
  topic: keywords
creator: Parvesh Kumar L
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-06-28
publication date: 2026-06-28
starting page: 34
ending page: 41

Spring Boot Framework for Rapid Development of Cloud Native and Enterprise Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Spring Boot
  topic: keywords
subject: Cloud Native Applications
  topic: keywords
subject: Enterprise Applications
  topic: keywords
subject: Microservices
  topic: keywords
subject: Java Framework
  topic: keywords
subject: DevOps
  topic: keywords
subject: Containerization
  topic: keywords
subject: Rapid Application Development
  topic: keywords
creator: Priyanka Abhishek
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-05-29
publication date: 2026-05-29
starting page: 27
ending page: 33

Intelligent Cloud Native DevOps Frameworks for Autonomous Infrastructure Management and AI Driven Service Orchestration in Hybrid AWS and Azure Ecosystems
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Native DevOps
  topic: keywords
subject: AIOps
  topic: keywords
subject: Hybrid Cloud Orchestration
  topic: keywords
subject: Autonomous Infrastructure Management
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: GitOps
  topic: keywords
subject: AWS
  topic: keywords
subject: Azure
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: Predictive Remediation
  topic: keywords
creator: Vincent Connor Bryant
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-05-19
publication date: 2026-05-19
starting page: 16
ending page: 26

Explainable Artificial Intelligence Driven Fraud Detection and Risk Prediction Framework for Secure Financial Services in Cloud Computing Enabled Banking Ecosystems
type: item
description: 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 simu…
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Risk Prediction
  topic: keywords
subject: Banking Ecosystems
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Financial Cybersecurity
  topic: keywords
subject: SHAP Analysis
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Behavioral Drift Detection
  topic: keywords
subject: Financial Risk Analytics
  topic: keywords
creator: Edgar Beverly Cleary
rights: Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2025-12-19
publication date: 2025-12-19
starting page: 21
ending page: 30

Event Driven Microservices Based Distributed Data Intelligence for Real Time High Throughput Enterprise Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Event-Driven Architecture
  topic: keywords
subject: Microservices
  topic: keywords
subject: Distributed Data Intelligence
  topic: keywords
subject: Real-Time Processing
  topic: keywords
subject: High Throughput
  topic: keywords
subject: Stream Processing
  topic: keywords
subject: Enterprise Applications
  topic: keywords
subject: Apache Kafka
  topic: keywords
creator: Hotakainen Paasilinna
rights: Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2025-12-28
publication date: 2025-12-28
starting page: 13
ending page: 20

Designing Resilient Cloud Computing Infrastructures through Advanced Zero Trust Architecture and Continuous Verification Mechanisms
type: item
description: 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.
subject: Articles
  topic: section
subject: Zero Trust Architecture
  topic: keywords
subject: Cloud Computing Security
  topic: keywords
subject: Continuous Verification
  topic: keywords
subject: Identity Governance
  topic: keywords
subject: System Resilience
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Policy Drift
  topic: keywords
subject: Security Orchestration
  topic: keywords
creator: Lithalsa Mariyana
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-04-10
publication date: 2026-04-10
starting page: 8
ending page: 15

Energy-Efficient Resource Allocation in Edge-Integrated Cloud Infrastructures
type: item
description: 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
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Cloud Infrastructure
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: SLA
  topic: keywords
subject: Green Computing
  topic: keywords
creator: Yuri Vasily Trifonov
rights: Copyright (c) 2021 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2021-12-24
publication date: 2021-12-24
starting page: 8
ending page: 13

Building Sustainable AI Microservices in Heterogeneous Digital Infrastructure Environments Using Hybrid Machine Learning Approaches and Scalable Service Design
type: item
description: 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.
subject: Articles
  topic: section
subject: AI microservices
  topic: keywords
subject: hybrid machine learning
  topic: keywords
subject: sustainable systems
  topic: keywords
subject: scalable design
  topic: keywords
subject: heterogeneous environments
  topic: keywords
subject: service-oriented architecture
  topic: keywords
creator: García Lorca Cervantes
rights: Copyright (c) 2020 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2020-01-19
publication date: 2020-01-19
starting page: 8
ending page: 13

Integrating Artificial Intelligence Techniques into Machine Learning Models: Enhancing Predictive Accuracy, Adaptability, and Decision Making in Complex Data Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Accuracy
  topic: keywords
subject: Adaptability
  topic: keywords
subject: Decision Making
  topic: keywords
subject: Hybrid Modelling
  topic: keywords
creator: Santhosh Agrwal
rights: Copyright (c) 2024 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2024-08-26
publication date: 2024-08-26
starting page: 8
ending page: 16

Developing Robust Testing Pipelines for Artificial Intelligence Algorithms in Clinical Decision-Making Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Clinical Decision-Making
  topic: keywords
subject: Testing Pipeline
  topic: keywords
subject: Model Validation
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Interpretability
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Deployment Strategy
  topic: keywords
creator: Clara Luana
rights: Copyright (c) 2024 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2025-03-24
publication date: 2025-03-24
starting page: 7
ending page: 13

Scalable Privacy Preservation in Distributed Healthcare AI Models Using Secure Multi-Party Computation and Federated Aggregation
type: item
description: 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.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Secure Multi-Party Computation
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Distributed AI
  topic: keywords
subject: Healthcare Data
  topic: keywords
subject: Model Aggregation
  topic: keywords
subject: Secure Aggregation
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Data Security
  topic: keywords
subject: Scalable AI
  topic: keywords
creator: Srividhya Siva
rights: Copyright (c) 2026 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2026-01-02
publication date: 2026-01-02
starting page: 1
ending page: 7

Secure Container Deployment Using Runtime Threat Detection and Image Hardening Techniques in Kubernetes Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Kubernetes Security
  topic: keywords
subject: Container Hardening
  topic: keywords
subject: Runtime Threat Detection
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: DevSecOps
  topic: keywords
creator: Birrissa Omari Yohannes
rights: Copyright (c) 2025 2025
date: 2025-11-14
publication date: 2025-11-14
starting page: 7
ending page: 12

Edge-to-Cloud AI Pipelines for Industrial IoT Using SAP Leonardo and Blockchain
type: item
description: 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.
subject: Articles
  topic: section
subject: Edge Computing, Blockchain
  topic: keywords
subject: SAP Leonardo
  topic: keywords
subject: Industrial IoT
  topic: keywords
subject: AI Pipelines
  topic: keywords
subject: Cloud
  topic: keywords
subject: Industry 4.0.
  topic: keywords
creator: Christian Johnkaiser
rights: Copyright (c) 2021 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2021-04-22
publication date: 2021-04-22
starting page: 1
ending page: 7

Dynamic Knowledge Representation Strategies for Autonomous Learning in Large-Scale Computational Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Dynamic knowledge representation
  topic: keywords
subject: autonomous learning
  topic: keywords
subject: computational networks
  topic: keywords
subject: ontology-based models
  topic: keywords
subject: neural-symbolic integration
  topic: keywords
subject: large-scale systems
  topic: keywords
creator: Daniel Nathanaël Yolan
rights: Copyright (c) 2024 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2024-08-26
publication date: 2024-08-26
starting page: 1
ending page: 7

Molecular Adaptations of Pathogenic Bacteria to Oxidative Stress During Host Immune Evasion
type: item
description: 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.
subject: Articles
  topic: section
subject: Oxidative stress
  topic: keywords
subject: pathogenic bacteria
  topic: keywords
subject: reactive oxygen species
  topic: keywords
subject: host immune evasion
  topic: keywords
subject: redox homeostasis
  topic: keywords
subject: bacterial virulence
  topic: keywords
subject: superoxide dismutase
  topic: keywords
creator: Ferdinand Laurens
rights: Copyright (c) 2024 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2024-02-25
publication date: 2024-02-25
starting page: 1
ending page: 7

Microbial Community Shifts in Response to Long-Term Heavy Metal Contamination in Agricultural Soils
type: item
description: 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.
subject: Articles
  topic: section
subject: Heavy metal contamination
  topic: keywords
subject: microbial community
  topic: keywords
subject: agricultural soils
  topic: keywords
subject: microbial diversity
  topic: keywords
subject: soil health
  topic: keywords
subject: functional shifts
  topic: keywords
subject: Actinobacteria
  topic: keywords
creator: Bastien Raphael
rights: Copyright (c) 2023 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2023-02-15
publication date: 2023-02-15
starting page: 1
ending page: 6

Characterization of Environmental Microbial Consortia Involved in Biodegradation of Industrial Organic Pollutants
type: item
description: 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.
subject: Articles
  topic: section
subject: Biodegradation
  topic: keywords
subject: microbial consortia
  topic: keywords
subject: industrial pollutants
  topic: keywords
subject: environmental microbiology
  topic: keywords
subject: metagenomics
  topic: keywords
subject: organic contaminants
  topic: keywords
creator: Jaqueline Anahí Carolina
rights: Copyright (c) 2022 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2022-03-03
publication date: 2022-03-03
starting page: 1
ending page: 7

Thermotolerance and Genetic Stability of Extremophilic Microorganisms Isolated from Geothermal Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Thermotolerance
  topic: keywords
subject: extremophiles
  topic: keywords
subject: geothermal environments
  topic: keywords
subject: genetic stability
  topic: keywords
subject: thermophilic microorganisms
  topic: keywords
subject: heat shock proteins
  topic: keywords
subject: genomic adaptation
  topic: keywords
creator: Mathias Frederik
rights: Copyright (c) 2020 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2020-02-11
publication date: 2020-02-11
starting page: 1
ending page: 7

Integrating Cloud and Edge Computing Architectures for Scalable, Low-Latency, and Intelligent Service Delivery in Distributed Systems
type: item
description: 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
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: cloud computing
  topic: keywords
subject: distributed systems
  topic: keywords
subject: latency
  topic: keywords
subject: intelligent services
  topic: keywords
subject: hybrid architecture
  topic: keywords
subject: scalability.
  topic: keywords
creator: Jhon Durai Raja
rights: Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2025-11-11
publication date: 2025-11-11
starting page: 1
ending page: 6

Design and Implementation of Context-Aware Systems for Mobile and Ubiquitous Computing: Enhancing Real-Time Adaptability and Intelligent User Interaction in Dynamic Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Context-aware systems
  topic: keywords
subject: mobile computing
  topic: keywords
subject: ubiquitous computing
  topic: keywords
subject: real-time adaptability
  topic: keywords
subject: intelligent interaction
  topic: keywords
subject: edge processing
  topic: keywords
subject: sensor fusion
  topic: keywords
creator: Neenu Jouse Maria
rights: Copyright (c) 2025 INTERNATIONAL JOURNAL OF RESEARCH AND APPLIED INNOVATIONS IN COMPUTER SCIENCE (IJRAICS)
date: 2025-03-19
publication date: 2025-03-19
starting page: 1
ending page: 6