International Journal of Computing Science and Systems (IJCSS)
type: channel
description: The International Journal of Computing Science and Systems (IJCSS) is an international, peer-reviewed, open-access journal dedicated to publishing high-quality research in computing science, information systems, and emerging technological innovations. IJCSS provides a global platform for researchers, academicians, industry experts, and practitioners to share cutting-edge advancements, theoretical developments, and practical applications that shape the future of computing and intelligent systems. The journal welcomes original research papers, review articles, case studies, technical reports, and experimental investigations covering a wide spectrum of computing-related domains, including but not limited to computer science, software engineering, distributed systems, artificial intelligence, cloud computing, data analytics, cybersecurity, and digital transformation. IJCSS is committed to maintaining rigorous academic and ethical standards through a transparent peer-review process managed by an international editorial board. As an open-access journal, all published articles are freely accessible to readers worldwide, promoting global knowledge exchange, scientific collaboration, and widespread dissemination of research findings. The journal’s mission is to bridge the gap between foundational computing theories and real-world systems, empowering technological innovation and contributing to the advancement of the global computing ecosystem. With its broad scope and diverse readership, IJCSS serves as a trusted resource for scholars, professionals, and institutions seeking impactful contributions in computing science and systems research.
language: en
publication name: International Journal of Computing Science and Systems (IJCSS)
items: Modern Enterprise Application Development Through APIs, Microservices, MuleSoft, and Spring Boot Technologies
items: Integrated Regulatory Affairs Strategies for Harmonizing International Medical Device Compliance Risk Assessment and Lifecycle Governance Across Multi-Jurisdictional Healthcare Markets
items: Advanced Reliability Engineering Models for Scalable AI and Machine Learning Workloads in Multi Cloud Infrastructure Environments
items: IJCSS_0602005
items: Enhancing High Throughput Business Applications Through Event Driven Microservices and Distributed Data Architecture Models
items: Artificial Intelligence-Driven Intelligent Systems: A Comprehensive Study on Adaptive Learning Models, Bias Mitigation, and Secure Deployment in Large-Scale IoT Environments
items: Design and Implementation of Data Engineering Workflows to Support Machine Learning and Artificial Intelligence in Real-Time Decision Systems
items: Integration of Data Quality Metrics with Machine Learning Models for Robust Decision-Making Systems
items: Evaluation of Transfer Learning Techniques for Low-Resource Natural Language Processing
items: End-to-End Machine Learning Pipelines Delivered Through Secure Microservices for Autonomous Operations in Digital Infrastructure Platforms
items: Assessing the Role of Continuous Testing in Reducing Failure Rates in AI-Driven Clinical Applications
items: Federated AI Model Deployment Across Multi-Cloud Microservices: Architecture, Challenges, and Security Frameworks
items: Continuous Threat Modeling and Security Validation for Agile Development Pipelines in Critical Healthcare Systems
items: A Hybrid Machine Learning Framework for Cross-Domain Fraud Detection Using Multimodal Data from Healthcare and Financial Systems
items: Optimizing Edge Computing Architectures for Real-Time Data Processing in IoT Networks
items: Neuro-Symbolic Integration Techniques for Scalable Reasoning in Next-Generation Intelligent Systems
items: Hierarchical Learning-Based Optimization of Data Flow in Large-Scale Cyber-Physical Systems
items: A Probabilistic Framework for Evaluating Reliability in Distributed Computing Environments with Uncertain State Transitions
items: Integrating Artificial Intelligence with Knowledge Graphs for Context-Aware Reasoning in Complex Problem Solving
items: Mitigating Model Drift in Continuous Machine Learning Pipelines Deployed on Cloud-Native Architectures
items: Exploring the Role of Personalized Content in Enhancing Engagement in Education Marketing
items: Evaluating Big Data Processing Techniques for Efficient Insights in High Velocity Data Environments
items: The Evolution of IT Governance Frameworks in Agile Organizations Undergoing Digital Transformation

Modern Enterprise Application Development Through APIs, Microservices, MuleSoft, and Spring Boot Technologies
type: item
description: Enterprise application development has undergone significant transformation with the adoption of Application Programming Interfaces (APIs), microservices architecture, MuleSoft integration platforms, and Spring Boot frameworks. Organizations increasingly require scalable, flexible, and interoperable systems capable of supporting digital transformation initiatives and rapidly changing business requirements. APIs facilitate seamless communication among distributed applications, while microservices promote modularity and independent deployment of services. MuleSoft provides a comprehensive integration platform that enables connectivity across cloud and on-premises systems, whereas Spring Boot simplifies the development of enterprise-grade applications through convention-over-configuration principles. This paper examines the role of these technologies in modern enterprise application development, discusses relevant literature, and highlights their collective contribution to agility, scalability, and operational efficiency in enterprise environments
subject: Articles
  topic: section
subject: Enterprise Applications
  topic: keywords
subject: APIs
  topic: keywords
subject: Microservices
  topic: keywords
subject: MuleSoft
  topic: keywords
subject: Spring Boot
  topic: keywords
subject: System Integration
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: Anish Anubhav. M
rights: Copyright (c) 2026 Anish Anubhav. M (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-28
publication date: 2026-05-28
starting page: 50
ending page: 56

Integrated Regulatory Affairs Strategies for Harmonizing International Medical Device Compliance Risk Assessment and Lifecycle Governance Across Multi-Jurisdictional Healthcare Markets
type: item
description: Medical device manufacturers operating across multiple jurisdictions face a regulatory environment characterized less by scientific disagreement than by administrative divergence, interpretive inconsistency, and asynchronous governance cycles. Regulatory convergence initiatives have expanded over the last decade through mechanisms such as the International Medical Device Regulators Forum (IMDRF), Medical Device Single Audit Program (MDSAP), and harmonized standards frameworks; yet compliance risk remains fragmented because authorities continue to evaluate identical technical evidence through jurisdiction-specific evidentiary thresholds, post-market surveillance expectations, cybersecurity doctrines, and lifecycle accountability structures. The friction lies in the mismatch between globally integrated product development systems and nationally bounded regulatory authority. A persistent problem. This paper develops an integrated regulatory affairs framework for harmonizing international medical device compliance risk assessment and lifecycle governance across multi-jurisdictional healthcare markets. Drawing from regulatory governance theory, enterprise risk management principles, lifecycle quality systems, and comparative regulatory analysis, the study proposes a multidimensional compliance architecture linking premarket evidence generation, market authorization strategy, post-market surveillance intelligence, cybersecurity oversight, and adaptive governance mechanisms. The evidence is contradictory at best regarding whether harmonization alone reduces compliance burden; several studies suggest that regulatory convergence may simply redistribute organizational complexity rather than eliminate it. Findings indicate that organizations adopting integrated regulatory intelligence systems, dynamic risk scoring methodologies, and lifecycle governance models demonstrate stronger compliance resilience than firms relying on sequential jurisdiction-specific approaches. Harmoniz…
subject: Articles
  topic: section
subject: Medical Device Regulation
  topic: keywords
subject: Regulatory Affairs
  topic: keywords
subject: Compliance Risk Assessment
  topic: keywords
subject: Lifecycle Governance
  topic: keywords
subject: International Harmonization
  topic: keywords
subject: IMDRF
  topic: keywords
subject: MDR
  topic: keywords
subject: FDA
  topic: keywords
subject: MDSAP
  topic: keywords
subject: Post-Market Surveillance
  topic: keywords
subject: Regulatory Intelligence
  topic: keywords
subject: Healthcare Markets
  topic: keywords
creator: Brandon Timothy
rights: Copyright (c) 2026 Brandon Timothy (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 39
ending page: 49

Advanced Reliability Engineering Models for Scalable AI and Machine Learning Workloads in Multi Cloud Infrastructure Environments
type: item
description: Large-scale AI and machine learning deployments inside multi-cloud infrastructures continue to expose a contradiction the industry has refused to confront directly: scalability has expanded faster than reliability modeling. Training pipelines now span heterogeneous orchestration layers, distributed GPU clusters, edge inference endpoints, and volatile API gateways, yet most operational frameworks still inherit assumptions from classical distributed systems theory developed for relatively stable enterprise environments. The evidence is contradictory at best. Existing reliability engineering models frequently optimize isolated metrics—availability, throughput, or fault tolerance—while ignoring cascading dependency failures generated through orchestration drift, asynchronous synchronization delays, and cross-provider latency instability. Small faults metastasize. Reliability therefore becomes less a property of architecture and more an unstable negotiation between infrastructure abstraction layers competing for control over state consistency. Contrary to established norms, this paper argues that reliability degradation in AI-centric multi-cloud systems is not driven primarily by hardware volatility or software defects in isolation, but by recursive coordination friction emerging between automation layers, dynamic scaling policies, and probabilistic workload scheduling. A hybrid reliability engineering framework integrating stochastic failure prediction, adaptive redundancy control, and chaos-driven resilience testing is evaluated against conventional cloud failover approaches. The results indicate that reliability gains plateau after redundancy saturation thresholds are exceeded, while operational shadow costs rise sharply through synchronization overhead and hidden orchestration leakage. The reality is simpler. More replication does not necessarily produce more resilience
subject: Articles
  topic: section
subject: Reliability Engineering
  topic: keywords
subject: Multi-Cloud Infrastructure
  topic: keywords
subject: AI Workloads
  topic: keywords
subject: Machine Learning Systems
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: Chaos Engineering
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Reliability Modeling
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Site Reliability Engineering
  topic: keywords
creator: Richardson Christian Walter
rights: Copyright (c) 2026 Richardson Christian Walter (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 29
ending page: 38

IJCSS_0602005
type: item
title: A Scalable Event-Driven Microservices Data Architecture for High Throughput Distributed Systems: Integrating Data Mesh Principles, Real-Time Stream Processing, and Resilient Polyglot Persistence for Consistent, Low-Latency Enterprise Analytics
description: Throughput collapses rarely originate from raw compute scarcity; they emerge from coordination failure across loosely coupled services that pretend to be independent while sharing hidden state through data contracts, schema drift, and latency-sensitive event propagation. The proposed architecture attempts to reconcile this contradiction by fusing event-driven microservices with data mesh principles, layering real-time stream processing over polyglot persistence to absorb volatility without forfeiting analytical consistency, yet the evidence is contradictory at best when subjected to heterogeneous enterprise workloads where temporal skew and data duplication metastasize into systemic inefficiencies. The reality is simpler. Distributed data systems lie. This work constructs a deliberately friction-aware architecture, rejecting idealized decoupling in favor of controlled dependency surfaces, embedding feedback-aware event streams and bounded consistency zones that trade strict correctness for operational survivability under high-throughput constraints, while empirical modeling suggests latency reductions of 27–41% under peak ingestion loads though at the cost of increased integration entropy and shadow operational overheads that remain poorly quantified. It works. Until it doesn't.
subject: Articles
  topic: section
subject: Microservices Data Architecture
  topic: keywords
subject: Event-Driven Systems
  topic: keywords
subject: Data Mesh
  topic: keywords
subject: Stream Processing
  topic: keywords
subject: Polyglot Persistence
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Low-Latency Analytics
  topic: keywords
subject: Data Consistency
  topic: keywords
subject: Systemic Friction
  topic: keywords
subject: High-Throughput Computing
  topic: keywords
creator: Anjenkaya Panwar
rights: Copyright (c) 2025 Anjenkaya Panwar (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-18
publication date: 2025-12-18
starting page: 31
ending page: 37

Enhancing High Throughput Business Applications Through Event Driven Microservices and Distributed Data Architecture Models
type: item
description: Traditional monolithic architectures often struggle to meet the scalability and low-latency demands of modern high-throughput business applications. This paper explores the synergistic integration of event-driven microservices and distributed data architecture models as a strategic solution. By decoupling service communication through asynchronous events and distributing data across polyglot persistence stores, systems can achieve superior resilience, elasticity, and throughput. We analyze core principles, data consistency challenges, and real-world deployment patterns, providing a comparative analysis and architectural diagram. The findings suggest that while operational complexity increases, the benefits in scalability and fault tolerance are indispensable for businesses operating at web scale.
subject: Articles
  topic: section
subject: Event-Driven Architecture
  topic: keywords
subject: Microservices
  topic: keywords
subject: Distributed Data
  topic: keywords
subject: High Throughput
  topic: keywords
subject: CQRS
  topic: keywords
subject: Event Sourcing
  topic: keywords
creator: Williams Markel
rights: Copyright (c) 2025 Williams Markel (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 21
ending page: 28

Artificial Intelligence-Driven Intelligent Systems: A Comprehensive Study on Adaptive Learning Models, Bias Mitigation, and Secure Deployment in Large-Scale IoT Environments
type: item
description: Artificial Intelligence (AI)-driven intelligent systems have become foundational in enabling scalable, adaptive, and autonomous decision-making within large-scale Internet of Things (IoT) environments. This paper examines the integration of adaptive learning models, bias mitigation strategies, and secure deployment mechanisms in distributed IoT ecosystems. It explores how machine learning techniques evolve dynamically with streaming data, addresses algorithmic fairness challenges, and outlines robust security architectures for real-world deployment. The study synthesizes existing literature, proposes a conceptual framework, and highlights key challenges and opportunities for future research. Visual representations, including flowcharts and tables, are incorporated to clarify system design and evaluation strategies
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Adaptive Learning Models
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: Secure Deployment
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Trustworthy AI
  topic: keywords
creator: Nikolai Pushkin
rights: Copyright (c) 2025 Nikolai Pushkin (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 14
ending page: 30

Design and Implementation of Data Engineering Workflows to Support Machine Learning and Artificial Intelligence in Real-Time Decision Systems
type: item
description: The rapid growth of machine learning (ML) and artificial intelligence (AI) applications has created a strong demand for robust data engineering workflows capable of handling real-time data processing and decision-making. Modern organizations increasingly rely on real-time analytics pipelines to transform large volumes of streaming and batch data into actionable insights. However, designing scalable, reliable, and efficient data engineering architectures that support ML-driven decision systems remains a significant challenge. This study explores the design and implementation of data engineering workflows tailored for real-time ML and AI systems. The paper discusses architectural components, workflow orchestration strategies, data ingestion mechanisms, and model deployment pipelines required for real-time decision-making environments. The research also examines existing literature on data pipelines, big data processing frameworks, and AI infrastructure. A conceptual architecture and workflow model are proposed to integrate data ingestion, transformation, feature engineering, and model serving within a scalable environment. The results highlight how automated pipelines and distributed processing frameworks significantly improve decision latency and model performance. The study contributes to the understanding of how well-designed data engineering workflows can enhance operational efficiency and enable reliable real-time AI systems.
subject: Articles
  topic: section
subject: Data Engineering
  topic: keywords
subject: Machine Learning Pipelines
  topic: keywords
subject: Real-Time Decision Systems
  topic: keywords
subject: Data Workflows
  topic: keywords
subject: Artificial Intelligence Infrastructure
  topic: keywords
creator: Rossi Osei Andersen,
rights: Copyright (c) 2024 Rossi Osei Andersen, (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2024-12-21
publication date: 2024-12-21
starting page: 22
ending page: 28

Integration of Data Quality Metrics with Machine Learning Models for Robust Decision-Making Systems
type: item
description: The efficacy of Machine Learning (ML) models is fundamentally constrained by the quality of the data upon which they are trained and deployed. This paper explores the critical integration of systematic Data Quality (DQ) metrics into the ML pipeline to build robust decision-making systems. We argue that moving beyond ad-hoc data cleaning to a continuous, metrics-driven assessment of data across its lifecycle—encompassing dimensions such as accuracy, completeness, consistency, and timeliness—is essential for improving model performance, fairness, and reliability. We present a framework for embedding DQ checks at key stages: pre-modeling (data profiling), in-training (monitoring for drift and anomalies), and post-deployment (feedback loops). Through conceptual diagrams and analysis, we demonstrate how quantified DQ scores can be used to trigger automated remediation, weight training instances, enrich feature sets, and provide interpretable diagnostics for model predictions. This integration fosters greater trust in AI systems by making their dependence on data quality explicit and manageable.
subject: Articles
  topic: section
subject: Data Quality
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Robustness
  topic: keywords
subject: Decision-Making
  topic: keywords
subject: Data Drift
  topic: keywords
subject: Model Monitoring
  topic: keywords
subject: Data-Centric AI
  topic: keywords
creator: Robinson Ethan Clark
rights: Copyright (c) 2025 Robinson Ethan Clark (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-28
publication date: 2025-12-28
starting page: 14
ending page: 21

Evaluation of Transfer Learning Techniques for Low-Resource Natural Language Processing
type: item
description: Natural Language Processing (NLP) has seen significant advancements due to the application of deep learning techniques and large-scale pre-trained language models. However, these models often perform suboptimally when applied to low-resource languages or domains with limited labeled data. This paper explores various transfer learning approaches aimed at addressing the low-resource NLP problem, evaluating their effectiveness, architecture designs, and performance trade-offs. Two diagrams illustrate the transfer learning process and a comparative model architecture. Two tables summarize benchmark results and dataset availability. Our analysis reveals that with carefully adapted transfer techniques, significant performance can still be achieved even under low-resource constraints
subject: Articles
  topic: section
subject: Transfer Learning
  topic: keywords
subject: Low-Resource NLP
  topic: keywords
subject: Fine-Tuning, Language Models
  topic: keywords
subject: Cross-lingual Adaptation
  topic: keywords
subject: Domain Adaptation
  topic: keywords
creator: Guzel Yakhina Sorokin
rights: Copyright (c) 2025 Guzel Yakhina Sorokin (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-12-27
publication date: 2025-12-27
starting page: 8
ending page: 13

End-to-End Machine Learning Pipelines Delivered Through Secure Microservices for Autonomous Operations in Digital Infrastructure Platforms
type: item
description: As digital infrastructures evolve in scale and complexity, the need for intelligent, autonomous operations has become imperative. This paper explores an architecture combining machine learning (ML) pipelines, microservices, and security frameworks to deliver end-to-end autonomous operations. We propose a modular, scalable system that leverages microservice containers to orchestrate ML models for infrastructure optimization. In this 2018 context, the integration of containerization (via Docker/Kubernetes), basic ML model deployment, and initial service mesh protocols is examined. The study highlights challenges, including data pipeline integrity, latency, and security, and proposes solutions using layered architecture and service-based isolation.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Microservices
  topic: keywords
subject: Digital Infrastructure
  topic: keywords
subject: Secure Pipelines
  topic: keywords
subject: Autonomous Operations
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: Containerization
  topic: keywords
subject: Edge Computing
  topic: keywords
creator: Josep Pla Saramago
rights: Copyright (c) 2026 Josep Pla Saramago (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 8
ending page: 12

Assessing the Role of Continuous Testing in Reducing Failure Rates in AI-Driven Clinical Applications
type: item
description: Artificial Intelligence (AI) has increasingly permeated clinical settings, offering diagnostic and decision-support solutions. However, the failure rates of these systems remain a significant concern, particularly in high-stakes environments like healthcare. This study investigates the impact of continuous testing—a development practice involving frequent, automated validation of software behavior—on the reliability of AI-driven clinical applications. Through a comprehensive analysis of existing systems, development practices, and test coverage metrics, we assess how continuous testing contributes to system robustness, error detection, and patient safety. Our findings indicate that integrating continuous testing reduces failure rates by up to 35%, ensuring more dependable clinical AI systems.
subject: Articles
  topic: section
subject: Continuous Testing
  topic: keywords
subject: AI in Healthcare
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Failure Rate Reduction
  topic: keywords
subject: Software Validation
  topic: keywords
subject: Machine Learning Reliability
  topic: keywords
creator: Murni Reza
creator: Antara Aulia
rights: Copyright (c) 2026 Murni Reza, Antara Aulia (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-07
publication date: 2026-01-07
starting page: 14
ending page: 20

Federated AI Model Deployment Across Multi-Cloud Microservices: Architecture, Challenges, and Security Frameworks
type: item
description: Purpose: This paper explores how federated AI models are deployed in multi-cloud environments using microservice architecture, identifying architectural solutions, security frameworks, and practical challenges. Design/methodology/approach: A descriptive design is used, analyzing key technological integrations, deployment models, and referencing prior foundational work in multi-cloud and federated computing before 2016. Findings: Deploying federated AI in distributed, cloud-native ecosystems introduces complexities in orchestration, heterogeneity management, and security enforcement. Practical implications: Enterprises can leverage this architecture to build scalable and privacy-preserving AI systems, though secure communication and compliance across providers remain ongoing challenges. Originality/value: This paper bridges classical multi-cloud security and architecture research with modern federated AI deployment needs, providing a unified framework that addresses both legacy and current gaps.
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: multi-cloud architecture
  topic: keywords
subject: microservices
  topic: keywords
subject: model orchestration
  topic: keywords
subject: security frameworks
  topic: keywords
subject: zero-trust
  topic: keywords
subject: containerization
  topic: keywords
creator: Hilary Mantel Ishiguro,
rights: Copyright (c) 2024 Hilary Mantel Ishiguro, (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2024-11-20
publication date: 2024-11-20
starting page: 16
ending page: 21

Continuous Threat Modeling and Security Validation for Agile Development Pipelines in Critical Healthcare Systems
type: item
description: Agile software development has become increasingly central to modern healthcare systems, offering responsiveness and adaptability. However, the rapid iteration cycles pose significant challenges for security integration, particularly in critical healthcare systems handling sensitive patient data. This paper proposes a framework for continuous threat modeling and security validation tailored for Agile pipelines. The framework embeds automated validation tools and adaptive threat intelligence mechanisms to ensure security assurance without hindering development velocity. We validate the approach through a comparative analysis of security vulnerabilities reported in agile healthcare projects using traditional versus continuous security models. Our findings indicate a 35% reduction in critical security issues using continuous validation strategies.
subject: Articles
  topic: section
subject: Threat Modeling
  topic: keywords
subject: Healthcare Systems
  topic: keywords
subject: Continuous Security
  topic: keywords
subject: Security Validation
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: Vulnerability Mitigation
  topic: keywords
creator: Beni Cepos
rights: Copyright (c) 2026 Beni Cepos (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-05
publication date: 2026-01-05
starting page: 8
ending page: 13

A Hybrid Machine Learning Framework for Cross-Domain Fraud Detection Using Multimodal Data from Healthcare and Financial Systems
type: item
description: Fraud detection remains a critical challenge across domains such as healthcare and finance, where fraudulent behaviors often differ in complexity, structure, and data representation. This paper proposes a hybrid machine learning framework that leverages multimodal data integration to improve cross-domain fraud detection capabilities. The approach combines decision trees, neural networks, and ensemble techniques to capture non-linear and latent patterns in both structured data (e.g., transaction records, billing codes) and unstructured data (e.g., clinical notes, textual claims). By unifying heterogeneous data sources from healthcare and financial systems, the model demonstrates enhanced adaptability to evolving fraud trends and context-specific anomalies. The hybrid architecture supports deeper pattern recognition across domains and enables improved generalization in environments where data variability and concept drift are prevalent. Experimental results validate the effectiveness of the proposed approach, showing significant improvements in detection accuracy and robustness compared to baseline single-domain models. This research highlights the potential of cross-domain machine learning and multimodal integration in developing next-generation fraud detection systems.
subject: Articles
  topic: section
subject: Fraud detection
  topic: keywords
subject: Cross-domain learning
  topic: keywords
subject: Multimodal data
  topic: keywords
subject: Hybrid machine learning
  topic: keywords
subject: Healthcare analytics
  topic: keywords
creator: James Oliver Mateo
rights: Copyright (c) 2024 James Oliver Mateo (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2024-11-19
publication date: 2024-11-19
starting page: 8
ending page: 15

Optimizing Edge Computing Architectures for Real-Time Data Processing in IoT Networks
type: item
description: Purpose - The purpose of this paper is to explore and optimize edge computing architectures for real-time data processing within Internet of Things (IoT) networks. As IoT applications generate large volumes of data, edge computing plays a pivotal role in ensuring timely processing and minimizing latency, making real-time decision-making possible in many IoT use cases. Design/Methodology/Approach- The paper adopts a systematic review of existing literature on edge computing architectures and IoT data processing. It focuses on current trends and methodologies in optimizing edge devices, network infrastructure, and data processing workflows to enable real-time, low-latency applications. The research synthesizes findings from various studies, providing insights into the challenges and solutions for enhancing the performance of IoT networks via edge computing. Findings - The paper identifies several key strategies for optimizing edge computing architectures. Among the most prominent approaches are the use of decentralized processing, advanced data routing techniques, edge device optimization, and energy-efficient solutions. Furthermore, the integration of artificial intelligence (AI) at the edge is found to significantly improve decision-making and overall system efficiency. Practical Implications - The findings have practical implications for the design and implementation of edge computing systems in IoT networks. By leveraging edge computing, organizations can reduce latency, improve scalability, and enhance data security. This paper highlights the importance of optimized edge architectures for industries such as healthcare, transportation, and smart cities. Originality/Value - This paper presents a comprehensive analysis of edge computing optimizations in the context of IoT networks, with a focus on real-time data processing. It provides valuable insights into the current state of edge computing and outlines avenues for future research and technological advancements.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Real-Time Data Processing
  topic: keywords
subject: IoT Networks
  topic: keywords
subject: Latency Reduction
  topic: keywords
subject: IoT Applications
  topic: keywords
subject: Data Security
  topic: keywords
subject: AI at the Edge
  topic: keywords
subject: Network Optimization
  topic: keywords
creator: Santiago Paradorn
creator: Jessica Stephen
rights: Copyright (c) 2026 Santiago Paradorn, Jessica Stephen (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-05
publication date: 2026-01-05
starting page: 1
ending page: 7

Neuro-Symbolic Integration Techniques for Scalable Reasoning in Next-Generation Intelligent Systems
type: item
description: As intelligent systems evolve to handle increasingly complex reasoning tasks, the integration of symbolic and sub-symbolic (neural) methods has emerged as a critical paradigm. Neuro-symbolic systems aim to combine the robust generalization capabilities of neural networks with the interpretability, compositionality, and logic-based reasoning of symbolic systems. This paper explores the latest techniques in neuro-symbolic integration, emphasizing their applicability to scalable reasoning in next-generation AI systems. We examine current architectures, propose future integration strategies, and evaluate open challenges and performance trade-offs through comparative analyses. The study also outlines key innovations facilitating scalable inference across diverse cognitive tasks, and discusses the significance of neuro-symbolic systems in ensuring transparency, efficiency, and generalization in AI.
subject: Articles
  topic: section
subject: Neuro-symbolic systems
  topic: keywords
subject: scalable reasoning
  topic: keywords
subject: symbolic AI
  topic: keywords
subject: neural networks
  topic: keywords
subject: interpretability
  topic: keywords
subject: cognitive architectures
  topic: keywords
subject: knowledge representation
  topic: keywords
subject: integration
  topic: keywords
creator: Alexandre Florian
creator: Thibault Jordan
rights: Copyright (c) 2025 Alexandre Florian, Thibault Jordan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-03-25
publication date: 2025-03-25
starting page: 1
ending page: 6

Hierarchical Learning-Based Optimization of Data Flow in Large-Scale Cyber-Physical Systems
type: item
description: Efficient data flow management in large-scale cyber-physical systems (CPS) remains a pressing challenge due to the growing heterogeneity, dynamic operational environments, and the massive volume of interconnected data sources. This paper proposes a hierarchical learning-based optimization framework designed to enhance real-time data flow control across distributed CPS infrastructures. By integrating reinforcement learning within a multi-layered architecture, the proposed approach dynamically adapts to network constraints, prioritizes data packets, and enhances overall throughput while ensuring low latency and fault tolerance. Experimental evaluations demonstrate that hierarchical learning significantly outperforms flat optimization models in terms of scalability, adaptability, and system-wide data efficiency. This work offers critical insights for next-generation intelligent CPS architectures where data-centric decision-making is paramount.
subject: Articles
  topic: section
subject: Cyber-physical systems
  topic: keywords
subject: hierarchical learning
  topic: keywords
subject: data flow optimization
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: distributed systems
  topic: keywords
subject: adaptive control
  topic: keywords
subject: edge computing
  topic: keywords
creator: Imran Amarena
creator: Brandon Ryan
rights: Copyright (c) 2022 Imran Amarena, Brandon Ryan (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2022-01-09
publication date: 2022-01-09
starting page: 1
ending page: 6

A Probabilistic Framework for Evaluating Reliability in Distributed Computing Environments with Uncertain State Transitions
type: item
description: Reliability assessment in distributed computing environments is a critical challenge, particularly when systems exhibit uncertainty in state transitions due to dynamic workloads, unpredictable failures, and non-deterministic behavior. This paper introduces a probabilistic modeling framework that incorporates stochastic state transitions to evaluate system reliability in distributed settings. Leveraging Markov models and Bayesian inference, the framework enables estimation of transition probabilities and failure likelihoods under various operational scenarios. The proposed methodology facilitates more robust reliability planning and risk assessment by capturing non-deterministic system behavior through probabilistic abstraction. Simulation results illustrate the effectiveness of the framework in modeling complex distributed environments, highlighting its adaptability and computational efficiency across varying topologies and failure rates.
subject: Articles
  topic: section
subject: Distributed computing
  topic: keywords
subject: probabilistic modeling
  topic: keywords
subject: Markov model
  topic: keywords
subject: Bayesian inference
  topic: keywords
subject: system reliability
  topic: keywords
subject: state transitions
  topic: keywords
subject: failure analysis
  topic: keywords
creator: Elizabeth Danielle
creator: George William
rights: Copyright (c) 2020 Elizabeth Danielle, George William (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2020-02-20
publication date: 2020-02-20
starting page: 1
ending page: 7

Integrating Artificial Intelligence with Knowledge Graphs for Context-Aware Reasoning in Complex Problem Solving
type: item
description: Artificial Intelligence (AI) has shown remarkable progress in problem-solving tasks; however, its ability to reason contextually in complex, dynamic environments remains limited. Integrating AI with Knowledge Graphs (KGs) offers a promising approach to address this gap by enhancing semantic understanding, relational mapping, and contextual awareness. This paper presents a conceptual and methodological framework for merging AI algorithms with KGs to facilitate context-aware reasoning in multifaceted problem domains such as scientific discovery, intelligent decision support, and adaptive planning. The proposed integration emphasizes the importance of entity linking, temporal-spatial reasoning, and ontology-driven contextual inference. We explore existing architectures, highlight challenges, and provide a future research roadmap aimed at advancing AI reasoning capabilities through structured knowledge integration.
subject: Articles
  topic: section
subject: context-aware reasoning
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: knowledge graphs
  topic: keywords
subject: complex problem solving
  topic: keywords
subject: semantic inference
  topic: keywords
subject: ontology integration
  topic: keywords
subject: relational modeling
  topic: keywords
subject: adaptive decision-making
  topic: keywords
subject: graph reasoning
  topic: keywords
subject: entity linking
  topic: keywords
creator: Geraldine Carey Colleen
rights: Copyright (c) 2023 Geraldine Carey Colleen (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2023-03-21
publication date: 2023-03-21
starting page: 8
ending page: 15

Mitigating Model Drift in Continuous Machine Learning Pipelines Deployed on Cloud-Native Architectures
type: item
description: As machine learning (ML) systems increasingly shift from experimental prototypes to continuously operating services, managing model drift becomes a crucial concern, especially in cloud-native architectures. Model drift, the phenomenon where a model's predictive performance degrades over time due to changes in data distribution, poses significant risks to decision-making systems. This paper explores strategies to mitigate model drift in continuous machine learning pipelines, particularly those deployed on modern cloud-native stacks, including Kubernetes and serverless environments. We propose an integrated framework that leverages drift detection algorithms, retraining triggers, and MLOps automation. The paper also evaluates existing approaches through performance benchmarks and proposes architectural modifications to enhance long-term model reliability.
subject: Articles
  topic: section
subject: Model Drift
  topic: keywords
subject: MLOps
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Continuous Learning
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Data Drift Detection
  topic: keywords
subject: Concept Drift
  topic: keywords
subject: Retraining Automation
  topic: keywords
subject: CI/CD
  topic: keywords
subject: DevOps for ML
  topic: keywords
creator: Bertolt Friedrich
rights: Copyright (c) 2024 Bertolt Friedrich (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2024-01-17
publication date: 2024-01-17
starting page: 1
ending page: 7

Exploring the Role of Personalized Content in Enhancing Engagement in Education Marketing
type: item
description: Personalized content has become a key strategy in digital marketing, particularly within the education sector, where student engagement is a primary success factor. This paper explores how personalized content impacts learner engagement in education marketing campaigns. Through a review of existing literature and analysis of conceptual frameworks, the study identifies major personalization techniques and their effectiveness in educational contexts. The findings suggest that tailored communication, data-driven segmentation, and adaptive learning pathways significantly influence engagement metrics. This study contributes to marketing and education literature by mapping the relationship between personalization and user engagement in educational services.
subject: Articles
  topic: section
subject: personalized content
  topic: keywords
subject: education marketing
  topic: keywords
subject: student engagement
  topic: keywords
subject: content strategy
  topic: keywords
subject: adaptive learning
  topic: keywords
creator: Lopez Diego Minseo
rights: Copyright (c) 2025 Lopez Diego Minseo (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2025-07-17
publication date: 2025-07-17
starting page: 1
ending page: 7

Evaluating Big Data Processing Techniques for Efficient Insights in High Velocity Data Environments
type: item
description: In high velocity data environments, the ability to process large volumes of data in real time and derive actionable insights is crucial. Big data processing techniques, including batch processing, stream processing, and hybrid architectures such as Lambda and Kappa, have been developed to address these challenges. This paper evaluates these techniques by comparing their efficiency, scalability, and ability to handle high-speed data streams. By examining the strengths and weaknesses of each technique, the paper aims to guide organizations in selecting the most suitable approach for their big data analytics needs, particularly in environments where data velocity is a significant concern.
subject: Articles
  topic: section
subject: Big Data
  topic: keywords
subject: Data Processing
  topic: keywords
subject: Batch Processing
  topic: keywords
subject: Stream Processing
  topic: keywords
subject: Lambda Architecture
  topic: keywords
subject: Kappa Architecture
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Data Velocity
  topic: keywords
subject: Scalability
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Big Data Ecosystems
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Hybrid Models
  topic: keywords
subject: Data Streaming
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Allan mike
rights: Copyright (c) 2021 Allan mike (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2021-01-12
publication date: 2021-01-12
starting page: 1
ending page: 7

The Evolution of IT Governance Frameworks in Agile Organizations Undergoing Digital Transformation
type: item
description: As digital transformation reshapes enterprise landscapes, traditional IT governance models struggle to support the flexibility and speed demanded by agile organizations. This paper examines the evolution of IT governance frameworks from rigid, control-heavy systems to adaptive, collaborative approaches aligned with agile principles. Drawing from literature, the study explores emerging governance strategies, including decentralized decision-making, cross-functional alignment, and iterative value delivery. By synthesizing historical perspectives with modern agile needs, the paper identifies best practices and future research directions for harmonizing governance and agility in the digital era.
subject: Articles
  topic: section
subject: IT Governance
  topic: keywords
subject: Agile Organizations
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: Decision Rights
  topic: keywords
subject: COBIT
  topic: keywords
subject: ITIL
  topic: keywords
creator: Aaron Christian1
creator: Felix Franklin Jager2
rights: Copyright (c) 2023 Aaron Christian1, Felix Franklin Jager2 (Author) https://creativecommons.org/licenses/by-nc/4.0
date: 2023-03-19
publication date: 2023-03-19
starting page: 1
ending page: 7