International Journal of Computing Science and Systems (IJCSS)
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.
Artificial Intelligence-Driven Intelligent Systems: A Comprehensive Study on Adaptive Learning Models, Bias Mitigation, and Secure Deployment in Large-Scale IoT Environments
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
Copyright (c) 2025 Nikolai Pushkin (Author) https://creativecommons.org/licenses/by-nc/4.0
Design and Implementation of Data Engineering Workflows to Support Machine Learning and Artificial Intelligence in Real-Time Decision Systems
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.
Copyright (c) 2024 Rossi Osei Andersen, (Author) https://creativecommons.org/licenses/by-nc/4.0
Integration of Data Quality Metrics with Machine Learning Models for Robust Decision-Making Systems
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.
Copyright (c) 2025 Robinson Ethan Clark (Author) https://creativecommons.org/licenses/by-nc/4.0
Evaluation of Transfer Learning Techniques for Low-Resource Natural Language Processing
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
Copyright (c) 2025 Guzel Yakhina Sorokin (Author) https://creativecommons.org/licenses/by-nc/4.0
End-to-End Machine Learning Pipelines Delivered Through Secure Microservices for Autonomous Operations in Digital Infrastructure Platforms
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.
Copyright (c) 2026 Josep Pla Saramago (Author) https://creativecommons.org/licenses/by-nc/4.0
Assessing the Role of Continuous Testing in Reducing Failure Rates in AI-Driven Clinical Applications
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.
Copyright (c) 2026 Murni Reza, Antara Aulia (Author) https://creativecommons.org/licenses/by-nc/4.0
Federated AI Model Deployment Across Multi-Cloud Microservices: Architecture, Challenges, and Security Frameworks
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.
Copyright (c) 2024 Hilary Mantel Ishiguro, (Author) https://creativecommons.org/licenses/by-nc/4.0
Continuous Threat Modeling and Security Validation for Agile Development Pipelines in Critical Healthcare Systems
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.
Copyright (c) 2026 Beni Cepos (Author) https://creativecommons.org/licenses/by-nc/4.0
A Hybrid Machine Learning Framework for Cross-Domain Fraud Detection Using Multimodal Data from Healthcare and Financial Systems
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.
Copyright (c) 2024 James Oliver Mateo (Author) https://creativecommons.org/licenses/by-nc/4.0
Optimizing Edge Computing Architectures for Real-Time Data Processing in IoT Networks
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.
Copyright (c) 2026 Santiago Paradorn, Jessica Stephen (Author) https://creativecommons.org/licenses/by-nc/4.0
Neuro-Symbolic Integration Techniques for Scalable Reasoning in Next-Generation Intelligent Systems
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.
Copyright (c) 2025 Alexandre Florian, Thibault Jordan (Author) https://creativecommons.org/licenses/by-nc/4.0
Hierarchical Learning-Based Optimization of Data Flow in Large-Scale Cyber-Physical Systems
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.
Copyright (c) 2022 Imran Amarena, Brandon Ryan (Author) https://creativecommons.org/licenses/by-nc/4.0
A Probabilistic Framework for Evaluating Reliability in Distributed Computing Environments with Uncertain State Transitions
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.
Copyright (c) 2020 Elizabeth Danielle, George William (Author) https://creativecommons.org/licenses/by-nc/4.0
Integrating Artificial Intelligence with Knowledge Graphs for Context-Aware Reasoning in Complex Problem Solving
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.
Copyright (c) 2023 Geraldine Carey Colleen (Author) https://creativecommons.org/licenses/by-nc/4.0
Mitigating Model Drift in Continuous Machine Learning Pipelines Deployed on Cloud-Native Architectures
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.
Copyright (c) 2024 Bertolt Friedrich (Author) https://creativecommons.org/licenses/by-nc/4.0
Exploring the Role of Personalized Content in Enhancing Engagement in Education Marketing
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.
Copyright (c) 2025 Lopez Diego Minseo (Author) https://creativecommons.org/licenses/by-nc/4.0
Evaluating Big Data Processing Techniques for Efficient Insights in High Velocity Data Environments
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.
Copyright (c) 2021 Allan mike (Author) https://creativecommons.org/licenses/by-nc/4.0
The Evolution of IT Governance Frameworks in Agile Organizations Undergoing Digital Transformation
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.
Copyright (c) 2023 Aaron Christian1, Felix Franklin Jager2 (Author) https://creativecommons.org/licenses/by-nc/4.0