International Journal of Modern Computing
The main aim of the International Journal of Modern Computing (IJMC) is to publish refereed, well-written original research articles and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Modern Computing (IJMC) is a peer-reviewed online journal and is published throughout the year.
Measuring the AI-Governance-Performance Nexus in Financial Decision-Making
Research on artificial intelligence in finance often reports model accuracy without explaining how institutional governance shapes real decision outcomes. This review examines the analytical relationship among artificial intelligence integration, data governance, and financial performance using the quantitative patterns reported in a large systematic evidence base. It summarises the distribution of research across time, countries, methods, technologies, sectors, and governance themes, then interprets the reported results from supported vector regression, quantile-sensitive analysis, structural equation modelling, multi-group comparison, and sensitivity testing. Machine learning, regulatory compliance, fraud detection, risk analytics, and credit scoring showed particularly strong associations. The structural model indicated that artificial intelligence contributes to financial outcomes directly and indirectly through data governance, supporting a partial mediation interpretation. Results also varied by governance maturity: basic data quality and compliance were more important in early implementation, while hybrid models and compliance automation became stronger in advanced environments. Temporal comparison suggested that direct relationships strengthened after 2020 while the mediating role of governance remained stable. The article evaluates the strengths and limits of these methods and proposes a research agenda based on multi-database evidence, longitudinal modelling, causal designs, and context-sensitive validation.
Copyright (c) 2025 International Journal of Modern Computing
Machine Learning for Biodiversity Monitoring: Success Stories and Lessons Learned
Biodiversity conservation depends on accurate, timely, and comprehensive data on species distributions, populations, and ecological interactions. Citizen science has emerged as a powerful tool for biodiversity monitoring, mobilizing millions of volunteers to collect observations across spatial and temporal scales that would be impossible for professional scientists alone. The integration of machine learning into biodiversity citizen science has accelerated dramatically over the past decade, with applications spanning species identification, population estimation, habitat modeling, and early warning systems for invasive species. This review synthesizes the current state of ML-enhanced biodiversity monitoring, examining successful projects that have leveraged computer vision, acoustic analysis, and multimodal learning to advance conservation science. We identify key success factors, including robust training data, transparent validation protocols, and effective volunteer engagement strategies. We also analyze lessons learned from challenges such as algorithmic bias, data quality concerns, and the limitations of automation for rare and cryptic species. Our findings indicate that while ML has significantly enhanced the efficiency and scope of biodiversity monitoring, its most effective applications are those that maintain human oversight for quality assurance and complex identification tasks. We conclude by proposing best practices for integrating ML into biodiversity citizen science and identifying priority areas for future research and development.
From Cameras to Classifiers: The Role of Computer Vision in Citizen Science
Computer vision has emerged as a transformative technology in citizen science, enabling automated analysis of images and videos at scales that would be impossible through human effort alone. This review examines the role of computer vision in citizen science, from camera trap projects that automatically capture wildlife images to mobile applications that provide real-time species identification. We analyze the technical approaches that have proven most effective, with particular focus on convolutional neural networks and transfer learning techniques that enable models to be trained on citizen science data. We examine applications across biodiversity monitoring, astronomy, medical imaging, and other domains, identifying success factors and limitations. We also address challenges including data quality, training data bias, model performance for rare species, and integration with volunteer workflows. We propose best practices for implementing computer vision in citizen science, including strategies for data collection, model training, validation, and volunteer engagement. We conclude by identifying emerging trends and future directions, including few-shot learning, multimodal approaches, and real-time analysis.
Copyright (c) 2026 International Journal of Modern Computing
Fair, Explainable, and Accountable AI: Human Oversight in Public-Sector Decision Systems
Public-sector artificial intelligence can shape access to services, inspections, benefits, taxation, healthcare, mobility, and other consequential decisions. Its legitimacy therefore depends on more than predictive performance. Systems must avoid unjustified discrimination, provide explanations suitable for affected people and reviewers, assign responsibility, support challenge, and preserve meaningful human control. This review analyses fairness, explainability, accountability, and participation as interconnected dimensions of AI governance. It draws on the evidence base of a systematic synthesis that retained 95 high-quality studies and governance frameworks from 2020 to mid-2025. The source literature shows that ethics and transparency are frequently discussed, yet bias mitigation, accountability mechanisms, citizen participation, and implementation procedures remain uneven. The article examines how bias enters through problem definition, data, labels, modelling, thresholds, deployment, and institutional practice. It distinguishes transparency from useful explanation and describes accountability as a chain of ownership, documentation, review, appeal, and correction. It also considers the limitations of nominal human-in-the-loop arrangements and proposes conditions for effective oversight. A practical assurance model is presented that connects principles to controls, metrics, and evidence. The review concludes that fair and accountable AI requires institutional capacity, stakeholder participation, and continuous monitoring, not a one-time technical test or public statement.
Ethical Challenges in AI-Powered Citizen Science: Privacy, Bias, and Transparency
The integration of artificial intelligence into citizen science has created unprecedented opportunities for scientific discovery, but it has also introduced significant ethical challenges that must be addressed to protect participants, maintain trust, and ensure the integrity of scientific research. This review examines the ethical dimensions of AI-powered citizen science, focusing on three interconnected areas: privacy, algorithmic bias, and transparency. We analyze how AI systems collect, process, and use volunteer data, raising concerns about data protection, consent, and the potential for commercial exploitation of citizen science contributions. We examine algorithmic bias in AI systems trained on citizen science data, including spatial, temporal, taxonomic, and demographic biases that can undermine data quality and scientific validity. We address transparency challenges, including the need for clear communication about how AI systems use volunteer data, how algorithms make decisions, and how volunteers can understand and challenge automated determinations. We propose an ethical framework for AI-powered citizen science that balances the benefits of AI integration with the protection of volunteer rights, scientific integrity, and public trust. We conclude by identifying priority areas for ethical guidance, governance, and research.
Data Quality Management in Healthcare: Challenges, Solutions, and Best Practices
Healthcare is one of the most data-intensive sectors, with patient records, clinical trials, public health surveillance, and research generating vast amounts of information that directly impact patient safety, clinical decision-making, and health policy. However, healthcare data quality remains a persistent challenge, with studies documenting widespread issues across multiple dimensions including accuracy, completeness, consistency, and timeliness. This review examines data quality management in healthcare, analyzing the unique challenges of healthcare data, including the complexity of clinical information, the diversity of data sources and formats, the sensitivity of patient data, and the high stakes of quality failures. We review the evidence on data quality in healthcare, drawing on studies from multiple settings and countries, and analyze the causes of quality problems, including training gaps, process weaknesses, and governance deficiencies. We examine solutions that have been implemented, including WHO's Data Quality Review toolkit, electronic health record validation, and quality improvement initiatives. We propose a comprehensive framework for healthcare data quality management that addresses technical, organizational, and ethical dimensions.
Beyond Species Identification: Emerging Roles of Machine Learning in Citizen Science Data Validation
Data validation is a critical yet resource-intensive component of citizen science projects. As the volume and complexity of citizen science data continue to grow, traditional approaches to quality assurance—relying on expert review or simple filtering rules are becoming increasingly inadequate. Machine learning offers powerful new capabilities for data validation, including automated anomaly detection, real-time quality scoring, observer expertise classification, and adaptive sampling strategies. This review examines the emerging roles of ML in citizen science data validation, moving beyond the well-studied application of species identification to encompass comprehensive quality assurance across diverse data types and scientific domains. We analyze the technical approaches that have proven most effective, including supervised and unsupervised learning for outlier detection, probabilistic modeling for uncertainty estimation, and ensemble methods for robust validation. We also address the challenges that arise when ML is applied to data validation, including the propagation of training data biases, the difficulty of validating novel observations, and the importance of maintaining human oversight. We propose a framework for ML-enhanced data validation that integrates automated screening, expert verification, and continuous learning, and we discuss implications for data quality, volunteer engagement, and scientific inference.
Copyright (c) 2024 International Journal of Modern Computing
Automating Data Quality Assurance in Citizen Science: Promise, Pitfalls, and Best Practices
Data quality assurance is a critical but resource-intensive component of citizen science projects. As project scale and data volume have grown, traditional expert-based quality assurance approaches have become increasingly inadequate, creating a need for automated and scalable validation methods. Machine learning has emerged as a promising solution, enabling automated screening, real-time validation, and adaptive quality assessment at scales that would be impossible through manual review. This review examines the application of machine learning to data quality assurance in citizen science, analyzing the promise, pitfalls, and best practices of automated validation. We examine the types of data quality issues that arise in citizen science, including identification errors, recording errors, sampling biases, and data fraud, and analyze how machine learning can address these issues. We review the technical approaches that have been applied, including anomaly detection, probabilistic validation, and quality scoring, and examine the conditions under which these approaches are most effective. We also address significant pitfalls, including training data bias, error propagation, model overconfidence, and the reduction of human oversight. Based on the analysis, we propose a framework for integrating machine learning into data quality assurance that balances automation with human expertise, scalability with reliability, and efficiency with volunteer engagement.
Static Analysis in Embedded and Electronic Systems: Challenges, Applications, and Future Perspectives
Static analysis has become a fundamental technology in embedded, cyber-physical, and electronic system software engineering due to the increasing complexity of modern software-controlled systems and the growing demand for safety, security, and reliability. This article examines the evolution of static analysis from an academic verification method to an essential industrial practice across safety-critical and security-sensitive domains. The study explores the role of Static Application Security Testing (SAST), smart contract verification, embedded and automotive system analysis, and AI/ML software testing. It further investigates major limitations of current static analysis methodologies, including scalability challenges, false positives, soundness–precision trade-offs, and benchmarking difficulties. The article also highlights how modern static analysis frameworks support compliance with international safety standards and enable the secure development of cloud-native, autonomous, and intelligent systems. Finally, the study concludes that static analysis will remain indispensable for ensuring software correctness, cybersecurity, functional safety, and trustworthiness in next-generation digital infrastructures and cyber-physical environments.
AI Data Governance Frameworks for Large Language Models: Supply Chain Management and Domain-Specific Applications
Artificial Intelligence (AI) data governance has become a critical component in ensuring the responsible, secure, and transparent deployment of AI systems across multiple domains. This article explores the implementation of AI data governance in supply chain management, healthcare, cybersecurity, and finance, highlighting how governance frameworks improve compliance, accountability, data integrity, privacy, and operational efficiency. The study further examines domain-specific governance strategies for Large Language Models (LLMs), emphasizing the need for tailored approaches that address sector-specific regulatory and ethical requirements. In addition, the article discusses major challenges associated with data governance for LLMs, including data quality and bias, privacy and security risks, transparency and explainability limitations, and scalability and complexity issues. The analysis reveals that although LLMs provide significant opportunities for automation, intelligent decision-making, and enhanced operational performance, their large-scale deployment introduces concerns related to ethical AI use, regulatory compliance, data ownership, and infrastructure costs. The article concludes that robust AI data governance frameworks are essential for building trustworthy, fair, and accountable AI systems while ensuring sustainable and secure integration of LLMs across diverse industries.
Artificial Intelligence Is Revolutionizing End-To-End Quality Assurance In Agile And DevOps Environments, From Test Case Design To Test Data Generation
A rethinking of Quality Assurance (QA) procedures is necessary in the modern software development environment, where Agile and DevOps approaches prioritize continuous integration and quick delivery. Examining two crucial areas—test case design and test data generation—this study delves into the revolutionary role of AI in improving end-to-end QA. Inefficient and perhaps quality-compromising traditional QA procedures can't keep up with the fast-paced Agile/DevOps settings. The emergence of AI as a potent tool for automating and optimizing these processes has greatly improved the efficiency with which teams can create test cases and realistic test data. We take a look at how test case design has changed over time, drawing attention to the shortcomings of traditional methods and the merits of AI-driven systems that employ user stories and requirements to generate tests automatically. Next, we'll discuss the importance of test data production, where AI can tackle problems like data privacy by creating different synthetic data and using masking and anonymization. Discussed as well is the use of AI into CI/CD pipelines, which shows how AI improves the efficacy and precision of deployment process testing. We also look at how AI may improve teamwork by facilitating communication and requirement analysis using Natural Language Processing (NLP) techniques. Ethical concerns, the necessity for human supervision, and guaranteeing the quality of AI-generated outputs are some of the remaining obstacles, despite substantial advantages. Finally, we go over several upcoming AI and QA developments that might take QA to the next level, like autonomous testing and predictive analytics. The importance of incorporating AI into QA procedures has been highlighted in this extensive investigation. This will allow enterprises to achieve higher software quality, shorter delivery cycles, and better performance in DevOps and Agile settings.
Artificial Intelligence in Software Quality Assurance for Agile and DevOps Environments: Challenges, Applications, and Future Directions
The rapid evolution of Agile and DevOps methodologies has significantly transformed modern software development by emphasizing continuous delivery, rapid iteration, and collaboration among multidisciplinary teams. In this environment, traditional software quality assurance (QA) approaches often struggle to keep pace with accelerated release cycles and increasingly complex software systems. Artificial Intelligence (AI) has emerged as a transformative technology capable of enhancing software testing, continuous integration/continuous deployment (CI/CD), test automation, defect prediction, and collaborative quality management. This paper explores the integration of AI into software quality assurance practices within Agile and DevOps ecosystems. The study examines AI-driven testing techniques, predictive analytics, intelligent test automation, self-healing test systems, AI-assisted collaboration tools, and AI-enhanced CI/CD pipelines. Additionally, the paper discusses ethical and operational challenges including data privacy, algorithmic bias, transparency, accountability, and the balance between automation and human expertise. Finally, future trends such as autonomous testing, generative AI for test artifact generation, and predictive QA analytics are analyzed. The study concludes that AI has the potential to fundamentally reshape software QA by improving efficiency, reliability, adaptability, and collaboration while still requiring human oversight and governance to ensure responsible and effective implementation.
Integration Patterns Driven by Events for Financially Sensitive Enterprise Platforms
Contemporary enterprise platforms functioning in financially sensitive contexts must ensure elevated reliability, auditability, and real-time responsiveness. Conventional synchronous integration methods frequently result in tight coupling, delay, and operational vulnerability in the context of high-volume financial transactions. Event-driven architecture (EDA) has evolved as a scalable model that facilitates loosely connected communication, asynchronous processing, and enhanced resilience in dispersed corporate systems. This paper analyses event-driven integration patterns tailored for financially sensitive enterprise platforms, including banking systems, payment gateways, government financial platforms, and enterprise resource planning (ERP) settings. It examines architectural elements including event producers, brokers, consumers, and event stores, and investigates patterns such as event notification, event-carried state transfer, event sourcing, and transactional outbox mechanisms. The study additionally examines dependability measures such as idempotent processing, assured delivery, replay capabilities, and audit-ready event logging necessary in regulated financial contexts. Furthermore, governance factors like compliance monitoring, data lineage tracking, and security procedures are assessed. The paper illustrates, via architectural diagrams, comparative tables, and analytical insights, how event-driven integration enhances scalability, operational resilience, and financial data integrity across enterprise platforms. The findings offer a systematic framework for organisations to implement when updating integration layers in transaction-critical systems, ensuring regulatory and operational reliability.
Incorporating AI-Driven Knowledge Graphs And Natural Language Processing For Astute Interpretation, Summarisation, And The Harmonisation Of Cross-Border Financial Reporting
In a context of more complex and globally integrated financial systems, issues of harmonisation in cross-border reporting are intensifying. Regulatory disparities, linguistic variations, data compartmentalisation, and the continued emergence of unstructured disclosures persist as impediments to the efficacy of transparency, compliance, and efficiency initiatives. This research presents a novel fusion of AI-driven Knowledge Graphs (KG) with Natural Language Processing (NLP), which we propose as a potential solution for enhancing financial interpretation and summarisation across jurisdictions. Knowledge Graphs (KGs) serve as organised semantic representations of financial institutions, their attributes, and interrelationships, enabling machines to comprehend and contextualise information. This architecture, when integrated with advanced NLP models such as transformers and specialised large language models (LLMs), can effectively and clearly extract, disambiguate, and summarise financial disclosures, audit reports, and regulatory filings. These features are especially beneficial for multinationals, auditors, and regulators seeking to cross-map diverging financial standards, such as IFRS and GAAP, or to automate compliance mapping. The document delineates a system architecture that leverages multi-source data, entity recognition, relation extraction, and multilingual semantic alignment utilising AI-augmented ontologies. Practical instances from the EU, ASEAN, and North America demonstrate how artificial intelligence-driven systems may streamline manual tasks, identify anomalies in reporting, and generate reconciled summaries for stakeholders across borders. The findings underscore the capability of NLP utilised in Knowledge Graphs, not just for automating reporting processes but also as a foundation for providing intelligent, transparent financial governance solutions.
Efficiently Integrating Security into DevOps Development Pipelines via DevSecOps: A Comprehensive Approach to Secure Software Delivery
In the contemporary digital environment, the necessity for swift software development and deployment has resulted in the extensive implementation of DevOps approaches. DevOps prioritizes communication between development and operations teams, facilitating continuous integration and continuous delivery (CI/CD). Nevertheless, conventional DevOps pipelines frequently neglect essential security considerations, resulting in vulnerabilities that may be identified only after deployment. To rectify this deficiency, DevSecOps—a paradigm that incorporates security as a collective obligation across the complete software development lifecycle (SDLC)—has arisen as a crucial improvement. This article examines the seamless incorporation of security inside DevOps pipelines through the adoption of DevSecOps concepts. It seeks to establish a systematic methodology for integrating automated security measures from the inception and throughout the development lifecycle. By advancing security measures earlier in the pipeline, organizations may identify and rectify vulnerabilities sooner, thereby mitigating risks and maintaining compliance without sacrificing delivery speed. A comprehensive literature review underscores the transition from conventional security models to contemporary DevSecOps frameworks. The document analyzes contemporary instruments and approaches, including Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), Software Composition Analysis (SCA), and Infrastructure as Code (IaC) scanning. It delineates the integration of these tools into CI/CD procedures for immediate vulnerability detection. The fundamental ideas of DevSecOps are examined, encompassing automation, policy-as-code, continuous monitoring, and threat modeling. The report also examines the cultural and organizational transformations required for effective deployment, highlighting the importance of collaboration across developers, security teams, and operations. This paper highlights the advantages, obstacles, and optimal practices of DevSecOps, emphasizing its essential function in contemporary safe software delivery. It finishes with insights on prospective advancements, like AI-driven threat detection and improved compliance automation, facilitating more resilient, secure, and agile development ecosystems.
AI-Enabled PII Lifecycle Governance in State Motor Vehicle Administration Systems: A Case-Driven Framework
Vehicle automation signifies a novel safety paradigm that may require a reevaluation of current safety supervision frameworks. This white paper provides an overview of the technical and regulatory framework regarding the safety of autonomous driving systems (ADS). It presents the most recent artificial intelligence and machine learning methodologies that facilitate ADS functionality. The paper examines the concepts of safety from the viewpoints of standards-setting bodies, federal and state regulations, and legal fields. The document delineates essential legislative alternatives based on themes presented in the White House’s Blueprint for an AI Bill of Rights, articulating a framework for ADS safety. The analysis finds that prospective ADS safety reforms may involve either the modification of the Federal Motor Vehicle Safety Standards (FMVSS) or a comprehensive risk analysis "safety case" methodology. The analysis examines case law regarding liability in robotics and judicial actions concerning consumer and commercial privacy, acknowledging that the advent of AI will transform liability frameworks, necessitating a meticulous approach to data collection that incorporates accountability and safeguards the privacy of consumers and organizations. This analysis underscores the necessity for policies that address human-machine interface concerns, emphasizing requirements for safety drivers and remote operators. This work underscores the necessity for collaboration among engineers, policy experts, and legal scholars to formulate a comprehensive Blueprint for ADS safety and identifies avenues for further research.
Artificial Intelligence in Healthcare: Applications, Challenges, and Future Perspectives
Artificial Intelligence (AI) has emerged as a transformative technology in the healthcare sector, offering significant opportunities to improve medical services, research, and patient care. AI-based technologies such as machine learning, deep learning, and natural language processing are increasingly used in various healthcare applications, including medical imaging, diagnostics, drug discovery, vaccine development, virtual patient care, and rehabilitation. These technologies also assist healthcare professionals in improving patient engagement, treatment adherence, and administrative efficiency. AI-driven systems can analyze large and complex datasets, support clinical decision-making, and reduce the workload of healthcare professionals. Despite these benefits, the adoption of AI in healthcare presents several challenges. Ethical and social concerns such as data privacy, security, algorithmic bias, accountability, and transparency remain major obstacles. Governance and regulatory frameworks are also required to ensure the safe and responsible use of AI technologies in clinical environments. In addition, technical limitations including data availability, model reliability, interpretability issues, and infrastructure costs may hinder the widespread implementation of AI systems. This article reviews the major applications of artificial intelligence in healthcare and highlights the ethical, governance, and technical challenges associated with its use. It also discusses the disadvantages and limitations of AI technologies and emphasizes the importance of developing responsible AI frameworks that align technological advancements with human values and healthcare needs. The study concludes that while AI has the potential to significantly enhance healthcare systems, careful planning, regulatory oversight, and collaboration between healthcare professionals and technology experts are essential to ensure its safe and effective integration.
Present Condition and Future Outlook on the Application of Machine Learning and Big Data Analytics in Multimorbidity Research
Multimorbidity, the coexistence of two or more chronic conditions in an individual, presents complex challenges for healthcare management and research. Traditional epidemiological and statistical methods often fail to capture the intricate interactions among diseases, functional impairments, and patient outcomes. Recent advances in machine learning (ML) and big data (BD) analytics offer promising strategies to address these challenges by enabling patient phenotyping, risk stratification, and prediction of disease progression using multimodal datasets, including electronic health records. Techniques such as clustering, deep learning, reinforcement learning, and temporal modeling facilitate the identification of disease patterns, trajectories, and treatment optimization for individuals with multimorbidity. Despite these advances, practical implementation in clinical settings is limited by data heterogeneity, methodological variability, and insufficient integration of domain expertise. Collaboration among clinicians, data scientists, and AI specialists is essential to develop standardized, validated research protocols and to translate ML/BD insights into actionable clinical strategies. Ultimately, the integration of these technologies promises to improve personalized care, population health management, and the creation of multidisciplinary guidelines for the management of multimorbidity.
From Information to Insight: A Data-Centric Approach to Strategic Decisions
In the modern digital era, data has become one of the most valuable organizational assets, driving innovation, efficiency, and competitive advantage. However, the mere availability of large volumes of data does not automatically translate into effective decision-making. The journey from data to decisions involves a complex process of data collection, integration, analysis, interpretation, and application within organizational contexts. This article explores the concept of transforming raw data into actionable insights that support informed, timely, and strategic decisions. Advances in data analytics, artificial intelligence, machine learning, and business intelligence tools have significantly enhanced the ability of organizations to extract value from structured and unstructured data. At the same time, challenges such as data quality issues, information overload, ethical concerns, and skill gaps continue to hinder effective data-driven decision-making. This article provides a comprehensive examination of the data-to-decision lifecycle, highlighting key technologies, methodologies, governance practices, and human factors involved in the process. By integrating technical, organizational, and strategic perspectives, the discussion demonstrates how data-driven approaches can improve accuracy, transparency, and accountability in decision-making. The article also examines real-world applications across various sectors and identifies future trends shaping the evolution of data-driven decision systems. Overall, the study aims to guide students, researchers, and practitioners in understanding how data can be systematically transformed into meaningful decisions that drive sustainable organizational success.
Designing Ethical ML Governance: Integrating Privacy, Fairness, and Organizational Performance
The rapid advancement of machine learning (ML) has transformed industries by enabling sophisticated data-driven decision-making, yet it has also introduced critical ethical challenges. Ethical data governance frameworks are essential to ensure that ML systems uphold privacy, fairness, and business optimization while addressing societal and organizational priorities. Privacy strategies, including anonymization, differential privacy, and compliance with regulations such as GDPR and CCPA, safeguard individuals’ data. Fairness involves mitigating biases in datasets and algorithms to promote equitable outcomes, while business optimization focuses on responsibly leveraging ML to maximize organizational value without compromising ethics. Effective frameworks integrate legal compliance, organizational policies, and technical solutions, incorporating privacy-preserving methods, fairness-aware models, and transparent decision-making. Key challenges include balancing trade-offs between privacy and utility, addressing bias, and ensuring scalable implementation. Case studies illustrate successful applications of these frameworks, demonstrating their potential to promote both ethical integrity and business innovation. Emerging trends, such as federated learning, AI ethics boards, and international collaboration on data standards, are pivotal for advancing responsible ML practices. Embedding ethics throughout the AI lifecycle from design to deployment and monitoring is critical. By adopting robust ethical governance frameworks, organizations can foster trust, meet regulatory requirements, and responsibly harness the full potential of ML technologies.
The Impact of Artificial Intelligence on Revolutionizing Metadata Management: Perspectives on Challenges, Prospects, and New Trends
This paper investigates the use of artificial intelligence (AI) in metadata management in libraries, focusing on the problems, potential, and future improvements. Metadata is critical for organizing, managing, and retrieving library resources, and AI has the potential to automate these processes. The study's major goals are to investigate the issues libraries face when implementing AI-based metadata systems, find potential for improving metadata procedures, and assess new trends in this field. A thorough literature study was used to examine important scholarly publications and glean useful insights. The findings indicate that data quality issues, ethical concerns, and technical constraints are the primary challenges facing libraries. At the same time, AI offers substantial opportunities, including automated metadata generation, improved resource discoverability, and efficient management of large digital collections. Predictive analytics and linked data adoption are predicted to increase in the future, which will have a big impact on metadata management procedures. The study concludes that although AI delivers considerable advantages, human supervision remains critical, and libraries must prioritize staff training and policy updates to ensure successful AI integration.
AI Data Governance for Large Language Models: Frameworks, Best Practices, and Future Directions
Large Language Models (LLMs) are rapidly transforming multiple sectors, including healthcare, finance, and cybersecurity, by enabling advanced data-driven insights and automation. However, the scale and complexity of LLMs introduce significant challenges in ensuring data privacy, ethical use, compliance with regulations, and mitigation of biases. AI data governance provides a structured approach to address these challenges by integrating robust frameworks, ethical guidelines, auditing mechanisms, and stakeholder collaboration throughout the LLM lifecycle. This article presents a comprehensive overview of AI data governance for LLMs, detailing critical components such as data collection, annotation, storage, management, usage, regulatory compliance, ethical frameworks, and accountability. It emphasizes best practices including the development of governance frameworks, leveraging AI-driven monitoring technologies, continuous improvement strategies, and human-in-the-loop collaboration to maintain data quality and trustworthiness. The study also examines real-world implementations in enterprises, showcasing case studies from industries like finance, telecom, and cloud services, highlighting the integration of frameworks such as IBM watsonx.governance and blockchain-based traceability approaches. Additionally, the article identifies open challenges, including scalability, cross-border compliance, security risks, data provenance, and bias mitigation, and suggests future research directions to create standardized, adaptive, and transparent governance systems. Overall, this work underscores the importance of a holistic, ethical, and regulatory-aware approach to AI data governance to ensure responsible, secure, and trustworthy deployment of LLMs across diverse domains.
Machine Learning Using Python (MLPY): A Comprehensive Study of Models and Techniques
Machine Learning with Python (MLPY) has emerged as a cornerstone of modern data-driven research and intelligent system development. Python’s simplicity, extensive libraries, and strong community support make it one of the most widely adopted programming languages for implementing machine learning models. This article presents a comprehensive overview of machine learning concepts using Python, focusing on both theoretical foundations and practical applications. It explores core learning paradigms, including supervised, unsupervised, and reinforcement learning, while emphasizing commonly used algorithms such as linear regression, decision trees, support vector machines, k-means clustering, and neural networks. The study highlights the role of popular Python libraries, including NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch, in facilitating efficient data preprocessing, model training, evaluation, and visualization. Additionally, the article discusses real-world applications of MLPY across various domains, such as healthcare, finance, natural language processing, and computer vision. Challenges related to data quality, model interpretability, and computational complexity are also addressed. By integrating conceptual explanations with practical insights, this work aims to provide learners, researchers, and practitioners with a clear understanding of how machine learning techniques can be effectively developed and deployed using Python to solve complex real-world problems.
Design and Implementation of a Responsible, Explainable, and Compliance-Driven AI Architecture for Enterprise-Scale Content Management Systems Integrating Generative Models, Retrieval Pipelines, and Real-Time Governance Controls
This paper introduces a data analytics-driven methodology for supplier onboarding and ERP-based compliance management that expedites qualification, enhances assurance, and reduces lifecycle risk. A standardized digital intake records identity, regulatory, ESG, cybersecurity, and tax credentials; deterministic rules authenticate necessary evidence, while a comprehensible gradient-boosted model assesses residual risk utilizing factors such as sector, jurisdiction, beneficial ownership depth, sanctions proximity, and historical incident rates. All processes are recorded in the ERP vendor master and procurement modules using regulated APIs. The approach establishes a golden-record strategy, reference taxonomies, and data-quality regulations to avert duplicate or incomplete vendor profiles. Continuous monitoring employs event streams and dashboards to identify status alterations, expired certificates, negative media, delayed attestations, and control deviations. Exceptions initiate structured remedial operations, whereas feedback loops recalibrate the model and adjust thresholds for idea drift. We assess the approach using a quasi-experimental design that compares matched business units prior to and following implementation. Results demonstrate a 32–45% reduction in onboarding lead time, a 28% decline in first-year compliance exceptions, and a 19% enhancement in audit-readiness ratings, all while upholding competition and diversity standards. Ablation analysis indicate that the most significant effects stem from master-data quality controls and the automatic ERP gates of the policy engine. A reference architecture, governance RACI, and value tracking framework are provided to facilitate expansion across multi-ERP environments. The contribution is threefold: firstly, a cohesive, analytics-driven methodology that integrates onboarding and compliance into a singular, data-oriented process; secondly, a transparent risk assessment framework linked with verifiable controls; and thirdly, actionable change-management strategies that facilitate value realization. Future endeavors will enhance causal inference, incorporate document intelligence, and investigate privacy-preserving data sharing.
Unraveling the Divide: How Data Governance and Data Management Shape Enterprise Success
Data governance has become a pivotal element in assessing the efficacy and enduring stability of IT systems in the contemporary data-centric landscape. It includes frameworks that assist organizations in overseeing data collection, storage, processing, utilization, and sharing, while ensuring alignment with business objectives, compliance with regulations, and adherence to ethical standards. The intricacies of data governance, especially regarding emerging technological frameworks, present considerable challenges. This systematic review seeks to examine the function of data governance in improving data quality, system stability, and regulatory compliance. It aims to identify prevalent challenges, successful methodologies, and strategic consequences for the implementation of data governance frameworks across various industries, with particular emphasis on small and medium enterprises (SMEs). We performed a systematic review of 68 qualifying studies published from 2014 to 2024, employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The studies included were evaluated for bias risk utilizing the Cochrane Risk of Bias Assessment Tool. The review encompassed various study types, comprising qualitative (60%), quantitative (19%), and mixed-methods (21%) research, to furnish a thorough understanding of the influence of data governance on IT performance. The results indicate that data governance profoundly affects system success by enhancing data quality, operational efficiency, and regulatory compliance. While qualitative studies primarily focused on in-depth analysis, there was a moderate presence of empirical validation via quantitative studies. Identified challenges encompass data migration difficulties, resistance to change, and budget limitations, especially during system upgrades. Effective practices entail aligning data governance with business strategies, utilizing hybrid models, and consistently revising governance policies to accommodate technological advancements such as artificial intelligence and big data. This review offers pragmatic suggestions for IT managers and policymakers to improve data governance frameworks. These encompass incremental implementation strategies, the formation of data governance committees, and the application of performance metrics to evaluate data quality and system robustness. Despite considerable advancements, additional research is necessary to tackle deficiencies concerning emerging technologies and the distinct challenges encountered by SMEs in developing regions.
Copyright (c) 2019 International Journal of Modern Computing
Data Quality Metrics How to Measure and Improve Accuracy
The quality of data, particularly its assessment, has been extensively examined in both research and practical applications. To facilitate economically driven management of data quality and decision-making under uncertainty, it is crucial to evaluate the data quality level using robust metrics. Nevertheless, if these measures are not well delineated, they may result in erroneous conclusions and financial detriment. Consequently, within a decision-oriented framework, we delineate five prerequisites for data quality measures. These requirements pertain to a measure designed to facilitate economically driven management of data quality and decision-making under uncertainty. We further illustrate the applicability and effectiveness of these requirements by assessing five data quality measures across several data quality dimensions. Furthermore, we examine the practical ramifications of implementing the outlined standards. The two most important requirements for data quality are consistency and accuracy. Database inconsistencies and errors are often caused by breaches of integrity requirements. To make a filthy database D consistent, automated techniques are required to locate a repair D0 that fulfils criteria and differs "minimally" from D. It's crucial to guarantee that the automatically generated fix D0 is accurate and makes sense. D0 should deviate from the "correct" data within a given range. This study explores practical approaches to improve data consistency and accuracy. We use conditional functional dependencies (CFDs) from [6] to ensure data consistency and detect mistakes that standard methods may miss. We present two techniques to increase data consistency: one for automatically computing a repair D0 that satisfies a set of CFDs, and another for progressively discovering a repair in response to clean database changes. We demonstrate that both challenges are insurmountable. We empirically validate the effectiveness and efficiency of our heuristic algorithms. We created a statistical strategy to ensure the accuracy of algorithmic fixes beyond a preset rate without requiring unnecessary human engagement.
Copyright (c) 2018 International Journal of Modern Computing
Data Profiling, The First Step Toward Achieving High Data Quality
This intellectual inquiry explores the profound influence of data quality on data analytics. The immaterial commences by delineating the multifarious aspects of data quality, which include accuracy, completeness, consistency, reliability, and timeliness. It is essential to comprehend and resolve these dimensions in order to fully realise the potential of data analytics tools and techniques. Subsequently, the abstract investigates the obstacles associated with guaranteeing data quality, such as the complex nature of data purification, data integration issues, and the changing nature of data sources. Additionally, this abstract delineates the methodologies and optimal procedures implemented to optimise data quality. The significance of techniques such as data profiling, data purification, and standardisation in the identification and correction of data inconsistencies is elucidated. Real-world case studies emphasise the critical relationship between the efficacy of data analytics methodologies and the integrity of the data. These case studies illustrate the tangible advantages that can be achieved by investing in data quality initiatives, such as enhanced consumer satisfaction, streamlined operational processes, and improved decision-making. The abstract also delves into the financial and reputational risks that are associated with substandard data, which range from flawed business strategies to erroneous predictive models. It promotes a proactive approach, in which organisations invest in sophisticated tools, experienced personnel, and robust data governance frameworks to guarantee the consistent quality of their data. Ultimately, businesses can achieve sustainable development by leveraging the true power of data analytics, which will drive innovation and cultivate a competitive advantage.
GDPR Compliance Challenges and How to Overcome Them
Startups and SMEs need focus, particularly technology startups; although they are fueled by innovation and advancing technology, they require improved data protection practices. This research seeks to collect data regarding the awareness of startups about the GDPR, pinpoint the main challenges encountered by technology startups in Catalonia since the GDPR's implementation in May 2018, and investigate (1) the potential correlation between the identified challenges and factors such as the number and type of employees hired, the size of the startup, the business sector, and the year of establishment; and (2) the resources, both time and financial, that startups have allocated towards compliance. The literature review identified gaps in the research and served as the foundation for examining the challenges encountered by startups due to the enforcement of the GDPR. Thirty-two challenges were identified concerning GDPR and categorized into four constructs: compliance costs, regulation complexity, government support, and process adaptation. The lack of adequate government support poses the greatest challenge for the Catalonian startups involved in the survey. This study represents one of the initial empirical investigations into GDPR compliance efforts and challenges faced by Catalan technology startups. It employs advanced statistical analysis techniques, beginning with ANOVA, and includes independent sample T-tests, correlation analysis, and regression analysis. Regrettably, the results cannot be generalized to all startups in Catalonia due to the failure to meet the required minimum sample size for representativeness; 116 responses were collected, whereas 314 were necessary to achieve the appropriate sample size. Nonetheless, this research offers a practical contribution by (1) presenting recommendations that enhance technology startups' understanding of the various challenges they need to tackle in order to comply with GDPR and (2) offering suggestions for the Catalan government to promote startup GDPR implementation.
Metadata is Key to Digital Transformation in Enterprises
The swift rise in global internet utilization and the escalating intricacy of digital landscapes have compelled firms to undertake digital transformation to maintain competitiveness. Despite previous studies emphasizing the significance of digital transformation, a substantial vacuum persists in the literature providing comprehensive and generalizable frameworks. Current models frequently pertain to certain sectors or are created by consultant companies, resulting in limited application across various organizational contexts. This study fills the vacuum by conducting a meta-analysis of diverse organizations—varying in size, sector, and geographic location—that have already experienced digital transformation. The objective is to discern prevalent trends and obstacles, and to provide a cohesive framework applicable across many industries. This inquiry is guided by two fundamental research questions: What are the similarities and contrasts among firms that have undergone digital transformation? What strategic measures may organizations do to expedite this process? The data indicate recurring themes in successful transitions, which were utilized to construct a preliminary framework. The model was subsequently evaluated against the identical instances to determine its applicability. The framework is designed to assist entrepreneurs, executives, change managers, and researchers in strategizing and implementing successful digital transformation programs.
Copyright (c) 2020 International Journal of Modern Computing
The Impact of Ransomware on Government Agencies: Lessons Learned and Future Strategies
Ransomware attacks on government agencies have escalated in both frequency and sophistication, posing significant challenges to national security, public safety, and operational continuity. These attacks encrypt critical data and demand ransom payments, often crippling essential services such as law enforcement, healthcare, and public utilities. Recent high-profile incidents, such as ransomware attacks on municipal governments and federal agencies, have underscored systemic vulnerabilities, including outdated legacy systems, inadequate cybersecurity infrastructure, and a lack of employee training. The financial and reputational damage resulting from such breaches can be extensive, with ransom demands often reaching millions of dollars and recovery efforts taking weeks or even months. Lessons learned from these incidents highlight the urgent need for proactive cybersecurity measures, including regular data backups, network segmentation, endpoint detection, and robust incident response protocols. Government agencies must adopt a zero-trust security model that minimizes unauthorized access and enforces strict authentication policies. Additionally, cybersecurity awareness training for employees is critical in preventing social engineering attacks, which often serve as initial entry points for ransomware infections. Collaborative efforts between federal, state, and local governments, as well as partnerships with private-sector cybersecurity firms, can enhance intelligence sharing and threat mitigation strategies. Legislative and policy-based approaches are also essential, with governments considering regulations that discourage ransom payments, mandate cybersecurity best practices, and enforce compliance with strict security standards. Emerging technologies such as artificial intelligence and machine learning can be leveraged to detect anomalies in network behavior and preemptively identify potential threats before they escalate into full-scale ransomware attacks. Future strategies should focus on comprehensive risk assessments, continuous monitoring, and the implementation of cyber-resilience frameworks that enable agencies to recover swiftly from cyber incidents. Governments must also allocate sufficient funding to modernize aging IT infrastructure, ensuring that systems are equipped with the latest security patches and defense mechanisms. As ransomware tactics continue to evolve, government agencies must remain vigilant, adopting a multi-layered security approach that integrates technological advancements, policy-driven solutions, and a culture of cybersecurity preparedness. The fight against ransomware requires a collective effort, and by implementing robust defense strategies, government entities can safeguard sensitive data, maintain public trust, and ensure the uninterrupted delivery of critical services.