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,...
In an era of digital transformation, government organizations handle vast amounts of sensitive data, making robust data governance essential for ensuring security, compliance, and efficiency. Public sector data governance encompasses policies, frameworks,...
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...
The implementation of advanced data analytics solutions has become paramount in higher education institutions, particularly within correctional education programs. The Department of Corrections (DOC) has faced persistent challenges in ensuring data...
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...
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...
Contemporary Artificial Intelligence (AI) systems require fluid, scalable, and intelligent data streams to facilitate real-time analytics, model training, and automated decision-making. Traditional data pipelines are frequently inflexible, labour-intensive,...
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...
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....
Through a data utilisation value chain, AI/Data commons will promote the construction of an open collaborative ecosystem based on PCI (Participation- Collaboration-Incentives). This will be accomplished by preserving data sovereignty and protecting...
The issue of ethical governance for robotics and artificial intelligence (AI) systems is examined in this paper. As a framework to guide ethical governance in robotics and AI, we present a roadmap that connects several components, such as ethics, standards,...
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...
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...
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...
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 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...
This research study analyzes the transformational impact of Generative AI on Oracle Database Automation, emphasizing its capacity to change data management and analytics. As enterprises increasingly depend on data-driven decision-making, efficient and...
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,...
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,...
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...
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...
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...
Small and medium-sized enterprises (SMEs) present a harder machine learning problem than either large corporate borrowers or individual consumers, for reasons the credit-risk and fraud-detection literatures document separately: SMEs are financially...
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...
On April 17, 2026, the Federal Reserve, OCC, and FDIC jointly issued SR 26-2, Revised Guidance on Model Risk Management, which supersedes both the foundational 2011 guidance (SR 11-7 / OCC Bulletin 2011-12) and the 2021 interagency statement on BSA/AML...
The main aim of the International Journal of Modern Computing (IJMC) is to publish refereed, well-written original research articles...
Haleema Tayyab
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