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...
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...
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...
The integration of technology into healthcare has revolutionized patient care, bringing forth innovations that enhance diagnostic accuracy, treatment efficacy, and operational efficiency. As medical software systems become increasingly integral to...
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,...
There has been a meteoric rise in the number of AI-based applications in road pavement engineering since the late 1980s. Key points in the decision-making process for pavement management include the use of such apps. They predict pavement degradation and do...
Deciphering the decision-making process of AI systems may be quite a challenge due to their complexity and lack of transparency. Because people could not know what goes into AI decision-making, this makes questions of justice and responsibility more...
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...
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...
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,...
But even in the age of AI-driven cloud ecosystems, we continue to grapple with how to maintain data reliability when workloads are constantly shifting, data comes from many places, and the real-time expectation is often acute. Data reliability methods that...
Utilising AI, ML, and DL in Cutting-Edge Business Strategies: A Comprehensive Overview .This research delves deeply into the ways in which AI, ML, and DL influence the creation and refinement of company strategies. It delves into the ways AI impacts...
Ethical concerns and dangers are inherent in the process of creating new technology. We take a look at several approaches to handling the potential dangers and ethical dilemmas posed by AI, one of the next technologies. The growth of AI might have both...
In 2017, we should finally see artificial intelligence (AI) implemented in real-world workplaces, rather not only in movies and TV shows. Concerns over the potential effects of artificial intelligence (AI) on traditional job functions have been making their...
The main aim of the International Journal of Modern Computing (IJMC) is to publish refereed, well-written original research articles...
Haleema Tayyab
Awan Tower, Peshawar, Pakistan
Monday – Friday, 9:00 AM – 6:00 PM (PKT)
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