Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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6248 research outputs found
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Examining the Impact of Demographics on Students’ Perceptions of Mobile-Assisted Language Learning
No full textMobile-assisted language learning (MALL) has witnessed significant development, with a growing number of university learners relying on their mobile devices for language learning. However, limited research has been conducted on the influence of demographics, including age, gender, and university level, on students\u27 perceptions of MALL. Addressing this research gap, the current study was conducted at the University of Sidi Mohamed Ben Abdellah, in Morocco, focusing on English department students. The study employed a quantitative approach, utilizing a questionnaire to collect data from 164 students. Through the examination of variables such as gender, age, and university level, the findings indicated that gender did not significantly impact students\u27 acceptance of MALL while age and university level emerged as influential factors in shaping students\u27 preferences. These findings highlight the potential effectiveness of MALL as a language learning tool. The results are particularly relevant for informing policymakers considering the implementation of MALL-based systems in higher education. Furthermore, educators are invited to consider their student cohorts\u27 diverse age and university-level characteristics and adapt MALL activities accordingly
Ethical and Legal Considerations of AI in IT Project Management: Addressing AI Biases, Data Privacy, and Governance
No full textAs Artificial Intelligence (AI) continues to reshape the IT project management field, ethical and legal issues are emerging as increasingly important. AI-powered tools boost productivity via automation, predictive analytics, and decision support; simultaneously, they also introduce risks associated with bias, data privacy, and governance. AI-powered biases in project management algorithms can result in unequal distribution of resources, discriminatory decision-making, and unforeseen outcomes. In addition, AI\u27s reliance on vast volumes of data raises privacy concerns, particularly in complying with global data protection laws such as GDPR, CCPA, and HIPAA. Governance frameworks are needed to render AI transparent, responsible, and ethically applied in IT project management. This article explores the possible risks of artificial intelligence in managing projects, examines the existing legal frameworks, and provides recommendations on how to mitigate biases embedded in AI, protect data privacy, and institute effective governance of AI. By addressing these issues, organizations can ethically leverage the power of AI while maintaining compliance and fostering trust in information technology project management processes
Leveraging AI-Driven Anomaly Detection for Enhanced Data Quality and Regulatory Compliance in Clinical Studies
No full textThe integration of artificial intelligence-driven anomaly detection systems has revolutionized data quality management and regulatory compliance in clinical studies. By leveraging advanced machine learning algorithms and pattern recognition capabilities, these systems enhance the detection and prevention of data inconsistencies while ensuring adherence to regulatory guidelines. The implementation demonstrates marked improvements in adverse event reporting, protocol deviation monitoring, and data standardization processes. Through automated validation frameworks and real-time monitoring capabilities, organizations can significantly reduce manual intervention requirements while maintaining high standards of data integrity. The evolution from traditional manual processes to AI-enabled monitoring represents a fundamental transformation in how clinical data quality is managed, leading to enhanced patient safety outcomes and more efficient trial operations
The Mobile Retail Revolution: AI\u27s Transformative Impact on Consumer Behavior and Industry Dynamics
No full textThe retail industry is experiencing a transformative shift driven by the integration of artificial intelligence and mobile technology. This technological convergence has revolutionized consumer shopping behavior by creating intelligent intermediaries between retailers and consumers. AI-powered mobile applications have enhanced personalization, inventory management, and optimized pricing strategies while ensuring robust security measures. The implementation of advanced machine learning models, sophisticated data processing architectures, and innovative personalization engines has led to enhanced customer experiences and operational efficiencies. The integration of physical and digital retail environments through technologies like RFID, computer vision, and augmented reality has created seamless shopping experiences. Looking ahead, emerging technologies such as federated learning, blockchain, and edge AI promise to further transform the retail landscape while addressing challenges in system scalability and integration complexity
AI-Driven Test Automation: Transforming Software Quality Engineering
No full textThe integration of artificial intelligence into test automation represents a paradigm shift in software quality engineering, addressing longstanding challenges of traditional testing methods. As applications grow increasingly complex with microservices architectures, cloud-native components, and frequent deployment cycles, AI-driven testing emerges as a solution to the brittleness and maintenance overhead of conventional approaches. By leveraging machine learning, natural language processing, computer vision, and self-learning systems, organizations can reduce script maintenance efforts while improving defect detection rates. These advanced frameworks enable automated test case generation, self-healing automation, predictive defect analysis, and enhanced performance testing capabilities. The transition from rule-based to intelligent testing follows an evolutionary path through augmentation, hybrid, intelligence-dominant, and autonomous phases, with each stage delivering progressive improvements in efficiency, accuracy, and scalability. AI-powered testing ultimately transforms quality assurance from a reactive verification activity into a proactive, adaptive mechanism capable of keeping pace with modern development practices
The Transformative Impact of Business Intelligence and Artificial Intelligence on Healthcare
No full textThis technical article explores the transformative impact of Business Intelligence (BI) and Artificial Intelligence (AI) on the healthcare industry, examining how these technologies are revolutionizing patient care and operational efficiency. The integration of advanced analytics with clinical workflows enables healthcare organizations to leverage vast amounts of data from electronic health records, medical imaging, and operational systems to drive evidence-based decision-making. This article discusses the architectural frameworks supporting healthcare data integration, visualization techniques enhancing clinical insights, AI applications augmenting diagnostic capabilities, and operational intelligence optimizing resource allocation. As these technologies continue to mature, they present unprecedented opportunities for precision medicine, population health management, and financial sustainability in healthcare delivery systems, ultimately creating a more responsive, efficient, and patient-centered healthcare ecosystem
AI-Driven Antibiotic Discovery: Addressing Antimicrobial Resistance Through Machine Learning
No full textAntibiotic resistance is a growing global issue, owing to the fast evolution of infections and the lessened efficacy of existing therapies. Unlike conventional medication development, antibiotics have the unique problem of being left behind as resistance develops. This has led to renewed interest in artificial intelligence (AI) and machine learning (ML) as approaches to expedite antibiotic discovery, particularly in the context of a slow and costly development process. This work reviews the increasingly widespread application of AI toward identifying antimicrobial peptides and small molecule drugs. These include prediction of antimicrobial activity, representation of compounds, assessment of drug-likeness, modelling of resistance mechanisms and de novo design of molecular classes. We also explore how open scientific principles, including reproducibility, openness, and data sharing, can be incorporated to accelerate preclinical research. We end by discussing emerging trends and future directions in antibiotic discovery, emphasizing how advances in machine learning are revolutionizing the field to tackle this urgent global challenge
Revolutionizing Financial Management: The Role of Agentic AI in SAP Finance
No full textThe integration of Agentic AI into SAP Finance represents a transformative advancement in enterprise financial management, combining autonomous decision-making capabilities with sophisticated data analytics to revolutionize traditional financial processes. This comprehensive article explores how Agentic AI is reshaping SAP Finance through enhanced automation of routine financial tasks, deployment of advanced predictive analytics for forecasting and risk assessment, and the provision of real-time financial intelligence that enables dynamic decision-making. By examining the technical architecture, implementation strategies, and organizational impacts, this article demonstrates how Agentic AI empowers finance professionals to transcend operational constraints and focus on strategic initiatives while simultaneously improving accuracy, compliance, and responsiveness in financial operations across the enterprise landscape
Converged IAM: Transforming Enterprise Identity Management in the Cloud Era
No full textThis article examines the transformation of Identity and Access Management (IAM) in the context of enterprise digital transformation and cloud adoption. It explores how traditional siloed approaches to IAM have created significant operational challenges and security vulnerabilities, leading organizations to adopt converged IAM solutions. The article investigates the benefits of integrating Access Management, Identity Governance and Administration, and Privileged Access Management into unified platforms. Through analysis of implementation cases across various industries, with particular focus on healthcare sectors, the article demonstrates how converged IAM solutions enhance security posture, streamline compliance processes, and improve operational efficiency. The article also examines the role of artificial intelligence and automation in modern IAM frameworks, highlighting their impact on threat detection, access management, and compliance monitoring
AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive
No full textThis article explores the transformative impact of AI-powered data platforms on enterprise supply chain management, focusing on architecture, implementation strategies, and performance optimization. The article examines how modern enterprises are leveraging artificial intelligence to enhance their supply chain operations through advanced analytics, cloud integration, and machine learning capabilities. The article presents a comprehensive analysis of key technical components, including real-time data processing, predictive analytics, and security frameworks, while evaluating their effectiveness in improving operational efficiency and decision-making processes. Through examination of implementation cases and performance metrics, the article demonstrates how AI-driven platforms are revolutionizing supply chain optimization, risk management, and compliance monitoring across various industries