Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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AI-Driven Healthcare Integration: A Comprehensive Technical Analysis of Diagnostic Transformation
No full textThe integration of artificial intelligence in healthcare represents a transformative shift in medical diagnostics and patient care delivery. This technological evolution encompasses advanced computational architectures for medical imaging interpretation, predictive analytics for risk stratification, and natural language processing for medical information extraction. The implementation of AI-driven patient interaction systems has enhanced healthcare communication while addressing critical challenges in data interoperability and ethical considerations. The advancement of performance optimization strategies, including model refinement techniques and scalable architectural solutions, has established a foundation for next-generation healthcare systems. These developments, combined with emerging quantum computing applications and edge AI implementations, mark a significant progression toward more efficient, transparent, and patient-centered healthcare delivery
The Power of AI-Driven Personalization: Technical Implementation and Impact
No full textAI-driven personalization represents a transformative force in customer engagement, utilizing advanced algorithms to deliver tailored experiences at individual levels. This article explores the architectural foundations, core algorithms, implementation challenges, evaluation frameworks, and industry-specific applications that power modern personalization systems. From collaborative filtering and deep learning networks to real-time processing engines and privacy-preserving techniques, the technological ecosystem supporting personalization continues to evolve rapidly. The discussion addresses how organizations overcome critical challenges including cold-start problems, data sparsity, and filter bubbles while measuring success through both technical and business metrics. By examining applications across e-commerce, media, finance, healthcare, education, and retail sectors, the content illuminates how domain-specific adaptations create value through dynamic pricing, adaptive interfaces, customized recommendations, and seamless omnichannel experiences
AI-Driven Smart Fabric Provisioning: Transforming Network Automation through Intelligent Orchestration and Dynamic Testing
No full textThe evolution of modern data centers demands innovative approaches to fabric provisioning, particularly when integrating new switches, hosts, and storage into existing infrastructures. This article introduces a Smart Fabric Provisioning solution powered by Agentic AI that transforms traditional manual processes into automated, intelligent workflows. By creating dynamic full-mesh fabrics with simulated environments, the solution addresses critical challenges in solution qualification and resource utilization during proof-of-concept phases. The AI-driven approach enables logical link manipulation to test various conditions without physical reconfiguration, significantly streamlining deployment workflows while reducing human error. This dual-purpose technology serves both as an internal efficiency tool for en
Agentic AI, Fabric Provisioning, Network Automation, Infrastructure Simulation, Proof-of-Concept Optimization
representing a paradigm shift in how network fabrics are provisioned, tested, and deployed
Vision Machine Learning for Efficient Defect Triaging in Repair Operations
No full textThe manufacturing industry along with electronics sectors experience a new technological revolution through Vision Machine Learning for their repair operations defect triaging procedures. The inspected quality control system based on ML enables fundamental change from human-operated methods by using deep learning constructs such as CNNs to perform automatic defect recognition and classification along with priority management tasks. Today\u27s move toward automated visual analysis solves three major problems: human inspector fatigue as well as variable human-based evaluation and restricted inspection speed. Advanced ML systems integrate multiple sensor types through transfer learning techniques to obtain both reduced training data needs and better detection precision and steadiness. The implementation structures of production systems include edge computing, cloud infrastructure and combination models which provide varying benefits throughout production settings. Research-based defect management workflows enhance optimized queue management and enable structured maintenance information storage and economic decision capability which shortens cycles and enhances repair quality. The deployment of these technologies in existing repair systems delivers operational effectiveness and quality upshots through supportive evaluation frameworks and continuous improvement procedures
Data Interfaces in Mental Health: Supporting Awareness, Not Surveillance
No full textDigital mental health interfaces represent a promising frontier bridging technology and psychological care, yet they must balance information provision with supportive design to avoid contributing to anxiety or surveillance concerns. These interfaces collect substantial personal data while facing challenges of information overwhelm, privacy vulnerabilities, accuracy limitations, and contextual understanding deficits. Effective mental health applications prioritize simplified layouts, empathetic visual design, and specialized data visualization techniques that enhance emotional intelligence without overwhelming users. The integration of artificial intelligence through machine learning and natural language processing enables personalized insights and emotional assessment, though these capabilities necessitate robust ethical frameworks centered on privacy protection and user autonomy. Despite implementation barriers including sensor accuracy issues and integration complexity, solutions like hybrid sensing approaches and human-in-the-loop systems offer practical pathways forward. Future directions point toward multimodal sensing, federated learning, just-in-time interventions, and digital phenotyping to create mental health interfaces that genuinely support psychological wellbeing while respecting individual agency
The Transformative Role of Artificial Intelligence in Accelerating Biomedical Research: A Focus on Alzheimer\u27s Disease
No full textThis article explores the transformative role of artificial intelligence in accelerating biomedical research, with a particular focus on Alzheimer\u27s disease. The article examines how AI platforms have revolutionized traditional research methodologies through enhanced data processing capabilities, improved diagnostic accuracy, and accelerated drug discovery processes. The article highlights significant advancements in four key areas: AI-enabled research platforms and infrastructure, early detection and diagnostic applications, genomic analysis and target discovery, and autonomous research systems. These innovations have led to unprecedented improvements in processing complex datasets, identifying early disease markers, analyzing genetic variations, and automating research processes. The article demonstrates how AI integration has dramatically reduced research timelines while maintaining high accuracy rates across various applications, potentially transforming the future of biomedical research and therapeutic interventions
Greenhouse Gases and their Role in Air Pollution and Global Warming
No full textToday, one of the most significant global challenges is the increase in climate change due to the excessive emission of greenhouse gases. Carbon dioxide gas, resulting from the combustion of fossil fuels, and methane are recognized as the primary greenhouse gases and the foremost contributors to climate change. Population density, increased vehicular traffic, industrial factories, and neglect of environmental concerns are major factors influencing the concentration of greenhouse gases in the atmosphere. Recent global studies indicate that since the onset of the Industrial Revolution—a period marked by a significant rise in fossil fuel consumption—human activity has played a crucial role in the process of climate change and global warming through the production and emission of greenhouse gases. Understanding how these types of pollution evolve requires attention to the various factors affecting their emission. Accordingly, this study collects and examines data obtained from library-based research using a descriptive-analytical method. Consequences of the greenhouse effect include flooding, reduction in potable water and agricultural products, increased soil erosion, the extinction of some plant and animal species, and the migration of certain population groups. These consequences underscore the necessity and importance of focusing on the use of renewable energy sources
Hybrid Learning Method in Teaching English for Medicine at the University of Bisha: Lecturers’ and students’ Perceptions
No full textThis study explores the implementation of hybrid learning, specifically through the Blackboard Learning Management System, as a pedagogical model in higher education. The purpose of the research is to examine the benefits and challenges associated with hybrid learning from the perspectives of lecturers and students, with a focus on its application in specialized disciplines such as medicine. The study adopts a qualitative design, employing semi-structured interviews, classroom observations, and document analysis to collect data from lecturers and students engaged in hybrid learning environments. The findings reveal that hybrid learning offers significant advantages, including enhanced flexibility, accessibility, and the integration of theoretical and practical knowledge. For lecturers, it facilitates innovative teaching strategies, fosters professional development, and enables efficient course management. For students, hybrid learning promotes self-discipline, time management, and active engagement while providing opportunities for real-world applications and lifelong learning. However, the study also identifies several challenges, including technological limitations, increased workload for lecturers, and skill gaps in navigating digital platforms. Additional issues, such as maintaining student engagement in asynchronous components, limited interaction in online activities, and assessment complexities, further underscore the need for targeted interventions. The research concludes that while hybrid learning is a transformative approach to modern education, its success requires robust technological infrastructure, comprehensive training for users, and institutional support. These measures can help address the identified challenges and optimize the benefits of hybrid models. The study highlights the importance of innovative pedagogical practices, tailored interventions, and a supportive digital ecosystem to enhance the effectiveness of hybrid learning, particularly in specialized and evolving academic fields such as medicine. This research provides valuable insights for educators, administrators, and policymakers aiming to implement or improve hybrid learning frameworks, contributing to the broader discourse on educational innovation in the digital age
Mobile Learning at Higher Education: Lecturers’ Perception of Mobile-Based Learning in Teaching English Paragraph Writing
No full textMobile technology has changed higher education teaching and learning. Mobile-Based Learning (MBL) provides flexible, accessible, and engaging ways to improve English language writing skills. Many studies have explored mobile learning from students\u27 perspectives, but few have examined how lecturers view and implement MBL, especially in Islamic Higher Education. To fill that gap, we investigated UIN Alauddin Samata, South Sulawesi English paragraph writing lecturers\u27 perspectives of MBL. The qualitative case study used interviews, classroom observations, and documentation analysis. The results show that lecturers like MBL as a classroom supplement. They shared materials, assigned tasks, and provided comments using LMS (Lentera), smartphones, and PCs. However, mobile tool limitations and technology limit the assessment of writing outcomes. According to the study, institutional support and blended learning approaches optimize MBL. These results add to digital pedagogy literature and suggest more studies on student perspectives and cross-institutional MBL writing instruction comparisons
ChatGPT in Translating Cultural Nuances in O Night by Gibran Khalil Gibran
No full textThis study investigates the challenges of using ChatGPT in translating the cultural nuances in the O Night poem by Gibran Khalil Gibran from Arabic into English. Then, the study compares ChatGPT’s translation with human translation. The sample consisted of 40 culture-bound expressions that were purposefully selected from the poem. They are analyzed using a mixed-methods approach based on Wang\u27s (2023) framework of the challenges embedded in AI translation tools. The findings reveal that the majority of the culture-bound expressions translated by ChatGPT are inadequate with the percentage (75%). This inadequacy is attributed to the fact that ChatGPT relies on denotative meaning due to its inability to translate the connotative meaning. Another challenge in ChatGPT is manifested in using an inappropriate linguistic style. Besides, its poor logical structure and its inability to capture the cultural nuances require human intervention to post-edit and refine the translation provided by ChatGPT. To overcome these challenges, the study recommends using contextual translation to convey the connotative and expressive meaning of poetic verses