Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Translation of Zero-Expressions by Microsoft Copilot and Google Translate
No full textA corpus of 318 English and Arabic zero-expressions used in general as well as specialized contexts as math, technology, law, military, economics, finance, and others was collected from Al-Maany Online dictionaries. The expressions were translated by Microsoft Copilot (MC) and Google Translate (GT) to find out the percentage of expressions correctly translated by both, the translation strategies used, and to explore the semantic, lexical, syntactic, and contextual inaccuracies that mistranslations reveal. It was found that 29% of the zero-expressions in the sample were correctly translated by both MC and GT. This percentage represents less than the correct translations of medical and Gaza-Israel War Terminology rendered by MC and GT. In 52% of the translations given by MC and 50% of the translations given by GT, the Arabic equivalent zero expressions consisted of a noun + a derived adjective صفرية الصفرية/ /صفري/ الصفري. In 31% of the data, MC gave definite equivalents (zero rating التصنيف الصفري) compared to 9% by GT. In 11%, GT rendered equivalents with an awkward word order (zero for zero approach صفر لنهج الصفر). In 12%, MC and GT gave similar Arabic equivalents with a reversed word order (zero fraction كسر الصفر (MC), صفر الكسر (GT). In 5%, MC and GT gave faulty Arabic equivalents with different derived forms (output zero إخراج الصفر (MC) & صفر المخرج (GT) instead of صفر مخرجات). The most common translation strategy used was word-for-word translation. Conceptual translation and modulation were not frequently used (zero position وضعية صفرية (MC), موضع الصفر (GT) instead of وضع الابتداء ; Zero duties واجبات صفرية instead of بدون رسوم). Zero expressions containing a polyseme were mistranslated (false zero صفر خاطئ (MC), صفر زائف (GT) instead of صفر غير حقيقي). Both MC & GT failed to give the underlying meaning of idiomatic phrases as الشمال صفر على which means has no value. Both gave a word-for-word translation zero on the north (MC) and zero to the north (GT), which are meaningless. Problems that AI has in translating zero-expressions are described and discussed in detail. The article concludes with some recommendations for AI specialists and translation pedagogy
Data Trust in Cloud-Based AI Systems: A Comprehensive Analysis
No full textThe integration of artificial intelligence with cloud-based systems has revolutionized data handling, processing, and security practices in modern enterprises. Data trust frameworks have emerged as essential components for balancing innovation with security and ethical considerations in AI implementations. These frameworks incorporate governance structures, technical safeguards, and operational protocols to ensure data integrity and reliability. Through systematic implementation of trust mechanisms, organizations can achieve enhanced security, improved operational efficiency, and increased stakeholder confidence. The evolution of data trust technologies, including advanced encryption methods and automated governance systems, continues to shape the future of secure AI operations in cloud environments. The implementation of these frameworks represents a paradigm shift in how organizations approach data security and AI deployment. By incorporating advanced cryptographic techniques, blockchain technology, and quantum-resistant algorithms, data trust frameworks provide robust protection against emerging threats while enabling seamless AI operations. The adoption of federated learning approaches and privacy-preserving computation methods has further enhanced the capability of organizations to maintain data confidentiality while leveraging AI capabilities. Additionally, the integration of automated monitoring systems and real-time validation protocols enables organizations to maintain consistent data quality standards across their operations. These advancements, combined with sophisticated identity management systems and access control mechanisms, create comprehensive trust architectures that support secure and ethical AI deployment while fostering innovation in cloud-based environments
Multimodal Deep Learning for Alzheimer’s Disease Diagnosis: Integrating Neuroimaging and Genetic Data
No full textConventional diagnosis of Alzheimer’s disease (AD) has usually relied upon data from individual modalities, which inherently restricts how data can be comprehended for understanding the disease process. To this end, in the current study, we present a novel multimodal deep learning framework that integrates clinical assessments, genomic information and imaging characteristics to enhance diagnosis and disease staging. This study uses Contrastive Stack Denoising Autoencoder and 3D CNNs to represent genetic data (e.g. single nucleotide polymorphisms, or SNPs), clinical test scores, and MRI scans. In addition to the correct categorization of people into three groups, AD, MCI, and CN. Compared with existing interpretability methods, this method selects the most prominent features by clustering them and performing perturbation analysis. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we show through experiments that our proposed deep learning framework outperforms traditional machine learning methods, including support vector machines, random forests, and k-nearest neighbors, for these imaging features. The multimodal model outperforms the single-modality models across all metrics, including accuracy, precision, recall, and F1 scores. This by itself validates the therapeutic relevance of the model, as it highlights classic AD proteins that are present in the disease, including the hippocampus, amygdala, and the Rey Auditory Verbal Learning Test (RAVLT), which are all widely known to be impacted in AD as per conventional medical knowledge of the disease
Creating Equitable Digital Healthcare: The Role of Content Platform Engineering in UI Optimization
No full textThis article examines how healthcare providers and health technology firms can optimize user interfaces for accessibility, ensuring that elderly and disabled patients can navigate telemedicine services effectively. The rapid digitization of healthcare, accelerated by the pandemic, has created both opportunities and barriers for vulnerable populations. While telehealth eliminates geographic and transportation limitations, poorly designed interfaces can erect new obstacles. Content platform engineering offers promising solutions through structured content models, adaptive delivery systems, and multimodal interaction patterns. The article explores technical foundations of accessible healthcare interfaces including semantic HTML architecture, progressive enhancement strategies, and ARIA implementation for dynamic content. It presents implementation strategies for addressing visual impairments, motor control limitations, and cognitive accessibility needs, along with frameworks for measuring success. A case study of Memorial Health System demonstrates how accessibility redesign can simultaneously improve patient outcomes and organizational efficiency. Future directions in healthcare accessibility include AI-driven personalization, biometric adaptation, and voice-first interface paradigms that further reduce barriers to equitable care access
The Technological Revolution in Agriculture: Cloud Computing as the Backbone of Smart Farming
No full textThis comprehensive review examines the pivotal role of cloud computing in revolutionizing agricultural practices through the integration of Internet of Things (IoT) technologies, advanced weather prediction systems, artificial intelligence analytics, and robust cloud infrastructure. The article explores how smart farming implementations leverage interconnected sensor networks to create continuous data pipelines from field conditions to decision support systems, enabling precision resource management and improved crop yields. It analyzes the technical aspects of weather data integration that combines satellite observations with ground measurements to provide early detection of adverse conditions. The review further investigates how machine learning algorithms transform agricultural data into actionable insights for disease detection, yield prediction, and resource optimization. Finally, it addresses the challenges and solutions related to cloud infrastructure design, data integration, interoperability, and security concerns that influence the adoption of these technologies across diverse farming contexts
Integrating Advanced Monitoring Technologies and Reliability Engineering for Proactive Wildfire Risk Management
No full textThe frequency and intensity of wildfires escalate because of environmental change together with anthropological activities and growing urban-wildland areas. The necessity for proactive wildfire risk management arises because it creates substantial economic losses and environmental damage and harmful impacts on public health thus becoming an essential worldwide concern. This paper investigates the integration of advanced remote sensing technologies with reliability engineering principles to establish a proactive risk management framework for wildfires. The research reviews state-of-the-art monitoring methods satellite imagery, unmanned aerial vehicle (UAV) surveillance, and ground-based sensor networks and assesses their operational performance through reliability metrics and statistical analysis. Case studies drawn from the USA, Australia, Europe, China, and the UK are examined to quantify detection improvements and overall system robustness. Using a combination of statistical methods (including regression analysis and Monte Carlo simulations) and predictive modeling (via machine learning algorithms), our findings indicate that integrated systems can improve early detection by up to 40% and reduce false alarms by approximately 30%. Implications for decision-making and resource allocation are discussed, and a proactive management framework is proposed that bridges the gap between monitoring technology performance and engineering reliability. By combining interdisciplinary research from geospatial science, artificial intelligence (AI), and reliability engineering, this study contributes a novel approach to wildfire risk management and underscores the necessity for continuous technological innovation and robust evaluation methods 
Breaking Down AI-Powered Case Categorization in Customer Support
No full textThis article explores the transformative impact of AI-powered case categorization in modern customer support environments. Traditional manual categorization processes create a significant administrative burden for support agents, reducing their capacity for substantive problem resolution while introducing inconsistencies that undermine service quality and analytics. AI-powered categorization systems address these limitations through sophisticated machine learning models, natural language processing capabilities, and continuous learning mechanisms that improve over time. The implementation of these systems in platforms like Salesforce\u27s Einstein Case Classification demonstrates how careful attention to evaluation metrics, threshold configuration, and integration with workflow systems can maximize operational benefits. Beyond efficiency gains, AI categorization delivers improved consistency, enhanced analytical capabilities, optimized resource allocation, and significant return on investment. The article examines both current implementations and emerging directions, including multimodal analysis, personalized categorization, predictive support modeling, generative response capabilities, and causal analysis that promise to further revolutionize customer support operations
Enterprise Applications Suite: Transforming Industry-Specific Digital Transformation Through AI and Cloud Computing
No full textEnterprise Applications Suite presents a comprehensive cloud-based solution transforming industry-specific digital transformation through artificial intelligence and cloud computing. This integrated platform delivers tailored functionalities for sectors including finance, manufacturing, retail, and healthcare through its multi-tenant architecture and embedded AI capabilities. Built on robust middleware and hosted on advanced cloud infrastructure, the suite offers pre-built workflows, regulatory compliance frameworks, and seamless integration with industry-specific tools. By addressing unique sectoral challenges with predictive analytics, automated processes, and real-time insights, Enterprise Applications Suite enables organizations to optimize operations, enhance customer experiences, and maintain competitiveness in rapidly evolving markets. This technical article demonstrates the architectural foundation, functional capabilities, and strategic impact of these applications across diverse industries, providing valuable insights for technology professionals seeking AI-driven cloud solutions for targeted business outcomes
Revolutionizing Real-Time Data Ingestion: A Novel Serverless Framework for Event-Driven Microservices
No full textThis article introduces EventFlow, a novel serverless framework designed for real-time data ingestion in event-driven microservice architectures. The framework addresses the limitations of traditional data processing architectures by leveraging cloud-native technologies and event-driven principles to deliver exceptional performance, cost-efficiency, and operational simplicity. Through comprehensive benchmarking and real-world case studies across multiple industries including fintech, manufacturing, and e-commerce, the article demonstrates EventFlow\u27s significant advantages in processing latency, throughput, scalability, and total cost of ownership. The architecture\u27s innovative approach eliminates infrastructure management overhead while providing zero-downtime deployments, automatic fault recovery, and simplified monitoring. The framework\u27s function-based programming model, language-agnostic interface, and comprehensive testing capabilities deliver substantial developer productivity improvements. Case studies validate EventFlow\u27s business impact through dramatic reductions in fraud detection time, manufacturing downtime, and improvements in e-commerce conversion rates, establishing it as a transformative solution for organizations requiring real-time data processing capabilities
Neural Interface AI: The Future of Personalized Mental Health Support
No full textThis article explores the integration of artificial intelligence and neural interface technology in revolutionizing mental health care delivery. The article examines how advanced Brain-Computer Interface (BCI) technology combined with AI enables real-time monitoring, early detection, and personalized intervention in mental health care. The article investigates the technical framework, adaptive learning capabilities, therapeutic integration, and clinical applications of these systems. Through comprehensive analysis of implementation results and clinical trials, the article demonstrates the potential of AI-powered neural interfaces in addressing global mental health challenges, improving treatment accessibility, and enhancing care delivery through continuous monitoring and personalized interventions