Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Enhancing Mental Health Interventions in the USA with Semi-Supervised Learning: An AI Approach to Emotion Prediction
No full textThe escalating prevalence of mental health challenges in the USA underscores the urgent need for innovative resolutions to enhance interventions and care. Accurate prediction of emotional states can empower mental health practitioners to provide timely and personalized support. The main objective of this study was to develop and evaluate semi-supervised learning models for emotion prediction in mental health. The present study\u27s prime focus is applying semi-supervised learning in the U.S. context to mental health datasets. The Emotion Prediction Dataset is one of the diverse datasets collected from different sources with the aim of gaining a wide understanding of emotional state conditions. It includes text data from social media platforms, such as Twitter and Facebook, where users express their feelings right at the moment; audio recordings from speech and interactions that capture vocal nuances and intonation; and physiological signals captured through wearable devices measuring heart rate, skin conductance, and facial electromyography. Logistic Regression, Random Forest, and Gradient Boosting are some of the models considered in this study. Model evaluation executed proven metrics such as accuracy, precision, recall, and F1-Score assesses performance comprehensively. Although all three models generally performed worse, the SVM model provided the most reliable predictions in the context of this dataset and may, therefore, be effective for emotion classification. Integrating emotion prediction models into existing mental health services offers a new paradigm in patient care. A strong framework for such integration should start with an assessment of the current platforms, highlighting key points where emotion prediction can complement the existing services. Emotion prediction models can significantly enhance support strategies by targeting interventions at predicted emotional changes. Mental health professionals will be able to create personal treatment plans in which the trends within the data denote specific emotional states the patient is most likely to experience. The consolidation of AI-powered emotion prediction algorithms into mental health services in the USA carries substantial ramifications for improving the quality of care and accessibility of mental health resources
FinTech Cloud-based data lakes: Performance, governance, and scalability
No full textThe rapid adoption of cloud-based data lakes and warehouses in financial institutions has transformed data management, enabling the handling of vast datasets critical for decision-making in trading, risk management, and customer analytics. This paper examines the pivotal roles of performance, governance, and scalability in the successful deployment of these systems as of March 2025. Performance is analyzed through the lens of query optimization and real-time analytics, highlighting how technologies like distributed computing enhance efficiency. Governance is explored with a focus on regulatory compliance, data security, and the implementation of robust frameworks to safeguard sensitive financial data against breaches and ensure adherence to global standards such as GDPR and Basel III. Scalability is evaluated as a core benefit, addressing the ability of cloud systems to dynamically adapt to fluctuating data demands while maintaining cost-effectiveness. The study synthesizes current industry practices, technological advancements, and challenges, revealing the interdependence of these three dimensions. Findings suggest that while cloud-based solutions offer significant advantages, financial institutions must navigate challenges such as data migration, latency in hybrid models, and governance complexities in multi-cloud environments. This paper contributes to the discourse on cloud adoption in finance by providing actionable insights for optimizing performance, ensuring compliance, and achieving scalable data architectures in an increasingly digital financial landscape
Meta-Prompting as a Solution to Students’ Prompt Engineering Difficulties for an Optimized Use of GenAI LLMs in the Context of Education: A Quasi-Experimental Study using Mistral Model
No full textThe emergence of prompt engineering as a rising field of GenAI has garnered attention with the purpose to resolve ambiguities accompanying its use. In essence, a perfect input prompt performing well on LLM A might not perform well on LLM B as there is a certain disparity in how each model behaves, along with students’ poor prompting competency can make of crafting excellent prompts almost impossible to achieve. To address this problematic issue, the present study sheds light on meta-prompting which can solve students’ countless difficulties encountered with forming appropriate prompts, besides disparities in how different LLMs respond to the same prompt. To this end, the study adopts a within-subjects quasi-experimental design, with a sample involving N=50 undergraduate students of the Higher School of Teachers – Moulay Ismail University. For data analysis, the study uses SPSS version 25 for statistical representation of data, and Python code executed on Google Colab coding environment in which the Wilcoxon Signed-Rank Test for paired samples was conducted. Results demonstrated that there is a strong significant difference between students’ self-crafted prompts and Mistral as a selected LLM’s meta-prompts in terms of prompt specificity, comprehensiveness, logical sequence of instructions, and good structure criteria. The direction of the difference was in a positive direction suggesting an increase in the overall rating scores between pre-test and post-test results. The present paper has also proven that students’ confidence with prompts significantly increases with the use of meta-prompting technique compared to traditional prompting. Ultimately, the study identifies limitations and offers recommendations orienting future research projects in the field of GenAI and LLMs
Revolutionizing Autonomous Cloud Infrastructure: AI-Driven Predictive Auto Scaling with Attribute-Based Instance Selection in AWS
No full textDynamic resource provisioning is essential for cost efficiency and performance in cloud computing, yet prevailing auto-scaling practices are predominantly reactive. This paper presents a novel framework that integrates advanced predictive analytics—employing a hybrid of LSTM and Transformer-based models—with Amazon EC2’s attribute-based instance selection in Auto Scaling Groups. Our system learns from 90 days of multi-resolution workload data and leverages adaptive statistical confidence metrics to adjust pricing thresholds for Spot Instances. Simulated experiments using real-world AWS workload traces demonstrate that our approach reduces scaling latency by 75%, improves resource utilization by 20–30%, and lowers costs by 35% compared to conventional threshold-based methods (p < 0.001). Additionally, a rigorous sensitivity analysis of key scaling parameters confirms the robustness of the proposed framework
Integrating Artificial Intelligence into Business Strategy: Opportunities and Challenges
No full textArtificial Intelligence (AI) has become a transformative business force, providing unparalleled opportunities for innovation, competitive advantage and efficiency. Artificial Intelligence (AI) is a technology that is here to stay and has the revolutionary potential to alter the corporate strategy landscape, so much so that it will improve the organization\u27s competitiveness in a time of crisis. This literature explores the role of AI, particularly innovations driven by machine learning, in improving operational effectiveness and optimizing strategic decision-making, which business process management can leverage. The emphasis on AI technology highlights its utility in achieving competitive advantage via the effective use of limited resources. The report illustrates how AI-driven solutions enhance corporate profitability and performance by providing predictive insights, streamlining marketing and management tasks, and using Big Data to study competitive market and customer behavior. It also addresses how artificial intelligence can be collectively used with present-day CRM systems and provide customized customer experiences in a rapidly changing landscape. The research provides some insights about the implementation of AI in corporate business models, with particular focus on the challenges of applying it in times of crisis. It encompasses persona generation, data quality measures, risk mitigation techniques and implementation prerequisites for successfully integrating AI technology to realize corporate agility and scalability for external disturbances
The Evolution of Data-Driven Supply Chain Finance Solutions
No full textThe evolution of supply chain finance has entered a transformative phase driven by data analytics, artificial intelligence, and digital technologies. This article examines how these innovations are reshaping financial relationships between buyers, suppliers, and financial institutions in supply chains worldwide. It explores the core data technologies revolutionizing supply chain finance, including ERP integration, advanced analytics, and automated financing platforms. The article further investigates how artificial intelligence and machine learning applications enhance credit risk assessment, enable dynamic pricing models, improve fraud detection, and streamline document processing through natural language processing. Additionally, it analyzes the impact of blockchain and distributed ledger technologies in automating payments through smart contracts, expanding access to financing through tokenization, and providing end-to-end traceability. While highlighting the significant benefits of these technologies, the article also addresses implementation challenges related to data quality, system integration, and change management requirements, offering insights for organizations seeking to optimize their supply chain finance operations in an increasingly digital ecosystem
The Next Horizon: Emerging Technologies Reshaping Customer Relationship Management
No full textThis article explores the transformative future of Customer Relationship Management (CRM) as it evolves beyond traditional boundaries through emerging technologies. It examines how artificial intelligence, extended reality, blockchain-based trust systems, IoT-powered intelligence, and convergent platforms are collectively reshaping how organizations engage with customers. The inquiry investigates how cognitive CRM systems are enabling hyper-personalization and autonomous relationship management, while immersive experiences are creating new dimensions of customer engagement. The analysis further examines how distributed trust technologies are establishing transparency across business ecosystems and how the convergence of these technologies is dissolving traditional CRM boundaries in favor of unified experience orchestration platforms. Through empirical studies and industry assessment, the article identifies key performance impacts, implementation challenges, and strategic considerations for organizations navigating this technological transformation
SAP CRM Trade Promotion Management and S/4HANA SD Integration: Streamlining Promotion Planning and Execution
No full textThis technical article explores the integration between the SAP CRM Trade Promotion Management (TPM) module and SAP S/4HANA Sales and Distribution (SD) systems, illustrating how this unified paradigm transforms the entire promotion lifecycle for consumer goods manufacturers. By bridging planning and execution processes through a synchronized data model, organizations can achieve seamless promotion management from conception through settlement. The integration delivers significant improvements in planning efficiency, execution accuracy, settlement automation, and analytical capabilities, addressing the challenges of traditional siloed methods. Through investigation of architecture, execution processes, financial integration, and analytics capabilities, the article demonstrates how this integration creates a continuous digital process that reduces operational inefficiencies while providing actionable intelligence for optimizing trade spend effectiveness
AI-Powered Portfolio Management: Transforming Wealth Management Through Intelligent Automation
No full textAI-enhanced portfolio management is revolutionizing the wealth management industry through the application of advanced technologies that enhance investment decision-making and client service delivery. This transformation extends beyond mere automation, fundamentally reimagining how portfolios are constructed, monitored, and optimized. Machine learning algorithms analyze vast datasets to identify complex patterns and correlations that human analysis might miss, while natural language processing technologies extract valuable insights from unstructured text sources to gauge market sentiment. Reinforcement learning systems continuously optimize portfolio rebalancing strategies to maintain desired risk exposures while minimizing costs. Despite these advancements, significant challenges remain, including concerns about data quality, algorithmic bias, and regulatory compliance. Financial institutions must implement explainable AI frameworks to ensure transparency and build trust with both clients and regulators. The future of wealth management likely involves hybrid advisory models where AI systems handle data-intensive tasks while human advisors focus on relationship management and complex planning. For enterprise solution architects and fintech developers, this paradigm shift necessitates new architectural frameworks that seamlessly integrate AI components with existing wealth management infrastructure while maintaining robust governance and data lineage capabilities. This evolution promises to democratize access to sophisticated investment strategies while delivering truly personalized portfolio solutions at scale
Integration Architecture Fundamentals for Healthcare Systems: A Framework for Seamless Interoperability
No full textIntegration architecture forms the foundation of modern healthcare information ecosystems, enabling disparate systems to communicate effectively and securely. The proliferation of specialized healthcare applications has created significant interoperability challenges, with fragmented systems hampering coordinated care delivery and operational efficiency. This article examines the fundamental components of healthcare integration architecture Application Programming Interfaces (APIs), middleware solutions, and data standards and their collective role in establishing seamless interoperability. The implementation of robust integration architecture yields substantial benefits across multiple dimensions of healthcare delivery, including enhanced clinical decision-making, streamlined operational workflows, improved patient experiences, and optimized financial outcomes. However, healthcare organizations face significant challenges in implementing effective integration solutions, including legacy system integration, security considerations, scalability requirements, and complex regulatory compliance. Strategic approaches to addressing these challenges involve implementing adapter patterns for legacy systems, incorporating comprehensive security measures, adopting scalable architectural approaches, and establishing formal data governance frameworks. The global significance of healthcare integration continues to grow, with the interoperability market expanding rapidly as organizations recognize the critical importance of connected systems for delivering high-quality, cost-effective care in an increasingly complex healthcare landscape