Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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The Business Value of IoT and Big Data Analytics
No full textThis study analyzes the disruptive impact of combining the Internet of Things (IoT) and Big Data Analytics on contemporary business models. This research investigates how these technologies use data-driven decision-making, better operational efficiency, and innovative business models across retail, healthcare, manufacturing, and logistics by integrating a review of current literature and evidenced case study examples across industries. Strategic issues to think about when developing a sustainable growth strategy with IoT and Big Data, building competitive advantage in value creation, transformation of market spaces, and return on investment are considered for companies. The findings indicate that the strategic deployment of IoT and Big Data provides room for new service-led business models and personalization in customer experience
Consumer Choice in Holiday Park Vacations: Exploring Decision-Making Beyond Economic Factors
No full textThis study examines the key factors influencing Chinese consumers when selecting holiday park vacations, beyond disposable income and pricing. It explores the hierarchy of consumer needs, external influences such as cultural and seasonal trends, and factors driving brand switching. Using survey data from 500 respondents across China, the study identifies major decision-making patterns and links them to established consumer choice theories. The findings provide insights for businesses to refine their marketing strategies and improve service offerings
Principles of Democratic Management in the Law on Agricultural Cooperatives: Limitations and Suggestions for Improvement
No full textThe article focuses on researching the principles of democratic management in Vietnamese cooperative law and on the basis of comparison with Vietnamese enterprise law and cooperative law in some typical countries. After the research process, the article concludes that the principle of democratic management in cooperative law has limitations such as: not ensuring members\u27 property ownership rights, specifically the right to dispose of property. From there, making capital contributing members less cautious when making decisions related to the cooperative. At the same time, cooperatives are at risk of being manipulated from outside
Artificial Intelligence and Digital Technologies in Finance: A Comprehensive Review
No full textThis study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to reshape consumer behaviors and financial systems. It also evaluates the implications of digital transformation on economic efficiency and the legal frameworks surrounding electronic payment systems. Ultimately, this study underscores the profound opportunities AI and digital technologies present for FinTech and offers insights relevant to both academic inquiry and policy-making
Strategic Management Accounting Usage in the Sri Lankan Service Sector
No full textStrategic Management Accounting (SMA) has emerged as a vital tool for organizations navigating dynamic and competitive business environments. While extensively studied in developed economies, research on SMA adoption in emerging markets, particularly in service-oriented economies like Sri Lanka, remains scarce. This study examines the extent of SMA usage in Sri Lankan service firms and investigates the impact of demographic factors such as firm size, industry type, and managerial experience on SMA adoption. Using a quantitative survey-based approach, data was collected from 202 service sector firms spanning industries such as banking, insurance, healthcare, hospitality, and information technology (IT). The results indicate that strategic pricing (M = 5.04), competitor position monitoring (M = 4.97), and customer profitability analysis (M = 4.96) are the most frequently employed SMA techniques, whereas life-cycle costing (M = 3.93) and lifetime customer profitability analysis (M = 3.98) are underutilized. Findings also reveal that listed firms and larger organizations demonstrate higher SMA adoption rates, whereas smaller firms face challenges due to resource constraints and a lack of expertise. This research contributes to the limited body of SMA literature in developing countries by offering empirical insights into the Sri Lankan service sector. The findings hold practical implications for policymakers, professional accountants, and industry leaders, emphasizing the need for targeted training programs, regulatory support, and digital transformation strategies to enhance SMA adoption
The Impact of COVID-19 on Banking and Finance: Challenges, Opportunities, and Future Directions
No full textThe COVID-19 pandemic significantly reshaped global economies, leaving a lasting impact on the banking and finance sectors. As financial institutions navigated economic uncertainty, liquidity constraints, and shifts in consumer behavior, the crisis exposed critical vulnerabilities while accelerating digital transformation. This paper examines these challenges, identifying key limitations in existing research, including data scarcity, regional biases, technological barriers, and socioeconomic disparities. By addressing these gaps, we propose strategic solutions such as enhancing data collection methods, leveraging diverse case studies, investing in financial technology, and promoting financial inclusion. Additionally, we outline future research directions in areas such as AI-driven banking, long-term consumer behavior shifts, sustainable finance, and global financial collaboration. The insights presented aim to equip policymakers, financial institutions, and researchers with the knowledge necessary to navigate the complexities of a post-pandemic financial landscape, fostering a more resilient, inclusive, and adaptive banking ecosystem
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
Machine Learning and Deep Learning Techniques for EEG-Based Prediction of Psychiatric Disorders
No full textEarly detection of psychiatric disorders as well as efficient treatment are difficult owing to their challenges, which require accurate prediction methods in healthcare. When combined with ML and DL techniques, EEG data promises to yield a promising method for enhancing diagnostic accuracy. In this study, the performance of a wide spectrum of ML and DL techniques for predicting psychiatric disorders from EEG datasets is evaluated and the best choice is found for a particular condition. The study carried an analysis based on public datasets representing diverse psychiatric disorders through systematic analysis. Advanced DL architectures comprising of CNNs and RNNs were compared against the classical traditional ML techniques such as RlForest and Support Vector Machines (SVMs). A comparison between these models was made based on key performance metrics such as accuracy, sensitivity, and specificity. Results showed that DL models, particularly CNNs, excel at feature extraction and classification over traditional ML methods with their highest accuracy of predicting major depressive disorder above 92%. But ML techniques were able to complete faster computationally, in spite of slightly lower predictive accuracy. As DL models excel at capturing complex patterns within EEG data, these findings suggest that there are increased computational demands associated with them. Following that, advanced pattern recognition capabilities associated with DL techniques benefit substantially from the predictive modeling offered by EEG, although their computational efficiency presents as a limitation. This study highlights the importance of hybrid methods combining the best properties of both ML and DL for psychiatric disorders prediction to get improved accuracy and scalability, which is conditioning this generation of safer diagnostic tools for clinical practice
Advancing Computational Intelligence: AI-Based Algorithm Design and Optimization in Programming
No full textThe research explores AI technique implementations in algorithm optimization and design frameworks to understand their crucial impact on programming challenges and efficiency increases. This investigation analyzes GPU performance through the GPU Benchmarks Compilation dataset while deeply assessing their impact on AI-based algorithm operation. The dataset provides detailed benchmarking information for GPUs that includes computational throughput combined with cost-performance ratios and energy efficiency metrics thereby establishing strong foundations for analyzing AI-driven computational developments. This research investigation uncovered major GPU capability evolutions which demonstrate why GPUs remain important for processing advanced AI processing models. The research unveils fundamental information about GPU evolution which shows how novel GPU developments deliver efficient scaling solutions for executing AI-based computational workloads. The research puts particular emphasis on energy efficiency because it addresses the growing computational needs of AI applications. This research examines the practical implications of its findings for computational intelligence frameworks that will exist in the coming years. The study reviews benchmark patterns to establish methods which optimize algorithm designs when utilizing enhanced GPU technology. The research discovers ways to combine AI methods with upcoming GPU technologies to develop advanced computational solutions that deliver maximum efficiency. The ongoing study supports computational intelligence research through its work to connect artificial intelligence methods with recent advancements in hardware. The research shows how AI-based algorithm optimization methods can propel breakthroughs in programming and problem-solving techniques. Findings from this research create an academic foundation for upcoming studies of GPU performance alongside AI integration which continues to further advance the discipline of computational intelligence with real-world applications
AI-Driven Machine Learning for Fraud Detection and Risk Management in U.S. Healthcare Billing and Insurance
No full textHealthcare fraud in the United States results in billions of dollars in financial losses annually, necessitating advanced technological solutions for fraud detection and risk management. Machine learning (ML) has emerged as a powerful tool in identifying fraudulent claims, mitigating risks, and enhancing financial security in healthcare billing and insurance (Anderson & Kim, 2023). This study examines the application of supervised and unsupervised ML techniques, such as decision trees, neural networks, and anomaly detection models, to detect fraudulent patterns in insurance claims (Wang et al., 2022). By analyzing large-scale electronic health records (EHRs) and claims datasets, ML algorithms can identify suspicious activities and reduce false positives, improving fraud detection accuracy (Garcia & Lee, 2023). Additionally, predictive analytics aids in risk assessment, enabling insurers and healthcare providers to proactively manage financial fraud risks (Brown et al., 2023). Despite its advantages, ML-based fraud detection systems face challenges, including data privacy concerns, interpretability issues, and regulatory compliance (Nguyen & Patel, 2023). This research highlights the effectiveness of AI-driven fraud detection models in minimizing financial losses and enhancing operational efficiency in the U.S. healthcare sector, with future implications for explainable AI and privacy-preserving ML solutions