Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Anomaly Detection in Financial Transactions Using Convolutional Neural Networks
No full textThe rise of digital financial systems has brought unprecedented convenience but has also exposed users and institutions to various fraudulent activities. Anomaly detection plays a critical role in ensuring financial security by identifying unusual transaction patterns that may indicate fraud or other irregularities. Traditional statistical and rule-based approaches, though effective to some extent, often fall short when dealing with the increasing volume and complexity of financial data. This study proposes a novel approach to anomaly detection in financial transactions using Convolutional Neural Networks (CNNs), a class of deep learning models primarily known for their success in image processing tasks. In this work, transactional data are preprocessed and transformed into structured formats suitable for CNN input. By treating sequences of financial transactions as temporal-spatial matrices, the CNN model learns intricate patterns that distinguish normal from anomalous behavior. Our methodology includes a comprehensive pipeline involving data normalization, feature engineering, and the construction of multi-channel representations to exploit CNNs\u27 strength in hierarchical feature learning. We evaluate our model on benchmark financial datasets and compare its performance against traditional machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Logistic Regression. The CNN-based model demonstrates superior performance in terms of accuracy, precision, recall, and F1-score. Additionally, it shows robustness in detecting rare anomalies while minimizing false positives, a critical requirement in real-time financial fraud detection systems. The results indicate that CNNs can effectively capture both local and global dependencies within financial transaction sequences, making them suitable for large-scale and high-dimensional data environments. This study contributes to the growing body of research advocating for the adoption of deep learning techniques in financial anomaly detection and opens up possibilities for integrating CNNs with real-time monitoring systems for enhanced financial security. Future research may focus on hybrid models combining CNNs with recurrent layers to capture long-term dependencies more effectively
Research on Iraqi EFL Learners’ Pronunciation: A Review Article
No full textReviewing existing research is of paramount importance for educational policy makers, practitioners, and researchers as it plays an important role in providing an updated overview of the current research focuses and identifying gaps and needs for future research. In the current study, an attempt is made to explore the landscape of research on Iraqi EFL learners’ pronunciation. The examined corpus included 16 published research articles meeting established criteria for study selection. The included studies were scrutinized for their characteristics, design, strengths and limitations. The findings reveal that while the existing research employs a variety of research designs, collects both qualitative and quantitative data, and covers a wide spectrum of pronunciation-related issues, it is mostly limited to university undergraduate learners. Additionally, many studies suffer from small sample sizes, utilize basic data collection tools, and adopt traditional (and subjective) data analysis methods, often neglecting advanced statistical analysis techniques. The present review underscores the need for further research to diversify participants across different educational levels, increase sample sizes, conduct longitudinal studies, employ advanced analysis tools, and broaden the research focus to include interventional studies and examination of instructional methods
The Role of Extracurricular Sports Programs in Fostering Leadership and Teamwork Skills among High School Students
No full textThe study investigated the impact of extracurricular sports programs on the development of leadership and teamwork skills among high school students at Emilio Aguinaldo College (EAC) Manila. Using a quantitative descriptive research design, 105 students were selected through purposive random sampling to participate in the study. Data were collected via structured questionnaires and analyzed using descriptive and inferential statistics to explore the relationships between students\u27 profiles (sex, age, grade level, and type of sport) and their development of leadership and teamwork skills. The findings revealed that extracurricular sports programs significantly enhance leadership and teamwork skills, with no significant differences in effectiveness based on demographic factors. Leadership skills were developed through team roles, decision-making, coaching, and skill transferability, while teamwork skills were fostered through collaboration, conflict resolution, and peer interactions. The study emphasized the consistent positive impact of these programs across diverse student groups but noted complex relationships between various skill components, suggesting a need for tailored program enhancements. Recommendations include broadening sports programs, integrating specific training for leadership and teamwork, and ensuring inclusive access for all students
Advancing Neurological Disease Prediction through Machine Learning Techniques
No full textLate prediction is a major health problem for neurological diseases and early prediction is essential to advance patient outcomes and allow timely intervention. Machine learning (ML) advances are enabling doctors to more efficiently and innovatively predict the onset of neurological conditions using complex biomedical data. In this study the assessment of the power of different ML algorithms to predict Parkinson’s disease, epilepsy, and multiple sclerosis is done to evaluate the relative performance and practical applications. In order to determine the effectiveness of ML techniques, a comprehensive review was done on the various ML techniques e.g. decision trees, k-nearest neighbors (KNN) and ensemble methods. Furthermore, the study validates the predictive capabilities of these approaches, using the Gradient Boosting and Support Vector Machines (SVM) for a case study on EEG and for EEG and clinical datasets. The models were evaluated and compared with respect to known key performance metrics such as accuracy, sensitivity and specificity. Results showed that Gradient Boosting performed best, and with an accuracy of 89% it could predict Parkinson’s earlier on in its first stages. In detecting seizure activity, KNN was very successful accounting for an accuracy of 85%, making it a useful tool for epilepsy diagnosis. The study demonstrated robust generalizability across diverse datasets with ensemble methods, broadly applicable to wider populations for neurological disease prediction. Finally, the study demonstrates that machine learning provides a highly flexible and efficient paradigm for making predictions of neurological disease, with potential for early diagnosis and intervention. The results suggest that ML can be a powerful tool to analyze very complex biomedical data, and in turn develop diagnostic tools targeted towards certain neurological disorders. The integration of ML models with real time clinical systems, and the extension of this to other diseases will further improve diagnostic precision and access in clinical practice
AI-Enhanced Multifunctional Smart Assistive Stick for Enhanced Mobility and Safety of the Visually Impaired
No full textIn today\u27s era of rapid advancement in technology, innovative assistive devices are transforming accessibility for visually impaired. Through the integration of assistive health technologies, embedded systems, and software engineering, the Smart Assistive Stick enables people to navigate on their own. Fundamentally, an Arduino microcontroller interprets the reflected signals to provide real-time feedback in the form of voice instructions or buzzer alerts. The ultrasonic sensor detects obstructions in three directions (front, left, and right) within a range of 0 to 30 cm. The stick is lightweight and reasonably priced, enhanced by GPS and GSM modules for location-based services and emergency alerts, and developed with SolidWorks for maximum efficiency and ergonomics. Additionally, the study uses cutting-edge artificial intelligence for object detection in response to the growing demand for affordable assistive devices. The information is then communicated to the user in audio form after captured photos are processed to classify different items, including people, cars, and other impediments. In the end, this dual strategy bridges the gap between accessibility and technology by facilitating independent mobility in a variety of contexts, such as public areas and senior living facilities, while also lowering human effort and raising environmental awareness
Predicting Energy Consumption Patterns with Advanced Machine Learning Techniques for Sustainable Urban Development
No full textAs urbanization continues to expand and evolve in the USA, power demand has increased manifold, and with it has arisen significant environmental problems in the form of increased greenhouse gas emissions and loss of resources. In this paper, we explore how future machine-learning techniques could predict power consumption in U.S. cities. The central aim of this research is to develop advanced machine learning models with the potential to effectively predict energy consumption in cities. This involves not only identifying the key variables behind energy consumption but also selecting and fine-tuning machine learning algorithms that are most capable of understanding the dynamics of urban energy intricacies. This study focuses on the energy consumption patterns in the large cities of the United States, recognizing the diversity of challenges and opportunities presented by different geographic and demographic situations. The dataset used in this research project offered a comprehensive view of energy consumption across various fields of household, commercial, and industrial consumption, giving a holistic view of energy dynamics within cities. It integrated data collected from smart meters that offer granular electricity consumption patterns at the level of individual households and businesses with weather reports that detail ambient conditions governing energy demand, such as temperature and humidity fluctuations. Government energy records add historical context and policy information, further enhancing the dataset and enabling close analysis of trends and patterns in energy consumption. The next phase was to select and train three distinct machine models to explore the energy consumption dataset, namely, Logistic Regression, Random Forest, and XG-Boost algorithms. Random Forest outperformed Logistic Regression and XG-Boost slightly in terms of accuracy and other evaluation metrics. However, all models exhibit relatively low accuracy, suggesting the need for further tuning, feature engineering, or alternative models to improve predictions. In major cities in the U.S. such as Los Angeles, Chicago, and New York, smart power forecasting based on AI is revolutionizing power distribution and power planning in cities. By utilizing advanced machine learning models, these cities can process vast amounts of information and predict power usage with high accuracy. The incorporation of artificial intelligence (AI) in urban power planning has been a defining feature of modern-day power management in the USA. Major cities such as Los Angeles, Chicago, and New York are increasingly adopting AI-powered power forecasting technologies to rationalize power distribution. The integration of machine learning insights in U.S. government-driven green construction is instrumental in driving sustainable construction in infrastructure. By utilizing data-driven approaches, policymakers are in a position to identify the optimal design methods and low-power technologies with high performance in buildings
Data-Driven Decision-Making and Strategic Leadership: AI-Powered Business Operations for Competitive Advantage and Sustainable Growth
No full textIn the modern era, data-driven business world, firms are under more and more pressure to use cutting-edge technology to maintain their competitiveness and achieve long-term success. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into company operations and leadership initiatives is pivotal to this transition. This research examines the influence of data-driven decision-making and strategic leadership on improving corporate performance using AI-powered solutions. This research explores the synergies between AI technology and leadership techniques, demonstrating how businesses may leverage data to enhance decision-making, promote innovation, and maintain development in a competitive environment. The initial portion of the study explores data-driven decision-making as a fundamental aspect of contemporary business practices. In the era of Big Data, enterprises are overwhelmed with extensive information, and AI technologies—particularly machine learning algorithms—are essential for deriving meaningful insights. These insights empower firms to make educated, real-time decisions that enhance efficiency and reveal new opportunities. AI and data analytics are transforming resource management, workflow optimization, and overall operational efficiency through customer behavior analysis and predictive maintenance. This paper\u27s secondary focus is on strategic leadership on the adoption of AI and ML. Contemporary leaders must traverse intricate technology environments and guide their enterprises through digital transformation. Strategic leadership in the current era necessitates a profound comprehension of AI technology and the possible difficulties they entail. Effective leaders must adopt AI technologies to enhance decision-making, while ensuring these tools are congruent with overarching business objectives. Furthermore, leadership in the AI era transcends technology; it involves fostering a culture of perpetual learning, creativity, and adaptation, wherein AI serves as a vital facilitator of corporate success rather than a disruptive element. This paper\u27s primary finding is that AI-driven business processes substantially enhance competitive advantage. AI technologies improve an organization’s capacity to promptly adapt to market fluctuations and customer requirements by automating mundane processes, streamlining supply chains, and delivering real-time information. Machine learning models facilitate organizations in forecasting trends, customizing services, and implementing swift strategic modifications. This proactive strategy is crucial for organizations aiming to maintain a competitive edge in a swiftly changing industry. Moreover, AI-driven methods significantly influence sustainable growth. AI solutions enhance resource allocation, minimize waste, and promote innovation, enabling organizations to develop sustainable models that are economically, socially, and ecologically responsible. This study examines how AI facilitates long-term growth plans, enabling firms to not only endure in a competitive market but also prosper over time
Utilizing Data Analysis and Project Management to Achieve Operational Excellence in Spinning Mills
No full textThe textile industry, particularly the spinning sector, operates in a fast paced and competitive environment where efficiency, product consistency, and cost control are critical to long-term success. Spinning mills often grapple with fluctuating market demand, rising operational costs, and the constant need to maintain high quality standards. In response to these challenges, the adoption of data analysis and project management practices has emerged as a transformative strategy. This article explores how the thoughtful application of data driven insights ranging from real-time production monitoring to quality control analytics can significantly enhance decision-making, reduce material waste, and improve energy efficiency. At the same time, structured project management approaches help streamline operations, implement technological upgrades, and ensure smoother coordination across departments. By integrating these tools, spinning mills can transition from reactive to proactive management, gaining the ability to forecast problems before they arise and allocate resources more effectively. Furthermore, this alignment with Industry 4.0 principles paves the way for smarter manufacturing, increased transparency, and improved customer responsiveness. The article serves as a practical roadmap for mill owners, managers, and engineers aiming to embrace digital transformation and drive sustainable growth in the spinning industry
AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights
No full textArtificial intelligence-driven decision intelligence represents a transformative force in contemporary enterprise strategy formulation and operational execution. This article examines the critical shift from traditional decision processes characterized by manual interventions and static analytics to dynamic, AI-augmented frameworks that enable organizations to respond proactively to complex business environments. Despite generating unprecedented volumes of operational data, many enterprises struggle to translate this abundance into actionable intelligence, creating a substantial gap between data collection and strategic utilization. This implementation disparity stems from technical barriers and organizational resistance, with cultural factors frequently outweighing technological limitations. The architecture of effective decision intelligence systems integrates diverse data streams through sophisticated preprocessing mechanisms and employs advanced analytical techniques to generate actionable recommendations. Applications span multiple domains, including supply chain optimization, financial operations, marketing personalization, and strategic planning. While offering substantial competitive advantages, these systems also introduce significant ethical challenges related to algorithmic bias, transparency, explainability, and accountability. Success requires multifaceted governance approaches that balance automation with human oversight, continuous monitoring for potential biases, and organizational capabilities that harmonize machine intelligence with human judgment in increasingly complex decision environments
Algorithmic Campaign Orchestration: A Framework for Automated Multi-Channel Marketing Decisions
No full textThis article examines the paradigm shift from traditional rule-based marketing automation to continuous experience optimization enabled by AI-driven decision engines. The article presents an architectural framework for real-time campaign orchestration systems that leverage predictive analytics, reinforcement learning, and natural language processing to dynamically personalize customer interactions across channels. Through multiple case studies across different industry sectors, the article demonstrates how these systems process multi-source data streams to make intelligent decisions in milliseconds, creating responsive customer journeys that adapt to behavioral signals and contextual cues. The article indicates significant improvements in engagement metrics, customer retention, and marketing return on investment compared to conventional batch-processing approaches. The article identifies implementation challenges, including technical integration barriers, data quality dependencies, and organizational readiness factors, while proposing solutions to these obstacles. This article contributes to the growing field of algorithmic marketing by establishing methodological guidelines for evaluating the performance of real-time decision systems and outlining a roadmap for future advancements in continuous optimization technologies