Saudi Cultural Missions Theses & Dissertations
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Item Restricted Agent-Based Simulation, Machine Learning, and Gamification: An Integrated Framework for Addressing Disruptive Behaviour and Enhancing Student and Teacher Performance in Educational Settings(Durham University, 2025) Alharbi, Khulood Obaid; Cristea, Alexandra IThe classroom environment is a major contributor to the learning process in schools. Young students are affected by different factors in their academic progress, be it their own characteristics, their teacher’s, or their peers’. Disruptive behaviour, in particular, is one of the main factors that create challenges in the classroom environment, by hindering learning and effective classroom management. To overcome these challenges, it is important to understand what causes disruptive behaviour, and how to predict and prevent it. While Machine Learning (ML) is already used in education to predict disruption-related outcomes, there is less focus on understanding the processes leading to the effect of disruptive behaviour on learning. Thus, in this thesis, I propose using Agent-Based Modelling (ABM) for the simulation of disruptive behaviour in the classroom, to provide teachers with a tool that helps them not only predict, but also understand how classroom interactions lead to disruptions. Reducing negative factors in the learning environment, like disruptive behaviour, is further supported by increasing positive factors, such as motivation and engagement. Therefore, the use of gamification is then introduced as a strategy to promote motivation and improve engagement by making not only the learning environment more rewarding, but also the ABM teacher simulation more appealing. This thesis focuses on these issues by designing and implementing for the first time an integrated approach that combines ABM and ML with gamification to simulate classroom interactions and predict disruptive behaviour. The ABM models the complex interactions between students, teachers, and peers, providing a means to study the processes leading to behavioural issues. Meanwhile, ML algorithms help predict learning outcomes with behaviours such as inattentiveness, hyperactivity, and impulsiveness. The simulation has revealed insights, such as the impact of peer influence on student behaviour and the varying effects of different types of disruptive behaviour, such as inattentiveness, hyperactivity and impulsiveness, on academic performance. The improved performance of the hybrid ML-ABM is shown by measuring results of simulation with ML integration using metrics like MAE, RMSE and Pearson correlation. Moreover, the inclusion of gamification elements was shown to improve engagement by increased login frequency and course completion rates in a MOOC setting, as well as be effective and appealing for teachers using the ML-ABM. In conclusion, this thesis presents the first comprehensive model that integrates ABM, ML, and gamification elements to explore educational outcomes in a disruptive classroom; it develops the first hybrid ML-ABM approach for predicting and managing classroom disruptive behaviour; it provides empirical evidence of the effectiveness of gamification in boosting student and teacher engagement; and it offers practical insights for educators and policymakers seeking to adopt innovative, technology-driven strategies for improving teaching and learning. The research lays a foundation for future studies, aiming to further explore and expand the capabilities of these technologies in an educational context.14 0Item Restricted Rasm: Arabic Handwritten Character Recognition: A Data Quality Approach(University of Essex, 2024) Alghamdi, Tawfeeq; Doctor, FaiyazThe problem of AHCR is a challenging one due to the complexities of the Arabic script, and the variability in handwriting (especially for children). In this context, we present ‘Rasm’, a data quality approach that can significantly improve the result of AHCR problem, through a combination of preprocessing, augmentation, and filtering techniques. We use the Hijja dataset, which consists of samples from children from age 7 to age 12, and by applying advanced preprocessing steps and label-specific targeted augmentation, we achieve a significant improvement of a CNN performance from 85% to 96%. The key contribution of this work is to shed light on the importance of data quality for handwriting recognition. Despite the recent advances in deep learning, our result reveals the critical role of data quality in this task. The data-centric approach proposed in this work can be useful for other recognition tasks, and other languages in the future. We believe that this work has an important implication on improving AHCR systems for an educational context, where the variability in handwriting is high. Future work can extend the proposed techniques to other scripts and recognition tasks, to further improve the optical character recognition field.42 0Item Restricted Explainable AI Approach for detecting Generative AI Imagery(Aston University, 2024-09-29) Alghamdi, Sara; Barns, ChloeThe rapid advancement of Artificial Intelligence (AI) and machine learning, particularly deep learning models such as Convolutional Neural Networks (CNNs), has revolutionized image classification across diverse fields, including healthcare, autonomous vehicles, and digital forensics. However, the proliferation of AI-generated images, commonly referred to as deepfakes, has introduced significant ethical, societal, and security challenges. Deepfakes leverage AI to create highly realistic yet synthetic media, complicating the ability to differentiate between authentic and manipulated content. This has heightened the need for robust tools capable of accurately detecting and classifying such media to combat the risks of misinformation, fraud, and erosion of public trust. Traditional models, while effective in classification, often lack transparency in their decision-making processes, limiting stakeholder trust. To address this limitation, this study explores the integration of Explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations), with CNNs to enhance interpretability and trust in model predictions. By employing CNNs for high-accuracy classification and XAI methods for feature-level explanations, the research aims to contribute to digital forensics and content moderation, offering both technical reliability and transparency. This study highlights the critical need for trustworthy AI systems in the fight against manipulated media, providing a framework that balances efficacy, transparency, and ethical considerations.41 0Item Restricted Enhancing Breast Cancer Diagnosis with ResNet50 Models: A Comparative Study of Dropout Regularization and Early Stopping Techniques(University of Exeter, 2024-09-20) Basager, Raghed Tariq Ahmed; Kelson, Mark; Rowland, SarehEarly detection and treatment of breast cancer depend on accurate image analysis. Deep learning models, particularly Convolutional Neural Networks (CNNs), have proven highly effective in automating this critical diagnostic process. While prior studies have explored CNN architectures [1, 2], there is a growing need to understand the role of dropout regularization and fine-tuning strategies in optimizing these models. This research seeks to improve breast cancer diagnosis by evaluating ResNet50 models trained from scratch and fine-tuned, with and without dropout regularization, using both original and augmented datasets. Assumptions and Limitations: This research assumes that the Kaggle Histopathologic Cancer Detection dataset is representative of real-world clinical images. Limitations include dataset diversity and computational resources, which may affect generalization to broader clinical applications. ResNet50 models were trained on the Kaggle Histopathologic Cancer Detection dataset with various configurations of dropout, early stopping, and data augmentation [3–6]. Performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics [7, 8]. The best-performing model was a ResNet50 trained from scratch without dropout regularization, achieving a validation accuracy of 97.19%, precision of 96.20%, recall of 96.90%, F1-score of 96.55%, and an AUC-ROC of 0.97. Grad-CAM visualizations offered insights into the model’s decision-making process, enhancing interpretability crucial for clinical use [9,10]. Misclassification analysis showed that data augmentation notably improved classification accuracy, particularly by correcting previously misclassified images [11]. These findings highlight that training ResNet50 without dropout, combined with data augmentation, significantly enhances diagnostic accuracy from histopathological images. Original Contributions: This research offers novel insights by demonstrating that a ResNet50 model without dropout regularization, trained from scratch and with advanced data augmentation techniques, can achieve high diagnostic accuracy and interpretability, paving the way for more reliable AI-powered diagnostics.12 0Item Restricted Quantifying and Profiling Echo Chambers on Social Media(Arizona State University, 2024) Alatawi, Faisal; Liu, Huan; Sen, Arunabha; Davulcu, Hasan; Shu, KaiEcho chambers on social media have become a critical focus in the study of online behavior and public discourse. These environments, characterized by the ideological homogeneity of users and limited exposure to opposing viewpoints, contribute to polarization, the spread of misinformation, and the entrenchment of biases. While significant research has been devoted to proving the existence of echo chambers, less attention has been given to understanding their internal dynamics. This dissertation addresses this gap by developing novel methodologies for quantifying and profiling echo chambers, with the goal of providing deeper insights into how these communities function and how they can be measured. The first core contribution of this work is the introduction of the Echo Chamber Score (ECS), a new metric for measuring the degree of ideological segregation in social media interaction networks. The ECS captures both the cohesion within communities and the separation between them, offering a more nuanced approach to assessing polarization. By using a self-supervised Graph Auto-Encoder (EchoGAE), the ECS bypasses the need for explicit ideological labeling, instead embedding users based on their interactions and linguistic patterns. The second contribution is a Heterogeneous Information Network (HIN)-based framework for profiling echo chambers. This framework integrates social and linguistic features, allowing for a comprehensive analysis of the relationships between users, topics, and language within echo chambers. By combining community detection, topic modeling, and language analysis, the profiling method reveals how discourse and group behavior reinforce ideological boundaries. Through the application of these methods to real-world social media datasets, this dissertation demonstrates their effectiveness in identifying polarized communities and profiling their internal discourse. The findings highlight how linguistic homophily and social identity theory shape echo chambers and contribute to polarization. Overall, this research advances the understanding of echo chambers by moving beyond detection to explore their structural and linguistic complexities, offering new tools for measuring and addressing polarization on social media platforms.22 0Item Restricted Adaptive encryption scheme for IoT sensors network(Cardiff University, 2024-09-05) Almalki, Ohud; Li, ShancangArtificial Intelligence (AI) and the Internet of Things (IoT) have revolutionised the way we live and work, bringing unpredictable levels of automation and decision-making. As a result, industries such as healthcare, finance, and smart cities have experienced significant changes. These technologies have transformed our lives to be more efficient, convenient, and connected. However, the rapid advancement of AI and IoT has also raised some concerns. Data privacy and security have become a major challenge with these systems processing massive amounts of sensitive personal and organisational information data. Highlighting the importance of implementing robust protection methods. This dissertation focuses on the different techniques used to maintain data privacy in AI and IoT ecosystems using privacy-preserving technologies (PETs), such as differential privacy (DP), federated learning (FL), and secure computation. These technologies are essential for compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Moreover, it is important to educate users about the associated risks of using AI and IoT and to encourage responsible behaviours. The core focus of this research is a dual-layer encryption schema that helps to protect sensitive data in IoT sensor networks by classifying the data as low and high-critical.9 0Item Restricted Pixels to Pavements(University College London, 2024) Hababi, Abdullah; Selby, EllyThe convergence of machine learning (ML) and the built environment is redefining traditional design decision-making processes. This report explores the integration of ML within architecture, urban design, and urban planning, emphasizing its transformative potential as a design decision making tool. The report delves into the historical context of digital tools in architecture and examines how ML is currently utilized in the built environment. Through a detailed methodology, the report analyzes ML’s role as a computational design aid, as a design facilitator or augmenter, and as a co-designer. This report aims to connect the idea of machine learning’s use in design decision-making processes in the built Environment to my design project. The impact of a literature review and case studies has helped extract and implement different key methods of machine learning in various stages of my design project, such as the data manipulation stage, form finding stage, design intervention placement stage, and simulation analysis of and for design decisions stage. Critical analyses focus on the role of data quality, human agency, and the limitations of ML, such as algorithmic bias and the potential erosion of human creativity. This report contends that ML can profoundly influence and effectively dictate design decision making in both an architectural and urban design context, through its aid as a computational design tool, design facilitator, and co-designer. The discussion emphasizes the necessity of human expertise in interpreting ML outputs and proposes a collaborative approach between human intuition and ML capabilities. The report concludes by advocating for a continuous dialogue between technology and human creativity to ensure ML serves as a valuable tool in shaping the built environment rather than a replacement for human ingenuity.10 0Item Restricted Supervised Machine Learning. A Strategic Approach for Financial Fraud Detection(University of Nottingham, 2024-03) Bashehab, Omar Sami; Wang, HuamaoFinancial fraud is an increasingly concerning issue in the present day. The rapidly growing rate of fraudulent activities has led to significant financial losses for many stakeholders. Card-not-present (CNP) fraud has risen with the growth of digital sales. The same benefits attracting online banking and transactions have attracted fraudsters and cybercriminals. Consequently, the incentive for fraud detection for mitigating financial risk is evident. However, traditional detectors are outdated, and rule-based systems fail to keep up with the dynamic innovative methodologies of cybercriminals. Thus, the ML-based system is needed. However, various challenges exist within ML-based detectors. Firstly, datasets are typically highly imbalanced and secondly, a lack of real-world datasets makes research extremely difficult. To tackle these problems, different resampling methods such as RUS, ROS, SMOTE and a hybrid sampling approach (ROS + RUS) were used and evaluated. Furthermore, a novel dataset was used, augmented from an original PAYsim real-world synthetic data. Furthermore, predictive models such as Decision Tree, Logistic Regression, Random Forests, Support Vector Machine and (Gaussian) Naïve Bayes were used with the different resampling methods in a comparative approach. Finally, the importance of data preprocessing and feature engineering was explored and evaluated amongst the classifiers. The experimental results illustrate the Random Forest, with Grid Search CV optimisation and RUS as well as feature engineering performed the best. The methodological approach exhibited an increase in F1 score, True Positive Rate, Recall and Accuracy for the classifier. The final model outputted an F1 score of 69%, ROC-AUC of 88% and True Positive Rate (TPR) of 93%.77 0Item Restricted LIGHTREFINENET-SFMLEARNER: SEMI-SUPERVISED VISUAL DEPTH, EGO-MOTION AND SEMANTIC MAPPING(Newcastle University, 2024) Alshadadi, Abdullah Turki; Holder, ChrisThe advancement of autonomous vehicles has garnered significant attention, particularly in the development of complex software stacks that enable navigation, decision-making, and planning. Among these, the Perception [1] component is critical, allowing vehicles to understand their surroundings and maintain localisation. Simultaneous Localisation and Mapping (SLAM) plays a key role by enabling vehicles to map unknown environments while tracking their positions. Historically, SLAM has relied on heuristic techniques, but with the advent of the "Perception Age," [2] research has shifted towards more robust, high-level environmental awareness driven by advancements in computer vision and deep learning. In this context, MLRefineNet [3] has demonstrated superior robustness and faster convergence in supervised learning tasks. However, despite its improvements, MLRefineNet struggled to fully converge within 200 epochs when integrated into SfmLearner. Nevertheless, clear improvements were observed with each epoch, indicating its potential for enhancing performance. SfmLearner [4] is a state-of-the-art deep learning model for visual odometry, known for its competitive depth and pose estimation. However, it lacks high-level understanding of the environment, which is essential for comprehensive perception in autonomous systems. This paper addresses this limitation by introducing a multi-modal shared encoder-decoder architecture that integrates both semantic segmentation and depth estimation. The inclusion of high-level environmental understanding not only enhances scene interpretation—such as identifying roads, vehicles, and pedestrians—but also improves the depth estimation of SfmLearner. This multi-task learning approach strengthens the model’s overall robustness, marking a significant step forward in the development of autonomous vehicle perception systems.37 0Item Restricted Deep Learning Approaches for Multivariate Time Series: Advances in Feature Selection, Classification, and Forecasting(New Mexico State University, 2024) Alshammari, Khaznah Raghyan; Tran, Son; Hamdi, Shah MuhammadIn this work, we present the latest developments and advancements in the machine learning-based prediction and feature selection of multivariate time series (MVTS) data. MVTS data, which involves multiple interrelated time series, presents significant challenges due to its high dimensionality, complex temporal dependencies, and inter-variable relationships. These challenges are critical in domains such as space weather prediction, environmental monitoring, healthcare, sensor networks, and finance. Our research addresses these challenges by developing and implementing advanced machine-learning algorithms specifically designed for MVTS data. We introduce innovative methodologies that focus on three key areas: feature selection, classification, and forecasting. Our contributions include the development of deep learning models, such as Long Short-Term Memory (LSTM) networks and Transformer-based architectures, which are optimized to capture and model complex temporal and inter-parameter dependencies in MVTS data. Additionally, we propose a novel feature selection framework that gradually identifies the most relevant variables, enhancing model interpretability and predictive accuracy. Through extensive experimentation and validation, we demonstrate the superior performance of our approaches compared to existing methods. The results highlight the practical applicability of our solutions, providing valuable tools and insights for researchers and practitioners working with high-dimensional time series data. This work advances the state of the art in MVTS analysis, offering robust methodologies that address both theoretical and practical challenges in this field.37 0