Saudi Cultural Missions Theses & Dissertations
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Item Restricted Computational Approaches for Drug Repositioning and Target Discovery in Alzheimer’s Disease(King Abdullah University of Science and Technology (KAUST), 2024) Alamro, Hind; Gao, XinAlzheimer’s Disease (AD) presents significant challenges to global healthcare systems due to its complex and progressive nature. Despite extensive research, the underlying mechanisms of AD lack clarity, and current treatments only alleviate symptoms without halting disease progression. Consequently, there is an urgent need for computational approaches that can accelerate research efforts and aid in the development of more effective treatments for AD. In this thesis, we address these critical challenges by developing computational and AI-based methods to improve the early detection of AD, identify novel biomarkers, and explore new therapeutic strategies through drug repositioning. To begin with, we focus on identifying key biomarkers associated with AD using gene expression datasets and then expand it to the identification of biomarkers through exploring the association between AD and its comorbidity, resulting in the discovery of new hub genes and miRNAs. Next, we examine the potential for drug repositioning by mining biomedical literature to uncover associations between drugs, targets, and diseases. This task was fulfilled by developing a systematic pipeline to extract valuable information from a curated collection of AD-related literature. The resulting data is subsequently used to construct a disease-specific knowledge graph, which is employed for drug repositioning using advanced graph-based techniques. Overall, this thesis contributes to AD research by employing computational methods, multi-data integration, and literature mining to provide new insights and therapeutic strategies. This work identifies key participants in AD progression and presents a pathway to accelerate the discovery of treatments through computational approaches.9 0Item Restricted Intelligent Data-Driven Models for Accurate Multi-factors Prediction of Carbon Credit Prices(Saudi Digital Library, 2025) Alshatri, Najlaa saad; Ghannam, SafaaThis thesis addresses the challenge of accurately predicting carbon credit prices, which are non-linear, non-stationary, and influenced by multiple correlated external factors such as energy prices, environmental indicators, and economic conditions. Accurate pricing is vital for transparency and effectiveness in carbon markets. A systematic literature review identified research gaps, leading to the development of a Carbon Credit Multi-Factor Prediction (CCMFP) model integrating factor identification and optimized prediction algorithms. The proposed Carbon Credit Multi-Factor Identification (CCMFI) model combines random forest regression with explainable AI to identify the most influential factors among 22 external variables. Feature reduction and extraction techniques, independent component analysis (ICA), nonlinear ICA (NLICA), and principal component analysis (PCA), were then applied, with extracted components used as inputs to SVR and MLP models. Using daily Australian Carbon Credit Units (ACCUs) prices as a case study, experiments evaluated the impact of different factor sets on prediction accuracy. The models achieved an R2 of over 97%, with optimal performance from factors including environmental technology patents, CO2 emissions, renewable energy adoption, global carbon allowances, coal and crude oil prices. These findings enhance market confidence, reduce financial risks, and support global climate change mitigation through effective carbon credit utilization.14 0Item Restricted Long-Term Dependency Margin Maximization Model (LTDM3): Dealing with Concept Drift in Personalized Learning Systems(Saudi Digital Library, 2023) Allogmany, Bander; Josyula, DarsanaAdvances in data analytics and intelligent technologies are enabling smart learning environments that promote personalized learning. Personalized learning systems where learners engage with information in a manner tailored to their unique needs, goals, and abilities have garnered significant academic research attention. If students can achieve their objectives faster than with traditional learning methods, it would increase their motivation and reduce their likelihood of dropping out. It can also offer educators a better understanding of each student’s learning process, enabling them to teach more effectively. Artificial intelligence (AI) plays a vital role in the development of personalized learning systems. Rapid advancements in AI technologies enable tracking and modifying of each student’s learning environment. Machine learning algorithms facilitate the determination of students’ learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, a deterioration of the model’s performance over time due to changes in data distribution. These arise due to factors affecting learning ability, including changes in family structure, parental involvement, peer relationships, learner behavior, personal interests, environmental influences such as nutrition and sleep, and so on. For successful personalization, it is critical that underlying predictive and classification models be able to adapt successfully to data changes that contribute to the drift phenomenon. This research proposes a method to address concept drifts in personalized learning systems that involve training using sequential features extracted automatically, noting when concept drifts are causing model deterioration, and automatically adjusting the trained model to improve model performance in the presence of drift. Unlike large language models (LLMs), which usually lack inherent capabilities for indicating that a concept drift has occurred, the approach presented in this dissertation can detect and point out instances of concept drift. Detecting concept drift is important for initiating specific interventions, whereas large language models tend to obscure or overlook such changes in the data. The proposed approach aims to enhance the accuracy and effectiveness of predictive models, ensuring personalized learning systems deliver pertinent and useful recommendations even when student preferences change. While conducting experiments using a real-world dataset related to students’ interactions with educational systems, the proposed model shows impressive results in managing concept drifts. Moreover, the proposed model shows resilience against two major types of drift: incremental and sudden drifts. This indicates that by using the proposed approach, we can ensure that the predictive models maintain their effectiveness in the presence of different types of drift.4 0Item Restricted Pseudo-Labeling for Deep Learning-Based Side-Channel Disassembly Using Contextual Layer and Feature Engineering(Saudi Digital Library, 2025) Alabdulwahab, Saleh Sami S; Son, YunsikEmbedded devices face critical cyber-attacks due to their lightweight design and the sensitive data they handle. Integrating cloud and embedded systems increases the need for security measures against threats. Among these threats are deep learning-based side-channel disassembly attacks, which can expose sensitive information or steal software intellectual properties. Conducting a security test to evaluate the systems against these threats is essential. However, the main challenges include a comprehensive and refined dataset for training deep learning-based side-channel attacks and the lack of public datasets; labeling and profiling such attacks are costly and time-consuming. Additionally, accurately disassembling a single instruction is difficult due to the multiple classes representing each instruction and the obfuscation caused by dummy instructions. This study aimed to create an advanced side-channel evaluation methodology that performs three main deep-learning tasks: profiling using context-aware pseudo-labeling techniques at an instruction level, a disassembly model enhanced with moving log-transformed temporal interaction features, and a sequence labeling model for the detection of dummy instructions using natural language processing techniques. Utilizing gated recurrent units, the proposed pseudo-labeling model achieved 0.996 R2 in estimating the power trace for the assembly instructions. The proposed features improved the disassembly model's accuracy to 0.993, outperforming the related works. Additionally, the detection of dummy instructions using a long short-term memory model reached an accuracy of 0.979. This study provides valuable insights and methodology for measuring the software robustness against side-channel attacks.16 0Item Restricted Machine Learning Techniques for Calorimeter Cluster Calibration of the CMS Particle Flow Algorithm(Baylor univeristy, 2025-05) ghazwani, Noorah; Kenichi, HatakeyamThe Electromagnetic Calorimeter (ECAL) and Hadronic Calorimeter (HCAL) are key components of the CMS detector. The ECAL is designed to measure the energies of electrons and photons, while the HCAL primarily measures the energies of charged and neutral hadrons. An algorithm called Particle Flow (PF) integrates information from various CMS sub-detectors to reconstruct and identify all particles produced in proton collisions. Photons and neural hadrons are reconstructed using calorimeter energy deposit clusters, and reconstruction of charged particle candidates and their separation from neutral particle candidates rely on measurements of charged particle tracks and calorimeter clusters. A proper calibration enhances particle identification and reduces the likelihood of misreconstructed energy excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. In this thesis, BDT is applied to calibrate PF ECAL clusters, while GNN is tested for hadronic cluster calibration.32 0Item Restricted Learning Based Ethereum Phishing Detection: Evaluation, Robustness, and Improvement(University of Central Florida, 2025) Alghuried, Ahod; Mohaisen, DavidPhishing attacks continue to pose a significant threat to the Ethereum ecosystem, accounting for a major share of Ethereum-related cybercrimes. To enhance the detection of such fraudulent transactions, this dissertation develops a comprehensive framework for machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and the vulnerability of detection models to adversarial attacks. By systematically evaluating these key practices, this work contributes to the development of more effective detection methods. The first part of the dissertation assesses the current state of phishing detection methods, identifying gaps in feature selection, dataset composition, and model optimization. We propose a systematic framework that evaluates these factors, providing a foundation for improving the overall performance and reliability of detection models. The second part explores the vulnerability of machine learning models, including Random Forest, Decision Tree, and K-Nearest Neighbors, to single-feature adversarial attacks. Through extensive experimentation, we analyze the impact of various adversarial strategies on model performance and uncover alarming weaknesses in existing models. However, the varied effects of these attacks across different algorithms present opportunities for mitigation through adversarial training and improved feature selection. Finally, the dissertation investigates how phishing detection models generalize across datasets, focusing on the role of preprocessing techniques such as feature engineering and class balancing. Our findings show that optimizing these techniques enhances model accuracy and robustness, making detection methods more adaptable to evolving threats. Overall, this work presents a comprehensive framework that addresses the critical elements of phishing detection in Ethereum transactions, offering valuable insights for the development of more robust and generalizable machine learning-based security models. The proposed framework has broad implications for improving blockchain security and advancing the field of phishing detection.22 0Item Restricted Detecting abuse of cloud and public legitimate services as command and control infrastructure using machine learning(Cardiff University, 2024) Al lelah, Turki; Theodorakopoulos, GeorgeThe widespread adoption of Cloud and Public Legitimate Services (CPLS) has inadvertently created new opportunities for cybercriminals to establish hidden and robust command-and-control (C&C) communication infrastructure. This abuse represents a major cybersecurity risk, as it allows malicious traffic to seamlessly disguise itself within normal network activities. Traditional detection systems are proving inadequate in accurately identifying such abuses. Therefore, this thesis is motivated by emphasizing the urgent need for more advanced detection techniques that are capable of identifying the C&C activity hidden within legitimate CPLS traffic. To assess the extent of the cyber threat of abusing CPLS, this thesis presents an ex- tensive Systematic Literature Review (SLR) encompassing academic and industry lit- erature. The review provides a comprehensive categorization of the attack techniques utilized to abuse CPLS as C&C infrastructure. The open problems uncovered through the SLR motivate this thesis to propose a novel Detection System (DS) capable of identifying malware that abuse CPLS as C&C communication channels. Furthermore, to evaluate our system robustness against attempts to evade detection, this thesis intro- duces the Replace Misclassified Parameter (RMCP) adversarial attack. The proposed detection system leverages Artificial Intelligence (AI) techniques, combining static and dynamic malware analysis methods to accurately identify CPLS abuse. The effective- ness of the proposed system is validated through extensive experiments, demonstrating its ability to detect novel and sophisticated attacks that evade traditional security measures. The outcomes of this thesis have significant implications for enhancing the security of cloud environments, contributing valuable knowledge and practical solutions to the field of cloud security.26 0Item Restricted Disinformation Classification Using Transformer based Machine Learning(Howard University, 2024) alshaqi, Mohammed Al; Rawat, Danda BThe proliferation of false information via social media has become an increasingly pressing problem. Digital means of communication and social media platforms facilitate the rapid spread of disinformation, which calls for the development of advanced techniques for identifying incorrect information. This dissertation endeavors to devise effective multimodal techniques for identifying fraudulent news, considering the noteworthy influence that deceptive stories have on society. The study proposes and evaluates multiple approaches, starting with a transformer-based model that uses word embeddings for accurate text classification. This model significantly outperforms baseline methods such as hybrid CNN and RNN, achieving higher accuracy. The dissertation also introduces a novel BERT-powered multimodal approach to fake news detection, combining textual data with extracted text from images to improve accuracy. By lever aging the strengths of the BERT-base-uncased model for text processing and integrating it with image text extraction via OCR, this approach calculates a confidence score indicating the likeli hood of news being real or fake. Rigorous training and evaluation show significant improvements in performance compared to state-of-the-art methods. Furthermore, the study explores the complexities of multimodal fake news detection, integrat ing text, images, and videos into a unified framework. By employing BERT for textual analysis and CNN for visual data, the multimodal approach demonstrates superior performance over traditional models in handling multiple media formats. Comprehensive evaluations using datasets such as ISOT and MediaEval 2016 confirm the robustness and adaptability of these methods in combating the spread of fake news. This dissertation contributes valuable insights to fake news detection, highlighting the effec tiveness of transformer-based models, emotion-aware classifiers, and multimodal frameworks. The findings provide robust solutions for detecting misinformation across diverse platforms and data types, offering a path forward for future research in this critical area.34 0Item Restricted HYBRID MACHINE LEARNING APPROACHES FOR SOC AND RUL ESTIMATION IN BATTERY MANAGEMENT SYSTEMS(Oakland University, 2024) Hawsawi, Tarik Abdullah; Zohdy, MohamedWith the fast development of electric vehicles (EVs), new technologies are needed to manage batteries more efficiently to optimize performance and more profound and longer battery use. A significant problem that must be solved successfully is accurate estimation of the State-of-Charge (SoC) to avoid fully discharging a battery. It shortens battery life and prolongs the time it takes to charge the battery. This dissertation introduces a new approach that uses Edge Computing and real-time predictive analytics to assess the status of EV batteries and send alerts when necessary, thus facilitating energy efficiency. The Edge Impulse platform is used to predict the Remain Useable Life RUL of batteries with enhanced accuracy using EON-Tuner and DSP processing blocks, enhancing computational capability and making it feasible for edge devices. Since traditional SoC estimations include tools like Kalman filters and Extended Kalman filters, which are effective but have a considerable drawback in estimating the SoC with changing battery parameters, this study proposes a multi-variable optimization method. The method enhances performance prediction after key parameters are iteratively adjusted, thus resolving the emergence hypotheses of most existing techniques. The system was designed and tested on Jupyter Notebook, and performance indicators of accuracy, MSE, and efficiency further validated the design. This study helps ensure proper energy use and long battery life for e-vehicles, which promotes clean energy use.7 0Item Restricted Facial Emotion Recognition via Label Distribution Learning and Customized Convolutional Layers(The University of Warwick, 2024-11) Almowallad, Abeer; Sanchez, VictorThis thesis attempts to investigate the task of recognizing human emotions from facial expressions in images, a topic that has been interest of to researchers in computer vision and machine learning. It addresses the challenge of deciphering a mixture of six basic emotions—happiness, sadness, anger, fear, surprise, and disgust—each presented with distinct intensities. This thesis introduces three Label Distribution Learning (LDL) frameworks to tackle this. Previous studies have dealt with this challenge by using LDL and focusing on optimizing a conditional probability function that attempts to reduce the relative entropy of the predicted distribution with respect to the target distribution, which leads to a lack of generality of the model. First, we propose a deep learning framework for LDL, utilizing convolutional neural network (CNN) features to broaden the model’s generalization capabilities. Named EDL-LBCNN, this framework integrates a Local Binary Convolutional (LBC) layer to refine the texture information extracted from CNNs, targeting a more precise emotion recognition. Secondly, we propose VCNN-ELDL framework, which employs an innovative Visibility Convolutional Layer (VCL). The VCL is engineered to maintain the advantages of traditional convolutional (Conv) layers for feature extraction, while also reducing the number of learnable parameters and enhancing the capture of crucial texture features from facial images. Furthermore, this research presents a novel Transformer architecture, the Visibility Convolutional Vision Transformer (VCLvT), incorporating Depth-Wise Visibility Convolutional Layers (DepthVCL) to bolster spatial feature extraction. This novel approach yields promising outcomes, particularly on limited datasets, showcasing its capacity to meet or exceed state-of-the-art performance across different dataset sizes. Through these advancements, the thesis significantly contributes to the advancement of facial emotion recognition, presenting robust, scalable models adept at interpreting the complex nuances of human emotions.22 0