Saudi Cultural Missions Theses & Dissertations

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Deepfake Technology: A Multi-Disciplinary Analysis of Technical, Social, Legal, and Ethical Issues
(Saudi Digital Library, 2026) Alanazi, Sami; Asif, Seemal
Advances in deep learning have enabled the creation of highly realistic synthetic media capable of manipulating faces, voices, and actions with increasing fidelity. These techniques support legitimate uses in entertainment, education, and digital content production. At the same time, misuse has raised concerns linked to misinformation, privacy infringement, identity fraud, and ethical accountability. This thesis examines deepfake technology as a socio-technical problem, integrating technical analysis with social, legal, and ethical perspectives to inform effective mitigation. Three strands structure the investigation. A critical review of the literature establishes the state of generative and forensic research. Fourteen expert interviews across artificial intelligence, law, ethics, and cybersecurity provide institutional and professional context. Alongside this, empirical experimentation evaluates deep learning approaches to deepfake detection under realistic conditions. Initial technical work focused on image-based forensics, leading to the development of AI-Guard, a convolutional detection system designed for practical deployment. Evaluation results indicate that AI-Guard achieved 98% validation accuracy and 93.2% test accuracy on a dataset exceeding 450,000 images, while supporting real-time inference on mobile platforms. Video forensics forms the second technical contribution. Building on insights from earlier experiments, the thesis introduces VIDS-Guard, a forensics-aware detection framework that combines frequency-domain features, YCbCr colour decomposition, and temporal Transformer-based attention. Trained on 26,975 videos, the system achieved an accuracy of 0.91, a Macro-F1 score of 0.90, and an AUC of 0.97, outperforming established benchmark models across multiple evaluation settings. The results suggest that architectural diversity and temporal modelling improve generalisation under dataset variation. Technical findings do not stand alone. Analysis of expert perspectives and regulatory gaps indicates that detection systems, if unreliable or poorly governed, may themselves contribute to harm. Effective responses therefore appear to require coordination between deployable forensic tools, legal clarity, and ethical safeguards. By aligning system design with institutional and societal considerations, this thesis advances both practical detection capability and regulatory understanding, contributing to transparency, accountability, and resilience within the digital media ecosystem.
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Intelligent Fault Detection for Belt Conveyor Idlers Using Machine Learning
(Saudi Digital Library, 2026) Alharbi, Fahad; Luo, Suhuai; Zhang, Hongyu; Chen, Zhiyong; Wheeler, Craig
Conveyor belt systems are essential components of modern mining, logistics, and manufacturing operations. However, faults in idlers, which are the rollers that support and guide the belt, can reduce system reliability, cause unplanned downtime, increase maintenance costs, and create safety risks. Conventional inspection methods, such as visual assessment and manual listening, are widely used to identify abnormal idler behaviour, but they are labour intensive, subjective, and difficult to apply across large conveyor systems. Acoustic monitoring offers a promising contactless alternative because developing faults often produce changes in sound before complete failure occurs. However, these fault related acoustic signatures can be subtle and may be masked by environmental and operational noise, making them difficult to interpret using traditional signal processing methods alone. Machine learning (ML) has therefore been increasingly applied to acoustic signals to automate feature extraction, fault detection, and classification. Nevertheless, existing ML approaches still face several important challenges, including the limited availability of labelled fault data, inadequate generalisation across recording conditions and sensing platforms, and insufficient modelling of the spatial and temporal characteristics of acoustic signals. This thesis addresses these challenges by developing intelligent fault detection (IFD) models that automatically analyse acoustic signals from belt conveyor idlers. The research follows a progressive methodology comprising supervised transfer learning, semi-supervised anomaly detection, task-specific spatial–temporal deep learning, and cross-domain Convolutional Neural Network (CNN)–Transformer adaptation. To support these investigations, acoustic datasets were collected using handheld microphones and drone-mounted recorders under multiple idler operating and fault conditions. The first study investigated supervised fault classification using the original handheld acoustic dataset, which contained 255 four-second samples representing Normal, Stage 1, Stage 2, and Stage 3 operating conditions. Embeddings were extracted using YAMNet, a pre-trained audio neural network, and processed using Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks to model contextual relationships in both forward and backward temporal directions. Attention mechanisms and an Extreme Gradient Boosting (XGBoost) classifier were also evaluated. The best-performing YAMNet–BiLSTM configuration achieved an accuracy of 90.59% and an F1-score of 90.57% for four-class fault-stage classification. Although this study established a strong supervised baseline, it required labelled examples from every operating condition and fault stage. This dependence limits practical application because faulty-idler recordings are relatively rare, costly to collect, and difficult to label in industrial environments. To address this limitation, the second study developed CASSAD (Chroma-Augmented Semi-Supervised Anomaly Detection), which was trained exclusively on normal operating samples during training. Following the first study, additional handheld recordings were collected, increasing the dataset from 255 to 468 acoustic samples. This larger dataset enabled CASSAD to be evaluated using a broader set of normal and abnormal operating recordings. CASSAD combines Chroma-STFT, Chroma-CQT, and Chroma-CENS representations with filtering, statistical aggregation, and a one-class support vector machine (OC-SVM). On the expanded 468-sample handheld dataset, the best-performing CASSAD configuration achieved an accuracy of 90.59%, a positive-class F1-score of 92.42%, and an Area Under the Receiver Operating Characteristic Curve of 96.29%. To enable a direct comparison with the YAMNet-based models, CASSAD was also evaluated on the original 255-sample dataset after the three fault stages had been combined into a single Abnormal class. In this binary evaluation, CASSAD achieved an accuracy of 93.00% and an F1-score of 93.25%, compared with an accuracy of 92.18% and an F1-score of 93.00% for the strongest YAMNet-based configuration. These results demonstrate that competitive anomaly-detection performance can be achieved without labelled abnormal samples during training. However, CASSAD provides only binary Normal–Abnormal decisions and uses temporally aggregated features, limiting its ability to distinguish fault severity and model changes in acoustic behaviour over time. To overcome these limitations, the third study developed TD-CLNet, a Time-Distributed CNN–Long Short-Term Memory (LSTM) architecture designed to perform multi-stage fault classification while learning spatial and temporal representations from the acoustic data. The expanded 468-sample handheld dataset was re-segmented into one-second samples and converted into log-Mel-spectrogram frames. TD-CLNet applies a shared CNN feature extractor to each frame and then uses an LSTM to model the resulting temporal feature sequence. This design enables the model to distinguish among Normal, Stage 1, Stage 2, and Stage 3 conditions while preserving temporal information that was reduced through the statistical aggregation used in CASSAD. Under four-fold cross-validation, TD-CLNet achieved a mean accuracy, precision, recall, and weighted F1-score of 92.1%. It outperformed the evaluated conventional CNN–LSTM configurations and provided a small improvement over the strongest YAMNet model re-evaluated on the same expanded dataset. Nevertheless, the sequential processing used by LSTM networks limits parallel computation and provides less direct access to broader global relationships within the acoustic feature sequence. To address these limitations and investigate cross-domain generalisation, the fourth study developed hybrid CNN–Transformer models using four pre-trained CNN backbones: ResNet-18, DenseNet-121, EfficientNet-B0, and ShuffleNet-V2. Two acoustic feature representations were investigated: Mel-spectrograms, which represent the distribution of signal energy across perceptually scaled frequency bands over time, and Mel-Frequency Cepstral Coefficients (MFCCs), which provide a compact representation of the short-term spectral envelope. The CNN backbones extracted local spectral representations, while Transformer encoders modelled broader contextual relationships within the feature sequences. In the first phase, the models were trained and evaluated using 0.5-second segments derived from the handheld source-domain recordings. The ResNet-18–Transformer models achieved a cross-fold mean accuracy of 96.9%, with a 95% confidence interval of 96.4%–97.6%, while a ResNet-18–Transformer ensemble increased the handheld-domain accuracy to 98.0%. In the second phase, the models trained on the handheld source-domain dataset were adapted to the drone-acquired target-domain dataset. The drone recordings represented a more challenging sensing environment because they were affected by rotor noise, changing recording distances, varying microphone positions, and environmental interference. Multiple fine-tuning strategies were evaluated, included full fine-tuning (Full-FT), freezing the CNN backbone (CNN-Frozen), freezing the Transformer encoder (TR-Frozen), and 𝐿2-SP regularisation. Mean–Covariance Alignment (MCA) was compared with a cross-entropy (CE) baseline and several established domain-adaptation methods. MFCCs produced the strongest CE baseline in several drone-domain experiments, achieving an accuracy of 90.5% under 𝐿2-SP. However, MFCC performance generally decreased when MCA was applied. In contrast, MCA improved the Mel-spectrogram Full-FT configuration, increasing accuracy from 84.7% to 86.0% and the F1-score from 83.4% to 85.6%. These findings demonstrate that domain-adaptation performance depends on the interaction among the acoustic representation, fine-tuning strategy, model architecture, and alignment objective; no single adaptation method was uniformly optimal across all evaluated configurations. In summary, this thesis establishes a connected research pathway for acoustic fault detection in belt conveyor idlers. It provides handheld and drone-acquired acoustic datasets, establishes supervised transfer-learning baselines, introduces CASSAD for anomaly detection without labelled abnormal training samples, develops TD-CLNet for task-specific spatial–temporal fault-stage classification, and proposes a two-phase CNN–Transformer framework for handheld-to-drone domain adaptation. Collectively, these contributions advance acoustic idler monitoring towards more accurate, data-efficient, and adaptable fault detection while identifying the need for further validation across additional industrial sites, conveyor configurations, sensing platforms, and operating conditions before large-scale real-world deployment.
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Mitigating Class Imbalance Through a Dynamic Training Regime: Application to Thyroid Nodule Classification
(Saudi Digital Library, 2026) Aljalis, Fatimah Ali S; Xinyu, Zhang; Vincent, C. S. Lee
Accurate classification of thyroid nodules from ultrasound images is complicated by the unequal distribution of benign and malignant cases, which can reduce the ability of deep learning models to identify clinically important abnormalities. Although a range of imbalance-handling techniques has been proposed, many depend on synthetic data generation, extensive model modifications, or computationally demanding architectures that may limit practical implementation. This research explores a training-oriented approach based on the Dynamic Balanced Training Regimes (DBTR) framework. Rather than altering the original dataset, the method combines learning from the natural class distribution with iterative training on balanced data subsets to strengthen recognition of underrepresented cases while retaining the characteristics of real clinical images. The proposed framework will be assessed using lightweight convolutional neural networks on the publicly available DDTI thyroid ultrasound dataset. The study aims to determine whether this strategy can improve classification performance under imbalanced conditions while remaining suitable for deployment in resource-constrained healthcare environments.
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ADVANCES IN REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION SYSTEM USING DEEP LEARNING TECHNIQUES FOR ENHANCED ACCESSIBILITY
(Saudi Digital Library, 2026) Alsharif, Bader; Ilyas, Mohammad
Advancements in technology have significantly contributed to the development of innovative tools aimed at improving communication and accessibility for individuals with hearing impairments. This dissertation explores various machine learning and deep learning techniques for recognizing American Sign Language (ASL) gestures, focusing on enhancing accessibility and bridging the communication gap between hearing-impaired and hearing individuals. Traditional machine learning models, such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), alongside deep learning architectures like AlexNet, ResNet-50, EfficientNet, ConvNeXt, and VisionTransformer, were investigated for their effectiveness. Experiments conducted on an extensive dataset of 87,000 ASL gesture images revealed exceptional recognition accuracy, with ResNet-50 achieving 99.98% and Random Forest reaching 99.55%, while other models performed within a range of 97% to 98%. Building on these findings, an innovative real-time recognition system was developed, integrating computer vision and deep learning techniques. The project initially utilized MediaPipe for precise hand movement tracking and YOLOv8, a state-of-the-art object detection model, to translate ASL gestures into text in real time. A comprehensive dataset of 29,820 annotated images was created to ensure strong generalization across diverse hand positions and lighting conditions. MediaPipe’s hand landmark annotations significantly enhanced input quality, improving the YOLOv8 models training accuracy. In addition, a more advanced framework was later designed that integrates YOLOv11 with MediaPipe for robust real-time ASL alphabet recognition. This system was trained on a large-scale dataset of 130,000 annotated images with custom keypoint-based annotations, enabling the model to capture subtle variations in hand and finger positions. Experimental evaluation demonstrated outstanding performance, achieving a mean Average Precision (mAP@0.5) of 98.2% with minimal latency, confirming its suitability for real-time applications in education, healthcare, and professional environments. Overall, the findings of this dissertation underscore the transformative potential of AI-driven solutions for ASL recognition. By bridging communication gaps through both traditional classification models and real-time deep learning frameworks, this work contributes to fostering inclusivity, accessibility, and independence for individuals with hearing impairments.
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Partial Learning for MIMO Detection
(Saudi Digital Library, 2024) Babulghum, Abdulaziz; ElHajjar, Mohammed; Ng, Soon Xin; Xu, Chao
Reliable and efficient multiple-input multiple-output (MIMO) detection remains a central challenge in modern wireless receivers. Optimal maximum-likelihood (Max-L) detection delivers the best performance. However, its exponential complexity is prohibitive, while linear schemes such as zero-forcing (ZF) and minimum mean square error (MMSE) are computationally attractive yet suffer from poor performance. Fully learned detectors improve robustness but introduce substantial parameter counts and computational complexity. Building on prior work on partial learning (PL), this thesis contributes a unified detection framework based on PL that addresses these trade-offs by applying learning only where it yields the most benefits: a subset of the weakest symbol streams, with the remaining streams detected using low-complexity linear detection. The first part of the thesis designs a soft-output PL demapper implemented with a small fully connected neural network (FCNN) for quasi-static channels and embeds it into an iterative detection scheme. The inner MIMO detector produces log-likelihood ratios (LLRs) that are exchanged with an outer convolutional decoder. EXIT charts and decoding trajectories are used to analyze convergence. Across representative 2×2 and 4×4 quadrature phase-shift keying (QPSK) systems, the iterative PL (Iter-PL) technique closes most of the gap to iterative Max-L and full-learning detectors while operating at a fraction of their complexity. Operation counts are reported and related to the number of learning-assisted streams *d*, demonstrating an explicit performance versus complexity trade-off. The second part extends Iter-PL to time-varying channels while also considering channel state information (CSI) error. The same FCNN-based soft demapper is trained using CSI errors. Results show that Iter-PL retains its iterative gains under 5% CSI error and remains markedly superior to purely linear detection. An adaptive PL strategy is further introduced to select *d* based on the average received signal-to-noise ratio (SNR), thereby achieving a near-constant target bit error rate (BER) with reduced average complexity. The final part addresses scalability in dynamic multi-user uplinks. A graph neural network (GNN)–based PL detector is proposed, where an approximate message passing (AMP) frontend supplies soft symbols and variance estimates to the GNN. The GNN then detects only the d weakest users, while ZF detects the remaining users. By operating on user graphs, the model generalizes across changing activity masks without requiring retraining and maintains a low parameter count. Simulations over multiple activity patterns consistently confirm low BER and favorable performance–complexity trade-offs. Overall, the thesis demonstrates that partial learning enables near-optimal soft detection with clear and quantifiable reductions in complexity, and that GNN-based partial learning offers the same benefits in multi-user scenarios. The proposed technique provides a practical approach to scalable, low-latency MIMO detection, making it suitable for evolving wireless systems.
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INVESTIGATING NOVEL ANALYSIS APPROACHES FOR STRUCTURAL CONDITION ASSESSMENT USING ULTRASOUND AND INFRARED DATA
(Saudi Digital Library, 2025) Alqurashi, Inad; Catbas, Necati
Aging civil infrastructure, particularly reinforced concrete bridges, is experiencing progressive deterioration that threatens safety, serviceability, and long-term performance. Traditional inspection methods such as visual examination and hammer sounding are limited in their ability to detect subsurface defects and are prone to subjectivity. This dissertation develops and validates an integrated, multi-modal structural condition assessment framework that combines rapid Infrared Thermography (IRT), high-resolution Ultrasound Tomography (UT), Artificial Intelligence (AI)-driven anomaly detection, and immersive Digital Twin (DT) visualization to overcome these limitations. The research advances three main areas: (1) a dual-mode IR–UT workflow exploiting the complementary strengths of each modality, enabling rapid surface screening with IRT and in-depth defect characterization with UT; (2) optimized deep learning (DL) models tailored to each modality, with a transformer-based Grounding DINO model applied to raw Infrared (IR) imagery for automated detection of thermal anomalies, and a lightweight You Only Look Once (YOLO)-v8n model applied to UT volumetric slices for detecting internal delaminations, voids, ducts, and rebar, both trained on large, segmentation-assisted, color-standardized datasets to ensure robust performance under diverse field conditions; and (3) integration of Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR), photogrammetry, and multi-modal non-destructive testing (NDT) data into a geo-referenced Virtual Reality (VR) environment to support real-time, collaborative decision-making. Laboratory testing on engineered specimens with embedded defects and field deployment on multiple in-service bridges, including the NASA Causeway Bridge, achieved high detection accuracy (mAP@0.5 up to 0.93 for UT using YOLOv8n and 0.80 for IRT using Grounding DINO), strong localization (Average IoU ≈ 0.80–0.90), and significant efficiency gains through targeted UT scanning. The VR-based DT enabled inspectors to seamlessly review thermal anomalies, volumetric UT slices, and 3D geometry in a single immersive scene, reducing defect confirmation time from several minutes to approximately one minute per location. By fusing complementary NDT modalities with AI models purpose-built for each data type and immersive visualization, this research delivers a scalable, repeatable, and field-validated methodology for rapid, objective, and data-rich condition assessment of reinforced concrete structures, with potential for broader application to other infrastructure types to enable proactive maintenance strategies and improved lifecycle management.
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Automated Synthetic Lung Tumor Generation for Training a U-Net Model on Lung CT Slices
(Saudi Digital Library, 2025) AlJoher, Sarah; Blumensath, Thomas
This thesis presents an automated pipeline for generating synthetic lung tumor CT images and corresponding segmentation masks to improve deep learning–based tumor segmentation in low-data settings. Real tumor regions are extracted from annotated CT scans and inserted into healthy lung slices using a 2D Tukey window and Poisson image blending to preserve realistic texture and boundaries. Ground truth masks are generated automatically using the Segment Anything Model and refined through morphological operations. The synthetic and real images are used to train a 2D U-Net segmentation model, which is evaluated across multiple experimental trials on an external dataset composed entirely of real pathological CT scans. Results show that models trained with carefully curated synthetic data match or outperform models trained on real data alone, demonstrating improved generalization and robustness. This work highlights the potential of automated synthetic data generation to reduce reliance on large, manually annotated medical imaging datasets.
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Computational Approaches for Drug Repositioning and Target Discovery in Alzheimer’s Disease
(King Abdullah University of Science and Technology (KAUST), 2024) Alamro, Hind; Gao, Xin
Alzheimer’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.
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Malignant Transformation of Oral Epithelial Dysplasia: Precision Diagnostics Utilizing a Deep Learning and Spatial Transcriptomics Predictive Modeling Approach.
(University of Maryland Baltimore, 2025) Alajaji, Shahd Abdullah; Sultan, Ahmed
Oral squamous cell carcinoma (OSCC) remains a major global health burden with limited improvements in overall survival over recent decades. Most OSCCs arise from oral potentially malignant disorders (OPMDs), including oral epithelial dysplasia (OED), which is currently graded subjectively by histopathological examination. The urgent need for objective, biologically informed risk stratification tools has driven the integration of artificial intelligence (AI), spatial transcriptomics, and functional genomics in oral cancer research. This thesis tests the central hypothesis that deep learning and spatial transcriptomic approaches can objectively predict the malignant transformation of OED by identifying histomorphological patterns and immune-epithelial gene signatures associated with malignant transformation zones and cancer progression. To evaluate this, three specific aims were pursued: 1. Develop and compare AI models for predicting malignant transformation of OED based on lymphocyte distribution and tissue morphology. 2. Identify spatially informed predictive biomarkers in proliferative leukoplakia (PL) using spatial transcriptomic profiling. 3. Functionally assess the role of mEAK-7, a novel regulator of non-canonical mTOR signaling, in OSCC initiation using a gene knockout mouse model. In Aim 1, we trained and evaluated multiple machine learning and deep learning models, including classical regressors, state-of-the-art neural networks, and weakly supervised pattern-recognition networks using a multi-institutional dataset of annotated whole slide images (WSIs) of OPMD cases with known transformation status. In Aim 2, spatial transcriptomics (10x Genomics Visium HD) was performed on PL samples to identify gene signatures predictive of transformation, with a focus on immune–epithelial interactions. In Aim 3, a 4NQO-induced oral carcinogenesis model was applied to mEAK- 7 knockout mice to assess its functional role in OSCC development. AI models demonstrated that lymphocyte infiltration patterns can predict malignant transformation, with deep learning models achieving accuracies up to 83.4% in distinguishing transformed from non-transformed cases. Spatial transcriptomics revealed downregulation of epithelial barrier genes (FLG, CASP14) and immune activation signatures (S100A8, S100A9, CD74) in transformation zones, supporting a model of barrier disruption and neoantigen-driven immune remodeling. The mEAK-7 knockout study showed significantly reduced OSCC incidence, implicating alternative mTOR signaling in OSCC initiation and validating spatial findings through in vivo functional evidence. In conclusion, this thesis presents an integrated, multi-modal investigation into the malignant transformation of OED, providing evidence that AI and spatial biology can complement conventional pathology in predicting cancer risk. The combined findings offer a foundation for future precision diagnostics in oral cancer prevention and identify novel molecular targets for early intervention.
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Pseudo-Labeling for Deep Learning-Based Side-Channel Disassembly Using Contextual Layer and Feature Engineering
(Saudi Digital Library, 2025) Alabdulwahab, Saleh Sami S; Son, Yunsik
Embedded 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.
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