Saudi Cultural Missions Theses & Dissertations
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Item Restricted Leveraging Deep Learning for Change Detection in Bi-Temporal Remote Sensing Imagery(University of Missouri-Columbia, 2024) Alshehri, Mariam; Hurt, J. AlexDeforestation in the Brazilian Amazon poses significant threats to global climate stability, biodiversity, and local communities. This dissertation presents advanced deep learning approaches to improve deforestation detection using bi-temporal Sentinel-2 satellite imagery. We developed a specialized dataset capturing deforestation events between 2020 and 2021 in key conservation units of the Amazon. We first adapted transformer-based change detection models to the deforestation context, leveraging attention mechanisms to analyze spatial and temporal patterns. While these models showed high accuracy, limitations remained in effectively capturing subtle environmental changes. To address this, we introduce DeforestNet, a novel deep learning framework that integrates advanced semantic segmentation encoders within a siamese architecture. DeforestNet employs cross-temporal interaction mechanisms and temporal fusion strategies to enhance the discrimination of true deforestation events from background noise. Experimental results demonstrate that DeforestNet outperforms existing models, achieving higher precision, recall, and F1-scores in deforestation detection. Additionally, it generalizes well to other change detection tasks, as evidenced by its performance on the LEVIR-CD urban building change detection dataset. This research contributes a robust and efficient framework for accurate change detection in remote sensing imagery, offering valuable tools for environmental monitoring and aiding global efforts in sustainable forest management and conservation.10 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.6 0Item Restricted Early Detection of Pleuropulmonary Blastoma Using Transformers Models(Bowie State University, 2024) Almenwer, Sahar; El-Sayed, HodaChildhood cancer is the second leading cause of death among children under the age of fifteen, according to the American Cancer Society. The number of diagnosed cancer cases in children continues to rise each year, leading to many tragic fatalities. One specific type of cancer, pleuropulmonary blastoma (PPB), affects children from newborns to those as old as six years. The most common way to diagnose PPB is through imaging; this method is quick, cost-effective, and does not require specialized equipment or laboratory tests. However, relying solely on imaging for early detection of PPB can be challenging because of lower accuracy and sensitivity. It is time consuming and susceptible to errors because of the numerous potential differential diagnoses. A more accurate diagnosis of PPB depends on identifying mutations in the DICER1 gene. Recent advancements in biological analysis and computer learning are transforming cancer treatment. Deep learning (DL) methods for diagnosing PPB are becoming increasingly popular. Despite facing some challenges, DL shows a significant promise in supporting oncologists. However, some advanced models possess a limited local receptive field, which may restrict their ability to comprehend the overall context. This research employs the vision transformer (ViT) model to address these limitations. ViT reduces computation time and yields better results than existing models. It utilizes self-attention among image patches to process visual information effectively. The experiments in this study are conducted using two types of datasets, medical images and genomic datasets, employing two different methodologies. One approach uses the ViT model combined with an explainability framework on large medical image datasets with various modalities. The other involves developing a new hybrid model that integrates the vision transformer with bidirectional long short-term memory (ViT-BiLSTM) for genomic datasets. The results demonstrate that the ViT model and the new hybrid model, ViT-BiLSTM, significantly outperform established models, as validated by multiple performance metrics. Consequently, this research holds great promise for the early diagnosis of PPB, reducing misdiagnosis occurrences, and facilitating timely intervention and treatment. These findings could revolutionize medical diagnosis and shape the future of healthcare.10 0Item Open Access Parking Occupancy Classification: Deep learning model compression for edge device classification(Queen Mary University of London, 2024) Tamim, Ziad; Ansari, Tayyab AhmedUrban areas face severe traffic congestion due to poorly managed parking systems. Advanced parking management, like automated and smart parking guidance systems, offers a feasible solution requiring real-tim occupancy data. Traditional sensor-based methods are costly and inefficient for large scale parking, whereas video-based sensing is more effective. Deep learning methods improve accuracy but have high computational costs, affecting real-time performance. Central servers or cloud platforms are often used but can be impractical due to high resource demands. Instead, utilising edge devices with model compression techniques—such as quantisation and knowledge distillation enhances efficiency. This study aims to boost the inference speed of parking classification algorithms by developing a custom model called QCustom based on the MobileNet Depthwise Separable Convolution blocks and using compression techniques to reduce the inference time further. Contributions include developing an edge-based real-time occupancy system, setting performance benchmarks, optimising models for edge devices, and testing on a prototype parking lot. The goal is efficient and accurate parking management for smart cities. Results of the paper include accuracy of 98.8% on the CNRPark-EXT dataset, real world implementation accuracy of 97.44%, and an inference speed for one parking slot of 0.746ms on the Raspberry Pi 5.30 0Item Restricted Next-Generation Diagnostics: Deep Learning based Approaches for Medical Image Analysis(Florida Institute of Technology, 2024-12) Alsubaie, Mohammed; Li, XianqiHigh-resolution medical imaging plays a pivotal role in accurate diagnostics and effective patient care. However, the extended acquisition times required for detailed imaging often lead to patient discomfort, motion artifacts, and increased scan failures. To address these challenges, advanced deep learning approaches are emerging as transformative tools in medical imaging. In this study, we propose a conditional denoising diffusion model-based framework designed to enhance the resolution and reconstruction quality of medical images, including Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopic Imaging (MRSI). The framework incorporates a data fidelity term into the reverse sampling process to ensure consistency with physical acquisition models while improving reconstruction accuracy. Furthermore, it leverages a Self-Attention UNet architecture to upsample low-resolution MRSI data, preserving fine-grained details and critical structural information essential for clinical diagnostics. The proposed model demonstrates adaptability across varying undersampling rates and spatial resolutions, as a network trained on acceleration factor 8 generalizes effectively to other acceleration factors. Evaluations on publicly available fastMRI datasets and MRSI data highlight significant improvements over state-of-the-art methods, achieving superior metrics in SSIM, PSNR, and LPIPS while maintaining diagnostic relevance. Notably, the diffusion model excels in preserving intricate structural details, detecting small tumors, and maintaining texture integrity, particularly in glioma imaging for mapping tumor metabolism associated with IDH1 and IDH2 mutations. These findings underscore the potential of deep learning-based diffusion models to revolutionize medical imaging, enabling faster, more accurate scans and improving diagnostic workflows across clinical and research applications.15 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.33 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.15 0Item Restricted TOWARDS ROBUST AND ACCURATE TEXT-TO-CODE GENERATION(University of Central Florida, 2024) almohaimeed, saleh; Wang, LiqiangDatabases play a vital role in today’s digital landscape, enabling effective data storage, manage- ment, and retrieval for businesses and other organizations. However, interacting with databases often requires knowledge of query (e.g., SQL) and analysis, which can be a barrier for many users. In natural language processing, the text-to-code task, which converts natural language text into query and analysis code, bridges this gap by allowing users to access and manipulate data using everyday language. This dissertation investigates different challenges in text-to-code (including text-to-SQL as a subtask), with a focus on four primary contributions to the field. As a solution to the lack of statistical analysis in current text-to-code tasks, we introduce SIGMA, a text-to- Code dataset with statistical analysis, featuring 6000 questions with Python code labels. Baseline models show promising results, indicating that our new task can support both statistical analysis and SQL queries simultaneously. Second, we present Ar-Spider, the first Arabic cross-domain text-to-SQL dataset that addresses multilingual limitations. We have conducted experiments with LGESQL and S2SQL models, enhanced by our Context Similarity Relationship (CSR) approach, which demonstrates competitive performance, reducing the performance gap between the Arabic and English text-to-SQL datasets. Third, we address context-dependent text-to-SQL task, often overlooked by current models. The SParC dataset was explored by utilizing different question rep- resentations and in-context learning prompt engineering techniques. Then, we propose GAT-SQL, an advanced prompt engineering approach that improves both zero-shot and in-context learning experiments. GAT-SQL sets new benchmarks in both SParC and CoSQL datasets. Finally, we introduce Ar-SParC, a context-dependent Arabic text-to-SQL dataset that enables users to interact with the model through a series of interrelated questions. In total, 40 experiments were conducted to investigate this dataset using various prompt engineering techniques, and a novel technique called GAT Corrector was developed, which significantly improved the performance of all base- line models.12 0Item Restricted ADAPTIVE INTRUSION DETECTION SYSTEM FOR THE INTERNET OF MEDICAL THINGS (IOMT): ENHANCING SECURITY THROUGH IMPROVED MUTUAL INFORMATION FEATURE SELECTION AND META-LEARNING(Towson University, 2024-12) Alalhareth, Mousa; Hong, SungchulThe Internet of Medical Things (IoMT) has revolutionized healthcare by enabling continuous patient monitoring and diagnostics but also introduces significant cybersecurity risks. IoMT devices are vulnerable to cyber-attacks that threaten patient data and safety. To address these challenges, Intrusion Detection Systems (IDS) using machine learning algorithms have been introduced. However, the high data dimensionality in IoMT environments often leads to overfitting and reduced detection accuracy. This dissertation presents several methodologies to enhance IDS performance in IoMT. First, the Logistic Redundancy Coefficient Gradual Upweighting Mutual Information Feature Selection (LRGU-MIFS) method is introduced to balance the trade-off between relevance and redundancy, while improving redundancy estimation in cases of data sparsity. This method achieves 95% accuracy, surpassing the 92% reported in related studies. Second, a fuzzy-based self-tuning Long Short-Term Memory (LSTM) IDS model is proposed, which dynamically adjusts training epochs and uses early stopping to prevent overfitting and underfitting. This model achieves 97% accuracy, a 10% false positive rate, and a 94% detection rate, outperforming prior models that reported 95% accuracy, a 12% false positive rate, and a 93% detection rate. Finally, a performance-driven meta-learning technique for ensemble learning is introduced. This technique dynamically adjusts classifier voting weights based on factors such as accuracy, loss, and prediction confidence levels. As a result, this method achieves 98% accuracy, a 97% detection rate, and a 99% F1 score, while reducing the false positive rate to 10%, surpassing previous results of 97% accuracy, a 93% detection rate, a 97% F1 score, and an 11% false positive rate. These contributions significantly enhance IDS effectiveness in IoMT, providing stronger protection for sensitive medical data and improving the security and reliability of healthcare networks.20 0Item Restricted Automatic Detection and Verification System for Arabic Rumor News on Twitter(University of Technology Sydney, 2026-04) Karali, Sami; Chin-Teng, LinLanguage models have been extensively studied and applied in various fields in recent years. However, the majority of the language use models are designed for and perform significantly better in English compared to other languages, such as Arabic. The differences between English and Arabic in terms of grammar, writing, and word-forming structures pose significant challenges in applying English-based language models to Arabic content. Therefore, there is a critical need to develop and refine models and methodologies that can effectively process Arabic content. This research aims to address the gaps in Arabic language models by developing innovative machine learning (ML) and natural language processing (NLP) methodologies. We apply the developed model to Arabic rumor detection on Twitter to test its effectiveness. To achieve this, the research is divided into three fundamental phases: 1) Efficiently collecting and pre-processing a comprehensive dataset of Arabic news tweets; 2) The refinement of ML models through an enhanced Convolutional Neural Network (ECNN) equipped with N-gram feature maps for accurate rumor identification; 3) The augmentation of decision-making precision in rumor verification via sophisticated ensemble learning techniques. In the first phase, the research meticulously develops a methodology for the collection and pre-processing of Arabic news tweets, aiming to establish a dataset optimized for rumor detection analysis. Leveraging a blend of automated and manual processes, the research navigates the intricacies of the Arabic language, enhancing the dataset’s quality for ML applications. This foundational phase ensures removing irrelevant data and normalizing text, setting a precedent for accuracy in subsequent detection tasks. The second phase is to develop an Enhanced Convolutional Neural Network (ECNN) model, which incorporates N-gram feature maps for a deeper linguistic analysis of tweets. This innovative ECNN model, designed specifically for the Arabic language, marks a significant departure from traditional rumor detection models by harnessing the power of spatial feature extraction alongside the contextual insights provided by N-gram analysis. Empirical results underscore the ECNN model’s superior performance, demonstrating a marked improvement in detecting and classifying rumors with heightened accuracy and efficiency. The culmination of the study explores the efficacy of ensemble learning methods in enhancing the robustness and accuracy of rumor detection systems. By synergizing the ECNN model with Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) networks within a stacked ensemble framework, the research pioneers a composite approach that significantly outstrips the capabilities of singular models. This innovation results in a state-of-the-art system for rumor verification that outperforms accuracy in identifying rumors, as demonstrated by empirical testing and analysis. This research contributes to bridging the gap between English-centric language models and Arabic language processing, demonstrating the importance of tailored approaches for different languages in the field of ML and NLP. These contributions signify a monumental step forward in the field of Arabic NLP and ML and offer practical solutions for the real-world challenge of rumor proliferation on social media platforms, ultimately fostering a more reliable digital environment for Arabic-speaking communities.42 0