Saudi Cultural Missions Theses & Dissertations
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Item Restricted Predicting Delayed Flights for International Airports Using Artificial Intelligence Models & Techniques(Saudi Digital Library, 2025) Alsharif, Waleed; MHallah, RymDelayed flights are a pervasive challenge in the aviation industry, significantly impacting operational efficiency, passenger satisfaction, and economic costs. This thesis aims to develop predictive models that demonstrate strong performance and reliability, capable of maintaining high accuracy within the tested dataset and showcasing potential for application in various real-world aviation scenarios. These models leverage advanced artificial intelligence and deep learning techniques to address the complexity of predicting delayed flights. The study evaluates the performance of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and their hybrid model (LSTM-CNN), which combine temporal and spatial pattern analysis, alongside Large Language Models (LLM, specifically OpenAI's Babbage model), which excel in processing structured and unstructured text data. Additionally, the research introduces a unified machine learning framework utilizing Gradient Boosting Machine (GBM) for regression and Light Gradient Boosting Machine (LGBM) for classification, aimed at estimating both flight delay durations and their underlying causes. The models were tested on high-dimensional datasets from John F. Kennedy International Airport (JFK), and a synthetic dataset from King Abdulaziz International Airport (KAIA). Among the evaluated models, the hybrid LSTM-CNN model demonstrated the best performance, achieving 99.91% prediction accuracy with a prediction time of 2.18 seconds, outperforming the GBM model (98.5% accuracy, 6.75 seconds) and LGBM (99.99% precision, 4.88 seconds). Additionally, GBM achieved a strong correlation score (R² = 0.9086) in predicting delay durations, while LGBM exhibited exceptionally high precision (99.99%) in identifying delay causes. Results indicated that National Aviation System delays (correlation: 0.600), carrier-related delays (0.561), and late aircraft arrivals (0.519) were the most significant contributors, while weather factors played a moderate role. These findings underscore the exceptional accuracy and efficiency of LSTM-CNN, establishing it as the optimal model for predicting delayed flights due to its superior performance and speed. The study highlights the potential for integrating LSTM-CNN into real-time airport management systems, enhancing operational efficiency and decision-making while paving the way for smarter, AI-driven air traffic systems.9 0Item Restricted Harnessing Machine Learning and Deep Learning for Analyzing Electrical Load Patterns to Identify Energy Loss(Saudi Digital Library, 2025) Alabbas, Mashhour Sadun Abdulkarim; Albatah, MohammadMeeting the challenges of energy requirements, consumption patterns, and the push for sustainability makes energy management in contemporary agriculture critically important. This study aims to devise a holistic model for energy efficiency in agricultural contexts by integrating modern computer vision methodologies for field boundary extraction together with anomaly detection techniques. To achieve the accurate segmentation of agricultural fields from satellite imagery, high-resolution imagery is processed using the YOLOv8 object detection model. The subsequently generated field feature datasets enable the smart grid data to serve as a basis for the anomaly detection process using the Isolation Forest algorithm. The methodology follows a multi-stage pipeline: data collection, preprocessing, augmentation, model training, fine-tuning, and evaluation. To validate accurate and reliable field boundary detection, evaluation metrics precision, recall, and mAP (mean Average Precision) are computed and analyzed. Subsequently, energy consumption data are processed for anomaly detection, enabling the identification of irregular and potentially inefficient consumption patterns. The findings indicate that YOLOv8 has a very high detection accuracy with an mAP score over 90%. Furthermore, the Isolation Forest algorithm has shown improved F1 scores over traditional approaches in detecting anomalies in energy consumption. This integrated method provides an automated and scalable solution in precision agriculture which allows users to monitor cultivation conditions and minimize energy consumption, thereby enhancing the energy efficiency and the overall decision-making framework. The study advances the convergence of artificial intelligence, remote sensing, and intelligent energy management systems, offering a basis for developing technological innovations that promote sustainablility in agriculture.24 0Item Restricted Paraphrase Generation and Identification at Paragraph-Level(Saudi Digital Library, 2025) Alsaqaabi, Arwa; Stewart, Craig; Akrida, Eleni; Cristea, AlexandraThe widespread availability of the Internet and the ease of accessing written content have significantly contributed to the rising incidence of plagiarism across various domains, including education. This behaviour directly undermines academic integrity, as evidenced by reports highlighting increased plagiarism in student work. Notably, students tend to plagiarize entire paragraphs more often than individual sentences, further complicating efforts to detect and prevent academic dishonesty. Additionally, advancements in natural language processing (NLP) have further facilitated plagiarism, particularly by using online paraphrasing tools and deep-learning language models designed to generate paraphrased text. These developments underscore the critical need to develop and refine effective paraphrase identification (PI) methodologies. This thesis addresses one of the most challenging aspects of plagiarism detection (PD): identifying instances of plagiarism at the paragraph-level, with a particular emphasis on paraphrased paragraphs rather than individual sentences. By focusing on this level of granularity, the approach considers both intra-sentence and inter-sentence relationships, offering a more comprehensive solution to the detection of sophisticated forms of plagiarism. To achieve this aim, the research examines the influence of text length on the performance of NLP machine learning (ML) and deep learning (DL) models. Furthermore, it introduces ALECS-SS (ALECS – Social Sciences), a large-scale dataset of paragraph-length paraphrases, and develops three novel SALAC algorithms designed to preserve semantic integrity while restructuring paragraph content. These algorithms suggest a novel approach that modifies the structure of paragraphs while maintaining their semantics. The methodology involves converting text into a graph where each node corresponds to a sentence’s semantic vector, and each edge is weighted by a numerical value representing the sentence order probability. Subsequently, a masking approach is applied to the reconstructed paragraphs modifying the v lexical elements while preserving the original semantic content. This step introduces variability to the dataset while maintaining its core meaning, effectively simulating paraphrased text. Human and automatic evaluations assess the reliability and quality of paraphrases, and additional studies examine the adaptability of SALAC across multiple academic domains. Moreover, state-of-the-art large language models (LLMs) are analysed for their ability to differentiate between human-written and machine-paraphrased text. This investigation involves the use of multiple PI datasets in addition to the newly established paragraph-level paraphrases dataset (ALECS-SS). The findings demonstrate that text length significantly affects model performance, with limitations arising from dataset segmentation. Additionally, the results show that the SALAC algorithms effectively maintain semantic integrity and coherence across different domains, highlighting their potential for domain-independent paraphrasing. The thesis also analysed the state-of-the-art LLMs’ performance in detecting auto-paraphrased content and distinguishing them from human-written content at both the sentence and paragraph levels, showing that the models could reliably identify reworded content from individual sentences up to entire paragraphs. Collectively, these findings contribute to educational applications and plagiarism detection by improving how paraphrased content is generated and recognized, and they advance NLP-driven paraphrasing techniques by providing strategies that ensure that meaning and coherence are preserved in reworded material.17 0Item Restricted Deep Learning based Cancer Classification and Segmentation in Medical Images(Saudi Digital Library, 2025) Alharbi, Afaf; Zhang, QianniCancer has significantly threatened human life and health for many years. In the clinic, medical images analysis is the golden stand for evaluating the prediction of patient prog- nosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of medical images is time- consuming and expensive for pathologists, radiologists and CT scans experts. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become main stream to segment tumours automatically, significantly reducing the workload of healthcare professionals. However, there still remain many challenging tasks towards medical images such as auto- mated cancer categorisation, tumour area segmentation, and relying on large-scale labeled images. Therefore, this research studies theses challenges tasks in medical images proposing novel deep-learning paradigms that can support healthcare professionals in cancer diagnosis and treatment plans. Chapter 3 proposes automated tissue classification framework called Multiple Instance Learning (MIL) in whole slide histology images. To overcome the limitations of weak super- vision in tissue classification, we incorporate the attention mechanism into the MIL frame- work. This integration allows us to effectively address the challenges associated with the inadequate labeling of training data and improve the accuracy and reliability of the tissue classification process. Chapter 4 proposes a novel approach for histopathology image classification with MIL model that combines an adaptive attention mechanism into an end-to-end deep CNN as well as transfer learning pre-trained models (Trans-AMIL). Well-known Transfer Learning architectures of VGGNet [14], DenseNet [15] and ResNet[16] are leverage in our framework implementation. Experiment and deep analysis have been conducted on public histopathol- ogy breast cancer dataset. The results show that our Trans-AMIL proposed approach with VGG pre- trained model demonstrates excellent improvement over the state-of-the-art. Chapter 5 proposes a self-supervised learning for Magnetic resonance imaging (MRI) tu- mour segmentation. A self-supervised cancer segmentation framework is proposed to re- duce label dependency. An innovative Barlow-Twins technique scheme combined with swin transformer is developed to perform this self supervised method in MRI brain medical im- ages. Additionally, data augmentation are applied to improve the discriminability of tumour features. Experimental results show that the proposed method achieves better tumour seg- mentation performance than other popular self- supervised methods. Chapter 6 proposes an innovative Barlow Twins self supervised technique combined with Regularised variational auto-encoder for MRI tumour images as well as CT scans images segmentation task. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative Barlow-Twins technique scheme is developed to represent tumour features based on unlabeled images. Additionally, data augmentation are applied to improve the discriminability of tumour features. Experimental results show that the pro- posed method achieves better tumour segmentation performance than other existing state of the art methods. The thesis presents four approaches for classifying and segmenting cancer images from his- tology images, MRI images and CT scans images: unsupervised, and weakly supervised methods. This research effectively classifies histopathology images tumour regions based on histopathological annotations and well-designed modules. The research additionally comprehensively segments MRI and CT images. Our studies comprehensively demonstrate label-effective automatic on various types of medical image classification and segmentation. Experimental results prove that our works achieve state-of-the-art performances on both classification and segmentation tasks on real world datasets16 0Item Restricted Breast Tumors AI-Based Early Identification using Screening Mammography for Adult Women(Saudi Digital Library, 2025) Almansour, Tareg Mohammed H; Abdelrazek, Elmetwally M; Elgarayhi, Ahmed; Medhet, TamerEarly detection of breast cancer (BrC) is one of the best strategies to prevent the disease's spread. This makes an autonomous diagnosis system based on deep learning (DL) attractive for improving the accuracy of detection and prediction. This study suggests employing transfer DL models to categorize BrC from mammograms. Furthermore, to identify BrC detection architectures, transfer DL models are applied to various well-known convolutional neural networks (CNNs). Three CNNs (NasNetMobile, EfficientNet-b0, and MobileNetV2) are adjusted in particular ways before being used. All systems use two types of optimizers: root mean square propagation (RMSP) and adaptive moment estimation (ADAM). The EfficientNet-b0 network attains 96.45% accuracy, 96.63% sensitivity, and 97.18% F1-score when using the ADAM optimizer. The experimental results demonstrate that EfficientNet-b0 outperforms other sophisticated CNN techniques and offers a number of advantages. Additionally, EfficientNet-b0 obtained an F1-score of 96.00%, a sensitivity of 96.55%, and an accuracy of 95.04% utilizing the RMSprop optimizer. To sum up, this work improves the identification of BrC for adult women by applying transfer DL models to digital mammography scans. The best-performing CNN among the three (NasNetMobile, EfficientNet-b0, and MobileNetV2) was EfficientNet-b0 optimized with ADAM and RMSprop. These results show how these structures could improve healthcare and increase the accuracy of BrC detection.15 0Item Restricted Improving Feature Selection in Medical Image Segmentation(Saudi Digital Library, 2025) ALABDULWAHAB, Abrar Sami S; Sang, Woong LeeColorectal cancer is considered one of the most common cancers worldwide, representing about one in 10 cancer cases and deaths globally. It starts as small, benign polyps which may turn into cancer. Early detection and removal of polyps is crucial to prevent colorectal cancer and ensure appropriate patient treatment. Due to the polyp features, accurately segmenting it can be challenging. Deep learning methods have been used to detect colorectal polyps by extracting the features. However, most of these approaches have limitations in handling polyp variations and often struggle with generalization when trained on small datasets or when encountering polyps with indistinct boundaries. Therefore, Duck-Net was proposed to segment polyps in colonoscopy images and address these challenges through its architecture, by creating a custom convolutional block and applying a secondary downsampling. However, Duck-Net has some limitations when it comes to polyps that have the same color as the colon, making it challenging for the model to detect these polyps. Therefore, Duck-Net performance needs further enhancement to segment and detect small-size, flat polyps, polyps with unclear edges, and subtle abnormalities, which are clinically significant for proper diagnosis. Attention mechanism, and Conv2DTranspose layer could be used to overcome such problems. Therefore, this thesis proposes a method based on a Duck-Net, integrated with the convolutional block attention module and conv2DTranspse to enhance feature representation, improve interpretability, generate higher-resolution outputs and the ability to capture vital small information from images consistently. This study confirmed that Duck-Net’s performance, when integrated with the convolutional block attention module block and conv2DTranspose layer, further enhanced image segmentation and outperformed the standard method in image segmentation and detection of polyps. It is feasible to segment and detect undetectable small-size, flat-shaped lesion polyps, and polyps with indistinct boundaries, which are considered factors for increased miss rate of colorectal cancer polyp detections.10 0Item Restricted Stress Detection: Leveraging IoMT Data and Machine Learning for Enhanced Well-being(Saudi Digital Library, 2025) Alsharef, Moudy Sharaf; Alshareef, Moudywe focus on the detection of acute stress, characterized by short-term physiological changes such as changes in heart rate variability (HRV), breathing patterns, and other bodily functions. Often measurable through wearable or contactless sensors. Accurate detection of acute stress is crucial in high-pressure environments, such as clinical settings, to reduce cognitive overload, prevent burnout, and minimize errors. Current research on stress detection faces multiple challenges. First, most proposed methods are not designed to identify stress in unseen subjects, limiting their generalizability and practical applicability. Second, due to the sensitive nature of stress-related physiological data and the risk of data leakage, insufficient attention has been paid to ensuring data privacy while preserving utility. Third, many existing studies rely on synthetically induced stress in controlled environments, overlooking real-world scenarios where stress can have severe consequences. Finally, nearly all research in this domain employs invasive IoMT sensors or wearable devices, which may not be practical or scalable for real-world applications. This thesis presents five key contributions in the field of stress detection using Internet of Medical Things (IoMT) sensors and machine learning. First, it introduces a deep learning model based on self-attention (Transformer), trained and evaluated using the WESAD dataset, a widely used benchmark collected from 15 participants under controlled stress tasks. The model achieved 96% accuracy in detecting stress and was validated using leave-one-subject-out (LOSO) cross-validation to demonstrate generalizability to unseen individuals. Second, to ensure data privacy, a differential privacy framework was integrated into the model. This approach adds noise during training to prevent sensitive data leakage and achieved 93% accuracy, confirming it is both private and effective. Third, the thesis introduces a new dataset called PARFAIT, collected from 30 healthcare workers during real hospital duties (ICU, ER, OR) using non-invasive HRV sensors and the Maslach Burnout Inventory (MBI) to label stress levels. This dataset supports real-world analysis of stress among physicians. Fourth, a cost-sensitive model is developed using XGBoost and the PARFAIT dataset, assigning higher penalties to stress misclassifications that could lead to medical errors. This model achieved 98% accuracy and reduced false negatives, making it suitable for clinical settings. Finally, a contactless radar-based system is presented to detect stress using ultrawideband (UWB) radar, capturing HRV and breathing data. A deep learning model achieved 92.35% accuracy, offering a non-wearable, scalable alternative. Although the radar-based model achieved a slightly lower accuracy (92.35%) compared to the wearable-based model (96%), it provides several important advantages. It works with out any physical contact, helps maintain user privacy, and can be more practical to deploy in clinical settings where wearable sensors may not be suitable. The small drop in accuracy is mainly due to the limitations of radar in measuring HRV precisely. However, by combining radar-based HRV with breathing features, the overall performance remains competitive. 314 0Item Restricted An Evaluation of Machine Learning and Deep Learning for Time Series Forecasting(Saudi Digital Library, 2025-08) Gadhi, Adel; Shelton, PeirisThis thesis investigates the use of machine learning and hybrid models to forecast time series data such as climate patterns, oil prices, Australian beer production, and sunspot activity. It examines traditional models like ARIMA and GARCH, as well as machine learning methods such as SVR, LSTM, RF, and DT, which better capture non-linear and complex relationships. The study also evaluates hybrid models like ARIMA-ANN and GARMA-LSTM, which consistently demonstrate superior forecasting accuracy across various datasets. The GARMA-LSTM model, in particular, proves effective for long-term forecasting, especially with sunspot and beer production data. Finally, the thesis applies an advanced deep learning system, WGAN-GP, to financial and climate data, showing that modern methods can move beyond classical assumptions and better capture complex, high-order dynamics.18 0Item Restricted Improving Induction Motor Fault Classification Accuracy Through Enhanced Multimodal Preprocessing, Artificial Image Synthesis, Deep Learning and Load-Adaptive Graph-Based Methods(Saudi Digital Library, 2024-11-06) Hejazi, Shahd Ziad M; Packianather,, Michael Liu, YingThis thesis aims to improve the accuracy of fault classification in Induction Motor (IM) bearings by developing and applying advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques for condition monitoring data. The proposed framework utilises several approaches, namely, Multimodal Data Preprocessing, Artificial Thermal Image Creation, Customised Radial Load Assessment, Multimodal Systems Decision Fusion, and Graph Convolutional Networks (GCN) on Tabular Datasets to achieve better classification accuracies over existing methods. This study's first significant contribution is the proposed novel approach in the preprocessing of multimodal condition monitoring data for classifying induction motor faults that employs Convolutional Neural Networks (CNNs), such as Residual Network-18 (ResNet-18) and SqueezeNet, to fuse vibration signals and thermal images. This approach enhances fault classification accuracy by 14.81% and proves exceptionally effective in scenarios with compromised image quality. Further refinement using Gramian Angular Field (GAF) processing enhances the detection of subtle fault indicators, achieving better accuracy than Continuous Wavelet Transform (CWT). Secondly, this thesis explores the creation of high-quality artificial thermal images using Wasserstein GAN with Gradient Penalty (WGAN-GP) and its conditional variant, conditional Wasserstein GAN with Gradient Penalty (cWGAN-GP), to address the scarcity of thermal imaging data. The artificial thermal images replicate complex thermal patterns of IMs under various fault conditions with remarkable accuracy, as evidenced by the improved Maximum Mean Discrepancy (MMD) scores and a 40.00% reduction in training times. The high fidelity of these artificially generated images, validated against real images, underscores their practical use in fault classification. Thirdly, the Customised Load Adaptive Framework (CLAF) introduces a novel approach to incorporating load variations into fault classification. Through a two-phase process involving ANOVA and optimal CWT, load-dependent fault subclasses—Mild, Moderate, Severe, and Normal (fault-free) or Healthy—are identified. The CLAF achieved an accuracy of 96.30% ± 0.50% in 18.155 s during five-fold cross-validation using a Wide Neural Network (WNN), demonstrating its ability to detect subtle fault variations across different Load Factors (LFs). Fourthly, building upon the CLAF’s load-dependent fault subclass structure, the research proposed two key methodologies for enhancing load-specific condition monitoring accuracy while optimising training time relative to complexity using the MFPT bearing dataset namely, the Load-Dependent Multimodal Vibration Signal Enhancement and Fusion (LD-MVSEF) method, and the Hybrid Graph-CNN Decision Fusion (HG-CDF) method. The LD-MVSEF employs a multimodal approach across multiple channels, with different signal encoding techniques achieving a fault classification accuracy of 99.04% ± 0.22% over five runs in 18 min 30 s. It performed particularly well in the Moderate class, achieving 99.15% ± 0.89% testing accuracy, and scored 97.20% ± 1.75% in the Mild class. The proposed HG-CDF combines the structural strengths of Graph Convolutional Networks (GCNs) with the pattern-detection capabilities of 1D-Convolutional Neural Networks (1D-CNNs) for CLAF load-dependent fault subclass classification. The study began by optimising the GCN through Taguchi experiments, converting tabular data into graph structures using the k-Nearest Neighbours method and achieving a mean accuracy of 89.01% ± 1.25 across nine configurations. HG-CDF further improved performance, reaching an overall accuracy of 99.19% in just 3 minutes and 28 seconds, surpassing LD-MVSEF in the Mild class with 98.92% accuracy while also providing a faster and more efficient solution. The methodologies proposed in this research significantly enhance the IM fault classification task, improve the decision-making process, and offer scalable solutions adaptable to other domains.11 0Item Restricted MULTIDIMENSIONAL APPROACHES IN BUG DETECTION FOR PARALLEL PROGRAMMING AND TEXT-TO-CODE SEMANTIC PARSING(University of Central Florida, 2025) Alsofyani, May; Wang LiqiangThis dissertation applies deep learning and large language models to two domains: parallel programming fault detection and text-to-code translation, aiming to enhance software reliability and natural language-driven code generation. Due to their unpredictable nature, concurrency bugs-particularly data race bugs— present significant challenges in fault detection for parallel programming. We investigate deep learning and LLM-based approaches for detecting data race bugs in OpenMP programs. Our proposed methods include a transformer encoder and GPT-4 through prompt engineering and fine-tuning. Experimental results demonstrate that the transformer encoder achieves competitive accuracy compared to LLMs, highlighting its effectiveness in understanding complex OpenMP directives. Expanding this research, we explore the role of LLMs in detecting faults in Pthreads, which requires a deep understanding of thread-based logic and synchronization mechanisms. We analyze ChatGPT's effectiveness in Pthreads fault detection through dialogue-based interactions and advanced prompt engineering techniques, including Zero-Shot, Few-Shot, Chain-of-Thought, and Retrieval-Augmented Generation. Additionally, we introduce three hybrid prompting techniques—Chain-of-Thought with Few-Shot Prompting, Retrieval-Augmented Generation with Few-Shot Prompting, and Prompt Chaining with Few-Shot Prompting—to enhance fault detection performance. In the semantic parsing domain, our research bridges the gap between natural language and executable code, focusing on text-to-SQL translation. To address SQL's limitations in statistical analysis, we introduce SIGMA, a dataset for text-to-code semantic parsing with statistical analysis capabilities. In addition, we address the gap in cross-domain context-dependent text-to-SQL translation for the Arabic language. While prior research has focused on English and Chinese datasets, no efforts have been made to explore Arabic cross-domain conversational querying. We introduce Ar-SParC, the first Arabic cross-domain, context-dependent text-to-SQL dataset. This dissertation contributes to fault detection in parallel programming and semantic parsing with statistical analysis, leveraging cutting-edge deep learning and LLMs techniques. Our findings advance bug detection in high-performance computing and natural language-based code generation, significantly improving software reliability and accessibility.34 0