SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Deep Multi-Modality Fusion for Integrative Healthcare(Queen Mary University of London, 2025) Alwazzan, Omnia; Slabaugh, GregoryThe healthcare industry generates vast amounts of data, driving advancements in patient diagnosis, treatment, and therapeutic discovery. A single patient’s electronic healthcare record often includes multiple modalities, each providing unique insights into their condition. Yet, integrating these diverse, complementary sources to gain deeper insights remains a challenge. While deep learning has transformed single-modality analysis, many clinical scenarios, particularly in cancer care, require integrating complementary data sources for a holistic understanding. In cancer care, two key modalities provide complementary perspectives: histopathology whole-slide images (WSIs) and omics data (genomic, transcriptomic, epigenomic). WSIs deliver high-resolution views of tissue morphology and cellular structures, while omics data reveal molecular-level details of disease mechanisms. In this domain, single-modality approaches fall short: histopathology misses molecular heterogeneity, and traditional bulk or non-spatial omics data lack spatial context. Although recent advances in spatial omics technologies aim to bridge this gap by capturing molecular data within spatially resolved tissue architecture, such approaches are still emerging and are not explored in this thesis. Consequently, integrating conventional WSIs and non-spatial omics data through effective fusion strategies becomes essential for uncovering their joint potential. Effective fusion of these modalities holds the potential to reveal rich, cross-modal patterns that help identify signals associated with tumor behavior. But key questions arise: How can we effectively align these heterogeneous modalities (high-resolution images and diverse molecular data) into a unified framework? How can we leverage their interactions to maximize complementary insights? How can we tailor fusion strategies to maximize the strengths of dominant modalities across diverse clinical tasks? This thesis tackles these questions head-on, advancing integrative healthcare by developing novel deep multi-modal fusion methods. Our primary focus is on integrating the aforementioned key modalities, proposing innovative approaches to enhance omics–WSI fusion in cancer research. While the downstream applications of these methods span diagnosis, prognosis, and treatment stratification, the core contribution lies in the design and evaluation of fusion strategies that effectively harness the complementary strengths of each modality. Our research develops a multi-modal fusion method to enhance cross-modality interactions between WSIs and omics data, using advanced architectures to integrate their heterogeneous feature spaces and produce discriminative representations that improve cancer grading accuracy. These methods are flexibly designed and can be applied to fuse data from diverse sources across various application domains; however, this thesis focuses primarily on cancer-related tasks. We also introduce cross-modal attention mechanisms to refine feature representation and interpretability, functioning effectively in both single-modality and bimodal settings, with applications in breast cancer classification (using mammography, MRI, and clinical metadata) and brain tumor grading (using WSIs and gene expression data). Additionally, we propose dual fusion strategies combining early and late fusion to address challenges in omics-WSI integration, such as explainability and high-dimensional omics data, aligning omics with localized WSI regions to identify tumor subtypes without patch-level labels, and capturing global interactions for a holistic perspective. We deliver three key contributions: the Multi-modal Outer Arithmetic Block (MOAB), a novel fusion method integrating latent representations from WSIs and omics data using arithmetic operations and a channel fusion technique, achieving state-of-the-art brain cancer grading performance with publicly available code; the Flattened Outer Arithmetic Attention (FOAA), an attention-based framework extending MOAB for single- and bimodal tasks, surpassing existing methods in breast and brain tumor classification; and the Multi-modal Outer Arithmetic Block Dual Fusion Network (MOAD-FNet), combining early and late fusion for explainable omics-WSI integration, outperforming benchmarks on The Cancer Genome Atlas (TCGA) and NHNN BRAIN UK datasets with interpretable WSI heatmaps aligned with expert diagnoses. Together, these contributions provide reliable, interpretable, and adaptable solutions for the multi-modal fusion domain, with a specific focus on advancing diagnostics, prognosis, and personalized healthcare strategies while addressing the critical questions driving this field forward.13 0Item Restricted Enhancing Gravitational-Wave Detection from Cosmic String Cusps in Real Noise Using Deep Learning(Saudi Digital Library, 2025) Taghreed, Bahlool; Patrick, SuttonCosmic strings are topological defects that may have formed in the early universe and could produce bursts of gravitational waves through cusp events. Detecting such signals is particularly challenging due to the presence of transient non-astrophysical artifacts—known as glitches—in gravitational-wave detector data. In this work, we develop a deep learning-based classifier designed to distinguish cosmic string cusp signals from common transient noise types, such as blips, using raw, whitened 1D time-series data extracted from real detector noise. Unlike previous approaches that rely on simulated or idealized noise environments, our method is trained and tested entirely on real noise, making it more applicable to real-world search pipelines. Using a dataset of 50,000 labeled 2-second samples, our model achieves a classification accuracy of 84.8% , recall 78.7% and false-positive rate 9.1% on unseen data. This demonstrates the feasibility of cusp-glitch discrimination directly in the time domain, without requiring time-frequency representations or synthetic data, and contributes toward robust detection of exotic astrophysical signals in realistic gravitational-wave conditions.13 0Item 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.11 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 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 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 Human Action Recognition Based on Convolutional Neural Networks and Vision Transformers(University of Southampton, 2025-05) Alomar, Khaled Abdulaziz; Xiaohao, CaiThis thesis explores the impact of deep learning on human action recognition (HAR), addressing challenges in feature extraction and model optimization through three interconnected studies. The second chapter surveys data augmentation techniques in classification and segmentation, emphasizing their role in improving HAR by mitigating dataset limitations and class imbalance. The third chapter introduces TransNet, a transfer learning-based model, and its enhanced version, TransNet+, which utilizes autoencoders for improved feature extraction, demonstrating superior performance over existing models. The fourth chapter reviews CNNs, RNNs, and Vision Transformers, proposing a novel CNN-ViT hybrid model and comparing its effectiveness against state-of-the-art HAR methods, while also discussing future research directions.33 0Item Restricted Rasm: Arabic Handwritten Character Recognition: A Data Quality Approach(University of Essex, 2024) Alghamdi, Tawfeeq; Doctor, FaiyazThe problem of AHCR is a challenging one due to the complexities of the Arabic script, and the variability in handwriting (especially for children). In this context, we present ‘Rasm’, a data quality approach that can significantly improve the result of AHCR problem, through a combination of preprocessing, augmentation, and filtering techniques. We use the Hijja dataset, which consists of samples from children from age 7 to age 12, and by applying advanced preprocessing steps and label-specific targeted augmentation, we achieve a significant improvement of a CNN performance from 85% to 96%. The key contribution of this work is to shed light on the importance of data quality for handwriting recognition. Despite the recent advances in deep learning, our result reveals the critical role of data quality in this task. The data-centric approach proposed in this work can be useful for other recognition tasks, and other languages in the future. We believe that this work has an important implication on improving AHCR systems for an educational context, where the variability in handwriting is high. Future work can extend the proposed techniques to other scripts and recognition tasks, to further improve the optical character recognition field.72 0Item Restricted Enhance Deep Learning for Cybersecurity Challenges in Software-Defined Networks(University of Warwick, 2024-09) Alsaadi, Sami; Leeson, Mark and Lakshminarayana, SubhashTraditional network devices, such as a router or switch, incorporate the control plane and the data plane. IT operators independently set traffic policies on each device. Nonetheless, this architectural setup raises operational expenses and complicates the dynamic adaptation and maintenance of secure network configurations. Software-defined Networking (SDN) represents a revolutionary approach to network management, offering enhanced flexibility. SDN promotes rapid innovation in networking by centralizing control and making it programmable. However, security concerns pose significant barriers to the broader adoption of SDN, as this new architecture potentially opens novel attack vectors previously non-existent or more challenging to exploit. Machine Learning (ML) strategies for SDN security rely heavily on feature engineering, requiring expert knowledge and causing delays. Therefore, enhancing intrusion detection is essential for protecting SDN architectures against diverse threats. The thesis develops techniques for detecting malicious activities in SDN using Deep Learning DL. It starts by evaluating CNNs on an SDN dataset, leading to a new CNN-based detection approach that employs a novel regularization method to reduce kernel weights and address overfitting, improving effectiveness against unrecognized attacks. Additionally, a semi-supervised learning method using an LSTM autoencoder combined with One Class SVM is introduced, specifically designed to detect DDoS attacks. This approach enhances the detection capabilities within SDN environments, showcasing the potential of DL in advancing network security.31 0