SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Features Selection Strategies for Classifying Heterogeneous Cardiovascular Disease Data(University of Liverpool, 2025-05) Aldosari, Hanadi; Conene, Frans; Zheng, YalinThe exponential growth of data across diverse domains has presented significant challenges, particularly in the integration and analysis of multi-modal datasets. This thesis addresses these challenges by proposing a Homogeneous Feature Vector Representation (HFVR) framework, designed to unify disparate data formats and facilitate holistic machine learning. The primary application of this research is the classification of Cardiovascular Disease (CVD), using data from multiple sources, including time series, images, video, and clinical records. The main research question guiding this thesis is: How can multiple heterogeneous data sources be effectively combined to support comprehensive and integrated machine learning analysis? The contributions of this work are twofold: (i) the development of technical methodologies for feature extraction, and (ii) the application of these methods within a medical context to enhance CVD diagnosis and prognosis. Five feature extraction techniques are presented to address the complexities of multi-modal data integration: 1-1D Motifs and Discords (1D-MD): This technique uses matrix profiles to extract recurring patterns (motifs) and anomalies (discords) from ECG time series data. It serves as a benchmark for classification models. 2-2D Motifs and Discords (2D-MD): This technique operates directly on ECG images to extract spatial motifs and discords, offering improvements in classification performance over 1D-MD. 3-2D Convolutional Neural Networks (2D-CNN): Pre-trained CNNs like ResNet-50 and VGG16 are used to extract hierarchical features from ECG images, significantly improving classification accuracy when integrated into the HFVR framework. 4-Multi-Frame 2D CNN (MF2D-CNN): Designed for video data, this method processes frames using CNNs and applies temporal aggregation to capture dynamic patterns while maintaining computational efficiency. 5-Spatio-Temporal 3D CNN (ST3D-CNN): Building on MF2D-CNN, this approach uses 3D convolutions to jointly analyze spatial and temporal dynamics in Echo video data. The research also includes the development of a bespoke, multi-modal dataset in collaboration with the Liverpool Heart and Chest Hospital (LHCH), combining ECG, Echo, and clinical data. This dataset was used to evaluate the proposed methods and demonstrate their real-world applicability. The HFVR framework outperformed single-modality approaches by integrating features from multiple data sources. Extensive experiments were conducted on public datasets (CPSC, GHS, GAF, eCAN, and dCAN) and the LHCH dataset. Evaluation metrics such as Accuracy, Precision, Recall, F1-score, and AUC were used, with ten-fold cross-validation and stratified sampling ensuring robustness. Traditional classifiers like Support Vector Machines (SVMs) and k-Nearest Neighbour (kNN) were also used to validate the HFVR framework. Results showed that combining MF2D-CNN with clinical and 2D-CNN features achieved the highest AUC of 93.3%, significantly outperforming baseline methods. Statistical analysis confirmed the robustness and scalability of the techniques. Overall, this thesis advances multi-modal data integration by presenting a unified framework for feature extraction and fusion. The HFVR framework paves the way for holistic machine learning and improved predictive accuracy. While focused on CVD classification, the techniques are generalizable to other domains. Future work will explore real-time implementation, enhanced extraction methods, and expansion to additional data types and domains, representing a major step toward scalable, integrated machine learning systems.12 0Item Restricted Enhancing Lip Synchronization in Deep Learning Models: An Evaluation of Supplementary Metrics for Wav2Lip Performance Optimization(Queen Mary University of London, 2025) Almelabi, Mohammed; Naich, Ammar YasirThe technology of lip synchronization aims at lip movements in videos with corresponding audio and has proven itself to be extremely useful in multimedia applications. The Wav2Lip model leverages deep learning to achieve high-quality lip-syncing videos that have become a leading approach in this field. This paper investigates the use of different evaluation metrics in assessing the performance of the Wav2Lip model. The purpose of this analysis is to improve the loss metric in training the loss function in training the model and provide insights into improving the development of lip synchronization models for more realistic results.14 0Item Restricted Intelligent Diabetes Screening with Advanced Analytics(University of Birmingham, 2024) Aldossary, Soha; Smith, PhillipDiabetes mellitus is a prevalent chronic disease with significant health implications worldwide. This project aimed to mitigate this pressing public health concern by using machine learning techniques and deep learning algorithms. I also established an online platform at which patients can enter their test results and health information and receive real-time diabetes detection and dietary recommendations based on their health profiles. Research has illustrated that models such as Gradient Boosting, Random Forest and Decision Trees perform well in diabetes prediction due to their ability to capture complex nonlinear relationships and handle diverse input features. Therefore, this project incorporated these models with others, such as the Support Vector Classifier and AdaBoost. Additionally, deep learning models, including Neural Networks, were utilised to explore intricate relationships within diabetes-related indicators. Notably, the Gradient Boosting model achieved an impressive accuracy of 99%, with 99% precision, 97% recall and 97% F1-score. To implement these solutions, I used Python as the programming language, employing libraries such as scikit-learn, NumPy, Pandas and Matplotlib, while Streamlit served as the app’s framework.18 0Item Restricted Facial Emotion Recognition via Label Distribution Learning and Customized Convolutional Layers(The University of Warwick, 2024-11) Almowallad, Abeer; Sanchez, VictorThis thesis attempts to investigate the task of recognizing human emotions from facial expressions in images, a topic that has been interest of to researchers in computer vision and machine learning. It addresses the challenge of deciphering a mixture of six basic emotions—happiness, sadness, anger, fear, surprise, and disgust—each presented with distinct intensities. This thesis introduces three Label Distribution Learning (LDL) frameworks to tackle this. Previous studies have dealt with this challenge by using LDL and focusing on optimizing a conditional probability function that attempts to reduce the relative entropy of the predicted distribution with respect to the target distribution, which leads to a lack of generality of the model. First, we propose a deep learning framework for LDL, utilizing convolutional neural network (CNN) features to broaden the model’s generalization capabilities. Named EDL-LBCNN, this framework integrates a Local Binary Convolutional (LBC) layer to refine the texture information extracted from CNNs, targeting a more precise emotion recognition. Secondly, we propose VCNN-ELDL framework, which employs an innovative Visibility Convolutional Layer (VCL). The VCL is engineered to maintain the advantages of traditional convolutional (Conv) layers for feature extraction, while also reducing the number of learnable parameters and enhancing the capture of crucial texture features from facial images. Furthermore, this research presents a novel Transformer architecture, the Visibility Convolutional Vision Transformer (VCLvT), incorporating Depth-Wise Visibility Convolutional Layers (DepthVCL) to bolster spatial feature extraction. This novel approach yields promising outcomes, particularly on limited datasets, showcasing its capacity to meet or exceed state-of-the-art performance across different dataset sizes. Through these advancements, the thesis significantly contributes to the advancement of facial emotion recognition, presenting robust, scalable models adept at interpreting the complex nuances of human emotions.17 0Item Restricted Optimizing Deep Learning Architectures for Enhanced Breast Cancer Detection on Mammography Images(University of Liverpool, 2024) Albalawi, Alaa; Anosova, OlgaBreast cancer is a major health issue affecting millions of women globally, and early detection through mammography is critical for improving survival rates. However, mammography often faces challenges, such as imbalanced datasets and poor image quality, especially in dense breast tissue, which complicates accurate detection. This project explores the use of deep learning techniques, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), to address these challenges and enhance breast cancer detection. Five models—ResNet50V2, MobileNetV2, VGG16, ResNet from scratch, and ViT—were compared using various evaluation metrics. Two datasets, RSNA and MIAS, were used, with preprocessing applied only to the RSNA dataset. The experiments were divided into three stages: the first stage evaluated the original RSNA dataset without preprocessing, the second stage tested the balanced and preprocessed RSNA dataset with and without data augmentation, and the third stage applied similar experiments on the MIAS dataset. The results showed that preprocessing and balancing the RSNA dataset significantly improved model performance, while data augmentation further enhanced accuracy and generalization. ViT models outperformed other CNN architectures, demonstrating superior detection abilities after augmentation. ResNet from scratch also showed strong results, benefiting from its controlled architecture that adapted well to high-resolution images. This study highlights how addressing class imbalance and optimising model architectures can lead to more effective breast cancer detection using deep learning.18 0Item Restricted Using Semantic Richness for Metaphor Detection using Deep Learning(University of Birmingham, 2024) Alnafesah, Ghadi; Lee, MarkABSTRACT The Natural Language Processing (NLP) encounters difficulties with metaphors, known for their creative and non-literal usage. Metaphors involve using words or phrases from one context in entirely different contexts, making the meaning less clear and requiring human interpretation for understanding. This dissertation places its focus on the semantic richness elements derived from the perceptual part of the semantic network. These elements serve as the main linguistic features integrated into vector representations. By extracting the semantic information encompassing concreteness, imageability, sensory experience, sentiment, and embodiment, this study seeks to explore the feasibility of detecting metaphors using deep learning models. The investigation is conducted using two experimental structures: sentence-level classification for the categorisation of entire sentences and word-level classification for individual words. These models are assessed across three metaphorical datasets: VUAMC, MOH-X, and TroFi. The main objective is to evaluate the impact of these semantic elements on the metaphor detection task, with the potential for enhancing model performance.35 0Item Restricted An Exploration of Word Embedding Models for Phishing Email Detection(University of Southampton, 2023-09-21) Alghamdi, Rawan; Hewitt, SarahPhishing emails are dangerous cyberattacks that attackers use to steal information. Manual solutions such as blacklists can be used to detect phishing emails. However, The emergence of machine learning solutions has made phishing email detection faster and easier. This study explored and compared the performance of three deep learning models for detecting text-based phishing emails. The models used different word embedding techniques: Word2Vec, FastText, and GloVe. All three models used a Long Short-Term Memory (LSTM) classifier. Two publicly available datasets were merged to create a balanced dataset of phishing and legitimate emails using only the body text of the emails, excluding the header. The first dataset is the Fraudulent E-mail Corpus - Nigerian Letter or ”419” Fraud, which contains phishing emails. The second dataset is the Enron Email Dataset, which contains legitimate emails. The Word2Vec- LSTM model achieved the best performance, with an F1 score of 98.62% and an accuracy of 98.62%. The FastText-LSTM also performed well, but its performance was slightly lower than the Word2Vec-LSTM model, with an F1 score of 95.73% and an accuracy of 95.73%. The GloVe-LSTM model performed poorly, with an F1 score of 55.79% and an accuracy of 60.53%. We therefore conclude that using different embedding techniques with the same classifier can result in different performances for detecting and classifying phishing and legitimate emails.82 0Item Restricted Utilising Technical Analysis, Commodities Data, and Market Indices to Predict Stock Price Movements with Deep Learning(Cardiff University, 2024) Aloraini, Osama Mohammed A; Sun, XianfangThis study investigates the efficacy of deep learning models, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), for forecasting stock price movements in the U.S. stock market. The dataset used includes 133 stocks across 19 different sectors and covers the period from 2010 to 2023. Moreover, to enrich the dataset, eleven technical indicators and their corresponding trading strategies, represented as vectors, were integrated along with market indices and commodities data. Consequently, various experiments were conducted to assess the effectiveness of different feature combinations. The findings reveal that the CNN model outperforms the LSTM model in both accuracy and profitability, achieving the highest accuracy of 59.7%. Furthermore, models demonstrated an ability to identify significant trend-changing points in stock price movements. Another finding shows that translating trading strategies into vector form plays a critical role in enhancing the performance of both models. However, it was observed that incorporating external features like market indices and commodities data led to model overfitting. Conversely, relying only on stock-specific features triggered a risk of model underfitting.64 0Item Restricted Enhancing Network Intrusion Detection using Hybrid Machine Learning and Deep Learning Approaches: A Comparative Analysis with the HIKARI-2021 Dataset(Saudi Digital Library, 2023-11-09) Alkhanani, Doaa; Batten, IanThis thesis presents an in-depth analysis of machine learning (ML) and deep learning (DL) methodologies for network intrusion detection, utilizing the HIKARI-2021 dataset. By leveraging models such as Random Forest, XG Boost, LSTM, and GRU, the study aimed to identify and classify malicious activities within network traffic. The models' performance was assessed primarily based on accuracy, as well as confusion matrix evaluations. Preliminary results indicate Random Forest achieved an accuracy of 93.77%, XG Boost attained 93.02%, LSTM reached 92.48%, and GRU reported 92.50%. These results were then compared to benchmark models from the literature, which achieved accuracies ranging from 98% to 99%. Through this comparative analysis, the research emphasizes the strengths, weaknesses, and the potential of each model in real-world scenarios. Notably, while the employed models showcased commendable performance, benchmark models exhibited slightly superior results, suggesting further room for model optimization and feature engineering. This research offers valuable insights into the evolving landscape of network security and sets the stage for further exploration in enhancing intrusion detection mechanisms.103 0Item Restricted Applications of Hyper-parameter Optimisations for Static Malware Detection(Saudi Digital Library, 2023-05-30) ALgorain, Fahad; Clark, JohnMalware detection is a major security concern and a great deal of academic and commercial research and development is directed at it. Machine Learning is a natural technology to harness for malware detection and many researchers have investigated its use. However, drawing comparisons between different techniques is a fraught affair. For example, the performance of ML algorithms often depends significantly on parametric choices, so the question arises as to what parameter choices are optimal. In this thesis, we investigate the use of a variety of ML algorithms for building malware classifiers and also how best to tune the parameters of those algorithms – a process generally known as hyper-parameter optimisation (HPO). Firstly, we examine the effects of some simple (model-free) ways of parameter tuning together with a state-of-the-art Bayesian model-building approach. We demonstrate that optimal parameter choices may differ significantly from default choices and argue that hyper-parameter optimisation should be adopted as a ‘formal outer loop’ in the research and development of malware detection systems. Secondly, we investigate the use of covering arrays (combinatorial testing) as a way to combat the curse of dimensionality in Gird Search. Four ML techniques were used: Random Forests, xgboost, Light GBM and Decision Trees. cAgen (a tool that is used for combinatorial testing) is shown to be capable of generating high-performing subsets of the full parameter grid of Grid Search and so provides a rigorous but highly efficient means of performing HPO. This may be regarded as a ‘design of experiments’ approach. Thirdly, Evolutionary algorithms (EAs) were used to enhance machine learning classifier accuracy. Six traditional machine learning techniques baseline accuracy is recorded. Two evolutionary algorithm frameworks Tree-Based Pipeline Optimization Tool (TPOT) and Distributed Evolutionary Algorithms in Python (Deap) are compared. Deap shows very promising results for our malware detection problem. Fourthly, we compare the use of Grid Search and covering arrays for tuning the hyper-parameters of Neural Networks. Several major hyper-parameters were studied with various values and results. We achieve significant improvements over the benchmark model. Our work is carried out using EMBER, a major published malware benchmark dataset of Windows Portable Execution (PE) metadata samples, and a smaller dataset from kaggle.com (also comprising of Windows Portable Execution metadata). Overall, we conclude that HPO is an essential part of credible evaluations of ML-based malware detection models. We also demonstrate that high-performing hyper-parameter values can be found by HPO and that these can be found efficiently.6 0