Saudi Cultural Missions Theses & Dissertations
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Item 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.12 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.10 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.33 0Item Restricted Modelling Efficient and Robust Solutions for Microbiology Image Analysis Using Deep Learning(The University of Queensland, 2024-06) Alhammad, Sarah; Lovell, BrianMicroscopic image analysis plays a crucial role in clinical microbiology laboratories for diagnostic purposes. Highly skilled microbiologists, also known as pathologists, are required to interpret vari- ous images, including Gram stain smears. These samples contain vital diagnostic information, such as identifying the presence and types of bacteria, evaluating specimen quality, and cell counting. However, manual interpretation of conventional glass microscopy slides remains a time-consuming, labour-intensive, and operator-dependent process. In high-volume pathology laboratories, implement- ing an artificial intelligent system could offer significant advantages by alleviating limitations faced by conventional pathology on a larger scale. Such a system would ensure enhanced accuracy, reduced workload for pathologists, and improved objectivity and efficiency. Consequently, this has motivated the research using data-driven techniques to develop automated interpretations of pathology images, particularly focusing on Gram stains. With the vast development and advancement in computer vision techniques, researchers have been able to explore the realm of Computer-Aided Diagnoses (CAD). The emergence of deep learning has revolutionised the analysis of pathology and medical images, moving away from traditional handcrafted features to leveraging the power of deep learning algorithms. Among these algorithms, Convolutional Neural Networks (CNNs) have demonstrated their ability to learn features from datasets, leading to enhanced performance and increased robustness of classifiers and detectors against variations Despite the extensive literature on pathology images, the automatic analysis of the Gram stain test using CNNs has not gained the same level of attention as other pathology tests such as breast cancer, lymphoma and colorectal cancer. It is exceedingly rare to find datasets relating to the very important Gram stain, and this data scarcity has likely hindered research on Gram stain automation and limited research in this area. This thesis aims to apply deep learning techniques to analyse pathology images, with a specific focus on Gram stain data. The aim is to discover novel approaches that can enhance the accuracy and efficiency of Gram stain analysis, bridging the gap in research and paving the way for advancements in this critical area. Initially, a CNN-based classifier was proposed for Gram-positive cocci bacteria subtypes in blood cultures. Throughout the study, the effect of downsampling, data augmentation, and image size on classification accuracy and speed was studied. To conduct these experiments, a novel dataset provided by Sullivan Nicolaides Pathology (SNP) consisting of three distinct bacteria subtypes, namely Staphylococcus, Enterococcus and Streptococcus were used. The sub-images were obtained from blood culture WSIs captured by the in-house SNP MicroLab using a ×63 objective without coverslips or oil immersion. The results show that a CNN-based classifier distinguishes between these bacteria subtypes with high classification accuracy. Secondly, existing CNN classification backbones operate under the assumption that all testing classes have been encountered during model training. However, in certain scenarios, it may be infeasible to collect all bacteria subtypes during the model training phase. CNNs are incapable of estimating their uncertainty, and they assume full knowledge of the world. To avoid misdiagnosis risk in the bacteria classification task, OpenGram a framework to open CNN classifier was proposed in this study that aims to tackle the problem of bacteria subtyping from an open-set perspective. Open-set recognition models can classify known instances and detect unknown samples of novel classes. OpenGram combines a CNN classifier with a Gaussian mixtures model to adapt to open-set classification. The results demonstrate OpenGram’s efficacy in accurately detecting unknown bacteria classes that were not encountered by the network during training, while maintaining the ability to classify known bacteria classes. Thirdly, most deep learning-based object detection methods rely on the availability of large sets of annotated training data, assuming that both training and testing data belong to the same feature space. However, these assumptions may not always hold true in real-world applications, particularly in the domain of pathology images. The process of collecting annotations for pathology images can be costly and labor-intensive. Additionally, testing supervised models on different distributions can degrade detector performance as these models might not be properly generalised to other domains. The objective was to tackle this lack of instance-level cell labels in Gram stain WSIs for the epithelial and leukocyte cell counting task. HybridGram, a framework with image translation and pseudo- labelling modules to completely avoid manual labelling on a new dataset was presented. The results demonstrate that HybridGram effectively bridges the performance gap between fully supervised and unsupervised models in this context.26 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.46 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.79 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