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Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667

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A Peer-to-Peer Federated Learning Framework for Intrusion Detection in Autonomous Vehicles
(Lancaster University, 2024-09) Alotaibi, Bassam; Bradbury, Matthew
As autonomous vehicles (AVs) increasingly rely on interconnected systems for enhanced functionality, they also face heightened cyberattack vulnerability. This study introduces a decentralized peer-to-peer federated learning framework to improve intrusion detection in AV environments while preserving data privacy. A novel soft-reordering one-dimensional Convolutional Neural Network (SR-1CNN) is proposed as the detection engine, capable of identifying known and unknown threats with high accuracy. The framework allows vehicles to communicate directly in a mesh topology, sharing model parameters asynchronously, thus eliminating dependency on centralized servers and mitigating single points of failure. The SR-1CNN model was tested on two datasets: NSL-KDD and Car Hacking, under both independent and non-independent data distribution scenarios. The results demonstrate the model’s robustness, achieving detection accuracies of 94.39% on the NSL-KDD dataset and 99.97% on the Car Hacking dataset in independent settings while maintaining strong performance in non-independent configurations. These findings underline the framework’s potential to enhance cybersecurity in AV networks by addressing data heterogeneity and preserving user privacy. This research contributes to the field of AV security by offering a scalable, privacy-conscious intrusion detection solution. Future work will focus on optimizing the SR-1CNN architecture, exploring vertical federated learning approaches, and validating the framework in real-world autonomous vehicle environments to ensure its practical applicability and scalability.
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Enhancing a Hyper-parameter Tuning of Convolutional Neural Network Model for Brain Tumor Classification using Whale Optimization and Grey Wolf Optimizer
(Newcastle University, 2024) Alkhudair, Haifa; Freitas, Leo
Brain tumors represent a global health issue, with about 11 new cases per 100,000 people annually. Therefore, it is crucial to develop faster and more accurate diagnostic solutions. This study develops and evaluates a convolutional neural network (CNN) model optimized using the Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) for classifying brain tumors. To achieve that, this work involved collecting and preprocessing an MRI brain tumor dataset, followed by building and training CNN models. Hyperparameters were optimized using WOA and GWO, and the performance of these optimized mod- els was compared against a non-optimized CNN. The WOA-optimized CNN outperformed both the non-optimized and GWO-optimized mod- els, achieving an accuracy of 93.4% and demonstrating superior general- ization across different classes. This study underscores the effectiveness of WOA in enhancing CNN models for medical image classification, of- fering promising approaches to enhancing the accuracy and reliability of brain tumor classification
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Generative AI for Mitosis Synthesis in Histopathology Images
(University of Surrey, 2024-09) Alkhadra, Rahaf; Rai, Taran; Wells, Kevin
Identifying mitotic figures has been established as an effective method of fighting cancer at its most vulnerable stage. Traditional methods rely on manual, slow, and invasive detection methods obtained from sectioned tissue samples to acquire histopathological images. Currently, Artificial Intelligence (AI) in oncology has produced a paradigm shift in the fight against cancer, also known as computational oncology. This is heavily reliant on the availability of mitotic figure datasets to train models; however, such datasets are limited in size, type, and may infringe on patient privacy. It is hypothesised that the potential of computational oncology can be realised by synthesising realistic and diverse histopathological datasets using Generative Artificial Intelligence (GenAI). This report demonstrates a comparison of Denoising Probabilistic Diffusion Models (DDPM) and StyleGAN3 in generating synthetic histopathology images, with mitotic figures. The MIDOG++ dataset containing human and canine samples with 7 types of tumours was used to train the models. Quality and similarity of generated and real images was evaluated using as Frechet Inception Distance (FID), Mean Square Error (MSE), Structural Similarity Index (SSIM), and Area Under the Curve (AUC) as a part of Receiver Operating Characteristic (ROC) study were incorporated. Our results suggests that the DDPM model is superior in terms of structural accuracy, however, StyleGAN3 capture the colour scheme better.
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Automated Pain Assessment Through Facial Expression Using Deep Learning and Image Processing
(University of Reading, 2024-09-13) Alsama, Morady; Patino, Luis
As pain is an unavoidable part of life, this study examines the use of facial expression tech nology in assisting individuals with pain. Accurate pain assessment in health care is essential, especially for non-verbal patients, since conventional methods largely fail because of the in herent subjectivity and self-reporting. Therefore, the present study develops and evaluates an automated pain assessment system through advanced analysis of facial expressions driven by contemporary deep learning techniques. It aims to generate a reliable and unbiased system for detecting and classifying pain intensity. A CNN-based system was developed using base models that apply ResNet-18 and ResNext-50 architectures. A custom-designed final layer was added to optimize classification accuracy, tailored explicitly for pain detection. Comprehensive data preprocessing strategies were used in the model to make it robust; it involved downsam pling and augmentation of the data. It was trained and validated on the UNBC-McMaster Shoulder Pain Expression Archive Database and the Radboud Faces Database, showing an impressive accuracy of over 90% on the training data. However, generalizing the models to unseen validation and test data proved challenging. These findings further articulate the crit ical imperative of enhancing generalisability across diverse patient populations for the system to perform effectively in real-world settings. The results underline the huge potential for deep learning in the automation of pain assessment, while future research remains on better mod eling generalization, promoting integration in clinical settings for a more objective, reliable, and consistent approach to pain management in health care settings.
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Enhancing DDoS attack Detection using Machine Learning and Deep Learning Models
(University of Warwick, 2023-09-26) AlObaidan, Fatimah; Raza, Hassan
Technology has become an essential part of our daily lives, indispensable for both individuals and enterprises. It facilitates the exchange of an extensive range of information across different spaces. However, Internet security is a critical challenge in today's digital age with growing dependence on IT services. Thus, various network environments can be vulnerable to attacks, causing resource depletion and hindering support for legitimate users. One of these attacks is the Distributed Denial of Service (DDoS) attack. The nature of this type of attack is such that it impacts the availability of the system. The impact to confidentiality is primary due to threat actors using DDoS as method to create chaos whilst lunching cyber-attacks on other part of infrastructures. Therefore, it is essential that DDoS attacks required sharper focus from a research perspective. The network intrusion detection system (NIDSs) are important tool to detect and monitor the network environment from DDoS attacks. However, NIDS tools suffer from several limitation such as detecting new attack and misclassified attacks. Therefore, Machine Learning (ML) and Deep Learning (DL) models are increasingly being used for automated detection of DDoS attacks. While several related works deployed ML for NIDS, most of these approaches ignore the appropriate pre-processing and overfitting problem during the implementation of ML algorithms. As a result, it can impact the robustness of the anomaly detection system and lead to poor model performance for zero-day attacks. In this research study, the researcher is proposing a new ML and DL approach based on hybrid feature selection and appropriate pre-processing operation to classify the network flow into normal or DDoS attacks. The results of the experiments carried out by researcher suggest the efficiency and the reliability of the proposed lightweight models in achieving high detection rate while minimising the detection time with less number of features. This project complies with following two CyBOK Skills areas: Network Security: The project evaluates the network security and introduces efficient, lightweight models for DDoS attack detection. Security Operations and Incident Management: The project enhances incident management capabilities by crafting ML that monitors network flows within NIDS.
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Assessing artificial intelligence MRI autocontouring in Raystation and the AutoConfidence uncertainty model for brain radiotherapy
(The University of Leeds, 2024-10) Alzahrani, Nouf; Henry, Ann; Nix, Michael; Murray, Louise; Al-qaisieh, Bashar
Abstract: Background: In radiotherapy, deep learning autosegmentation (DL-AS) and automation of quality assurance (QA) have the potential to efficiently standardize and enhance the quality of contours. Aim: To assess the performance of DL-AS in delineating organs-at-risk (OARs) in brain RT using the RayStation Treatment Planning System. Secondly, to build and test a novel artificial intelligence QA model called AutoConfidence (ACo). Methods: Retrospective MRI and CT cases were randomly selected for training and testing. DL-AS models were evaluated from geometric and dosimetric perspectives, focusing on the impact of pre-training editing. The ACo model was evaluated using two sources of autosegmentation: internal autosegmentations (IAS) produced from the ACo generator and two external DL-AS with different qualities (high and low quality) produced from RayStation models. Results: The edited DL-AS models generated more segmentations than the unedited models. Editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AS performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the MR and CT DL-AS models. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than for other OARs, for all models. ACo had excellent performance on both internal and external segmentations across all OARs (except lenses). Mathews Correlation Coefficient was higher on IAS and low-quality external segmentations than high-quality ones. Conclusion: MRI DL-AS in RT may improve consistency, quality, and efficiency but requires careful editing of training contours. ACo was a reliable predictor of uncertainty and errors on DL-AS, demonstrating its potential as an independent, reference-free QA tool.
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A Quality Model to Assess Airport Services Using Machine Learning and Natural Language Processing
(Cranfield University, 2024-04) Homaid, Mohammed; Moulitsas, Irene
In the dynamic environment of passenger experiences, precisely evaluating passenger satisfaction remains crucial. This thesis is dedicated to the analysis of Airport Service Quality (ASQ) by analysing passenger reviews through sentiment analysis. The research aims to investigate and propose a novel model for assessing ASQ through the application of Machine Learning (ML) and Natural Language Processing (NLP) techniques. It utilises a comprehensive dataset sourced from Skytrax, incorporating both text reviews and numerical ratings. The initial analysis presents challenges for traditional and general NLP techniques when applied to specific domains, such as ASQ, due to limitations like general lexicon dictionaries and pre-compiled stopword lists. To overcome these challenges, a domain-specific sentiment lexicon for airport service reviews is created using the Pointwise Mutual Information (PMI) scoring method. This approach involved replacing the default VADER sentiment scores with those derived from the newly developed lexicon. The outcomes demonstrate that this specialised lexicon for the airport review domain substantially exceeds the benchmarks, delivering consistent and significant enhancements. Moreover, six unique methods for identifying stopwords within the Skytrax review dataset are developed. The research reveals that employing dynamic methods for stopword removal markedly improves the performance of sentiment classification. Deep learning (DL), especially using transformer models, has revolutionised the processing of textual data, achieving unprecedented success. Therefore, novel models are developed through the meticulous development and fine-tuning of advanced deep learning models, specifically Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT), tailored for the airport services domain. The results demonstrate superior performance, highlighting the BERT model's exceptional ability to seamlessly blend textual and numerical data. This progress marks a significant improvement upon the current state-of-the-art achievements documented in the existing literature. To encapsulate, this thesis presents a thorough exploration of sentiment analysis, ML and DL methodologies, establishing a framework for the enhancement of ASQ evaluation through detailed analysis of passenger feedback.
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Novel Deepfakes Detection Strategies: Insights from Prosopagnosia
(Newcastle University, 2024-10) Alanazi, Fatimah; Morgan, Graham
The credibility of audio and video content, which is essential to our perception of reality, is increasingly challenged by advancements in deepfake generation techniques. Existing detection models primarily focus on identifying anomalies and digital artifacts. However, the rapid evolution of technology enables the creation of sophisticated deepfakes that can evade these methods. This thesis investigates the effectiveness of different facial features for deepfake detection in images and face recognition in individuals with prosopagnosia. It examines whether there is a correlation between the facial features prioritized by AI models for deepfake detection and those emphasized in training programs aimed at enhancing face recognition in individuals with prosopagnosia. Additionally, it assesses the impact of occluding each facial feature during training on AI model performance and identifies which facial elements individuals with prosopagnosia find most challenging to recognize. Inspired by research into prosopagnosia, which highlights the importance of internal facial features like the eyes and nose, this study proposes a novel approach to deepfake detection. The methodology involves identifying critical facial features, applying face cut-out techniques to create training images with various occlusions, and evaluating AI models trained on these datasets using EfficientNet-B7 and Xception models. The results indicate that models trained with occluded datasets performed better, with the EfficientNet-B7 model achieving a higher accuracy rate (92%) when core facial elements (eyes and nose) were covered, compared to models trained on datasets without occlusions or with occlusions covering external features. This suggests that focusing on features outside the face’s center improves detection accuracy. The findings also highlight that facial cues beneficial for individuals with prosopagnosia do not uniformly translate to equivalent value for AI models. This research demonstrates that detection systems can be more effective by focusing on a small region of the face, contributing significantly to the improvement of deepfake detection methods and enhancing our understanding of face recognition processes.
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Feature Selection for High Dimensional Healthcare Data
(University of Surrey, 2024-01) Alayed, Abdulrahman; Kouchaki, Samaneh
In today’s digital landscape, researchers frequently encounter the complexity of handling highdimensional datasets. At times, data mining and machine learning methods struggle when confronted with immense datasets, leading to inefficiencies. The presence of extensive raw data with numerous features can negatively impact machine learning algorithms, affecting accuracy, increasing overfitting, and amplifying complexity. This is primarily due to the inclusion of redundant and irrelevant data, which hampers the learning process. However, employing feature selection techniques can effectively address these challenges. By selectively choosing relevant features, these techniques enable machine learning algorithms to operate more efficiently. They contribute to faster training, reduce model complexity, enhance accuracy, and mitigate overfitting issues. The primary objective of this project is to create an automatic variable selection pipeline by choosing the best features among various innovative feature selection techniques. The pipeline incorporates different categories of variable selection methods: Filter methods, Wrapper methods, Embedded methods, and Hybrid Method. The variable selection techniques are applied to the MIMIC-III (Medical Information Mart for Intensive Care) dataset, which is reachable at no cost. This database is well-suited for the project's goals, as it is a centralized database containing details about patients admitted to the critical care unit of a vast regional hospital. The dataset is particularly useful for forecasting the likelihood of death pst-ICU admission during hospital stay. To achieve this goal, the project employs six classification techniques: Logistic Regression (LR), K-nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The project systematically evaluates and compares the model's performance using various assessment metrics.
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Home Monitoring in Interstitial Lung Disease
(University College London, 2024) Althobiani, Malik Abdulmalik; Hurst, John R; Porter, Joanna; Russell, Anne-Marie; Folarin, Amos
Introduction: Interstitial lung disease (ILD) comprises a variety of conditions affecting the parenchyma of the lung, with a diverse incidence. Some patients are prone to rapid progression, while others are susceptible to exacerbations. Forced vital capacity (FVC) is used as an endpoint in clinical trials for novel idiopathic pulmonary fibrosis (IPF) therapies. However, it is often measured every three months, resulting in lengthy monitoring periods to identify meaningful treatment responses or disease trajectories. Home spirometry may enable more regular monitoring, potentially allowing for faster detection of ineffective treatment and reductions in clinical trial size, duration, and cost. Individuals with ILD often experience cough, shortness of breath, anxiety, exercise limitation, and fatigue, impacting their quality-of-life (QoL). Conventional indicators of disease progression, such as pulmonary function tests (PFT), may not completely capture the severity of symptoms experienced by patients. Continuous remote patient monitoring involving more than FVC may provide a more complete and real-time assessment of physiological parameters and symptoms. However, the views of clinicians and patients are poorly understood, as is the feasibility and utility of delivering such an approach. Aim: To systematically gather, summarise and evaluate the evidence from clinical trials for feasibility, reliability, and detection of exacerbations and/or disease progression in patients with ILD. To understand the views of clinicians and patients about home monitoring in patients with ILD. To investigate the feasibility and utility of a 4 contemporary approach to patient care using commercially available technology to detect disease progression in patients with ILD through continuous monitoring of physiological parameters and symptoms. Methods: A systematic review was conducted assessing studies on home monitoring of physiological parameters and symptoms to detect ILD exacerbations and progression. This was followed by an international survey of clinicians to explore their perspectives on using telehealth for remote ILD health care support. A patient survey was then conducted to quantify patients’ use of and experiences with digital devices. These preliminary studies informed the development of the research question and main PhD hypotheses. To test these hypothesis, two subsequent studies were conducted. Firstly, a feasibility study that assessed the feasibility, acceptability, and value of remote monitoring using commercially available technologies over 6 months period. Secondly, a prospective observational cohort study that evaluated a real-time multimodal program using commercially available technology to detect disease progression in patients with ILD through continuous monitoring of physiological parameters and symptoms. Results: The systematic review provided supportive evidence for the feasibility and acceptability of home monitoring in patients with ILD and identified priorities for future research. The findings of the follow-up studies indicated that although health care professionals recognised the potential benefits of home monitoring, their adoption rate was low due to barriers like lack of organisational support, technical issues, and 5 workload constraints. Although the findings of the mixed-methods study have demonstrated that digital devices are widely used among patients with ILD, the views and perspectives regarding the use of these devices is varied. The prospective multi- centre observational cohort study provided evidence supporting the feasibility and acceptability of remote monitoring to capture both subjective and objective data from varied sources in patients with respiratory diseases. The high engagement level observed from the passively collected data suggests the potential value of wearables for long-term, user-friendly remote monitoring in chronic respiratory disease management. The main study is one of the first to employ a comprehensive multimodal remote monitoring system to investigate the potential of home-monitoring to detect progression in patients with ILD. The results demonstrate the potential of multimodal home-monitoring to assess associations between physiological parameters and symptoms with disease progression, and to detect disease progression in patients with ILD. Moreover, the results suggest a strong correlation between hospital and home measurements of forced vital capacity in patients with ILD. Conclusion: Taken collectively, the findings presented in this thesis supports the use of a multimodal home-monitoring system, and the potential role for physiological parameters and symptoms to detect ILD progression. It provides a contemporary, personalised approach to patient management. These results provide a critical initial step towards further evaluating the value of home-monitoring for ILD management. However, larger, longitudinal validation studies are required. Future research could explore the potential of machine learning algorithms on this data for real-time detection of ILD disease progression. Machine learning models could provide early detection of changes in lung function and alert patients and healthcare providers to acute and chronic changes and empower patients to better self-manage their disease. This could allow for timely interventions and more personalised management of ILD.
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