Saudi Cultural Missions Theses & Dissertations

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Facial Emotion Recognition via Label Distribution Learning and Customized Convolutional Layers
(The University of Warwick, 2024-11) Almowallad, Abeer; Sanchez, Victor
This 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.
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The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review
(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, Thomas
This research explores the implementation of IoT-based predictive maintenance within railway systems, focusing on the technologies, cost implications, reliability, safety, and barriers identified in the literature. The study systematically reviews 30 peer-reviewed journals to assess the current state of IoT applications in the railway sector. Critical IoT technologies such as sensors, wireless sensor systems, and edge processing are examined in their role in enhancing predictive maintenance practices. The research highlights significant long-term cost savings associated with IoT adoption, despite high initial implementation costs. Furthermore, the study evaluates how IoT technologies contribute to improved reliability and safety by enabling real-time monitoring and predictive analysis. However, several barriers to widespread adoption are identified, including technical integration challenges, financial constraints, regulatory hurdles, and organisational resistance. The findings underscore the need for a strategic approach that will help tackle all obstacles by realising the benefits of IoT-predictive maintenance in the railway sector. This study offers significant insights for stakeholders, offering a deep understanding of the challenges of IoT-based predictive maintenance in railways. Future research directions are suggested, emphasising the importance of long-term studies, holistic approaches, and the integration of emerging technologies to address the identified barriers.
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Explaining Machine Learning Classifiers For Android Malware Detection
(King's College London, 2024-08-03) Bin Hazzaa, Zaid; Pierazzi, Fabio
The prevalence of Android malware continues to rise, and traditional approaches are proving ineffective against the evolving tactics of direct attacks. Manually inspecting applications is no longer a practical solution. Machine learning has demonstrated success in various domains, and its high performance in Android malware detection positions it to be effectively deployed in real-world scenarios. However, real-world results have yet to align with experimental findings, and the unique requirements of the security field have led to a lack of trust in its practical application. This research aims to address this issue by utilizing best practices for conducting experiments to eliminate experimental bias and employing explanation methods to enhance the transparency and robustness of the classifier. These measures are critical for building trust among security experts, with transparent, learning-based malware detection being a paramount necessity in the security system. Providing thorough explanations is key to informed decision- making. The research utilizes activities, services and receivers feature sets from Drebin feature extraction to explore the significance of feature sets and employs explanation methods to gain deeper insights into the model.
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The Complexity of The System & Decisions Through A Digital Participatory Approach
(UCL, 2024-07) Khayat, Abdulaziz; Philippe, Morel
This report explores the potential of using computational tools to reinterpret the legal text of the city and trace its impact. It discusses the laws, regulations, and decision-making processes to construct cities drifting away from bureaucratic arbitrary existing city governance and decision-making models. To develop such a system for governing and making urban decisions in cities, first, we must understand the nature of cities. Analyzing the city will shed light on the complexity of its components and its nature being a multi-faceted organism. Hence, the first section contains literature reviews of prominent works of major architectural figures to uncover the bureaucratic narrative of architecture. In the second section, multiple approaches to understanding cities are discussed. The third section explores the computational potential of participatory planning. The fourth section explores the concept of text similarity in machine learning and the urban environment. Finally, the last section demonstrates the application of the discussed tools and concepts.
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Evaluation of Use of Artificial Intelligence (AI) and Machine Learning to Practice and Master Colonoscopic Skills.
(University of Dundee, 2024-08) Alshahrani, Norah Abdullah; Tang, Benjie
Abstract Objective The abstract concisely summarizes the research project "Evaluation of Use of Artificial Intelligence (AI) and Machine Learning to Practice and Master Colonoscopic Skills." It outlines the background of flexible colonoscopy, highlighting its importance in diagnosing and treating colorectal diseases. The study emphasizes the potential of VR simulators to provide a safe, controlled training environment. It identifies the need for quantitative data defining the number of procedures required to achieve competence in VR training. The research aims to demonstrate the effect of the use of AI and machine learning in colonoscopy traning.by conducting experiments with novice subjects and collecting and analyzing data. The expected outcome is to provide quantified evidence supporting the use of VR and AI in colonoscopy training, ultimately improving training methods and enhancing patient safety. Methods The methodology of this study involves a mixed approach where novice subjects undergo hands-on training on VR colonoscopy systems. Participants are selected based on specific criteria, and consent is obtained before involvement. The study utilises a VR simulator alongside physical phantom models to ensure comprehensive training. Detailed experimental procedures are followed, including simulation-based training sessions and performance assessments. Data is collected systematically through observation, performance metrics, and feedback and analysed using statistical methods such as SPSS to quantify the proficiency-gain curve and evaluate the effectiveness of VR training in mastering colonoscopic skills. Results This study included colonoscopy examinations performed on eight volunteers four times and compared with four experts who were examined 500 times. The results indicated that the average time taken to complete the procedure varied between (5:03 to 13:10 minutes) and the time to reach the cecum (4:58 to 10:10 minutes), with statistically significant differences between volunteers (P = 0.03) in the time to reach the cecum. The comparison between the expert group and volunteers also showed statistically significant differences between experts and volunteers in some aspects, such as the time taken to reach the cecum (2:22 minutes for experts versus 7:37 minutes for volunteers). Although the percentage of time in which a clear vision was maintained was higher among experts (96.75%) compared to volunteers (92.62%), this percentage among volunteers was also statistically significant, reflecting the importance of training and practice in improving this skill. Conclusion The conclusion of this study indicates that using VR simulators and AI in colonoscopy training significantly enhances skill acquisition, reduces the proficiency-gain curve, and ensures a safer training environment. The data analysis shows a marked improvement in performance among novice subjects trained with VR, validating the effectiveness of this approach. The study provides quantified evidence supporting the integration of VR and AI technologies in medical training programs, suggesting that such methods effective.
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Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review
(University of Manchester, 2024) Almohawis, Alhaitham; Yong, Sin
Abstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.
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Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review
(Queen’s University, Belfast, 2024) Alfaifi, Yahya; Clarke, Susan
Artificial Intelligence (AI) has the potential to significantly improve pre-hospital care, especially in emergency medical services (EMS). However, its current application remains scattered, with varying integration levels across care stages. This scoping review aims to map and assess existing research on AI applications within pre-hospital care without focusing on specific AI technologies, such as machine learning (ML), deep learning (DL), or decision support systems (DSS). The review reflects the current research landscape, capturing how AI is utilised across critical stages such as call-taking, dispatch, and on-scene assessment. Using the framework developed by Arksey and O’Malley (2005), a systematic search was conducted across multiple databases to identify studies relevant to AI in pre-hospital care. The scope was deliberately broad to capture a comprehensive view of the available literature, focusing on identifying areas where further research is needed. The findings indicate that DSS is commonly used to support decision-making in call-taking and dispatch, while more advanced AI applications like ML and DL show potential in predictive analytics and real-time decision-making. However, these technologies are still in their early stages of real-world implementation. This review highlights the gaps in AI research, particularly in the later stages of prehospital care, such as transport and handover. Further exploration is necessary to unlock AI’s full potential in enhancing EMS operations and outcomes.
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Reducing Type 1 Childhood Diabetes in Saudi Arabia by Identifying and Modelling Its Key Performance Indicators
(Royal Melbourne Institute of Technology, 2024-06) Alazwari, Ahood; Johnstone, Alice; Abdollahain, Mali; Tafakori, Laleh
The increasing incidence of type 1 diabetes (T1D) in children is a growing global health concern. Reducing the incidence of diabetes generally is one of the goals in the World Health Organisation’s (WHO) 2030 Agenda for Sustainable Development Goals. With an incidence rate of 31.4 cases per 100,000 children and an estimated 3,800 new cases per year, Saudi Arabia is ranked 8th in the world for number of T1D cases and 5th for incidence rate. Despite the remarkable increase in the incidence of childhood T1D in Saudi Arabia, there is a lack of meticulously carried out research on T1D in children when compared with developed countries. In addition, it is crucial to recognise the critical gaps in current understanding of diabetes in children, adolescents, and young adults, with recent research indicates significant global and sub-national variations in disease incidence. Better knowledge of the development of T1D in children and its associated factors would aid medical practitioners in developing intervention plans to prevent complications and address the incidence of T1D. This study employed statistical, machine learning and classification approaches to analyse and model different aspects of childhood T1D using local case and control data. In this study, secondary data from 1,142 individual medical records (359-377 cases and 765 controls) collected from three cities located in different regions of Saudi Arabia have been used in the analysis to represent the country’s diverse population. Case and control data matched by birth year, gender and location were used to control confounders and create a more robust and clinically relevant model. It is well documented that genetic and environmental factors contribute to childhood T1D so a wide range of potential key performance indicators (KPIs) from the literature were included in this study. The collected data included information on socioeconomic status, potential genetic and environmental factors, and demographic data such as city of residence, gender and birth year. Several techniques, such as cross-validation, hyperparameter tuning and bootstrapping, were used in this study to develop models. Common statistical metrics (coefficient of determination, R-squared, root mean squared error, mean absolute error) were used to evaluate performance for the regression models while for the classification models accuracy, sensitivity, precision, F score and area under the curve were utilised as performance measures. Multiple linear regression (MLR), artificial neural network (ANN) and random forest (RF) models were developed to predict the age at onset of T1D for all children 0-14 years old, as well as for the most common age group for onset, the 5-9 year olds. To improve the performance of the MLR models, interactions between variables were considered. Additionally, risk factors associated with the age at onset of T1D were identified. The results showed that MLR and RF outperformed ANN. The logarithm of age at onset was the most suitable dependent variable. RF outperformed the others for the 5-9 years age group. Birth weight, current weight and current height influenced the age at onset in both age groups. However, preterm birth was significant only in the 0-14 years cohort, while consanguineous parents and gender were significant in the 5-9 age group. Logistic regression (LR), random forest (RF), support vector machine (SVM), Naive Bayes (NB) and artificial neural network (ANN) models were utilised with case and control data to model the development of childhood T1D and to identify its key performance indicators. Full and reduced models were developed to determine the best model. The reduced models were built using the significant factors identified by the individual full model. The study found that full LR had the highest accuracy. Full RF and SVM with a linear kernel also performed well. Significant risk factors identified as being associated with developing childhood T1D include early exposure to cow’s milk, high birth weight, positive family history of T1D and maternal age over 25 years. Poisson regression (PR), RF, SVM and K-nearest neighbor (KNN) were then used to model the incidence of childhood T1D, taking in the identified significant risk factors. The interactions between variables were also considered to enhance the performance of the models. Both full and reduced models were created and compared to find the best models with the minimum number of variables. The full Poisson regression and machine learning models outperformed all other models, but reduced models with a combination of only two out of three independent variables (early exposure to cow’s milk, high birth weight and maternal age over 25 years) also performed relatively well. This study also deployed optimisation procedures with the reduced incidence models to develop upper and lower yearly profile limits for childhood T1D incidence to achieve the United Nations (UN) and Saudi recommended levels of 264 and 339 cases by 2030. The profile limits for childhood T1D then allowed us to model optimal yearly values for the number of children weighing more than 3.5kg at birth, the number of deliveries by older mothers and the number of children introduced early to cow’s milk. The results presented in this thesis will guide healthcare providers to collect data to monitor the most influential KPIs. This would enable the initiation of suitable intervention strategies to reduce the disease burden and potentially slow the incidence rate of childhood T1D in Saudi Arabia. The research outcomes lead to recommendations to establish early intervention strategies, such as educational campaigns and healthy lifestyle programs for mothers along with child health mentoring during and after pregnancy to reduce the incidence of childhood T1D. This thesis has contributed to new knowledge on childhood T1D in Saudi Arabia by: * developing a predictive model for age at onset of childhood T1D using statistical and machine learning models. * predicting the development of T1D in children using matched case-control data and identifying its KPIs using statistical and machine learning approaches. * modeling the incidence of childhood T1D using its associated significant KPIs. * developing three optimal profile limits for monitoring the yearly incidence of childhood T1D and its associated significant KPIs. * providing a list of recommendations to establish early intervention strategies to reduce the incidence of childhood T1D.
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The Role of Artificial Intelligence in Breast Cancer Screening Programmes: A Literature Review and Focus Upon Policy Implications
(The University of Edinburgh, 2024) Alrabiah, Alanoud; Hellowell, Mark
Background: Breast cancer (BC) is a leading cause of morbidity and mortality amongst older women, leading to the introduction of screening programmes to support earlier detection and improved survivability. Current screening programmes rely upon the performance of radiologists in terms of accuracy; however, evidence shows that both under and overdiagnosis means screening also results in harms to some women. Artificial intelligence is then a promising technology for improving the accuracy of mammogram screening. Aim: To describe the potential roles of AI in BC screening, and the potential benefits, limitations and risks in these roles. Methods: PubMed, SCOPUS, and CINAHL were searched. Primary research studies published in English and in the last ten years, investigating the accuracy of AI systems for screening BC, were eligible for review. Evidence was appraised using the CASP (2024) checklists and data analysed narratively. Results: 14 studies were found eligible for review, mostly adopting a retrospective study design or laboratory study design. Roles for AI in BC screening include as a standalone system replacing radiologists entirely, as risk stratification systems used before radiologist readings, or as reader aids. While some studies reported AI systems to be superior, others reported accuracy to be inferior to radiologist readings. Differences in results could be due to variations in AI system or radiologist performance. Conclusion: There is insufficient evidence to support the use of AI in BC screening programmes, and more robust, prospective studies comparing readings from clinical practice are urgently required. Policy must also be implemented to regulate the use of AI until there is sufficient evidence to support its use.
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Exploring Malnutrition in Residential Aged Care: A Study on Nursing Notes using Natural Language Processing and Large Language Models
(University of Wollongong, 2024-03-21) Alkhalaf, Mohammad; Yu, Ping
Population ageing has led to an increasing demand for services for the older people. Residential aged care facilities (RACFs) in Australia provide a range of services for older people who can no longer live independently at home. These include accommodation, personal care, health care services and social and emotional support. Despite efforts for comprehensive care, managing nutrition for older people has been complex in RACFs. Malnutrition has emerged as a prevalent issue within these facilities, raising serious health concerns. Therefore, understanding and addressing malnutrition becomes a critical concern for the Australian government. To date, there has been a reliance on nutrition screening tools to assess older people’s nutritional care needs. Conducting these assessments require adequate healthcare training, and is time consuming, thus are not implemented as frequently as needed to timely uncover the risk of malnutrition for older people. In Australia, the majority of RACFs have established electronic health record (EHRs) system to capture and record care recipients’ information. These include medical diagnosis, regular nursing assessment, weight chart, care plan, periodic review, incident and infection review, and nursing progress report. Therefore, RAC EHRs contain wealth of information that can be mined to support aged care services. The advancement in natural language processing (NLP) technologies, in specific, large language models (LLMs), provides an opportunity to uncover useful insight from the RAC EHRs. Therefore, this PhD research is dedicated to extend NLP technology to the under-studied area RAC, design, implement and evaluate LLM applications in nutrition management among older individuals living in RACFs. It aims to design and develop a sophisticated machine learning framework capable of analysing both structured and unstructured EHR data to gain comprehensive insights into the malnutrition issue. Drawing from literature insights, the study initiates by employing word embedding techniques integrating with cosine similarity and UMLS ontology to extract nutrition- related terms from nursing notes in RACFs. This led to the uncover of language style and terminology used by the practicing nursing and aged care workers in manage nutrition for the older people under their care. Subsequent development of 13 extraction rules identifies relevant notes indicative of malnutrition, forming the basis for a training data set of 2,278 relevant nursing notes, which is utilized in LLM implementation. To enhance the LLM understanding of nursing notes, we randomly selected 500,000 notes for pre-training a domain specific LLM based on the established RoBERTa model. This is followed by fine-tuning the LLM specifically for malnutrition note detection. Achieving an impressive F1-score of 0.96, our model significantly surpassed previous models, ensuring more accurate classification of notes documenting malnutrition. Furthermore, we developed a framework integrating generative LLM, Llama 2, and retrieval augmented generation (RAG) system to extract comprehensive summary information from malnutrition-related notes. This framework demonstrates high accuracy (90%) in identifying malnutrition risk factors from 1,399 notes. It generates detailed summaries about nutrition status from EHRs with 99% of accuracy. Our study reveals a malnutrition prevalence rate of approximately 33% in the studied RACFs. There are 15 main categories and 43 subcategories of malnutrition risk factors. For the first time, this research identified the primary risk factors of malnutrition in RACFs, including poor appetite that affects 17% of older people. This is followed by insufficient oral intake and dementia progression. To enhance malnutrition predictive capabilities, we fine-tuned the RAC domain specific model to address the sequence length limitation of the RoBERTa model, 512 tokens. This is achieved by extending the sequence length to support 1,536 tokens. Augmented with risk factors, our model achieved an F1-score of 0.687, demonstrating its effectiveness in predicting malnutrition risk one month before the event onset. In conclusion, this research designs, develops and evaluates an innovative AI framework that leverages advanced AI technologies, particularly NLP and domain- specific LLMs, to tackle malnutrition among older people in residential aged care facilities. By analysing text data in EHR, The AI framework identifies risk factors, summarises nutrition information, and predict malnutrition one-month before the event onset. After thorough evaluation by domain experts, the AI framework can be implemented as an automated assessment tool. Its implementation into aged care services will alleviate the time burden associated with nutrition care for health and aged care practitioners, supporting them in identifying risk factors of malnutrition for the old people under their care, and manage malnutrition efficiently. The framework’s scalability extends beyond residential aged care facilities. It can be further extended to other healthcare settings to improve nutrition care effectiveness and quality of life for consumers.
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