Saudi Cultural Missions Theses & Dissertations
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Item Unknown Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract 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.12 0Item Unknown Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review(Queen’s University, Belfast, 2024) Alfaifi, Yahya; Clarke, SusanArtificial 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.38 0Item Restricted 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, MarkBackground: 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.12 0Item Restricted Sketch compression(University of Surre, 2023-09) Alsadoun, Hadeel Mohammed; song, Yi-Zhe; Ashcroft, AlexanderIn the rapidly evolving field of digital art and animation, traditional sketching techniques often rely on pixel-based methods, leading to less meaningful representations. This dissertation aims to transform this paradigm by rigorously investigating the efficacy of autoencoders for vector sketch compression. We conducted experiments using two distinct neural network architectures: Long Short-Term Memory (LSTM) and Transformer-based autoencoders. The Transformer model, which has significantly impacted the field of sequence-to-sequence tasks, especially in natural language processing, serves as a focal point of our study. Our experiment aims to answer a compelling question: Can these impressive results be replicated in the domain of vector sketch compression? The answer is a resounding yes. The Transformer model not only excelled in reconstructing sketches but also simplified the strokes and enhanced the overall quality of the sketch, achieving an impressive 85.03% classification accuracy. The LSTM model, known for its ability to capture temporal dependencies, served as our baseline, achieving a classification accuracy of 56.139% on a pre-trained classifier. Our findings strongly advocate for the adoption of Transformer-based models in vector sketch compression, offering a more compact and semantically rich representation. The LSTM model’s respectable performance also suggests its potential utility in less complex scenarios. Overall, this study opens new avenues for research in digital art, particularly in optimizing Transformer architectures for sketch compression.12 0Item Restricted MISINFORMATION DETECTION IN THE SOCIAL MEDIA ERA(Howard University, 2024-04-22) Alzahrani, Amani; Rawat, Danda B.As social media becomes the main way of getting information, the spread of misinformation is a serious and widespread problem. Misinformation can take many forms, such as text, video, and audio, and it can travel quickly through different platforms, affecting the quality and trustworthiness of the information that users access around the world. Misinformation can have negative effects on how people think, act, and interact, and it can even endanger social peace. This study aims to tackle the complex problem of misinformation by presenting a comprehensive approach that addresses various forms of deceptive content on social media with a focus on Twitter ( currently X). Twitter stands out as a dynamic and influential microblogging service that enables users to share real-time updates, news, and opinions in concise 280-character messages known as tweets. We introduce a hybrid deep learning model that incorporates Feature-based models at both tweet and user levels, complemented by pre-trained text embedding models such as Global Vectors (GloVe) and Universal Sentence Encoders (USE). Through careful evaluation on a real-world dataset, our approach proves effective in detecting textual misinformation. Recognizing the vital need to verify the reliability of information on social media, we propose a method to assess user credibility. Our solution involves evaluating the credibility of users based on their profiles to enhance the rumors detection model. This study proposes a novel mechanism for assessing a user’s credibility. Additionally, we extended our study capabilities to address the challenges posed by deceptive video content spread on social media using DeepFake technology. As the rapid advancement of deepfake technology threatens the integrity of audio and video content, we present a novel approach combining Optical Flow (OF) algorithms with a Convolutional Neural Network (CNN) to enhance deepfake video detection. This comprehensive strategy addresses the diverse challenges posed by misinformation, credibility assessment, and deepfake detection in the dynamic landscape of social media.36 0Item Restricted Deep Learning-Based Digital Human Modeling And Applications(Saudi Digital Library, 2023-12-14) Ali, Ayman; Wang, PuRecent advancements in the domain of deep learning models have engendered remarkable progress across numerous computer vision tasks. Notably, there has been a burgeoning interest in the field of recovering three-dimensional (3D) human models from monocular images in recent years. This heightened interest can be attributed to the extensive practical applications that necessitate the utilization of 3D human models, including but not limited to gaming, human-computer interaction, virtual systems, and digital twin. The focus of this dissertation is to conceptualize and develop a suite of deep learning-based models with the primary objective of enabling the expeditious and high-fidelity digitalization of human subjects. This endeavor further aims to facilitate a multitude of downstream applications that leverage digital 3D human models. The endeavor to estimate a three-dimensional (3D) human mesh from a monocular image necessitates the application of intricate deep-learning models for enhanced feature extraction, albeit at the expense of heightened computational requirements. As an alternative approach, researchers have explored the utilization of a skeleton-based modality, which represents a lightweight abstraction of human pose, aimed at mitigating the computational intensity. However, this approach entails the omission of significant visual cues, particularly shape information, which cannot be entirely derived from the 3D skeletal representation alone. To harness the advantages of both paradigms, a hybrid methodology that integrates the benefits of 3D human mesh and skeletal information offers a promising avenue. Over the past decade, substantial strides have been made in the estimation of two-dimensional (2D) joint coordinates derived from monocular images. Simultaneously, the application of Convolutional Neural Networks (CNNs) for the extraction of intricate visual features from images has demonstrated its prowess in feature extraction. This progress serves as a compelling impetus for our investigation into a hybrid architectural framework that combines CNNs with a lightweight graph transformer-based approach. This innovative architecture is designed to elevate the 2D joint pose to a comprehensive 3D representation and recover essential visual cues essential for the precise estimation of pose and shape parameters. While SOTA results in 3D Human Pose Estimation (HPE) are important, they do not guarantee the accuracy and plausibility required for biomechanical analysis. Our innovative two-stage deep learning model is designed to efficiently estimate 3D human poses and associated kinematic attributes from monocular videos, with a primary focus on mobile device deployment. The paramount significance of this contribution lies in its ability to provide not only accurate 3D pose estimations but also biomechanically plausible results. This plausibility is essential for achieving accurate biomechanical analyses, thereby advancing various applications, including motion tracking, gesture recognition, and ergonomic assessments. Our work significantly contributes to enhancing our understanding of human movement and its interaction with the environment, ultimately impacting a wide range of biomechanics-related studies and applications. In the realm of human movement analysis, one prominent downstream task is the recognition of human actions based on skeletal data, known as Skeleton-based Human Action Recognition (HAR). This domain has garnered substantial attention within the computer vision community, primarily due to its distinctive attributes, such as computational efficiency, the innate representational power of features, and robustness to variations in illumination. In this context, our research demonstrates that, by representing 3D pose sequences as RGB images, conventional Convolutional Neural Network (CNN) architectures, exemplified by ResNet-50, when complemented by as tute training strategies and diverse augmentation techniques, can attain State-of-the-Art (SOTA) accuracy levels, surpassing the widely adopted Graph Neural Network models. The domain of radar-based sensing, rooted in the transmission and reception of radio waves, offers a non-intrusive and versatile means to monitor human movements, gestures, and vital signs. However, despite its vast potential, the lack of comprehensive radar datasets has hindered the broader implementation of deep learning in radar-based human sensing. In response, the application of synthetic data in deep learning training emerges as a crucial advantage. Synthetic datasets provide an expansive and practically limitless resource, enabling models to adapt and generalize proficiently by exposing them to diverse scenarios, transcending the limitations of real-world data. As part of this research’s trajectory, a novel computational framework known as "virtual radar" is introduced, leveraging 3D pose-driven physics-informed principles. This paradigm allows for the generation of high-fidelity synthetic radar data by merging 3D human models and the principles of Physical Optics (PO) approximation for radar cross-section modeling. The introduction of virtual radar marks a groundbreaking path towards establishing foundational models focused on the nuanced understanding of human behavior through privacy-preserving radar-based methodologies.16 0Item Restricted Exploring AI-Powered Image Generation for Fashion Collaborations: A Deep Learning Approach to Blending Shoe Designs from Multiple Brands(Saudi Digital Library, 2023-09-07) AlJoudi, Sarh; Marchioni, EnricoThis thesis presents an approach for fashion shoe design generation using Generative Adversarial Networks (GANs) with a focus on the WGAN-GP, ProGAN, and StyleGAN architectures. The primary objective is to assess the visual quality, diversity, and adherence to the fashion trends of the generated shoe designs. A set of evaluation metrics, including Fréchet Inception Distance (FID), Earth Mover’s Distance (EMD), Maximum Mean Discrepancy (MMD), and K-Nearest Neighbours (KNN) accuracy, are employed to quantitatively evaluate the performance of each model. The evaluation results demonstrate that ProGAN outperforms both WGAN-GP and StyleGAN in all metrics, achieving the lowest FID, EMD, and MMD scores. ProGAN generates visually appealing and diverse shoe designs, that closely resembling real-world fashion trends. While WGAN-GP also achieves acceptable results, StyleGAN faces challenges with the droplet effect and noisy colours in the generated designs, resulting in lower performance in all evaluation metrics. Nevertheless, StyleGAN’s style-mixing capability showcases its potential for creating novel and creative shoe designs. The qualitative evaluation further confirms the ProGAN as the preferred choice for generating high-quality and fashion-forward shoe designs. Its robustness, progressive growing approach, and architectural stability contribute to its outstanding performance. The discussion also highlights the potential implications of GAN-based fashion image generation in the fashion industry and creative domains, such as trend forecasting and virtual garment display. The findings suggest that ProGAN holds promise as a reliable and creative tool for generating fashion-forward shoe designs and adopting StyleGAN 2 with the Noise-Conditional AdaIN (NCAIN) layer may address the droplet effect problem and enhance visual coherency in the generated designs. Ultimately, the results and discussions pave the way for further advancements and applications of GAN-based fashion design in the fashion industry and creative domains. Keywords: Deep learning, shoe design, GANs, style-mixing.Item Restricted Universal Deep Learning for Signal Detection in Wireless Networks(Saudi Digital Library, 2023-11-25) Albagami, Khalid; Li, Geoffrey YeRecently, applying deep learning (DL) in physical layer communications for channel estimation and signal detection has extensively been studied in the literature. The majority of the existing studies investigating the use of DL techniques to address the channel estimation and signal detection problem focus on analysing channel impulse response (CIR) that are generated from only one channel type distribution such as additive white Gaussian channel Noise (AWGN) and Rayleigh channel. Although this field is well-researched in the literature, DL models are yet to be widely adopted in practice for channel estimation and signal detection. The main challenge is that a pre-trained DL model exhibits sub-optimal performance after transitioning to a different wireless channel environment. Moreover, with the limited resources and processing units available at the base station, it is a cumbersome task to re-collect data and re-train the model whenever the wireless environment changes. In this thesis, we study and investigate the feasibility of applying universal deep neural network (Uni-DNN) model consisting of two cascaded DL models where the first one works as a wireless channel classifier and the second one as a signal detector. The channel classifier DL model works on identifying the wireless channel distribution that impaired the transmitted signal. Then, the signal detector DL model utilises this information along with the received signal to recover the transmitted signal. Three different Uni-DNN architectures were developed namely architectures A, B and C utilising a combination of deep neural network (DNN) and convolutional neural network (CNN) models. The proposed architectures are trained and tested on multiple frequency selective and frequency flat fast fading wireless channels and their bit error rate (BER) performance is compared to both conventional DL models and popular channel estimation techniques such as minimum mean square error (MMSE) and least square (LS) for orthogonal frequency division multiplexing (OFDM) multiplexing scheme under AWGN with signal-to-noise ratio (SNR) ranging from 0 to 20dB. The Uni-DNN architecture C with cascaded DNN-CNN has shown the most promising results out of the three in adapting to multiple wireless channels, outperforming conventional DL models, LS and nonperfect-CSI MMSE conventional channel estimators in low pilot frequency density. In addition, the theoretical and practical Uni-DNN model inference time complexity analysis shows that such a model is faster to compute compared to MMSE and can be deployed in real-time applications.Item Restricted A Comparison of Time Series and Deep Learning Methods for Predicting Stock Prices(Saudi Digital Library, 2023-03-23) Alajmi, Shahad; Ji, LanpengStock market is one of the most competitive financial markets where investors need to know the trend of prices in advance. There have been many improvements and advancements in the application of neural networks in the financial industry. In this research, two advanced methods were used to simulate and predict the close stock prices of Saudi Telecom Company (STC). The first method was the autoregressive integrated moving average (ARIMA) and the second method was a by using a class of deep learning neural networks called recurrent neural networks (RNN). ARIMA (p,d,q) was the statistical method selected as a time series model based on the level, trend ,and seasonality of data. Additionally, the order of p and q is based on an autocorrelation function ‘ACF’ ,and a partial autocorrelation function ‘PACF’, the optimal model of this research was ARIMA (1,0,28). Moreover, RNN uses long short-term memory layers (LSTMs), dropout regularisation, activation functions, a loss function, which is the mean square error (MSE), and the Adam optimiser to simulate the predictions. The unique characteristic of LSTMs is that the model is able to store previous data over time and use this data to predict future prices. The structure of the LSTM consists of five layers: one input layer, three hidden layers ,and one output layer. The methods used to measure the performance of predictions of each model are the mean absolute percentage error ‘MAPE’ and the root squared mean error ‘RMSE’. ARIMA (1,0,28) is the model that was found to have a lower error between actual and predicted prices. After analysing each model, ARIMA model’s prediction accuracy was 96.3% and RNN’s accuracy was 93.8%; we concluded that the ARIMA model is better than the RNN model for forecasting the close stock prices of STC. This research include important data which can benefit investors and companies to make economical decisions, such as when to buy or sell shares.Item Restricted DEEP LEARNING APPROACHES FOR OBJECT TRACKING AND MOTION ESTIMATION OF ULTRASOUND IMAGING SEQUENCES(Saudi Digital Library, 2023) Alshahrani, Mohammed; Almekkawy, MohamedIn recent decades, object tracking and motion estimation in medical imaging have gained importance. It is a powerful tool that can be used to improve diagnostic accuracy and therapy efficiency. This importance has led researchers to search for faster and more accurate algorithms for object tracking. Different approaches have been used to perform object tracking, such as object detection, motion estimation, and similarity matching, which are the focus of this study. Different avenues can be used to address similarity matching. First, the classical method, which takes an object and searches for a similar object in the subsequent frame (because it is an object tracking in a video sequence) by examining all the sub-windows in the subsequent frame and measuring a cost function between the reference object and the sub-window. This approach is inefficient and cannot achieve real-time tracking. The deep learning method for similarity matching utilizes twin convolutional networks that produce a feature map that is later combined using a correlation layer. This layer provides a score map that points to a high-similarity area. This study examined and developed object tracking algorithms to track objects of interest in the human liver using a correlation filter-based neural network (CFNet). The dataset used in this study was CLUST-2D, which was provided by the Swiss Federal Institute of Technology in Zürich (ETH). It contains approximately 96 ultrasound sequences of the liver from different patients. Three versions of the CFNet network were tested in this study. First, baseline-CFNet was used for training. Baseline-CFNet struggled to track objects under significant displacements and deformations. To address this limitation of the baseline-CFNet, a second version was developed. Advanced-CFNet is the second version of CFNet implemented in this study. This is the main contribution of this study. This version incorporates a dynamic template update and motion prediction modules, which improve object tracking by preventing tracker drift and maintaining the template from being polluted with inappropriate appearances of the tracked object. The third version implemented in this study is Kalman-CFNet, which utilizes a linear Kalman filter to estimate an object's motion and enhance its robustness against unexpected motions. The comparative analysis demonstrated the superiority of Advanced-CFNet, as it achieved lower root mean square error (RMSE) values than the other methods, particularly in challenging scenarios. These findings highlight the effectiveness of the advanced CFNet for object tracking in liver ultrasound imaging.