Saudi Cultural Missions Theses & Dissertations
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Item Restricted CADM: Creative Accounting Detection Model in Saudi-Listed Companies(Saudi Digital Library, 2025) Bineid, Maysoon Mohamed; Beloff, NataliaIn business, financial statements are the primary source of information for investors and other stakeholders. Despite extensive regulatory efforts, the quality of financial reporting in Saudi Arabia still requires improvement, as prior studies have documented evidence of creative accounting. This practice occurs when managers manipulate accounting figures within the boundaries of the International Financial Reporting Standards to present a more favourable image of the company. Although various fraud detection methods exist, identifying manipulations that are legal yet misleading remains a significant challenge. This research introduces the Creative Accounting Detection Model (CADM), a deep learning (DL)-based approach that employs Long Short-Term Memory (LSTM) networks to identify Saudi-listed companies engaging in creative accounting. Two versions of the model were developed: CADM1, trained on a simulated dataset based on established accounting measures from the literature, and CADM2, trained on a dataset tailored to reflect financial patterns observed in the Saudi market. Both datasets incorporated financial and non-financial features derived from a preliminary survey of Saudi business experts. The models achieved training accuracies of 100% (CADM1) and 95% (CADM2). Both models were then tested on real-world data from the Saudi energy sector (2019–2023). CADM1 classified one company as engaging in creative accounting, whereas CADM2 classified all companies as non-creative but demonstrated greater stability in prediction confidence. To interpret these results, a follow-up qualitative study involving expert interviews confirmed CADM as a promising supplementary tool for auditors, enhancing analytical and oversight capabilities. These findings highlight CADM’s potential to support regulatory oversight, strengthen auditing procedures, and improve investor trust in the transparency of financial statements.12 0Item Restricted Long-Term Dependency Margin Maximization Model (LTDM3): Dealing with Concept Drift in Personalized Learning Systems(Saudi Digital Library, 2023) Allogmany, Bander; Josyula, DarsanaAdvances in data analytics and intelligent technologies are enabling smart learning environments that promote personalized learning. Personalized learning systems where learners engage with information in a manner tailored to their unique needs, goals, and abilities have garnered significant academic research attention. If students can achieve their objectives faster than with traditional learning methods, it would increase their motivation and reduce their likelihood of dropping out. It can also offer educators a better understanding of each student’s learning process, enabling them to teach more effectively. Artificial intelligence (AI) plays a vital role in the development of personalized learning systems. Rapid advancements in AI technologies enable tracking and modifying of each student’s learning environment. Machine learning algorithms facilitate the determination of students’ learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, a deterioration of the model’s performance over time due to changes in data distribution. These arise due to factors affecting learning ability, including changes in family structure, parental involvement, peer relationships, learner behavior, personal interests, environmental influences such as nutrition and sleep, and so on. For successful personalization, it is critical that underlying predictive and classification models be able to adapt successfully to data changes that contribute to the drift phenomenon. This research proposes a method to address concept drifts in personalized learning systems that involve training using sequential features extracted automatically, noting when concept drifts are causing model deterioration, and automatically adjusting the trained model to improve model performance in the presence of drift. Unlike large language models (LLMs), which usually lack inherent capabilities for indicating that a concept drift has occurred, the approach presented in this dissertation can detect and point out instances of concept drift. Detecting concept drift is important for initiating specific interventions, whereas large language models tend to obscure or overlook such changes in the data. The proposed approach aims to enhance the accuracy and effectiveness of predictive models, ensuring personalized learning systems deliver pertinent and useful recommendations even when student preferences change. While conducting experiments using a real-world dataset related to students’ interactions with educational systems, the proposed model shows impressive results in managing concept drifts. Moreover, the proposed model shows resilience against two major types of drift: incremental and sudden drifts. This indicates that by using the proposed approach, we can ensure that the predictive models maintain their effectiveness in the presence of different types of drift.4 0Item Restricted An Evaluation of Machine Learning and Deep Learning for Time Series Forecasting(Saudi Digital Library, 2025-08) Gadhi, Adel; Shelton, PeirisThis thesis investigates the use of machine learning and hybrid models to forecast time series data such as climate patterns, oil prices, Australian beer production, and sunspot activity. It examines traditional models like ARIMA and GARCH, as well as machine learning methods such as SVR, LSTM, RF, and DT, which better capture non-linear and complex relationships. The study also evaluates hybrid models like ARIMA-ANN and GARMA-LSTM, which consistently demonstrate superior forecasting accuracy across various datasets. The GARMA-LSTM model, in particular, proves effective for long-term forecasting, especially with sunspot and beer production data. Finally, the thesis applies an advanced deep learning system, WGAN-GP, to financial and climate data, showing that modern methods can move beyond classical assumptions and better capture complex, high-order dynamics.32 0Item Restricted Human Activity Monitoring for Telemedicine Using an Intelligent Millimeter-Wave System(University of Dayton, 2025) Alhazmi, Abdullah; Chodavarapu, VamsyThe growing aging population requires innovative solutions in the healthcare industry. Telemedicine is one such innovation that can improve healthcare access and delivery to diverse and aging populations. It uses various sensors to facilitate remote monitoring of physiological measures of people, such as heart rate, oxygen saturation, blood glucose, and blood pressure. Similarly, it is capable of monitoring critical events, such as falls. The key challenges in telemonitoring are ensuring accurate remote monitoring of physical activity or falls by preserving privacy and avoiding excessive reliance on expensive and/or obtrusive devices. Our approach initially addressed the need for secure, portable, and low-cost solutions specifically for fall detection. Our proposed system integrates a low-power millimeter-wave (mmWave) sensor with a NVIDIA Jetson Nano system and uses machine learning to accurately and remotely detect falls. Our initial work focused on processing the mmWave sensor's output by using neural network models, mainly employing Doppler signatures and a Long Short-Term Memory (LSTM) architecture. The proposed system achieved 79% accuracy in detecting three classes of human activities. In addition to reasonable accuracy, the system protected privacy by not recording camera images, ensuring real-time fall detection and Human Activity Recognition (HAR) for both single and multiple individuals at the same time. Building on this foundation, we developed an advanced system to enhance accuracy and robustness in continuous monitoring of human activities. This enhanced system also utilized a mmWave radar sensor (IWR6843ISK-ODS) connected to a NVIDIA Jetson Nano board, and focused on improving the accuracy and robustness of the monitoring process. This integration facilitated effective data processing and inference at the edge, making it suitable for telemedicine systems in both residential and institutional settings. By developing a PointNet neural network for real-time human activity monitoring, we achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Furthermore, the proposed system provided activity data reports, tracking maps, and fall alerts and significantly enhanced telemedicine applcations by enabling more timely and targeted interventions based on objective data. The final proposed system facilitates the ability to detect falls and monitor physical activity at both home and institutional settings, demonstrating the potential of Artificial Intelligence (AI) algorithms and mmWave sensors for HAR. In conclusion, our system enhances therapeutic adherence and optimizes healthcare resources by enabling patients to receive physical therapy services remotely. Furthermore, it could reduce the need for hospital visits and improve in-home nursing care, thus saving time and money and improving patient outcomes.28 0Item Restricted Forecasting OPEC Basket Oil Price and Its Volatilities Using LSTM(University College London, 2024-09) Almazyad, Sulaiman; Hamadeh, LamaThe global economy is greatly affected by oil prices, which have an impact on everything from consumer goods prices to transportation expenses. Forecasting these prices accurately is crucial for energy security, company strategy, and economic planning. Traditional statistical models such as ARIMA and SARIMA have been used for such forecasts, but struggle with the non-linear patterns inherent in oil price movements. This research explores the use of Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Network (RNN) built to manage longterm dependencies, in predicting the OPEC reference basket oil price and its associated volatility, ultimately improving the accuracy of these forecasts. The model is built upon historical datasets of the OPEC Reference Basket (ORB), and its efficacy is assessed using a variety of performance indicators, including RMSE, MAE, and MAPE. The outcomes reveal that the LSTM model is16 0Item Restricted Exploring Advanced Deep Learning, foundation and Hybrid models for Medical Image Classification(University of Surrey, 2024-09) Kutbi, Jad; Carneiro, GustavoThis dissertation explores the use of advanced deep learning architectures, foundation models, and hybrid models for medical image classification. Medical imaging plays a critical role in the healthcare industry, and deep learning models have demonstrated significant potential in improving the accuracy and efficiency of diagnostic processes. This work focuses on three datasets: RetinaMNIST, BreastMNIST, and FractureMNIST3D from the MedMNISTv2 datasets, each representing different imaging modalities and classification tasks. The significance of this work lies in its comprehensive evaluation of state-of-the-art models, including ResNet, Vision Transformers (ViT), ConvNeXt, and Swin Transformers, and their effectiveness in handling complex medical images. The primary contributions of this research are the implementation and benchmarking of modern architectures on these datasets, as well as the investigation of hyperparameter optimization using Optuna. Pretrained models and hybrid architectures such as CNN-ViT, SwinConvNeXt and CNN-LSTM were explored to enhance performance. Key results demonstrate that models like ConvNeXt-tiny (pretrained) and CLIP achieved high accuracy and AUC scores, particularly in BreastMNIST and RetinaMNIST datasets, setting new performance benchmarks. The combination of Swin and ConvNeXt using feature fusion was shown to improve model robustness, especially when handling multi-class and 3D data.20 0Item Restricted Integrating Sentiment and Technical Analysis with Machine Learning for Improved Stock Market Predictions(University of Dundee, 2024-07-30) Almubarak, Maha Sofyan A; Mazibas, Murat; Kwiatkowski, AndrzejThis thesis advances stock forecasting by integrating sentiment analysis from Twitter as social media platform with traditional technical indicators, employing machine learning (ML) techniques. The research identifies gaps in existing literature, particularly in the use of appropriate validation methods and the balance of statistical metrics with financial benchmarks. It proposes a comprehensive methodology that incorporates Time Series Cross- Validation and hyperparameter tuning to enhance the adaptability and economic robustness of forecasting models. The empirical analysis unfolds in three chapters: 1. Technical Analysis within LSTM models to predict movements of the SPY ETF, validated through Time Series Cross-Validation to ensure robustness, focusing on both accuracy and financial performance. 2. Integration of Sentiment Analysis to assess its impact on model responsiveness and financial outcomes, demonstrating improved predictive accuracy. 3. Application to a Diverse Stock Portfolio, where models are applied to 10 different stocks across various sectors, confirming the models’ effectiveness and practical utility in real-world trading strategies. Key findings suggest that incorporating sentiment analysis significantly enhances the predictive precision of models, particularly in volatile market conditions. This synergy between technical indicators and sentiment data not only boosts accuracy but also enriches the models’ economic performance, offering valuable insights for traders and academic researchers exploring complex financial markets.21 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 Leveraging Machine Learning for Enhanced Detection and Classification of Brain Pathologies Using EEG(Saudi Digital Library, 2023-11-09) Albaqami, Hezam; Hassan, Ghulam Mubashar; Datta, AmitavaMaintaining brain health is vital due to its role in controlling all body functions. This thesis introduces novel methods for the problem of automated brain diagnostic tasks using electroencephalogram (EEG). Several contributions have been made, including wavelet-based feature extraction methods and novel deep-learning architectures for detecting and classifying brain pathologies. Additionally, novel methods of feature dimensionality reduction, data fusion, and data augmentation are proposed. The proposed solutions are rigorously assessed using extensive EEG datasets consisting of patients from a wide demographic range to evaluate the generalization capabilities. This thesis offers significant contributions to biomedical signal processing for diagnostic tasks.64 0Item 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.26 0