Saudi Cultural Missions Theses & Dissertations
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Item Restricted Credit Card Fraud Prediction Using Machine Learning Model(University of Essex, 2024-08) Alanazi, Mohammed; Walton, MichaelThe widespread adoption of credit cards has significantly increased the frequency of fraudulent activities. This has resulted in considerable financial losses for both consumers and financial institutions. As the use of credit cards continues to grow, the challenge of protecting transactions against unauthorized access has become more serious than ever. This research focuses on creating a solution using machine learning to accurately and effectively identify fraudulent credit card transactions. It addresses the issue of uneven transaction data by employing advanced methods such as logistic regression, XGBoost, LightGBM, and a hybrid model. The research involves thorough data preparation, model development, and careful assessment using measures “such as accuracy, precision, recall, F1 score, and ROC AUC”. This research leverages sophisticated machine learning techniques and tackles the specific challenges associated with imbalanced data. The study aims to significantly enhance the detection of fraudulent transactions while reducing false positives. The ultimate goal is to boost the security of financial systems, thus providing better protection against fraud, and to improve trust and reliability in credit card transactions.57 0Item Restricted Machine Learning Based Predication of Diabetes(University of Notingham, 2024) Almhmadi, Anas; Wilkinson, RichardDiabetes mellitus, commonly known as diabetes, is a chronic disease related to the metabolic system of humans. It is part of a broader category of chronic diseases, including cardiovascular disease, acute kidney infection, eye problems, and foot ulcers. Currently, 537 million people worldwide are living with diabetes, a figure expected to rise to 643 million by 2030. Given the limited availability of medical professionals, there is an increasing need to develop automated tools to assist decision-making for various diseases using prevalence datasets. This dissertation focuses on the implementation of both deterministic models, such as decision trees, random forests, support vector machines, and neural networks, and probabilistic models, including logistic regression, Naïve Bayes, Gaussian Naïve Bayes, and nonparametric Naïve Bayes, for binary diabetes classification. Seven input features—age, gender, BMI, blood glucose level, HbA1C level, hypertension (yes/no), and heart disease (yes/no)—along with the binary response variable (diabetes), are utilized to develop these classification models. The dataset comprises 100,000 patients and eight features, with a significant class imbalance: 91.5% do not have diabetes. Among the models, the decision tree exhibited the highest balanced accuracy of 98.48%, with a sensitivity of 100% and a specificity of 96.95%. The decision tree outperformed all other models when applied to the imbalanced data. For the balanced data, the random forest model demonstrated superior performance (except logistic regression) with a balanced accuracy of 92.42%, sensitivity of 92%, and specificity of 92.85%. These models can be further refined by considering additional relevant variables and applying advanced deep-learning models.11 0Item Restricted SIGNS, LANGUAGE, AND SPATIAL PRACTICES IN THE LINGUISTIC LANDSCAPE OF TAIF(University of Sussex, 2024-05-17) Alamri, Abdulrazaq; Piazza , Roberta; Robinson, JustynaThis study explores the Linguistic Landscape (LL) of Taif, and examines government and commercial signs to reveal the interplay of languages and power dynamics through the medium of public signage. Taif is the fourth most populated city in Saudi Arabia after Riyadh, Jeddah and Makkah. However, while the linguistic landscapes of Jeddah and other Saudi cities have been studied, Taif remains an under-researched city, despite offering a rich multicultural scenario. The study integrates linguistic and visual dimensions to contribute to the existing LL research in Saudi Arabia. Unlike earlier studies, it also combines analysis of de facto language use with predictions concerning future language display. The study’s mixed-method approach uncovers Taif’s multilingualism using inferential statistics to predict the occurrence of languages, and fusing linguistic and semiotic analyses to develop a comprehensive understanding of power relations reflected in the city’s LL. After analysing 4,714 signs, socioeconomic factors emerged as significant determinants of the LL. The old area of Sharqiyah and the gentrified area of Shubra predominantly feature monolingual MSA (Modern Standard Arabic) signs, while Shihar, an expensive area, displays a mixture of MSA and English. The primary languages shaping the LL of Taif are MSA, Classical Arabic, English, Urdu, Informal Arabic, Romanised Arabic and Arabicised English. Inferential statistics (logistic regression) revealed that subject matter and function were the strongest predictors of language choice, while sign placement had minimal impact. Applying a multimodal approach to the data, the social, political and religious meanings of such visual elements as typography, colour saturation and salience, as well as top-bottom and left-right visual design are highlighted. The findings encompass aspects such as the interplay of local and global discourses, reflecting openness to bilingualism and unequal access to spaces. They also highlight the societal relevance of MSA, which reflects the country's formality and social solidarity, as well as the significance of euphemisms in signs, such as those relating to hookah smoking. The interplay of English and Arabicised English reveals themes of beauty, trendiness, luxury, local advertising and the iconicity of English, encapsulating cultural and linguistic dynamics. The cultural undertones of duty and repelling envy within religious discourses are explored. The study also investigates the role of Informal Arabic in softening governmental directive discourse and in promoting local authenticity and intertextual connections. The research further underscores the pragmatism and collective identity evident in ethnicity-related signs. Taif's LL reveals a blend of tradition and modernity, with English and migrant languages complementing Arabic, reflecting its cosmopolitanism and economic drivers. The omission of minority languages in signs suggests a gap between the LL and the city's true linguistic makeup. The study also highlights Taif’s deeply rooted religious discourse and the rise of Informal Arabic alongside the official language used by the authorities, underscoring the city's nuanced sociolinguistic dynamics. Keywords: Linguistic Landscape (LL), Multilingualism, Logistic Regression, Multimodality.32 0Item Restricted Predicting Drugs Metabolized by Cytochrome Enzymes(University of Glasgow, 2023-12-06) Alshammari, Mariam; Lever, jakeIn the era of rapid technological evolution, embracing the strength of machine learning, deep learning, and other computational approaches merged with biological and biochemical domains has enhanced multiple medical applications. Drug discovery is one of the fields that have been rapidly developing. This project will focus on predicting drugs metabolized by Cytochrome enzymes. Therefore, using machine learning, deep learning, and pre-trained approaches would illustrate the strength of recent computational methods used in the medical field; consequently, it will reduce the limitations of traditional techniques in drug development by limiting the cost and time during clinical trials. This study will prepare the dataset to extract descriptors and build Logistic Regression, Support Vector Machine, Random Forest, Recurrent Neural Networks, ChemBERTa, and Galactica, along with parameter tunning to evaluate the best model through ROC Curve, Confusion Matrix, and F1-score. This proposed study shows that random forest outperformed other models with a 0.907 f1-score.21 0Item Restricted Sentiment Analysis in Online Social Networks(SDL, 2023-05-19) Assery, Ahmad Ali; Zarbaf, MonasadatThe convenience of being able to shop from home has led to the rise of e-commerce in today's highly digitized society. Before buying anything online, customers are required to read hundreds of reviews. Tracking and analyzing customer feedback may be challenging when there are millions of online reviews for a single product. However, in today’s age of machine learning, if a model were used to polarize and comprehend from them, thousands of input and information might be gained quickly and easily. As a result, sentiment analysis has emerged as a distinct field of research that integrates NLP and text analytics to identify and categorize the emotional tone of written content. In this dissertation, we investigate the difficulty of labelling reviews as positive, negative, or neutral. For massive amounts of supervised data, like those seen in the Amazon dataset, we have found success using KNN, Logistic regression, and Random Forest Classifiers. Meanwhile, the greatest results were obtained using the Logistic and random forest classifiers, and we plan to develop a web application using these models to categorize the reviews in real time. Finally, this research delves into sentiment analysis and opinion mining with regards to product feedback.44 0