Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Modelling Emergency Attendances and Waiting Times in Scotland(The University of Edinburgh, 2024-07) Alkathiri, Shaymaa; Wood, SimonBackground and Aims: Emergency Departments (EDs) in Scotland face significant challenges due to fluctuating patient loads and increased waiting times in recent years. This research aims to analyse and predict total attendances and waiting times in Emergency Departments in Scotland. The purpose is to offer insightful analysis, modelling, and predictions to Public Health Scotland, enhancing the efficiency of emergency healthcare services. Data: The analysis is based on two datasets provided by Public Health Scotland: the monthly statistics dataset and the weekly statistics dataset. Method: We developed four Generalized Additive Models (GAMs) to predict the two main outcomes: the number of attendances in Emergency Departments and the number of attendances categorized by waiting times (within 4 hours and over 4 hours). GAMs were chosen for their ability to model complex, non-linear relationships effectively by incorporating temporal and geographical factors.19 0Item Restricted Semantic Analysis of Amazon Reviews of Sustainable Products(University of Leeds, 2024-02-18) Alotaibi, Amal; Dimitrova, VaniaOnline shopping has grown to be an essential part of modern living, garnering a wealth of client input. This project advances the field of consumer feedback mining and semantic and sentiment analysis of customer reviews since, when applied effectively, it can enhance goods, services, or marketing initiatives. This project proposes a framework using Natural Language Processing (NLP) techniques to find customer preferences related to sustainability through mining customer reviews (CR) text. First, implement the LDA and sLDA models using the Gensim package in Python to extract sustainable topics from CR. After that, implement the BERTopic model to find the sustainability aspect in (CR). Then, the overall sentiment for every review in each topic was calculated using the Vader sentiment library in Python. Lastly, interpret the results and generate helpful insights for brand managers. The Amazon product review data is used in this study, and we use Food and Grocery Sustainable Products. The findings of the proposed framework are promising, as we were able to identify the most discussed topics in sustainability aspects of products and produce an assessment that provides information about the aspects that the customers are most satisfied with and that can be improved. However, the sLDA model and the BERTopic model achieve the goal but not the expectation. especially BERTopic, it was not accurate enough for weakly supervised text classification. Also, the Vader sentiment tool did not meet expectations because of the complexity of CR. However, the text analyst specialist found that the structure is flexible enough to allow for future development and increased usage. Ultimately, we think that these data will help brand managers create and improve future products, which will raise consumer satisfaction and boost revenue and profitability.25 0Item Restricted INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, AvielThe proliferation of interconnected smart devices, once ordinary household appliances, has led to an exponential increase in sensitive data collection and transmission. The security and privacy of IoT devices, however, have lagged behind their rapid deployment, creating vulnerabilities that can be exploited by malicious actors. While security attacks on IoT devices have garnered attention, privacy implications often go unnoticed, exposing users to potential risks without their awareness. Our research contributes to a deeper understanding of user privacy concerns and implications caused by data collection within the vast landscape of the Internet of Things (IoT). We uncover the true extent of data accessible to adversarial individuals and propose a solution to ensure data privacy in precarious situations. We provide valuable insights, paving the way for a more informed and comprehensive approach to studying, addressing, and raising awareness about privacy issues within the evolving landscape of smart home environments.13 0Item Restricted The Role of Data Visualization in Web-Based Data Analysis Tools(Swansea University, 2023) Alzahrani, Batool; Gaya, RandellIn the age of increasing data importance, individuals and organisations must be able to effectively comprehend and use the data they gather. This project presents a web-based data analysis platform that aims to provide a user-friendly alternative to conventional spreadsheet applications. The system employs Laravel, a PHP-based web application framework, to construct a platform that facilitates data input, visualisation, and processing functionalities, serving the needs of a variety of users, from novices to professionals. The platform is designed to give customers enhanced data analysis capabilities, enabling them to collect real-time data, analyse it effectively, and extract practical insights. The project hereby seeks to develop a robust and scalable data storage and processing framework that prioritises security while remaining user- friendly. This framework will employ cutting-edge methods, including statistical analysis and machine learning algorithms, to give the platform valuable features, such as strong user authentication, preconfigured calculation routines, templates, and efficient data input/output capabilities. The overall aim is to improve the user experience and enable informed decision-making by emphasising dynamic table generation, interactive data visualisation, graph creation, and export capabilities. The primary objective hereby is to provide a comprehensive and adaptable web-based data analysis tool that fulfils the changing requirements of modern society, where expertise in data science is essential for achieving success in many sectors.33 0Item Restricted Feature extraction for high dimensional healthcare data(University of Surrey, 2024-02-19) Alanazi, Bader Bander D; Kouchaki, SamanehABSTRACT In the contemporary era of digital technology, the healthcare sector is faced with an abun-dance of huge databases, mostly due to the widespread adoption of machine learning and data mining methodologies. Nevertheless, the substantial complexity of large datasets pre-sents notable obstacles, such as the predicament known as the 'curse of dimensionality'. The primary objective of this project is to tackle these issues by formulating methodologies that enable the automated extraction of characteristics from complex Intensive Care Unit (ICU) data, which consists of numerous dimensions. The ultimate aim is to utilise these methodol-ogies to anticipate the likelihood of in-hospital death following admission to the ICU. The utilises a variety of advanced feature extraction methods, encompassing both linear and nonlinear approaches such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Autoencod-ers. The aforementioned methodologies are employed on the MIMIC III dataset, encompassing data pertaining to a population of around fifty-one thousand patients. Every patient can be identified by their distinct admission identification number. The primary objective of this study is to assess methodologies for the automated extraction of features that can be subsequently employed in healthcare applications. The study addi-tionally investigates the potential of employing more sophisticated and advanced machine learning models, such as deep learning models, to effectively capture intricate patterns and relationships within the data characterised by a high number of dimensions. Further could explore the practical application of these extracted traits in real-world healthcare contexts, perhaps resulting in the development of more precise and efficient predictive models and enhanced patient outcomes. This study makes a valuable contribution to the domain of machine learning in the healthcare sector, with a specific focus on the automated extraction of features from complex datasets to predict in-hospital mortality. The results of this study have the potential to contribute to the progress of data-driven solutions in the field of healthcare.13 0