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 The Role of Natural Language Processing in Early Detection of Mental Health Conditions from Social Media Data(Saudi Digital Library, 2025) Alasery, Aidh; Lauria, StashaMental health disorders such as anxiety, depression, and schizophrenia are increasing rapidly and affect a significant proportion of the global population. As a result, the affected patients suffer negative consequences such as high financial costs of treatment and a poor quality of life. The reliance on traditional clinical methods to diagnose mental health problems further leads to delays in identifying the disorders among affected individuals. An emerging approach to address the delay is the adoption of artificial intelligence (AI) through natural language processing (NLP) models, which can evaluate real-time social media content to identify individuals at risk of mental health problems. The current research sought to identify how NLP techniques could be adopted for the early diagnosis and detection of mental health illnesses from social media interactions. Data was collected using the scoping review method, where 20 qualitative peer reviewed journal articles were identified and assessed. To evaluate the findings obtained in the study, thematic analysis was adopted. The generated insights indicated that using deep learning techniques, including recurrent neural networks (RNNs) and classification machine learning methods, such as decision trees (DT), facilitated the detection of mental health illnesses. Further insights revealed that techniques such as data anonymisation were effective for privacy preservation, and explainable AI (XAI) were useful in upholding the privacy of user data during the data collection phase. Additionally, various advantages of NLP models were elaborated, including accuracy, generalisability, and fairness. However, challenges such as risks of bias and breaching the privacy of user data were also identified. In future work, there is a need to investigate how the NLP models can be enhanced further by integrating more technologies, such as big data.36 0Item Restricted Exploring Nonlinear Associations and Interactions of Risk Factors for Breast Cancer Incidence Using Machine Learning Approaches(Imperial College London, 2024-08) Alqarni, Lina; Heath Alicia; Berrington, AmyBACKGROUND: Breast cancer is influenced by a complex array of risk factors. This study aimed to identify nonlinear associations and interactions between various risk factors and breast cancer incidence using computationally efficient, interpretable methods. METHODS: Data from the Generations Study, a long-term prospective cohort of 104,423 women, were analysed. Risk factors evaluated included demographic, medical, reproductive, hormonal, and lifestyle variables. We compared the performance of traditional Cox proportional hazards models with tree-based methods, including Classification and Regression Trees (CART) and random forests, using the C-statistic. SHapley Additive exPlanations (SHAP) values were extracted to interpret random forest outputs, highlighting key risk factors and interactions. Stability selection was applied to enhance computational efficiency and identify the most stable and important variables. RESULTS: The multivariable Cox model achieved the highest predictive accuracy with C-index of 0.657, slightly outperforming the random forest model (C-index of 0.650). However, the random forest model revealed nonlinear associations and interactions not captured by the Cox model. Age, family history of breast cancer, and benign breast disease were among the most critical factors identified, with complex interactions noted between age, body mass index at entry, and family history with other risk factors such as hormone replacement therapy duration, oral contraceptive duration, and smoking pack-years. Stability selection effectively reduced the number of variables without compromising model performance. CONCLUSIONS: While linear models capture dominant associations, tree-based models like random forests offer additional insights into complex, nonlinear relationships among breast cancer risk factors, highlighting the potential for more personalised screening and prevention strategies16 0Item Restricted Entrepreneurship dynamics for women entrepreneurs' inclusion in emerging markets: a case study on Saudi Arabia(University of Glasgow, 2024-05-16) Alzahrani, Eidah; Keston-Siebert, Sabina; Gordon, JillianThis study explored the ways in which institutions affect women that either support or hinder their entrepreneurial endeavours and considers how women can leverage these institutions to their advantage. Few studies have focused on women entrepreneurs' interactions in their local economic and social contexts (Cavallo et al., 2018). This study explores the role of institutions in women's entrepreneurship in Saudi Arabia amidst COVID-19 and post-pandemic. The data collection for this study utilised multiple methods including: conducting interviews with (31) Saudi women and (6) institutional representatives; remote observation of entrepreneurial events and document analysis. A thematic analysis approach was adopted to examine the data and identify recurring patterns and themes within the discussion. This research examines the intricate relationship between institutions and women's entrepreneurial activities. Through analysing how institutions can influence and be influenced by women, this research has uncovered the potential for mutual benefits and opportunities that exist within this dynamic. The study utilises complementary frameworks integrating institutional impact and identity play as theoretical lenses with a ‘multi-level model’ (McAdam et al., 2019) and ‘do context framework’ (Baker and Welter, 2020) to gain a better understanding of how entrepreneurs interact with their contexts. This study makes a crucial contribution by emphasizing the pivotal role that women entrepreneurs have in shaping society, offering invaluable insights, and acting as a catalyst for empowering women in the field of entrepreneurship. The decisions they make, the risks they take, and their resilience in the face of challenges are influenced by beliefs, faith, self-worth, confidence, tenacity, self-awareness, risk-taking, adaptability, autonomy and independence, determination, and self-empowerment. Such attributes serve as the defining characteristics of their entrepreneurial journey. The importance of institutional influences is highlighted by the constantly changing environment in which these women operate, thus illustrating their significance. By examining the connection between women and institutions in the field of entrepreneurship, this research provides valuable insights of the complex mechanisms that influence women's interactions and highlight the transformative power of women entrepreneurs to shape institutions and society. Thus, through the development of a multidimensional theoretical framework, it illustrates the continuous interaction between entrepreneurs and their environments in a continual cycle. Through this, women have influenced family, cultural norms and institutional policies in subtle ways. The study describes bidimensional process influences and leverages their contexts to make entrepreneurial decisions, providing a framework for further research.47 0