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Capstone Thesis: Ameena & Zainab
No full textAmeena & Zainab follows two inseparable teenage girls in Saudi Arabia as they search for personal identity amid parental influence and individual challenges. Raised in a conservative environment, their bond is tested when personal beliefs and expectations collide. As tensions rise, they must confront what it truly means to grow up—and whether growing apart is sometimes part of that journey.
The film was sparked by a deeply personal event that prompted the director to revisit the evolution of a formative friendship. What began as private reflections and memories gradually grew into a cinematic exploration of adolescence, resilience, and the impact of religious instruction taught through fear rather than understanding. Influenced by films such as Wajda and Theeb, the project uses visual storytelling to mirror emotional change, shifting from warm, bright hues to cooler, muted tones and combining intimate close-ups with isolating wide shots to capture the characters’ inner worlds.
The production journey proved as dramatic as the narrative itself. Midway through filming, an unexpected setback involving the lead actress forced urgent script revisions and scene mergers to preserve both schedule and vision. Casting across different age groups introduced further challenges and surprising rewards, while close collaboration with editors, a colorist, and sound designers shaped the final aesthetic and emotional resonance. Six rounds of editing ultimately refined the pacing to a festival-friendly 14-minute cut.
Ameena & Zainab speaks first to young Saudi women but reaches far beyond, inviting audiences everywhere to reflect on adolescence, parental expectations, and the complexities of friendship. It stands as both personal closure for the filmmaker and a contribution to the growing landscape of Saudi cinema that celebrates authentic local stories with universal appeal
Cultural Influences on Mental Health Stigma: How Society Views Mental Health
No full textAbstract
Mental health stigma continues to be a major barrier to seeking psychological support in Arab and
Muslim societies, especially in Saudi Arabia, where cultural and religious values strongly
influence how mental illness is viewed. This study explored whether presenting mental health
education through an Islamic lens could help reduce people’s reluctance to seek professional help.
Using a quantitative, cross-sectional approach, 71 participants completed a stigma scale survey
before and after watching a culturally sensitive educational video concerning Islamic values. A
paired samples t-test showed a significant drop in avoidance scores after the intervention (p =
.003), suggesting that religiously framed content helped lower stigma. The findings align with
previous research on the importance of culturally relevant strategies and show that using familiar
religious frameworks can improve understanding and acceptance of mental health care. While the
results are encouraging, the study's generalizability is limited due to its small, mostly female
sample and short-term design. Future studies should examine long-term outcomes and explore how
different interpretations of Islam shape mental health attitudes. Overall, this research adds valuable
insight into how culturally informed methods can support stigma reduction in religious and
community-oriented settings
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No full textBackground: Depression is a widespread psychological issue among patients with cardiac diseases, which affects treatment adherence, recovery, and overall quality of life. Spiritual-based interventions (SBIs) have gained attention for their potential to alleviate depressive symptoms. However, there is a dearth of research investigating the efficacy of these interventions in the Middle East and North Africa (MENA) region.
Objective: This scoping review aims to synthesize available evidence on the efficacy of spiritual-based interventions in reducing depressive symptoms among cardiac patients in the MENA region.
Methods: The present research used a systematic approach to searching electronic databases such as SCOPUS, Web of Science, and ESBECOhost Arab research world in the English language from 2015 to 2025, based on the Arksey and O'Malley framework. Papers were identified based on spiritual-based Intervention addressing depressive symptoms among cardiac patients in the MENA region. Studies were analyzed using the Population-Concept-Context (PCC) framework, as outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR).
Results: The analysis pinpoints numerous Spiritual Interventions, such as prayer, mindfulness, and Faith-based counseling, as usual practices. The results showed that SBIs are related to a decrease in depression symptomology and enhancement of psychological wellbeing. Nevertheless, variability in the study models, small amounts of samples, and sparse long-term studies are also some of the current study's limitations.
Conclusion: This research indicates that the efficacy of spiritual-based interventions can reduce depressive signs in cardiac patients in the MENA region. However, additional research is required to ascertain long-term efficacy and cross-cultural effectiveness.N
Unlocking the potential of Arabic NLP : High-quality dataset and preprocessing tool for Arabic large Language models
No full textArabic remains one of the most widely spoken yet technologically underserved languages in the field of Natural Language Processing (NLP), especially within academic and formal domains. This project addresses two critical
gaps in Arabic NLP: the scarcity of high-quality domain-specific Arabic datasets for low-resource LLMs and the lack of automated frameworks tailored to the complexities of the Arabic language. Arabic remains underrepresented
in large-scale NLP research due to data sparsity, high morphological richness, and limited domain-specific corpora — particularly in academic and educational contexts. To bridge this gap, we developed a curated academic dataset that captures formal Arabic usage across disciplines, aimedat enhancing the training and evaluation of Arabic Large Language Models (LLMs). In parallel, we built a robust, modular framework for large-scale Arabic data preprocessing. This framework automates advanced linguistic refinement stages including deep normalization, morphological transformation, diacritization, and distributed deduplication across multiple GPUs, as well as semantic scoring using LLM-based annotators. By integrating data from Common Crawl and additional Arabic sources such as books and journals, and applying data-centric AI techniques and morphological analysis, the framework ensures high linguistic semantic coherence. Our output is a high-quality, academic-specific Arabic dataset. That was validated through intrinsic evaluations—grammar correctness, lexical diversity, readability, and topic coherence—and extrinsic evaluations on downstream tasks.These outcomes validate the framework’s effectiveness and its potential to accelerate the development of Arabic AI systems. This project supports Saudi Vision 2030 by advancing Arabic AI and aligning with SDG 4 through academic resource accessibility and SDG 9 through scalable NLP tools
Consumer Trust in Digital Payment Systems
No full textThis study explores consumer trust in digital payment systems and its impact on adoption rates. Through qualitative interviews, it identifies key factors influencing trust, including security measures, user experience, transparency, and perceived risks. Recommendations focus on enhancing trust through robust security, transparent policies, and tailored support for diverse demographics, especially for the elderly. This study presents the results of five in-person interviews I conducted with Saudi Arabian respondents of various ages on their experiences with digital payment systems. According to the interview, the most significant aspects impacting digital payments are trust, security measures, demography, and reputation, while risk, transparency, and experience are critical for assessing service provider credibility. It sheds light on the crucial aspect of customer services that affects digital payment trust and ensures that the consumer will continue to use these services. Consumers desire transparency and robust security measures. Positive experiences with digital payments boost trust, while negative ones raise concerns. Trust in digital payment systems varies based on cultural attitudes, demographics, and age. Prioritizing security measures, transparent policies, and trustworthy customer service can enhance trust.N
Loneliness and susceptibility to social pain mediate the association between autistic traits and psychotic experiences in young non-clinical adults.
No full textUnderstanding of the mechanisms involved in the occurrence of psychotic experiences (PEs) in highly autistic individuals is crucial for identifying appropriate prevention and intervention strategies. This study aimed to investigate the mediating role of susceptibility to social pain and loneliness in the relationship between autistic traits (ATs) and PEs in adults from the general population of 12 Arab countries. This cross-sectional study is part of a large-scale multi-country research project. A total of 7646 young adults (age range 18-35 years, mean age of 22.55 ± 4.00 years and 75.5% females) from twelve Arab countries (i.e., Algeria, Bahrain, Egypt, Iraq, Jordan, Kingdom of Saudi Arabia, Kuwait, Lebanon, Morocco, Oman, Palestine, and Tunisia) were included. Mediation analyses showed that, after adjusting over confounding variables, both loneliness (indirect effect: Beta = 0.18; Boot SE = 0.02; Boot CI 0.14; 0.21) and social pain (indirect effect: Beta = 0.03; Boot SE = 0.01; Boot CI 0.001; 0.05) partially mediated the association between ATs and PEs. Higher ATs were significantly associated with more loneliness and susceptibility to social pain, and directly associated with more severe PEs. Finally, higher loneliness and susceptibility to social pain were significantly associated with greater PEs scores. Findings indicated that individuals with higher ATs tend to experience greater loneliness and feel more pain from rejection, which can in turn be associated with higher levels of PEs. Interventions targeting susceptibility to social pain and loneliness as a means of mitigating PEs among highly autistic adults should be considered
A Dynamic KNX-Based Energy Management System Utilizing Machine Learning and Probabilistic Models for Renewable Energy Optimization
No full textThis thesis presents a comprehensive analysis and optimization of an intelligent Energy Management System (EMS) specifically developed for residential buildings, integrating real-time occupancy-driven controls and advanced machine learning (ML) predictive modeling. Initially, a baseline energy scenario was established using HOMER Pro, simulating a grid-connected hybrid renewable energy system featuring a 200 kW photovoltaic (PV) array and a 100 kW inverter for a mid-rise residential building located in Jeddah, Saudi Arabia. Comparative analysis revealed significant hourly energy savings of approximately 15.33% through the implementation of occupancy-responsive automation based on KNX control systems.
To further refine energy predictions and optimization, predictive modeling using Support Vector Regression (SVR) and alternative ML techniques was conducted utilizing the high-resolution Solace dataset. Bayesian hyperparameter optimization was systematically applied, enhancing the predictive reliability of SVR. At a moderate dataset scale (10,000 sampling points), SVR achieved robust predictive with an RMSE of 0.0781 kWh and R² of 0.9195) and practical computational efficiency. However, at a larger scale (100,000 sampling points), SVR accuracy declined notably with an RMSE of 0.0904 kWh and R² of 0.8854, highlighting an inherent performance trade-off related to dataset size and computational demand. In contrast, Random Forest maintained superior accuracy with an RMSE of 0.0702 kWh and R² of 0.9310 at large scale, albeit with significantly increased computational overhead.
The SVR predictive optimization (at moderate scale) further enhanced annual energy savings by approximately 16.48% compared to the baseline scenario, totaling approximately 61,799 kWh/year. These findings validate the clear economic, operational, and environmental benefits of incorporating predictive machine learning into intelligent occupancy-driven EMS frameworks, emphasizing the importance of strategically balancing predictive accuracy with computational resources and dataset granularity
