http://repository.aaup.edu/jspui/handle/123456789/1 Arab American University Fri, 15 May 2026 06:06:30 GMT 2026-05-15T06:06:30Z http://repository.aaup.edu/jspui/handle/123456789/3855 Title: A Proposed Training Program Based on Emotional Intelligence to Enhance the Mental Health of Students at Arab American University رسالة ماجستير Authors: Masae’d, Madleen Jihad Naji$AAUP$Palestinian Abstract: In view of the growing psychological and academic demands on our university students, it is necessary to seek ways that effectively promote mental health and coping. Emotional intelligence is one of the main psychological factors that can assist in enhancing a student's psychological quality of life, reduce stress, anxiety and depression, and in addition to enhancing social interaction and academic performance. From this perspective, this study aimed to identify the level of mental health, emotional intelligence and academic pressure of Arab American university students. Then, propose a training program based on emotional intelligence to enhance the mental health of students. The study population consisted of all undergraduate students enrolled in various faculties at Arab American University and a convenient sample of 300 students. The study adopted a developmental research approach aimed at designing a proposed training program based on emotional intelligence to enhance the mental health of students at the Arab American University. A questionnaire was used as the primary data collection tool to measure levels of emotional intelligence, levels of depression, levels of anxiety, levels of stress, and levels of academic pressure among the students. The results obtained from the questionnaire were used to identify students’ needs and to guide the development of the proposed training program. The results showed that the level of emotional intelligence among undergraduate students varied. with 37.3% of students demonstrating a low level, 50.3%, showing a medium level, and only 12.3% having a high level of emotional intelligence. Many students also experienced significant mental health challenges. The highest percentage, 45.7%, reported an extremely severe level of depression; 51.7%, had an extremely severe level of anxiety; and 38.7% exhibited a severe level of stress. In terms of academic pressure, 69.7% of students V experienced a medium level of pressure, which was the highest among the categories reported. Description: Master \ Innovation in Education Thu, 01 Jan 2026 00:00:00 GMT http://repository.aaup.edu/jspui/handle/123456789/3855 2026-01-01T00:00:00Z http://repository.aaup.edu/jspui/handle/123456789/3854 Title: Using Machine Learning to Detect Network Client Health Security in Zero Trust Architecture رسالة ماجستير Authors: Tanina, Montaser I.M.$AAUP$Palestinian Abstract: Modern organizations face increasing cybersecurity challenges as cyberattacks expand due to remote work, cloud services, and Bring Your Own Device (BYOD) policies. Zero Trust Architecture (ZTA) has emerged to address these challenges by applying a "never trust, always verify" model to every user and device. This thesis targets a critical vulnerability in ZTA: the real-time assessment of endpoint security health. We propose a machine learning based framework for continuously assessing device security health and integrating this information into ZTA decision-making processes. A comprehensive dataset was created by collecting data from multiple sources (such as update status, antivirus presence, vulnerabilities, and system behavior indicators) from different environments. To overcome the limitations of real-world data, synthetic data augmentation techniques (including a GPT-based) were applied, expanding the dataset while maintaining realistic distributions. Each device was assessed using a Device Risk Measure (DRM) that combines factors such as compromise likelihood and potential impact, enabling the training of supervised learning models with clear accept/deny labels. Several machine learning algorithms (such as support vector machines, decision trees, and ensemble methods) were trained and evaluated based on their ability to classify devices as "healthy" (acceptable) or "unhealthy" (should be denied from network access). The models achieved high accuracy in distinguishing device trust levels, with the best-performing model exceeding 99% classification accuracy. The integration of feature extraction highlighted the most critical security features contributing to device risk. The results demonstrate the potential for effectively integrating data-driven adaptive device health checks into a zero-trust (ZTA) model. This approach enables dynamic policy implementation, allowing the policy decision point to trust or quarantine devices based on their current risk level. This helps reduce the attack surface and prevents compromised or non-compliant devices from compromising the network. The research has significant implications for cybersecurity practices, providing a blueprint for enhancing ZTA implementations using machine learning, ultimately improving automated threat prevention and organizational resilience Description: device health check, Zero trust Architecture, Machine Learning, Risk Assessment, Network Access control Thu, 01 Jan 2026 00:00:00 GMT http://repository.aaup.edu/jspui/handle/123456789/3854 2026-01-01T00:00:00Z http://repository.aaup.edu/jspui/handle/123456789/3853 Title: نحو تطوير نظرية الطوارئ في إدارة الصراع الاستراتيجي: مقاربة نقدية لدمج نظريات القيادة في بُنية التحالف المُهيمن. رسالة ماجستير Authors: جرادات, علي حازم محمد$AAUP$Palestinian Abstract: اعتمدت الدراسة المنهج النوعي النقدي التحليلي، موظفةً استراتيجيات "إشكالية الأدبيات" و"نقل النظريات عبر الحقول" المستمدة من منهجية "المنقّب-المستكشف"، حيث جرى تحليل أكثر من 150 دراسة علمية وفصلاً أكاديمياً محكماً للوصول إلى مرحلة الإشباع النظري. وقد سعت الدراسة من خلال هذا الإطار إلى دمج أربعة أنماط قيادية محورية أثبتت فعاليتها في السياقات المتأزمة، وهي: القيادة التحويلية، القيادة التبادلية، القيادة التعاونية (الخدماتية)، ونموذج تبادل القائد والأعضاء (LMX)، وذلك لتطوير فهم أعمق لدور القيادة العليا في تشكيل الاستجابة التنظيمية. خلُصت الدراسة إلى اقتراح "نموذج نظري هجيني" يتجاوز النظرة الوصفية التقليدية نحو بناء "بُنية توليدية" تُنتج المعنى والشرعية وتُفكك شبكات السلطة. ويتكون هذا النموذج من أربعة أبعاد متكاملة تضمن فاعلية الاستجابة: البعد الإدراكي التحليلي (تأطير الأزمة)، البعد العلائقي-النفوذي (هندسة شبكات الثقة)، البعد الإجرائي-الاتصالي (تفعيل موقع العلاقات العامة)، والبعد الرمزي-التأويلي (إنتاج السردية التنظيمية). وتؤكد النتائج أن موقع ممارسي العلاقات العامة داخل "المجموعة الداخلية" للقيادة يُعد متغيراً حاسماً لنجاح إدارة الصراع الرمزي. تتجلى الأهمية الاستراتيجية لهذه الدراسة في قدرتها على التكيّف مع السياق العربي والفلسطيني المعقد؛ حيث اقترح الباحث تطبيقات عملية للمنظمات الحكومية والإغاثية الفلسطينية لمواجهة أزمات تآكل الثقة والانغلاق الاتصالي. وتوصي الدراسة بضرورة إعادة هندسة "التحالف المهيمن" ليكون فضاءً للحوار والذكاء الجماعي، بما يضمن تحويل الأزمات من "حالات طوارئ" منكمشة إلى "فرص تأويلية" لإعادة بناء الهوية المؤسسية والشرعية المجتمعية. Description: Master \ Contemporary Public Relations Thu, 01 Jan 2026 00:00:00 GMT http://repository.aaup.edu/jspui/handle/123456789/3853 2026-01-01T00:00:00Z http://repository.aaup.edu/jspui/handle/123456789/3852 Title: Analyzing Customer Reviews on Banks Mobile Applications: A Bilingual Approach Using Natural Language Processing for Topic Detection and Sentiment Analysis رسالة ماجستير Authors: Maswadeh, Aseel “Mohammed Osama$AAUP$Palestinian Abstract: Mobile banking applications have become a central channel for financial interaction, service delivery, and customer engagement in the Arab world due to the rapid expansion of digital banking services. This study aims to analyze Arabic user sentiment toward mobile banking applications and to identify the main topics that influence user satisfaction and dissatisfaction, in order to support service improvement and decision-making in the banking sector. The study was conducted in 2025 using user reviews collected from Google Play for mobile banking applications affiliated with the top 100 Arab banks listed by The Banker (2024). Data were collected using a Python-based data extraction tool, resulting in more than 361,000 reviews. The dataset was filtered to include Arabic-language reviews only, while preserving the natural country distribution across the MENA region, reflecting real usage patterns and dialectal diversity. This dataset represents the study population, from which a labeled subset was used as the analytical sample. The methodology involved developing a domain-specific Arabic preprocessing pipeline that includes text normalization, dialect handling, negation preservation, and meaningful bigram construction. Several annotation approaches were examined and compared, including manual labeling, rating-based sentiment inference, and GPT-assisted classification. Based on reliability and consistency, manual labeling was adopted as the final reference annotation. Class imbalance was addressed using class weights, SMOTE, and random oversampling. Sentiment classification was evaluated using traditional machine-learning models, an LSTM model, and transformer-based Arabic language models (AraBERT and MARBERT). Topic identification was conducted using a dual strategy combining probabilistic Latent Dirichlet Allocation (LDA) and GPT-based topic generation. The results show that transformer-based Arabic models achieve the highest performance, with MARBERT outperforming all baselines due to its strong ability to capture dialectal and contextual information. Topic analysis indicates that positive reviews mainly emphasize ease of use, convenience, and successful transactions, whereas negative reviews are dominated by reliability related issues such as login failures, unstable updates, slow performance, and application crashes. The study recommends prioritizing application reliability, strengthening pre-release testing, and adopting continuous sentiment monitoring to improve mobile banking services. Description: Master \ Data Science and Business Analytics Thu, 01 Jan 2026 00:00:00 GMT http://repository.aaup.edu/jspui/handle/123456789/3852 2026-01-01T00:00:00Z