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Defense

Sunday, April 7, 2024
Defense of a Master’s Thesis by Batoul Hamad in the Strategic Planning and Fundraising Program

Researcher Batoul Ahmed Hamad, a student in the Master’s Program in Strategic Planning and Fundraising, has defended her thesis titled "Enhancing Corporate Social Responsibility Practices and Their Impact on Sustainable Development in The Banking Sector in Palestine".

This study aimed to assess the degree of application of social responsibility in the banking sector in Palestine, and its impact on sustainable development. A quantitative approach was followed to answer the research questions and test hypotheses to summarize the dimensions of enhancing corporate social responsibility practices and their impact on sustainable development in the banking sector in Palestine. The researcher relied on the questionnaire instrument.

Wednesday, April 3, 2024
Defense of a Master’s Thesis by Abd Al Ghani Takrouri in the Strategic Planning and Fundraising Program

Researcher Abd Al Ghani Hafez Takrouri, a student in the Master’s program in Strategic Planning and Fundraising has defended his thesis titled "Strategic Planning and Financial Sustainability in the Palestinian NGOs: The Enabling Role of Regulatory Framework".

This study investigates the dynamic interaction between strategic planning statements and sustainability practices in the context of Palestinian NGOs. The research focuses on the unique social and political landscape of Palestine, and explores how strategic planning processes within these NGOs can be improved to integrate and advance sustainability goals, through an in-depth examination of case studies, interviews and the relevant literature.

Wednesday, April 3, 2024
Defense of a Master’s Thesis by Ayat Najjar in the Cyber security Program

Researcher Ayat Awad Najjar, a student in the Master’s Program in Cyber Security has defended her thesis titled "Detecting Written Documents by ChatGPT for the Cybersecurity Domain Using Machine Learning".

This research focuses on creating a robust model for detecting texts produced by large language models popular in the field of natural language processing, based on the assumption that machine learning technology can detect machine learning technology. The first study explores the field of cyber security and highlights the potential risks associated with using AI-generated texts in malicious ways.

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