Isra Ibrahim Zawahra

I graduated from Al-Quds Open University in Palestine with a Bachelor’s degree in Computer Information Systems, Information Security minor, with a grade of “Very Good”.

During my studies, I completed courses in software development, database design, network programming, systems analysis, artificial intelligence, and management information systems. My specialization in information security included topics such as network security, cybersecurity principles, information protection strategies, security protocols, ethical hacking, and data encryption techniques.

This comprehensive education provided me with a solid foundation in the theoretical and practical aspects of computer information systems and information security, preparing me to effectively meet the challenges in the ever-evolving field of information technology.

I enrolled in the Master's Program in Artificial Intelligence and Data Analytics at the Graduate School of Palestine Ahliya University in the second semester of 2023, driven by my deep passion for AI techniques and their applications in analyzing big data and addressing real-world problems. During my studies, I gained in-depth knowledge in the fields of machine learning, neural networks, statistical data analysis, and predictive modeling, as well as practical experience through research and applied projects.

I submitted my Master's thesis on January 22, 2025, and it addressed a specialized topic in the applications of AI to improve performance and analyze patterns in a specific field (Improving Fracture Detection: An AI-Based Approach to Wrist X-Ray Analysis). I received a "Very Good" grade for the thesis.

I am currently working on converting the thesis into two research papers for publication in peer-reviewed scientific journals in the field of artificial intelligence and scientific research. This aims to contribute to the development of academic knowledge and share research findings with the scientific community.

This certification, issued by Cisco, provided comprehensive knowledge in cybersecurity operations, including network monitoring, threat detection, and incident response. The program emphasized hands-on experience with security tools and technologies used in real-world Security Operations Centers (SOCs). Through this course, I developed a strong understanding of cybersecurity principles, intrusion analysis, and the implementation of security policies, preparing me for roles related to cyber defense and security monitoring.

I added to my skills a specialized course in web design, where I learned how to create interactive websites using HTML and CSS programming languages, in addition to improving the user experience (UX) and designing user interfaces (UI). This course enhanced my ability to develop responsive and effective websites that meet the needs of users.

During my training in the Archives Department of the Bethlehem Municipality, I entered and organized citizen data using Microsoft programs such as Excel and Word, which contributed to improving file management and digital archives. I also acquired skills in data processing, input accuracy, and teamwork, which enhanced my contribution to improving the services provided.

I worked as a freelance assistant in data analysis and research project development, designing advanced analytical models using tools such as Python (Pandas, NumPy, Matplotlib) and Excel. Tasks included preparing reports and analytical files, developing machine learning models to extract patterns and information from data, and providing integrated research solutions based on accurate scientific foundations. I also contributed to the preparation of documents and presentations in accordance with established academic standards, in addition to providing technical support in project implementation and analysis of results.

I developed a smart home e-pharmacy to help seniors organize their medication. It reminds them to take their medications and alerts them when the medication expires or runs out. The project utilized data analysis and artificial intelligence techniques to ensure accurate appointments and recommendations, while also designing an easy-to-use interface tailored to the needs of the target group. The project contributed to enhancing the independence of seniors and improving their adherence to medical treatment.

This project aimed to develop an advanced image captioning system by integrating Convolutional Neural Networks (CNN) for image feature extraction and Long Short-Term Memory (LSTM) networks for generating natural language descriptions. The model was trained on large-scale image datasets and fine-tuned to produce accurate and contextually relevant captions. Emphasis was placed on descriptive analysis, enabling the system to generate meaningful and coherent sentences that capture the visual content and context of images. The outcomes of this project were accepted and published as a book chapter in Springer, showcasing its academic and practical relevance in the field of computer vision and natural language processing.

This project focused on the automatic detection and classification of photovoltaic (PV) solar panels in satellite imagery using machine learning techniques. I applied and compared multiple models, including convolutional neural networks (CNNs), to improve the accuracy and speed of panel recognition. The system was trained and validated using real-world datasets and demonstrated significant potential for use in smart grid management and renewable energy infrastructure planning. The project focused on satellite image preprocessing, feature extraction, model training, and performance evaluation. The results are currently being published in a scientific research journal.

This project aimed to develop an intelligent system for email classification using various data analysis and natural language processing (NLP) techniques. The primary objective was to categorize incoming emails into predefined classes such as spam, promotional, personal, or work-related content. The project involved cleaning and preprocessing textual data, applying feature extraction methods (e.g., TF-IDF and word embeddings), and implementing classification algorithms such as Naive Bayes, Support Vector Machines (SVM), and Logistic Regression. Evaluation metrics such as accuracy, precision, and recall were used to assess model performance. The project highlights the practical integration of data science and machine learning to streamline digital communication management.