Information and Computer Science‎

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https://hdl.handle.net/20.500.11888/10130

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Now showing 1 - 5 of 172
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    Deer Balak Alena
    (2025-06-18) Farah Assaf; Lial Awwad
    Deer Balak Alena which means "Take care of us" in the Palestinian dialect) is a public safety system designed to enhance security and emergency response in Palestine. The platform delivers real-time risk alerts and combats misinformation by verifying the credibility of news reports using AI-powered mechanisms. The system enables users to report incidents such as roadblocks, security threats, and emergency situations. Verified alerts are then distributed to both residents and emergency services through multiple communication channels, including offline methods, ensuring accessibility even without an internet connection. The system aims to create a secure and reliable digital space for emergency communication by implementing advanced trust and security mechanisms. These ensure the authenticity of users and content, verify submitted reports, and uphold community standards. By integrating predictive analytics, the system can anticipate potential risks and assist both authorities and individuals in making informed decisions. Strong security protocols are applied to protect user data and maintain system integrity. Through a combination of alert notifications, emergency coordination, and misinformation control, Deer Balak Alena plays a vital role in building a safer and more informed community.
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    Mzad Palestine
    (2025-06-19) Waleed Dweikat; Karim Mithqal; Saad Odeh
    Abstract MzadPalestine is a machine learning-powered auction platform designed to modernize traditional auction practices for Palestinian communities. Integrated with payment gateways like Strip, the platform serves two primary stakeholders: buyers (bidders) and sellers, alongside administrators. The system streamlines auction workflows through real-time bidding, ML-driven price predictions, and secure payment processing. Key features include a dark mode interface, post-auction review moderation, and compliance with GDPR/PCI-DSS standards for secure transactions. Designed as a responsive web application, MzadPalestine prioritizes accessibility, transparency, and cultural relevance, with plans for mobile app development in future phases. The platform supports dual transaction models: real-time auctions and fixed-price "Buy Now" purchases. Expanding beyond traditional auctions, MzadPalestine integrates comprehensive jobs and services marketplaces. The jobs module connects employers with job seekers, supporting both traditional employment sectors and emerging digital opportunities. The services marketplace enables local professionals and craftspeople to offer their expertise, from technical services to cultural crafts, with secure booking systems. These additional modules leverage the same robust infrastructure, and community-focused approach as the core auction platform. By bridging technology with localized practices, the platform aims to reduce information asymmetry, enhance trust, and drive economic empowerment across Palestinian regions.
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    AL-NAJAH Market
    (2025-02-02) Mohammad Rafeeq Tanbour; Yazeed Kamel Zahran
    The "An- Najah University Market" project is an innovative online platform created specifically for the students of An- Najah National University. Its main goal is to provide a comprehensive and integrated marketplace that addresses both the academic and personal needs of students. The platform allows students to buy a wide range of products, such as textbooks, electronics, accessories, and study supplies, while also offering the unique opportunity to sell their used or personal items for a small commission. By facilitating a convenient and accessible shopping experience, the platform aims to simplify the process of purchasing academic materials, technology, and personal items. Furthermore, it seeks to support student entrepreneurship by enabling students to list and sell their products directly on the platform. The website also offers daily deals, special discounts, and secure payment methods, ensuring that students can make purchases at competitive prices. With a user friendly interface, continuous customer support, and advanced features like order tracking, the "An-Najah University Market" platform promises to be a reliable and efficient tool that meets the evolving needs of university students.
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    ENHANCING ARABIC TEXT COMPREHENSION THROUGH Automated Grammar-Aware Question Generation
    (2025-02-03) Deema Jabi; Raneen Sawalmeh
    In recent years, the integration of Natural Language Processing (NLP) techniques into Arabic language education has gained significant attention. This project focuses on enhancing Arabic text comprehension through the development of an automated, grammar-aware question generation system. The system leverages advanced NLP models to analyze Arabic texts, ensuring a deep understanding of their syntactic and semantic structures. By incorporating Arabic grammar rules, the system generates contextually relevant and linguistically accurate questions tailored to the text. This approach not only aids in improving reading comprehension skills but also fosters a better understanding of Arabic grammar among learners. The project combines rule-based techniques with machine learning models, ensuring both precision and adaptability across diverse Arabic dialects and literary styles. The proposed solution has potential applications in educational platforms, language learning tools, and automated assessment systems, making it a valuable contribution to Arabic language technology and education. Keywords: Natural Language Processing, Annotation Systems, Similarity Measures, Online Quizzes.
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    Securing web applications using machine learning
    (2025-02-03) Osama Salahat; Yousef Assaf; Ameer Younis
    Web applications play a crucial role in today’s digital world, providing a wide range of services and handling sensitive data. However, their widespread use also makes them prime targets for cyberattacks, particularly SQL Injection (SQLi) and Cross-Site Scripting (XSS). These attacks can lead to data breaches, unauthorized access, and even complete system compromise. Traditional Intrusion Detection Systems (IDS) rely on predefined rules, which often fail to keep up with evolving attack techniques, leaving systems vulnerable. This project introduces a machine learning-based solution to protect web applications from SQLi and XSS attacks. Using a large dataset of real attack payloads, the system applies Term Frequency-Inverse Document Frequency (TF-IDF) to extract meaningful patterns from web inputs. A Logistic Regression model then analyzes these patterns to classify incoming requests as either safe or malicious. To enhance security further, the system is integrated with pfSense, a powerful open-source firewall, through its REST API. This integration not only detects threats but also blocks malicious users in real time, adding an extra layer of protection. Testing results show that the model achieves an impressive 97% accuracy in detecting attacks, highlighting its effectiveness. By leveraging machine learning, this approach overcomes the limitations of traditional IDS, offering an automated, scalable, and adaptive solution for web security. This project demonstrates the potential of AI-driven cybersecurity in defending modern online systems against ever-evolving threats.