Information and Computer Science
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- ItemAL-NAJAH Market(2025-02-02) Mohammad Rafeeq Tanbour; Yazeed Kamel ZahranThe "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.
- ItemENHANCING ARABIC TEXT COMPREHENSION THROUGH Automated Grammar-Aware Question Generation(2025-02-03) Deema Jabi; Raneen SawalmehIn 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.
- ItemSecuring web applications using machine learning(2025-02-03) Osama Salahat; Yousef Assaf; Ameer YounisWeb 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.
- ItemodeConnect: A Dynamic Platform for Learning Programming(2025-02-03) Abdullah Hamza; Monawar Abu Shalbak; Ameera HakawatiWe have built a web-based interactive platform for online programming languages learning, starting with Python at its core. The platform aims to assess students in a highly effective manner through a learning path that is structured, whereby students can advance through lessons and exercises with points and levels-ish on progress and performance. A placement test will also help such people determine the most appropriate entry level for the user in question. Several other options include that of registering either as a student or a teacher. A teacher can add new languages, lessons, and exercises into the system, after being vetted by administrators of the platform. These administrators are responsible for granting permissions to the teachers for letting them to add content like programming language and lessons and ensure the quality and relevance of the material being added. This functionality further magnifies the platform's adaptability to various educational needs, maintaining it as a scalable and lean app for any new updates around programming trends. In general, this platform aspires to offer an uninterrupted learning ecosystem that develops each individual and promotes cooperative learning.
- ItemFIRE AND SMOKE DETECTION SYSTEM(2025-02-03) Mohammad Ammar Jararrah; Amr Adnan BadranThe Fire and Smoke Detection System is an AI-powered solution designed for real-time detection of fire and smoke in video streams. Utilizing a YOLO-based deep learning model, the system processes video feeds from cameras, ensuring high accuracy in identifying potential fire hazards. The system comprises two main components: a Python server, responsible for video analysis and detection, and a .NET MAUI client, which provides users with real-time alerts and live monitoring capabilities. With features such as automatic network discovery, QR code-based remote access, and efficient communication between client and server, the system offers a seamless experience for both local and remote fire detection. This project aims to enhance fire safety by providing an accessible and efficient monitoring tool for homes, businesses, and industrial environments