Information and Computer Science‎

Permanent URI for this collection

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

Browse

Recent Submissions

Now showing 1 - 5 of 159
  • No Thumbnail Available
    Item
    HISBA
    (2024-07-02) Mohammed Najeh Ghanim; Haneen Bashar Hashaika; Hala Zahi Najim
    The project aims to provide a comprehensive solution for farmers by providing a technological platform that enables them to market their products directly, without the need for intermediaries. This initiative seeks to enable farmers to achieve higher profits and enhance the sustainability of their agricultural busi- nesses. The project also seeks to promote value-added industries by converting surplus agricultural products into value-added products. This effort aims to en- hance the role of women in the agricultural sector and empower them economi- cally. In addition, the project seeks to raise awareness about the importance of agriculture and its products among consumers, and encourage them to support local farmers and buy their products. Which contributes to strengthening the local economy and developing the agricultural sector
  • No Thumbnail Available
    Item
    ADEL
    (2024-07-05) Amr Qabaha; Zuhair Madmouj
    ADEL is a comprehensive Software-as-a-Service (SaaS) web application designed to optimize the management processes of law firms. Developed using (Laravel-PHP, Firebase, Livewire, and MySQL). ADEL offers a robust and user-friendly platform for lawyers, clients, and managers. Key features include a real-time chatting system for seamless communication, strong security measures, calendar management for efficient scheduling, document upload capabilities, and a comprehensive client management system. Additionally, ADEL includes a billing and financial section, an OCR feature for automated document processing, and a customizable notification system. Lawyers can join existing offices using subscription codes, while clients can link to specific lawyers. ADEL aims to digitize and securely store paper-based documents, enhancing the overall efficiency and effectiveness of law firms.
  • No Thumbnail Available
    Item
    Elite-shop E-Commerce
    (2024-07-02) Asem Assi.; Hamza Mahmoud
    Our graduation project is an e-commerce application designed to address the constraints and limitations faced by stores on social media platforms. The primary aim of the project is to provide a dedicated website for stores and trading, offering a more efficient and user-friendly experience for both sellers and buyers. By transitioning from social media to a specialized e-commerce platform, stores can benefit from better organization, enhanced functionality, and increased visibility. This project leverages MongoDB for database management, ensuring scalable and flexible data handling. The ultimate goal is to create an environment where businesses can thrive and customers can enjoy a seamless shopping experience.
  • No Thumbnail Available
    Item
    GRID REAL ESTATE DEVELOPMENT
    (2024-07-07) Raghad Nouri; Maha Khilfeh
    Grid Real Estate Development Our website aims to provide a digital environment that connects real estate owners and project owners, where each can benefit from the artificial intelligence integrated into the platform to enhance their experience. The site allows real estate owners to display their properties in an advanced manner and enables project owners to search for suitable properties for their projects based on precise criteria and other user evaluations.
  • No Thumbnail Available
    Item
    Optimizing Pneumonia Detection from Chest X-ray
    (2024-07-04) Yousef Taha; Yazeed Rashed
    Pneumonia remains a critical global health concern, necessitating quick and precise diagnostic strategies to improve patient outcomes. In response, this study investigates how ensemble learning techniques can significantly enhance the accuracy of lightweight deep learning models in diagnosing pneumonia from chest X-ray images. To achieve this, we utilized five advanced convolutional neural networks: MobileNet, EfficientNetB0, DenseNet121, NasNetMobile, and ResNet. We trained each model on a curated dataset of labeled chest X-rays and optimized it through extensive hyperparameter tuning. We combined the predictive outputs of these models using a soft voting ensemble method, leveraging their collective strengths to enhance classification performance. The ensemble model demonstrated superior accuracy and reliability, achieving higher accuracy and recall compared to individual models. Confusion matrix and ROC curve analysis further validated the ensemble's performance, highlighting its improved ability to distinguish between normal and pneumonia-affected images. This study showcases the potential of ensemble learning in medical image analysis, providing a robust system that can significantly assist clinicians in early and accurate diagnosis of pneumonia.