http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/1196 2026-05-24T15:12:37Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17235 Photolancer: A web-based Freelancing Marketplace to Hire Photographer Shovon, Nowras Nafiz The Photolancer project has the potential to completely change how people in Bangladesh interact and hire photographers. By offering a sophisticated and user-friendly platform created especially for booking and providing photography services, Photolancer aims to address the difficulties that both clients and photographers experience. Using cutting-edge e-commerce technologies, Photolancer's strategy aims to give customers a flawless experience. The implementation of secure transaction processes will guarantee the privacy and security of personal data on a variety of devices. The project's strategic adoption of contemporary techniques fits with the anticipated technological development in the area, guaranteeing Photolancer's continued leadership in the freelance photography industry. a sustained and impactful presence within the country wide ecommerce marketplace. In precis, Photolancer, in its future trajectory, stands as a testament to innovation in e-book transactions, user-centric layout, and a dedication to meeting the evolving needs of ecommerce book-based enthusiasts in Bangladesh. The project's foresight and strategic positioning reflect a willpower to growing a reliable and colorful virtual market for hiring photographers. Project Report 2025-01-13T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17234 Performance Analysis of Banner and Poster Detection using Machine Learning Algorithms Sumon, Md.Shazzad Hossain This research project, conducted within the Computer Science and Engineering Department at Daffodil International University, focuses on the implementation of YOLOv8, a real-time object detection system, for the purpose of Banner and Poster Detection in the unique visual context of Bangladesh. Leveraging diverse datasets collected physically from local areas and annotated using the Roboflow website, the study explores the key elements contributing to the high accuracy of YOLOv8 in detecting banners. The model's architecture, including advancements in YOLOv8's latest version, bounding box regression, and confidence scoring, facilitates precise localization with confidence scores reaching 99.99%. The use of normalized coordinates and probability distribution further enhances the model's ability to generalize across different image sizes. Multiobject detection capabilities, training on diverse datasets, and post-processing strategies implemented by Ultralights' engine contribute to the model's robust performance. The research project attains a remarkable accuracy of 99.99%, validating the efficacy of YOLOv8 in automated banner detection tasks. The outcomes not only showcase the model's strengths but also hold significant implications for real-world applications, offering a reliable and accurate system for detecting banners and posters in the dynamic visual landscape of Bangladesh. Project Report 2025-01-13T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17233 Fresh Groceries - Your Trusted Web based Online Shopping Ryhan, Apu Fresh Groceries is a web-based online shopping platform designed to offer a seamless and user-friendly grocery shopping experience. This report provides a comprehensive overview of the project’s development lifecycle, focusing on the identification of user needs, system design, and implementation. The system aims to address the inefficiencies of traditional grocery shopping by offering features such as product filtering, real-time inventory updates, multiple payment options, and user-friendly interfaces. Developed using modern web technologies, Fresh Groceries provides a secure, scalable, and intuitive platform for users. The project’s findings underscore its potential impact on simplifying grocery shopping and enhancing customer satisfaction. Project Report 2025-01-01T00:00:00Z http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17202 Distinguishing between AI-Generated and Human-Written Content in the Modern Digital Landscape using Deep Learning Emu, Md. Atiq Morshed This study focuses on the growing difficulty of recognizing text that produced by machines in a time when artificial intelligence is extensively used. The study proposes a novel method based on a Long Short-Term Memory (LSTM), GRU and Hybrid architecture to distinguish AI-generated content and human-written text with remarkable accuracy. By employing advanced techniques for text preprocessing, vectorization, and embedding, we achieved an efficient design with average computational demands. We tested the models on a large dataset, the model demonstrated outstanding performance. The best model achieved an accuracy of 98.31% and the best F1-score is 0.98. These findings show the outstanding ability of the model to generalize well on unseen data, proving the potential of using it in real-world applications. The model's stability and reliability are backed by highly similar outcomes of the training and validation phases with minimal overfitting due to excellent regularization strategies. The confusion matrices and the full classification reports gave in-depth insights into the model's strengths and weaknesses, thus enhancing its applicability. Project Report 2025-01-13T00:00:00Z