http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/16 2026-06-05T20:31:10Z 2026-06-05T20:31:10Z Shovon, Nowras Nafiz http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17235 2026-05-23T10:03:56Z 2025-01-13T00:00:00Z

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 Sumon, Md.Shazzad Hossain http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17234 2026-05-23T10:03:22Z 2025-01-13T00:00:00Z

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 Ryhan, Apu http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17233 2026-05-23T10:01:35Z 2025-01-01T00:00:00Z

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 Shanto, Emon Islam http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17232 2026-05-23T10:00:16Z 2025-01-19T00:00:00Z

Zero-shot Learning for Predicting Unseen Student Activities in Educational Platforms Shanto, Emon Islam Zero-shot learning (ZSL) has emerged as a groundbreaking approach in machine learning, enabling models to classify unseen categories by leveraging the relationships between known and unknown categories through semantic knowledge. Within educational platforms, accurately predicting and understanding student activities is vital for personalizing learning, tracking progress, and improving adaptive learning systems. However, the wide diversity of potential student activities makes collecting labeled data for every scenario unfeasible, posing a significant limitation to traditional supervised learning methods. This study addresses this limitation by introducing a ZSL framework specifically designed to predict unseen student activities. The proposed framework leverages semantic embeddings, such as word2vec and BERT, to establish meaningful connections between known and unknown activities, enabling accurate predictions without labeled examples. The framework is thoroughly evaluated using real-world datasets of student interaction logs, with performance assessed across metrics such as accuracy, precision, recall, and F1-score. The results highlight the framework's ability to deliver strong predictive performance while providing valuable insights into the relationships between activity categories. By bridging the gap between labeled and unlabeled data, this research showcases the transformative potential of ZSL in advancing educational platforms. It demonstrates how ZSL can enhance adaptive learning systems, foster student engagement, and equip educators withactionable insights, driving the development of smarter, more personalized educational technologies. Project Report

2025-01-19T00:00:00Z