Please use this identifier to cite or link to this item:
http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9962| Title: | JUIT-OLX |
| Authors: | Ekaghara, Mayank Tomar, Aditya Bharti, Monika [Guided by] |
| Keywords: | YOLO OLX e-commerce Machine learning |
| Issue Date: | 2023 |
| Publisher: | Jaypee University of Information Technology, Solan, H.P. |
| Abstract: | juitOLX is an e-commerce website designed to provide students with a sustainable and cost-effective way to buy and sell items they need. To promote sustainability and legal compliance, the website has incorporated a machine learning (ML) model using the YOLOv3 algorithm. This model detects illegal objects in images uploaded by students before they are listed on the website. juitOLX is built using the MERN stack, which includes MongoDB, ExpressJS, ReactJS, and NodeJS, providing a robust and scalable platform with real-time updates for buyers and sellers. The website offers features such as image uploads, search functionality, messaging system, and a seamless and transparent buying and selling experience. By promoting sustainability, responsible consumption, and legal compliance, juitOLX has the potential to revolutionize e-commerce platforms for students. |
| Description: | Enrollment No. 191406, 191431 |
| URI: | http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9962 |
| Appears in Collections: | B.Tech. Project Reports |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| JUIT-OLX.pdf | 6.59 MB | Adobe PDF | View/Open |
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