Smart shopping carts by Fitting alerts
Download| Volume 2 Issue 2 2021 | |
|---|---|
| Author(s): |
Adnan Alam Khan
Suresh Kumar
Tahira Adnan
Dr.Muhammad Taha Jilani |
| Abstract | Humans are drawn to social media by marketing that are based on cognition. Users are primarily interested in the apparel section; what is their opinion on the size of clothes? Most designers produce dresses based on overall ethnicity, while others use free-size dresses, mostly undergarments, which is not a wise clothing approach. Is this study’s assessment on the future of online clothes negative? Is there any way to save money on the internet with this strategy? Any adroit method that can make buyer and seller happy. By merging deep learning and neural networks, this study is taking the initiative to overcome this challenge. Initially, participants in this research were asked to accept cookies, which are a type of intelligent agent, sometimes known as a we-agent that is loaded as an app on your browser. When you place an order, it establishes a connection with the shopping server and browses recommended cloths for the user by calculating or matching user fittings attributes and cloths attributes, such as Chinese shoe size 42 but user wears 10 sizes, both sizes are the same but attributes are defined for China from 10 to 45+, whereas shoe sizes in our country are generally 0-12. This study depicts an intelligent agent is mandatory to bridge attribute gap to save time. An intelligent agent is the ultimate remedy to address this problem. The goal of the project is to offer users an idea and to estimate which brand and size will be the best for them based on data/input supplied by consumers. The agent, which compares user qualities with available attributes from the shopping cart (the name of this study is fitting alerts) and follows a deep learning algorithm, will produce .the best result |
| Keywords | clothing shopping cart, deep learning, artificial intelligent agent, attribute comparison |
| Year | 2021 |
| Volume | 2 |
| Issue | 2 |
| Type | Research paper, manuscript, article |
| Recognized by | Higher Education Commission of Pakistan, HEC | Category | Y | Journal Name | ILMA Journal of Technology & Software Management | Publisher Name | ILMA University | Jel Classification | -- | DOI | - | ISSN no (E, Electronic) | 2709-2240 | ISSN no (P, Print) | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://ijtsm.ilmauniversity.edu.pk/arc/Vol2/i2/pdf/5.pdf | Page | 31-34 |