Podrobná bibliografie
| Název: |
Intelligent Web Data Extraction System for E-commerce. |
| Autoři: |
Selvy, P. Tamije, Anitha, M., Varthan, L. R. Vishnu, Sethupathi, P., Adharsh, S. P. |
| Zdroj: |
Journal of Algebraic Statistics; 2022, Vol. 13 Issue 3, p63-68, 6p |
| Témata: |
ELECTRONIC commerce, DATA extraction, DEEP learning, SHORT-term memory, ARTIFICIAL neural networks |
| Abstrakt: |
Data is crucial in today's world for whichever domain is taken to offer a higher and more intuitive experience for a user, Sometimes data is readily available in raw format, and Sometimes we need to extract the data from other websites/sources using advanced web scraping technologies. However, a web scraper that is based on a set of rules fails in the real world because the website's content dynamically and rapidly changes over time which in turn also changes the HTML contents of the website content. This study investigates a mechanism to allow automated web data extraction, In this study, an intelligent web data extraction using convolutional and Residual Neural Networks (ResNet) is developed. Usage of ResNet in the training layer accelrates the over all learning speed of the model. [ABSTRACT FROM AUTHOR] |
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Copyright of Journal of Algebraic Statistics is the property of New York Business Global LLC and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: |
Complementary Index |
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