Intelligent Web Data Extraction System for E-commerce

被引：0
|
作者：
Selvy, P. Tamije
Anitha, M.
Varthan, L. R. Vishnu
Sethupathi, P.
Adharsh, S. P.
机构：
来源：
JOURNAL OF ALGEBRAIC STATISTICS | 2022年 / 13卷 / 03期
关键词：
Adaptive web scraping; Deep learning; Long Short-Term Memory (LSTM); Web data extraction; You only look once (Yolo); ResNet;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：63 / 68
页数：6
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