Efficient Distributed Web Crawler Using Hefty and Enhanced Bandwidth Algorithms for Drug Website Search

Abstract For this study, I completed a comprehensive review of punishment clauses in the Korean Legal Code from 1985 to 2016. Using a web crawler and text analysis, I gathered data on the laws and then identified the content of the penal sentence in each clause. By investigating the data, I was able to quantify and assess changes over time in: (1) the number of punishment clauses; (2) the severity of sentences; and (3) the balance between imprisonment and fines. In order to examine the causes of these changes, I separated the data into different sentence levels and sectors. I found that low-level punishment clauses had grown quickly, and some of the sectors responsible for the change included civil engineering and sex offenses. This comprehensive review of the penal sentences revealed issues of concern related to overcriminalization, overpenalization, and an imbalance of punishment level in the Korean Legal Code.

Download Full-text

A Social Media Mining and Ensemble Learning Model: Application to Luxury and Fast Fashion Brands

Information ◽

10.3390/info12040149 ◽

2021 ◽

Vol 12 (4) ◽

pp. 149

Author(s):

Yulin Chen

Keyword(s):

Public Participation ◽

Brand Positioning ◽

Image Model ◽

Web Crawler ◽

Fast Fashion ◽

Social Media Mining ◽

Fashion Brand ◽

Community Information ◽

Brand Information ◽

Media Mining

This research proposes a framework for the fashion brand community to explore public participation behaviors triggered by brand information and to understand the importance of key image cues and brand positioning. In addition, it reviews different participation responses (likes, comments, and shares) to build systematic image and theme modules that detail planning requirements for community information. The sample includes luxury fashion brands (Chanel, Hermès, and Louis Vuitton) and fast fashion brands (Adidas, Nike, and Zara). Using a web crawler, a total of 21,670 posts made from 2011 to 2019 are obtained. A fashion brand image model is constructed to determine key image cues in posts by each brand. Drawing on the findings of the ensemble analysis, this research divides cues used by the six major fashion brands into two modules, image cue module and image and theme cue module, to understand participation responses in the form of likes, comments, and shares. The results of the systematic image and theme module serve as a critical reference for admins exploring the characteristics of public participation for each brand and the main factors motivating public participation.

Download Full-text

Face retrieval system based on elastic web crawler over cloud computing

Multimedia Tools and Applications ◽

10.1007/s11042-020-10271-3 ◽

2021 ◽

Author(s):

M. E. ElAraby ◽

M. Y. Shams

Keyword(s):

Cloud Computing ◽

Retrieval System ◽

Web Crawler ◽

Face Retrieval

Download Full-text

A Novel Architecture for Deep Web Crawler

International Journal of Information Technology and Web Engineering ◽

10.4018/jitwe.2011010103 ◽

2011 ◽

Vol 6 (1) ◽

pp. 25-48 ◽

Cited By ~ 7

Author(s):

Dilip Kumar Sharma ◽

A. K. Sharma

Keyword(s):

Cost Effective ◽

Deep Web ◽

Web Data ◽

Web Crawler ◽

Web Information ◽

General Search ◽

Web Crawlers

A traditional crawler picks up a URL, retrieves the corresponding page and extracts various links, adding them to the queue. A deep Web crawler, after adding links to the queue, checks for forms. If forms are present, it processes them and retrieves the required information. Various techniques have been proposed for crawling deep Web information, but much remains undiscovered. In this paper, the authors analyze and compare important deep Web information crawling techniques to find their relative limitations and advantages. To minimize limitations of existing deep Web crawlers, a novel architecture is proposed based on QIIIEP specifications (Sharma & Sharma, 2009). The proposed architecture is cost effective and has features of privatized search and general search for deep Web data hidden behind html forms.

Download Full-text