scholarly journals WEB SCALE INFORMATION EXTRACTION USING WRAPPER INDUCTION APPROACH

2014 ◽  
pp. 18-24
Author(s):  
RINA ZAMBAD ◽  
JAYANT GADGE
Keyword(s):  
Information Extraction ◽  
Data Extraction ◽  
Extraction Methods ◽  
Experimental Results ◽  
Search Query ◽  
Wrapper Induction

Information extraction from unstructured, ungrammatical data such as classified listings is difficult because traditional structural and grammatical extraction methods do not apply. The proposed architecture extracts unstructured and un-grammatical data using wrapper induction and show the result in structured format. The source of data will be collected from various post website. The obtained post data pages are processed by page parsing, cleansing and data extraction to obtain new reference sets. Reference sets are used for mapping the user search query, which improvised the scale of search on unstructured and ungrammatical post data. We validate our approach with experimental results.

A FRAME WORK FOR WEB INFORMATION EXTRACTION AND ANALYSIS

2013 ◽  
Vol 7 (2) ◽  
pp. 574-579 ◽  
Author(s):  
Dr Sunitha Abburu ◽  
G. Suresh Babu
Keyword(s):  
Information Extraction ◽  
Data Extraction ◽  
Research Work ◽  
Web Pages ◽  
Web Documents ◽  
E Learning ◽  
Structured Information ◽  
Frame Work ◽  
Effective Decision ◽  
The Web

Day by day the volume of information availability in the web is growing significantly. There are several data structures for information available in the web such as structured, semi-structured and unstructured. Majority of information in the web is presented in web pages. The information presented in web pages is semi-structured.  But the information required for a context are scattered in different web documents. It is difficult to analyze the large volumes of semi-structured information presented in the web pages and to make decisions based on the analysis. The current research work proposed a frame work for a system that extracts information from various sources and prepares reports based on the knowledge built from the analysis. This simplifies  data extraction, data consolidation, data analysis and decision making based on the information presented in the web pages.The proposed frame work integrates web crawling, information extraction and data mining technologies for better information analysis that helps in effective decision making.   It enables people and organizations to extract information from various sourses of web and to make an effective analysis on the extracted data for effective decision making.  The proposed frame work is applicable for any application domain. Manufacturing,sales,tourisum,e-learning are various application to menction few.The frame work is implemetnted and tested for the effectiveness of the proposed system and the results are promising.

Unique views on obesity-related behaviors and environments: research using still and video images (Preprint)

2018 ◽  
Author(s):  
Jordan Carlson ◽  
J. Aaron Hipp ◽  
Jacqueline Kerr ◽  
Todd Horowitz ◽  
David Berrigan
Keyword(s):  
Physical Activity ◽  
Ethical Issues ◽  
Data Extraction ◽  
Dietary Assessment ◽  
Extraction Methods ◽  
Image Capture ◽  
Still Images ◽  
Key Informants ◽  
Technological Advances ◽  
Computer Scientists

BACKGROUND Image based data collection for obesity research is in its infancy. OBJECTIVE The present study aimed to document challenges to and benefits from such research by capturing examples of research involving the use of images to assess physical activity- or nutrition-related behaviors and/or environments. METHODS Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. RESULTS Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (N = 13; e.g., participants using smartphones for dietary assessment), researcher generated (N = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (N = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). CONCLUSIONS Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes are critical. CLINICALTRIAL NA

Implementation of Web Application for Disease Prediction Using AI

2020 ◽  
pp. 5-9
Author(s):  
Manasvi Srivastava ◽  
◽  
Vikas Yadav ◽  
Swati Singh ◽  
◽  
...  
Keyword(s):  
Web Application ◽  
Ad Hoc ◽  
Data Extraction ◽  
Extraction Methods ◽  
Web Page ◽  
Web Based ◽  
Web Extraction ◽  
Web Scraping ◽  
Audio Video ◽  
Manual Extraction

The Internet is the largest source of information created by humanity. It contains a variety of materials available in various formats such as text, audio, video and much more. In all web scraping is one way. It is a set of strategies here in which we get information from the website instead of copying the data manually. Many Web-based data extraction methods are designed to solve specific problems and work on ad-hoc domains. Various tools and technologies have been developed to facilitate Web Scraping. Unfortunately, the appropriateness and ethics of using these Web Scraping tools are often overlooked. There are hundreds of web scraping software available today, most of them designed for Java, Python and Ruby. There is also open source software and commercial software. Web-based software such as YahooPipes, Google Web Scrapers and Firefox extensions for Outwit are the best tools for beginners in web cutting. Web extraction is basically used to cut this manual extraction and editing process and provide an easy and better way to collect data from a web page and convert it into the desired format and save it to a local or archive directory. In this paper, among others the kind of scrub, we focus on those techniques that extract the content of a Web page. In particular, we use scrubbing techniques for a variety of diseases with their own symptoms and precautions.

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irvin Dongo ◽  
Yudith Cardinale ◽  
Ana Aguilera ◽  
Fabiola Martinez ◽  
Yuni Quintero ◽  
...  
Keyword(s):  
San Francisco ◽  
Data Extraction ◽  
Qualitative Evaluation ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Advantages And Disadvantages ◽  
Web Scraping ◽  
Shared Information

Purpose This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

scholarly journals Evaluation of speech signal features extraction methods

2020 ◽  
Vol 2 (1) ◽  
pp. 69-78
Author(s):  
Ismail Shayeb ◽  
Naseem Asad ◽  
Ziad Alqadi ◽  
Qazem Jaber
Keyword(s):  
Speech Signal ◽  
High Speed ◽  
Research Paper ◽  
Extraction Methods ◽  
Features Extraction ◽  
Experimental Results ◽  
Extraction Time ◽  
Digital Signals ◽  
Human Speech ◽  
Signal Features

Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

scholarly journals The COVID-19 pandemic shifted the Veterans Affairs System toward being a payer and virtual care provider: is it sustainable?

2021 ◽  
Author(s):  
Liam Rose ◽  
Linda Diem Tran ◽  
Steven M Asch ◽  
Anita Vashi
Keyword(s):  
Financial Incentives ◽  
Care Delivery ◽  
Data Extraction ◽  
Medical Personnel ◽  
Extraction Methods ◽  
Virtual Care ◽  
Large Expansion ◽  
Virtual Visits ◽  
The Difference ◽  
One Year

Objective: To examine how VA shifted care delivery methods one year into the pandemic. Study Setting: All encounters paid or provided by VA between January 1, 2019 and February 27, 2021. Study Design: We aggregated all VA paid or provided encounters and classified them into community (non-VA) acute and non-acute visits, VA acute and non-acute visits, and VA virtual visits. We then compared the number of encounters by week over time to pre-pandemic levels. Data Extraction Methods: Aggregation of administrative VA claims and health records. Principal Findings: VA has experienced a dramatic and persistent shift to providing virtual care and purchasing care from non-VA providers. Before the pandemic, a majority (63%) of VA care was provided in-person at a VA facility. One year into the pandemic, in-person care at VA's constituted just 33% of all visits. Most of the difference made up by large expansions of virtual care; total VA provided visits (in person and virtual) declined (4.9 million to 4.2 million) while total visits of all types declined only 3.5%. Community provided visits exceeded prepandemic levels (2.3 million to 2.9 million, +26%). Conclusion: Unlike private health care, VA has resumed in-person care slowly at its own facilities, and more rapidly in purchased care with different financial incentives a likely driver. The very large expansion of virtual care nearly made up the difference. With a widespread physical presence across the U.S., this has important implications for access to care and future allocation of medical personnel, facilities, and resources.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

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