A computational model to analyze medical web pages for optimized search results

Googling for Health Information

Local and global topics in text modeling of web pages nested in web sites

The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN). Index Terms: World Wide Web, Web page classification, textual categorization, HTML, Artificial Neural Networks, ANN.

The coronavirus pandemic has forced education at all levels to change from face-to-face mode to online learning. In keeping with that purpose, Universities are releasing a significant number of educational resources on the Web to support virtual education. Final users, who need these educational resources, explore the Web through search engines such as Google, Yahoo, Yandex, or Bing; unfortunately, the search results they obtain lack accuracy and are not necessarily adequate to their requirements. This problem is because Web resources release does not consider their visibility or ease of being found. One way to improve the experience of users who browse the Web is by delivering more appropriate content in response to their searches. An alternative to enhancing the meaning of web searching results is embedding structured semantic markup in the HTML of web pages through standards such as JSON-LD and Schema.org vocabulary, in compliance with W3C recommendation. Search engines can interpret this markup to understand the resources being published and, consequently, improve the rightness of search results. For example, Google uses the structured semantic markup to show rich fragments, Rich Snippets, or even Knowledge Graph in user searches.This research proposes a framework that enables a systematic analysis of the websites of the top-ranking universities, focused on the educational content they provide to review the embedded semantic markup annotated by using JSON-LD and the Schema vocabulary. To this end, a worldwide list of the universities that are part of the top international ranking has been compiled. Then, by using Web Scraping techniques, we have analyzed these universities' Websites in search of educational resources and reviewed if the embedded structured markup is included. Finally, data mining techniques have been used to describe and organize the educational resources obtained.The contribution of this work is two-fold. Firstly, the analysis of embedded structured markup that uses Schema vocabulary and JSON-LD format in university websites. This analysis is relevant since previous research has not explicitly focused on the educational field or has not used a specific dataset within this context. Secondly, the proposal of a framework that allows accomplishing this type of analysis of embedded structured markup from a data collection phase to obtaining results and indicators on the data. It addresses the data mining process from download to the final data analysis to get information. The proposed framework consists of eleven components distributed in three well-defined layers: data access layer, service layer, and application layer. The framework component development process is defined by merging two methodologies, Design Science Research (DSR), to guide the creation of an artifact, and CRISP-DM, to address the data mining process. The architecture of the framework integrates tools such as Scrapy (Python), for web scraping and crawling functions, MongoDB for manipulating semi-structured data with a NoSQL management mode, Redis as an in-memory database (auxiliary) that through queries allows to determine if the URLs that are extracted in the Web Scraping process have already been processed or not (duplicate control), and Apache Kafka as a communication intermediary and facilitator of the flow or exchange of information between the other components.Moreover, this work provides a data set made up of the HTML pages of the universities' Web sites that can be used for further analysis.

With the exponentially growing amount of information available on the Internet, an effective technique for users to discern the useful information from the unnecessary information is urgently required. Cleaning web pages for web data extraction becomes critical for improving performance of information retrieval and information extraction. So, we investigate to remove various noise patterns in Web pages instead of extracting relevant content from Web pages to get main content information. In this paper, we propose an approach that detect multiple noise patterns and remove these noise patterns from Web pages of any Web sites. The method first build DOM tree for any Web page. Our approach is based on the basic idea of Case-Based Reasoning (CBR) to find noise pattern in current Web page by matching similar noise pattern kept in Case-Based. We also apply a back propagation neural network algorithm to classify the stored various noise patterns by matching similar noise data in current Web page. We have implemented our method on several commercial Web sites and News Web sites to evaluate the performance and improvement of our approach. Experiments show that results leads to a more accurate and effectiveness of the approach.

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.

providing health information to consumers [1,2]. Even cancer patients, who are not in the typical demographic group of Internet users, often name the Internet as the second most important source for cancer information after health professionals [3,4,5], and people are more satisfied with information they receive from the Web than from other media [6]. The aim of this chapter is to give an overview of best practices for developing and providing consumer health information on the Web and to provide a framework for best practices, quality criteria, and methods for quality assurance and evaluation. Developers and health information providers are faced with “everyday” practical questions such as how to ensure, monitor, and continuously improve the quality of the information they publish. End-users (consumers) are also interested in “quality criteria” and best practices, as they wish to have “markers” (attributes of a Web site or Web page or a health information provider) that can be used to predict the “quality” in order to select “trustworthy” health information.The issue of quality criteria is also important for third-party expert evaluators working for gateways or portals, or other intermediaries such as librarians or healthcare workers putting together lists of “recommended” health Web sites, because they need to specify and apply selection criteria for the sites they endorse. Similarly, organizations that are in the business of Web site certification need to have a checklist of quality criteria to justify their decisions to certify a Web site. Finally, policy makers are interested in the subject of what makes a good (or bad) health Web site, especially in the context of making regulatory and legislative policy decisions. There has been considerable debate and research on the variable quality of health Web sites [7] and how to best evaluate health information Web sites. One reason for the controversy is that “quality” is an elusive concept and implies subjectivity. Quality can be broadly defined as the “totality of characteristics of a product or service that satisfy stated or implied needs of the user” [8,9]. This definition stresses that “quality” is determined by the concordance (or gap) between individual user needs and the actual attributes of the service or product. Perceived quality (or satisfaction) is further confounded by the users’ prior expectations, which also vary individually. In other words, certain aspects of quality seem to be in the eye of the beholder, and can be measured only by the gap between user needs and attributes of the Web site or service [10]. This apparent subjectivity has led to a pessimistic view that quality of Web sites cannot be measured or evaluated, at least not by third parties [11]. The fact that we are talking about “health information” makes the issue even more problematic, as in medicine there are often “gray” areas with no clear black-or-white 4 Design and Evaluation of Consumer Health Information Web Sites

The behavioral web users’ access patterns help website administrator/web site owners to take major decisions in categorizing web pages of the web site as highly demanding pages and medium demanding pages. Human beings act as a spider surfing the web pages of the website in search of required information. Most of the traditional mining algorithms concentrate only on frequency/support of item sets (web pages set denoted as ps in a given web site), which may not bring considerably more amount of profit. The utility mining model focuses on only high utilities item sets (ps). General utility mining model was proposed to overcome weakness of the frequency and utility mining models. General utility mining does not encompass website topology. This limitation is overcome by a novel model called human behavioral patterns’ web pages categorizer (HBP-WPC) which considers structural statistics of the web page in addition to support and utility. The topology of the web site along with log file statistics plays a vital role in categorizing web pages of the web site. The web pages of the website along with log file statistics forms a population. Suitable auto optimization metric is defined which provides guidelines for website designers/owners to restructure the website based on behavioral patterns of web users.KeywordsWeb miningReorganizationLog fileWeb site topology

With the maturity of network technology, the deterministic factor of a 'successful' web site does not as much depend on its connectivity to the Internet, rather the content of web pages plays a more important role. The inclusion of dynamic data, for example, has become an increasingly desirable feature of a web page. Web servers that support dynamic web pages are capable of retrieving data at the time of user requests. Rather than being statically included in the web page, these dynamically retrieved data may physically reside in various file systems or database servers across the Internet.JAVA, a new object-oriented programming language, has become a buzzword since its introduction by Sun Microsystems in 1994 [1, 2, 3]. It was claimed that JAVA, via its ability to embed applets in a web page, would make the content of a web page alive and dynamic. Data and information on a web page can now be updated dynamically when users request them.In this paper, we discuss our experience of developing JAVA applets that use various types of JDBC (JAVA Database Connectivity) drivers to connect to and then manipulate data stored in SQL (Structured Query Language) database servers. We first start with the discussion of general client/server model involving JDBC, and then introducing the procedure of setting up programming projects employing this approach in an undergraduate database course.

At present, there are many security risks from the network, so it has practical significance for students in the university to learn the knowledge about Web pages and website security performance and have Web pages and web site security performance detection capability. It is described that the recent organization of a number of students to carry out a Web page and Web site security performance experiments. In experiments students’ security awareness on the Web page and website are trained through the use of security vulnerability detection tools and enable students to have basic security performance testing capabilities of web page and website

Many people use the World Wide Web to get information; however, it is not always easy to obtain the desired information, since the number of Web sites and Web pages is huge, and most of them are not structured. To solve this problem, some Web site managers visualize their sites' structure and/or keyword information; this is an effective way of developing an easy-to-navigate Web site. Even if both functions are provided, however, in conventional systems they are provided as separate functions, and no relation exists between them; a user cannot see and operate a site map and keyword information as related information. Providing a site map with keywords is a new technique for visualizing and navigating Web sites. It displays both a Web site's structure and keyword information on a site map and displays the relation between them. This technique not only allows both sets of information to be visualized on the same screen, but also makes it possible to interact with one through the other: a user can interact with a Web site's structure through its keyword information, and vice versa.

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

The World Wide Web is a dominant global communication medium and knowledge repository. It is used by a great number of people with a variety of computer skills hence its usability is critical. As with many large information collections, the challenge with web usability is understanding the structure of a collection of information objects (web pages) to find relevant ones for satisfying a specific information need. Web sites are organized in a hyperlinked structure that somewhat addresses this challenge. However, this "connectedness" also causes the now well-known "lost in cyberspace" phenomenon where one may get confused within the complex organization of a web site. Meanwhile, information exploration on the web is not limited to browsing a web site. The problem of finding relevant information applies to a collection of pages that come from various web sites as in the case of the results of a "less than perfectly constructed" search query. Information visualization has been proposed as a way to cope with these problems by taking advantage of people's innate perceptual skills to support their cognitive skills. Many paradigms have been proposed for the visual presentation of web spaces (i.e. structured or unstructured collection of web pages). This study surveys these paradigms to provide a map of where the research in this field is, and what directions future research and practice can take. For this, we introduce a classification scheme to help in the systematic understanding of web visualization and for providing a framework for the development of future visualizations.

We develop a novel framework that aims at automatically adapting previously learned information extraction knowledge from a source Web site to a new unseen target site in the same domain. Two kinds of features related to the text fragments from the Web documents are investigated. The first type of feature is called, a site-invariant feature. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of feature is called a site-dependent feature. These features are different in the Web pages collected from different Web sites, while they are similar in the Web pages originating from the same site. In our framework, we derive the site-invariant features from previously learned extraction knowledge and the items previously collected or extracted from the source Web site. The derived site-invariant features will be exploited to automatically seek a new set of training examples in the new unseen target site. Both the site-dependent features and the site-invariant features of these automatically discovered training examples will be considered in the learning of new information extraction knowledge for the target site. We conducted extensive experiments on a set of real-world Web sites collected from three different domains to demonstrate the performance of our framework. For example, by just providing training examples from one online book catalog Web site, our approach can automatically extract information from ten different book catalog sites achieving an average precision and recall of 71.9% and 84.0% respectively without any further manual intervention.

How Well Do Firms Implement Strategy When They Design Internet Web Pages?