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The rapid growth of web has made it a huge source of information which will make the availability of data easier and more efficient if its content is well organized. Automatic classification of web pages is one of the major methods in the Web Content Mining (WCM) which can be of great value in the development and maintenance of web directories. Based on the analysis done, CMAC neural network showed faster learning in high dimensional problems, but considering the heavy data on the web, the main challenge in web page classification is how to deal with high dimensional feature space which increase the memory required by CMAC neural network. In the present paper a Classifier-CMAC neural network model is proposed for use in content based web page classification which requires less memory. The results reveal that the proposed model is more useful than any other algorithms.

This work describes the CitationFinder, a knowledge-based system for the automatic classification of Web pages which contain citations of publications. The system counts on a knowledge base of production rules with associated certainty factors. It was constructed on the basis of a corpus of 1.000 sample pages and presented a very satisfactory performance: 88,6% precision and 97% recall for a test corpus of 300 pages.

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.

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 lack of sufficient labeled Web pages in many languages, especially for those uncommonly used ones, presents a great challenge to traditional supervised classification methods to achieve satisfactory Web page classification performance. To address this, we propose a novel Nonnegative Matrix Tri-factorization (NMTF) based Dual Knowledge Transfer (DKT) approach for cross-language Web page classification, which is based on the following two important observations. First, we observe that Web pages for a same topic from different languages usually share some common semantic patterns, though in different representation forms. Second, we also observe that the associations between word clusters and Web page classes are a more reliable carrier than raw words to transfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which we want classify Web pages, through two different paths: word cluster approximations and the associations between word clusters and Web page classes. Due to the reinforcement between these two different knowledge transfer paths, our approach can achieve better classification accuracy. We evaluate the proposed approach in extensive experiments using a real world cross-language Web page data set. Promising results demonstrate the effectiveness of our approach that is consistent with our theoretical analyses.

Clustering techniques have been applied to categorize documents on Web and extract knowledge from Web. In this paper, we introduce a novel clustering method into Web page clustering, which is an extension of affinity propagation (AP). This method is called partition adaptive affinity propagation (PAAP), which can automatically rerun AP procedure to yield optimal clustering results and eliminate number oscillations if they occur. Experiments are carried out to compare PAAP with K-means and AP on ten different Web page data sets. The results verify that PAAP can find better clusters when compared with similar methods. And the results also demonstrate that PAAP is robust and effective when clustering Web pages.

These days, the Internet is developing at an exponential rate and can cover just about any data required. Nonetheless, the immense measure of web pages makes it more difficult to effectively discover the target data by a user. Therefore, an efficient method, for classifying this huge amount of data is essential if the web pages are to be exploited to its full potential. In the domain of automatic web page classifier many approaches have been tried to solve this problem using different Machine learning-based algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree, K-Nearest Neighbor (K-NN) and Neural Networks. However, there is a lack of comparison between these algorithms to find a better framework for the classification and analysis of health related web pages. In this research study, we compare two commonly used supervised Machine Learning algorithms; Support Vector Machines (SVM) and Naive Bayes to classify web pages which provide drugs related information of patients for example side effects, patient action and follow-up information for patients. We use Unified Medical Language System (UMLS) to annotate the health related concepts in Web pages and train SVM and Naive Bayes classifiers in General Architecture for Text Engineering to classify health related and non-health related Web pages. The evaluation was performed using K-fold cross validation using four runs on a data set of fifty Web pages. Results found that SVM performed better to classify health and non-health related pages in terms of precision, recall and F-measure.

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.

Automatic processing of websites is of great importance for applications such as search engine that extract information from web pages. Search engines use meta tag values when classifying pages of websites. Meta tag names can change for different languages. For example, for login page, entries such as login, login page or giris, giris sayfasi may change from language to language. When the websites are examined, it can be seen that each of the pages created for the same purpose has similar designs. In this study, a deep learning based model was proposed for functional classification of web pages, regardless of language. Transfer learning was used to reduce the cost during the feature extraction process from recorded web page images. Finally, the results of two different experiments are presented for show the effectiveness of the proposed method in the classification of web pages according to their functions.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

Automatic web page classification can be used in domain-specific search engines to help users get the specific information more conveniently and precisely on Internet. The semantic similarity and noisy data in domain-specific web pages make traditional classifier perform poorly on them. In this paper, a dictionary-based multilingual web page classification method is proposed to try to improve the classification performance. A domain-specific dictionary is constructed in the method to intensify the domain-specific knowledge in the pages. An automatic encoding detection and integration method is also introduced in the classifier to extract Chinese and English information precisely from the multilingual pages. After verified in the experiments, the method is integrated into a real domain-specific search engine where it shows good effectiveness. Keywords-Web page classification, Search engine, Domainspecific knowledge, Dictionary

In 2020, Most Filipinos are using the internet due to COVID-19 pandemic lockdowns. The internet is not limited to adults and children might be exposed to online adult content and abuse. The Philippine Internet Service providers fail to capture pornographic web pages that are not for child viewing. A Web Page classifier would help in detecting and classifying web pages. In this study, a total of 12000 web pages with adult content and academic web pages were collected using scrapy and existing datasets from DMOZ were used to create a Support Vector Machine (SVM) multi-class classifier. To improve the accuracy of the SVM model, data preprocessing was performed to remove noisy and irrelevant data from the dataset. The text in the web pages was used to train the SVM classifier by using Term Frequency and Inverse Document Frequency, Count vectorizer, and Word2vec Skip-gram embedding with TF-IDF as a multiplier. A series of experiments were conducted using multiple word embedding techniques. The SVM model built using word2vec with TF-IDF multiplier outperforms the SVM model built using TF-IDF and Count Vectorizer. The word embedding generated using word2vec was generated with a window size of 9 and a vector dimension of 900. The SVM model built using word2vec shows an S6% accuracy. The SMV model is deployed in the Django framework and a chrome plugin was created to use the SVM model using REST API.

We propose a generic multiple classifier system based solely on pairwise classifiers to classify web pages. Web page classification is getting huge attention now because of its use in enhancing the accuracy of search engines and in summarizing web content for small-screen handheld devices. We have used a Support Vector Machine (SVM) as our core pair-wise classifier. The proposed system has produced very encouraging results on the problem web page classification. The proposed solution is totally generic and should be applicable in solving a wide range of multiple class pattern recognition problems.KeywordsSupport Vector MachineHandwritten CharacterSupport Vector ClassifierMultiple Classifier SystemStructure Support Vector MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

How to embed more secret message into web page with keeping good imperceptibility simultaneously is a trade-off problem for information hiding schemes. This paper presents a novel web page information hiding method based on three inherent features of current web page's HTML Code matrix. This method firstly exploits chaotic transform to deal with secret message, then embeds each bit of secret message into cover web page according to the random odevity of character amount contained by each row(column) in cover web page's HTML code matrix. Its process is very simple to understand and easy to implement. It can improve the largest embedded capacity (LEC) of cover web page greatly with keeping good imperceptibility at the same time. We test our approach on two test data sets which include 1000 web pages in total. The experiment results show that the max LECs for these two data sets are up to 11291 bits and 13545 bits respectively. The average LECs for two data sets has improvement of 80.2% and 24% more than that of other web page information hiding method .

With the phenomenal growth of World Wide Web and the huge number of web sites with so many web pages, finding relevant information becomes quite difficult for the web users. Web site promoters also need to ensure that their web site is on the top of the search results for better market share. Finding the right combination of keywords and placing them at the appropriate places may help in bringing the web site on the top of search results when a search engine searches the web sites and creates indexes. Over the years web site optimization research has reported encouraging results. Despite promising results, data mining techniques for web sites classification have hardly been applied. An appropriate implementation of classification of web pages may help designing the web pages accordingly. Here in this work data mining using R’s web scraping technique has been used for performing extraction of web pages and also pair of java tools called “website” and “webscrap” were developed to automatically get pages and for further analysis of the downloaded pages. This is the first attempt to analyze medical related nomenclature using.