Efficient classification model of web news documents using machine learning algorithms for accurate information

Mulahuwaish, Aos; Gyorick, Kevin; Ghafoor, Kayhan Zrar; Maghdid, Halgurd S.; Rawat, Danda B.

doi:10.1016/j.cose.2020.102006

Cited by 22 publications

(15 citation statements)

References 8 publications

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“…These graphs are classified into the same group based on features that are identical. Four machine learning classifiers, Support Vector Machine (SVM), K‐Nearest Neighbors (kNN), Decision Tree (DT), and Long Short‐Term Memory (LSTM), are compared in Mulahuwaish et al (2020), and an approach for classifying web documents was also proposed (Papadakis et al, 2016) which extended the k‐Nearest Neighbor classification algorithm, allowing the method to operate on data represented using a graph (Faroughi et al, 2021) instead of traditional vector‐based models.Many methods of FSM have been applied to numerous biomedical problems (Mrzic et al, 2018). Because a lot of biological datasets can be modeled and represented as a set of graphs or a single graph, they require conversion from a typical representation format into another format of graph.…”

Section: Applications Of Fsmmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future.

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Section: Applications Of Fsmmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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“…Mulahuwaish et al [5] proposed Random Forest algorithm is used in data mining techniques to find the best values from the random one. But the data status has changed rapidly; the selection of random samples from the input is very difficult to understand.…”

Section: Literature Surveymentioning

confidence: 99%

“…Raghavendra R, et al: Web Information Extraction methods using Web Content Minning (WCM) for Webapplications Last web usage mining is the concept that is used to utilize the contents of the websites by preprocessing, pattern discovery, and pattern analysis. The optimization concept is to reduce the time taken and exact data-driven from the huge network plays a major role in web mining techniques [5]. The figure 1 denotes the types of mining on websites from huge networks.…”

Section: Introductionmentioning

confidence: 99%

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Raghavendra¹,

Niranjanamurthy²

2022

IJCDS

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In the digital world era, data was generated by humans and machines are huge in volume and have been accessed through websites on the internet platform. Most of the transactions happened on web product items, web news, and web advertisements. Web Information Extraction (WIE) is the technique where the information on websites is extracted accurately within a time using Web Content Mining (WCM) concept. Every second, new data has been generated in different locations and the contents of the websites have changed rapidly at various intervals during processing time. The live time and location of the data have changed each time when internet users processing web applications. So extracting the information from the web page or website is a challenging one with accuracy and latency on websites. Classic algorithms and data mining techniques are used to preprocess the generated data with a certain time but the validity of those has not been maintained on the web server. Perhaps, their special features have taken for doing extraction using web mining techniques. The recently advanced concepts such as Deep Learning with Recurrent Neural Networks (RNN) are used to perform Web Information Extraction on various websites over the large network by keeping hold of the data status at each second in memory while doing the processing. The technique Long Short-Term Memory (LSTM) is used to hold the status in intermediate memory then all generated data in web applications send this status to RNN for further classifications. Classification methods are used in Artificial Neural Networks (ANN), it would train the input data from the large network and segregate them based on the algorithms used by the user. Finally, the deep learning concept is combined with all recent trends with input models as an embedded layer. Social media information is up-to-date with its originality and validity also keeps track fully in larger networks by using this technique. This paper suggested the best methods to implement the web information extraction concepts in web content mining from different websites on larger clusters/networks using deep learning LSTM techniques.

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“…Mulahuwaish et al [18] recently proposed a method to classify web documents using the Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbour (KNN). These approaches rely on document frequency-based features to improve classification efficiency.…”

Section: Background and Related Workmentioning

confidence: 99%

“…LSTM: The LSTM model captures the contextual information of the text of the web pages in a forward direction. Moreover, it utilizes the last hidden state to categorize the web pages [18,43]. The word embedding layer resolves the data sparsity problem in the BOW and TF-IDF machine learning approaches.…”

mentioning

confidence: 99%

Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

Nandanwar

Choudhary

2021

Symmetry

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Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.

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Efficient classification model of web news documents using machine learning algorithms for accurate information

Cited by 22 publications

References 8 publications

Subgraph mining in a large graph: A review

Subgraph mining in a large graph: A review

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

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