Design of Kids-specific URL Classifier using Recurrent Convolutional Neural Network

Rajalakshmi, R.; Tiwari, Hans; Patel, Jay; Kumar, Ankit; Karthik, R.

doi:10.1016/j.procs.2020.03.260

Cited by 13 publications

(8 citation statements)

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“…The proposed RCNN-DCLSTM is compared with the existing CBGRU [23], HDLN [24], and TLFM [25] method for the metrics such as accuracy, precision, recall, and F-1 measure.…”

Section: Comparative Analysis and Resultsmentioning

confidence: 99%

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A Novel Deep RCNN-DCLSTM Classifier for Finding the Quality of Web-Page Based On Review Dataset

Srivast,

Pandey,

Agarwal

2023

Preprint

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Nowadays, internet has numerous of web contents but it is difficult to find the web page quality. For predicting the quality of web page, a technique is necessary. Therefore, for determining the quality of the web page, a novel Residual Convolutional Neural network and Drop Connect Long Short Term Memory (RCNN-DCLSTM) technique is proposed. It consists of two stages: pre-processing and classification. In the preprocessing stage, tokenization, identification of slang, stop word removal, and lemmatization processes improve the level of accuracy during classification. In the classification stage, the proposed deep learning classifier based on RCNN-DCLSTM is used to classify the quality of web page as very high quality, high quality, moderate quality, low quality, and very low quality based on reviews. Here, the Drop connect regulation system on hidden-to-hidden weight metrics with LSTM is used to avoid the fitting problem. The proposed RCNN-DCLSTM accuracy is tested on four data sets and compared with previous methods. Based on the estimation result, the proposed RCNN-DCLSTM gives the accuracy of 0.91, precision of 0.909, recall of 0.908, and F-1 measure of 0.91. Hence, it is proved that the proposed RCNN-DCLSTM technique accurately finds the quality of web page.

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“…The proposed RCNN-DCLSTM is compared with the existing CBGRU [23], HDLN [24], and TLFM [25] method for the metrics such as accuracy, precision, recall, and F-1 measure.…”

Section: Comparative Analysis and Resultsmentioning

confidence: 99%

“…Rajalakshmi et al [23] proposed a combination of CNN and Bidirectional Gated Recurrent Unit (CBGRU) for web page classification based on whether kids are specific or not. The input URL is preprocessed and given to the embedding layer for conversion into a vector.…”

Section: Related Workmentioning

confidence: 99%

A Novel Deep RCNN-DCLSTM Classifier for Finding the Quality of Web-Page Based On Review Dataset

Srivast,

Pandey,

Agarwal

2023

Preprint

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“…For this reason, these datasets are not applicable to the field of cyber parental control. Table 1 also shows that only [14][15][16][17][18] created applicable datasets for the field of cyber parental control; however, none of these is publicly available. Given these factors, there is a need to create a ground truth dataset that contains objectionable and unobjectionable web content data.…”

Section: Related Workmentioning

confidence: 99%

Ground Truth Dataset: Objectionable Web Content

Altarturi

Anuar

2022

Data

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Cyber parental control aims to filter objectionable web content and prevent children from being exposed to harmful content. Succeeding in detecting and blocking objectionable content depends heavily on the accuracy of the topic model. A reliable ground truth dataset is essential for building effective cyber parental control models and validation of new detection methods. The ground truth is the measurement for labeling objectionable and unobjectionable websites of the cyber parental control dataset. The lack of publicly accessible datasets with a reliable ground truth has prevented a fair and coherent comparison of different methods proposed in the field of cyber parental control. This paper presents a ground truth dataset that contains 8000 labelled websites with 4000 objectionable websites and 4000 unobjectionable websites. These websites consist of more than 2 million web pages. Creating a ground truth objectionable web content dataset involved a few phases, including data collection, extraction, and labeling. Finally, the presence of bias, using kappa coefficient measurement, is addressed. The ground truth dataset is available publicly in the Mendeley repository.

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“…Thus, self-attention is an alternative to RNN models. LSTM / BGRU-based deep learning architectures are not restricted to the above areas alone, but were applied in URL classification problems (Rajalakshmi et al, 2020a). The effect of combining the attention mechanism with the BGRU-based models was studied by Rajalakshmi et al (2020b) for identifying the kids-relevant web pages.…”

Section: Movie Editormentioning

confidence: 99%

Self-attention based sentiment analysis with effective embedding techniques

SivakumarSoubraylu¹,

RajalakshmiRatnavel²

2021

IJCAT

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The problem of sparse vector representation exists in handling the large-scale data and also semantics of words are not considered in many existing works of sentiment analysis. Effective word embedding techniques improve the task of sentiment analysis by overcoming the above two problems. It is a challenging task, when the review is expressed in multiple sentences and the entire sentence needs to be considered instead of individual words to determine the sentiment. To achieve this task, we have proposed a novel LSTM-based deep learning architecture that applies a sentence embedding using universal sentence encoder along with an attention layer. To evaluate the proposed approach, we have carried out various experiments on IMDB data set. From the experimental results, it is observed that the proposed method is significantly better than the other word-embedding-based approaches with an improvement of 5% and we have achieved a F1 score of 89.12% for our approach.

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Design of Kids-specific URL Classifier using Recurrent Convolutional Neural Network

Cited by 13 publications

References 13 publications

A Novel Deep RCNN-DCLSTM Classifier for Finding the Quality of Web-Page Based On Review Dataset

A Novel Deep RCNN-DCLSTM Classifier for Finding the Quality of Web-Page Based On Review Dataset

Ground Truth Dataset: Objectionable Web Content

Self-attention based sentiment analysis with effective embedding techniques

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