f) 2020 - 82 Documents
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Item DEEP NEURAL NETWORK FOR EVALUATING WEB CONTENT CREDIBILITY USING KERAS SEQUENTIAL MODEL(Springer Link, 2020-08-08) Manjula, R; Vijaya, M SWeb content credibility determines the measure of acceptable and reliable of the web content that is observed. Content will prove to be unreliable if it is not updated, and it is not controlled for remarkable, and therefore, web content credibility is considerably essential for the people to assess the content. The analysis of content credibility is a vital and challenging task as the content credibility is outlined on crucial factors. This paper focuses on building deep neural network (DNN)-based predictive model using sequential model API to evaluate credibility of a webpage content. Deep neural network (DNN) is considered as an extremely promising decision-making architecture, and it performs feature extraction and transformation with the use of refined statistical modeling. A corpus of 400 webpage contents has been developed, and the factors like readability, freshness, and duplicate content are defined and captured from the webpage content. These features are redefined, and a new set of features is self-learned through the deep layers of neural network. Numeric labeling is used for defining credibility, wherein five-point Likert scale rating is used to denote the content credibility. By using sequential model, KerasRegressor with ADAM optimizer and a multilayer network is generated for building DNN-based predictive model and discovered that deep neural network outperforms other general regression algorithms in prediction scores.Item MEASURING WEB CONTENT CREDIBILITY USING PREDICTIVE MODELS(Springer Link, 2020-01-30) Manjula, R; Vijaya, M SWeb content credibility is a measure of believable and trustworthy of the web content that is perceived. Content can turn out to be unreliable if it is not up-to-date and it is not measured for quality or accuracy and therefore, web content credibility is important for the individuals to access the content or information. The analysis of content credibility is an important and challenging task as the content credibility is expressed on essential factors. This paper focus on building predictive models to discover and evaluate credibility of a web page content through machine learning technique. A corpus of 300 web page contents have been developed and the factors like Readability, Freshness, Duplicate Content are defined and captured to model the credibility of web content. Two different labeling such as binary labeling and numeric labeling are used for defining credibility. In case of binary labeling, the high and low credibility of web content are represented by 1 and 0, respectively, whereas in case of numeric labeling five-point scale rating is used to mark the content credibility. Accordingly, two independent datasets have been developed. Different regression algorithms such as Linear Regression, Logistic Regression, Support Vector Regression (SVR) are employed for building the predictive models. Various experiments have been carried out using two different datasets and the performance analysis shows that the Logistic Regression model outperforms well when compared to other prediction algorithms.