ACOA: Archimedes conditional autoregressive optimization algorithm based RMDL for web data classification

Web data classification has become a subject of great value due to the increase of premium data source in the web data and as the pointer to utilizing this source of data. Deep learning is a technique that requires a significant quantity of well-designated data that is difficult and high consumption time to gather explicitly. To bridge this gap, a hybrid approach named Archimedes conditional autoregressive optimization algorithm (ACOA) is established for web data classification. Firstly, the input web page is fed up with the Bidirectional Encoder Representations from Transformers (BERT) tokenization phase. Secondly, Aspect term extraction (ATE) is done by the tokens for the identification of phrases selected by opinion indicators in review sentences. Thirdly, feature extraction is performed by obtained suitable features. Lastly, web data classification is performed by Random Multimodel Deep Learning (RMDL) that is tuned using ACOA. ACOA is an incorporation of Archimedes optimization algorithm (AOA) with the Conditional Autoregressive Value at Risk (CAViaR) model. The presented approach ACOA is evaluated with metrics like, precision, recall and F1-Score, which acquires the maximum values as 91.6%, 94.7% and 93.1%.