Drug Recommendation Using Sentiments Analysis & Sequence of Covide Protein Prediction Using Deep Learning

Abstract

nowadays, participating moments on communicative networks have enhanced something comprehensive. Sharing impressions, opinions, and good representations to formulate our sentiments within the text without using a lot of information. Understanding the underpinnings of biology begins with obtaining an accurate description of protein structure. Despite recent advancements in experimental methods significantly improving our ability to experimentally identify protein structures, the gap between the number of protein sequencing and recognized protein structures continues to grow.. In this work, Bidirectional Long Short-Term memory (BiLSTM) deep learning classification algorithm has used for prediction of COVID-19 protein sequence. For the implementation of this work first we extract aspect from review data and identified the class labels. According class label and extracted features by NLP we recommend the during for COVID-19. We recommend an innovative method to reconstruct it into a binary distribution as well as traditional machine learning classification problem and utilize an in-depth knowledge strategy to determine the reconstructed problem. Our hybrid approach will provide better classification accuracy over classical machine learning algorithms