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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2191777


Sentiment Analysis is an ongoing field of research in text mining that is concerned with the computational treatment of textual views, sentiments, and subjectivity. It's the task of distinguishing between positive and negative viewpoints, emotions, and assessments. Sentiment analysis has been the topic of intensive research since its inception. During COVID-19, there has been a subtle increase in the usage and the time spent on the social networking sites by people as most of the daily operations have moved online. Moreover, in addition to the illness itself, the pandemic has led to dread, anxiety, stress, concern, repugnance, and poignancy in individuals all around the world. Considering these indicators, experts are paying close attention to Twitter data analysis during this pandemic. BERT is used as a transfer learning model and this work analyses the efficacy of fine-tuning it for the task of opinion mining by comparing it to a baseline model that includes a TF-IDF vectorizer and a Naïve Bayes classifier. Its performance is also compared to that of Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and XGBoost classifier. To determine the most effective settings for the BERT model, hyper-parameter tweaking is used. After two epochs of training at a learning rate of le-5 and batch size of 16, the maximum accuracy of 87.6% is attained. These results outperform all of the machine learning models examined in this study. This work tackles a comprehensive overview of the last update in this field. It can be beneficial to scholars in this domain because it encapsulates the most well-known Sentiment analysis methodologies and their comparison in single research work. © 2022 IEEE.

International Journal of Information System Modeling and Design ; 13(9), 2022.
Article in English | Scopus | ID: covidwho-1975017


Healthcare sector stocks are a very good opportunity for investors to obtain gains faster most of the time in a year and mostly during this COVID pandemic. Purchasing a healthcare stock of a certain company indicates that you hold a part of the company shares. Specifically, various examinations have been led to anticipate the development of financial exchange utilizing AI calculations, such as SVM and reinforcement learning. A collection of machine learning algorithms are executed on Indian stock price data to precisely come up with the value of the stock in the future. Experiments are performed to find such healthcare sector stock markets that are difficult to predict and those that are more influenced by social media and financial news. The impact of sentiments on predicting stock prices is displayed and the accuracy of the final model is further increased by incorporating sentiment analysis. © 2019 American Society of Mechanical Engineers (ASME). All rights reserved.

2nd International Conference on Data Science and Applications, ICDSA 2021 ; 288:457-465, 2022.
Article in English | Scopus | ID: covidwho-1598962


The COVID-19 pandemic has wreaked havoc on the planet, making it difficult for the average person to go about their daily lives. There has been an increase in cases all over the world, and the infection rate has only risen in recent months. Governments have instituted stringent lockdowns to combat the high rate of infections, and it has become important to maintain social distance. The aim of this paper is to predict B-cell epitopes based on a variety of factors in order to aid in the development of a vaccine (SARS-CoV-2). We combine various attributes to determine the data set’s significance and create an effective predictive model. Our proposed approach would aid vaccine development by allowing vaccine makers to make informed decisions about suitable proteins and peptides relevant to the SARS-CoV-2 virus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.