Vaibhav Rupapara

TL;DR: In this paper, the authors analyzed the heart failure survivors from the dataset of 299 patients admitted in hospital and found significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient's survivor prediction.

TL;DR: In this paper, the authors performed Covid-19 tweets sentiment analysis using a supervised machine learning approach using a bag-of-words and the term frequency-inverse document frequency.

TL;DR: In this paper, an ensemble approach, called regression vector voting classifier (RVVC), was introduced to identify the toxic comments on social media platforms, which merges the logistic regression and support vector classifier under soft voting criteria.

TL;DR: In this article, seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. And the proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score.

TL;DR: A novel Google App numeric reviews & ratings contradiction prediction framework using Deep Learning approaches is proposed that significantly predicts unbiased star rating of app.