Matloob Khushi

TL;DR: This study presents a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 and supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.

TL;DR: Reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.

TL;DR: A model is developed for credit default prediction by employing various credit-related datasets and the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset, and the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers.

TL;DR: In this article, the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) was used to identify the best imbalance techniques suitable for medical datasets.

TL;DR: In this article, the authors classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Recurrent Neural Networks (RNN), Reinforcement Learning, and other deep learning method such as HAN, NLP, and Wavenet.