An Empirical Study of Pre-Trained CNN Models on COVID-19 CT Scan Images

As COVID-19 is highly infectious, the prevention of this disease is mandatory. The instant diagnosis of this disease is obligatory to stop the infection. The most commonly used procedure for COVID-19 detection is the RT-PCR test. But this process is very time-consuming and as a result, it allows the covid infected persons to spread the infection before they come to know the test result. So, in this paper, we used the method of detecting COVID-19 from CT scan images as a replacement for the conventional RT-PCR test. But this alternative method has its demerit too. To diagnose COVID-19 from these CT scan images, the analysis of a radiologist expert is required. So, we have used a deep-learning based method for automatic detection of covid infection from the CT scan images. We have used six pre-trained models: ResNet50, Xception, DenseNet121, DenseNet201, MobileNet, MobileNetV2 and their accuracy are 97.38%, 92.35%, 95.56%, 93.55%, 93.95%, and 92.94% respectively. © 2022 IEEE.

A Cross-Corpus Deep Learning Approach to Social Media Emotion Classification

Emotion Detection refers to the identification of emotions from contextual data in the form of written text, such as comments, posts, reviews, publications, articles, recommendations, conversations, and so on. Because of the Internet's exponential uptake and the recent coronavirus outbreak, social media platforms have become a crucial means of sharing thoughts and ideas throughout the entire globe, creating rapid data growth through users' contributions on various platforms. The necessity to acquire knowledge of their behaviors is a matter of great concern for both internet safety and privacy. In this study, we categorize emotional sentiments using deep learning models along with hybrid approaches such as LSTM, Bi-LSTM, and CNN+LSTM. When compared to existing state-of-the-art methods, the experiments showed that the suggested strategy is more robust and achieves an expressively higher quality of emotion detection with an accuracy rate of 94.16%, including strong F1-scores on complex and difficult emotion categories such as Fear (93.85%) and Anger (94.66%) through CNN+LSTM. © 2022 IEEE.