Machine Learning Approaches to Assess Mood of the News Editorial

Social media and news channels have always been vital sources for spreading information and raising awareness about recent occurrences. As reported by a survey, 82 percent of respondents from India stated that they sourced their news online, which included social media, as of the year 2021, making it a popular form of accessing news1. For a long time, information about COVID - 19 has been one of the most popular topics. News channel networks and editorials were one of the first places where knowledge regarding COVID - 19 was widely disseminated. In this study, sentiment analysis models have been developed to categorize tweets by some of India's most well-known news stations into positive and negative during the COVID - 19 virus was new in India from June 2020 to July 2020. We attempted to do so by developing nine various models based on different datasets and classification algorithms to investigate the news channels' tweets more thoroughly. According to our findings, the model that provided us with the highest accuracy and performance has been trained using the NLTK Dataset and the Logistic Regression Classifier. © 2022 IEEE.

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

Corona Virus has caused a disruption to the normalcy in the world and require thorough patient management. Looking at the present scenario and the kind of pandemic that it has turned out to be, this paper aims at providing an enhancement to the RT-PCR way of testing and uses the chest X-Rays to detect the presence and severity of the corona virus in a body to successfully differentiate between pneumonia and the Covid-19. The main motive is to help doctors and medical experts with an advance aid to the nursing of critically affected patients. This is feasible because the X-Ray machines are widely available throughout the country and they can assist in the advanced detection of the disease. Around 6000 images of the three kinds of chest X-Rays of patients with pneumonia, Covid, as well as completely normal patients, were used in the process. The paper concludes with the explicit comparison of all the models and their results. Primarily, a simple CNN model was opted for the scrutiny and then later on VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2 pre-trained models were utilised for anatomizing Covid-19 with respect to pneumonia and normal cases. In case of CNN, the maximum accuracy that was attained was 95.30% whereas, for the VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2, the maximum accuracies in correctly predicting the diseases were 95.63%, 96.02%, 94.82%, 95.23% and 93.39% respectively. © 2021 IEEE.

Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

Severe Acute Respiratory Syndrome Corona Virus 2 or SARS-CoV-2 (COVID-19) has affected 21 million people worldwide and is responsible for 0.75 million deaths (as of August 2020). Declared as a pandemic by the WHO, the virus has affected almost every country after originating in China articulating how contagious the virus is. With reasonable social measures, countries have already shown depreciation in coronavirus cases. This paper deals with detecting and distinguishing the COVID-19 disease from normal patients through frontal chest X-ray scans using Convolutional Neural Networks. Different combinations of parameters were used to train multiple CNNs and their behavior was noted. After thorough experimentation of different models, the best model which achieved an accuracy of 0.98 on the test set with a loss of 0.036 was nominated. The selected model’s more intuitive accuracy metrics were calculated and its intermediate convolutional neural activations and receiver-operating characteristics curve is shown. © 2021 Taru Publications.