ABSTRACT
Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better. © 2022 IEEE.
ABSTRACT
The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The global recession due to the pandemic has knocked the business landscape and brought the world to its knees. There were a number of renowned companies that made the headlines for being the top industry hard hits. Nonetheless, there were businesses that survived this pandemic and navigated the COVID complexities so effectively that it tipped the scales in their favor. We attempt to study the factors that helped these businesses masterfully work their way through the conundrums of coronavirus pandemic. We first build a dataset that entailed information pertinent to businesses and relevant COVID-related information that was sourced from Yelp and other platforms. We used a variety of classifiers to make predictions about the survival of these businesses followed by that after assessing their performance through varied methods. The model efficiency was classified based on several rating techniques to evaluate both underperforming and profitable businesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.