Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.