Prediction of covid-19 growth and trend using machine learning approach.

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Evolution of Outbreaks, Lessons Learnt and Challenges Towards “New Normalcy”—Post COVID-19 World

A new era in epidemics started due to unhealthy practices, population density, environmental changes, migration and deforestation. The rapidity in the spread is primarily due to globalization as we moved to the industrial revolution where everything is internet-connected. In past 30 years, the trend exhibits an increase in the number of epidemics challenging the social well-being, the economy and to some extent the national security. And this translates to the impact on the industrial growth, the race of future together fighting with the newest of the viruses. This paper analyzes and reviews the outbreaks from the start of the revolutionary steam power generation to the modern days, their impact to generate new values to the society that translated the newer solutions to become new norms. Impact on the outbreaks on the various key sectors and the measures that lead us to overcome is presented. We present the new normal which would become normal in the near future, the post COVID-19 scenario. © 2022, Institute of Technology PETRONAS Sdn Bhd.

Prediction of COVID-19 using hyperparameter optimized convolutional neural network

All over the world, there are heavy cases of COVID-19 patients those exhibiting the symptoms. In a very short period of time, this pandemic virus has become drastic across the country. A fast detection of corona spread is necessary for both the infected and uninfected person for the further spreading. The preexisting techniques used methods like Linear Regression, Support Vector Machine (SVM) and Naive Bayes are not producing better results. Our aim is to bring out better outcomes and to produce good accuracy. Instead of machine learning we opt for deep learning approaches in our work. Image preprocessing will be done by Histogram Equalization algorithm and further the image classification is done by Convolution Neural Network (CNN) architectures such as VGG-16 and ResNet-50 by using 350 images of X-ray datasets. From the comparison, VGG-16 produce better train and test accuracy of 92% and 98.4% .Hence the accuracy of VGG-16 was further tuned using Hyper Parameter Optimization using Tensor Board which produces better outcomes. © 2021 Karadeniz Technical University. All rights reserved.