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Studies in Systems, Decision and Control ; 382:1-22, 2022.
Article in English | Scopus | ID: covidwho-1391725


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.

Mater Today Proc ; 2021 Apr 15.
Article in English | MEDLINE | ID: covidwho-1185156


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.