Performance evaluation of regression models for COVID-19: A statistical and predictive perspective

Abstract

Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.

The effects of COVID-19 on safety practices in construction projects

“…These new safety measures were resulted from the new regulations enforced by Saudi government to control the spread of COVID-19. Similar measures were enforced in other different countries all workplaces such as social distancing which was difficult to implemented in some areas like urban spaces unless it is considered in early stages of urban design [29] . The other thing that needs to be reconsidered about these new safety measures is the reviewing the existing guidelines and standard with the aid of digital transformation [30] , however, in construction projects it is required to see how social distancing could be controlled where the work environment needs on physical attendance and the possibility of social distancing is difficult.…”

Reduction of false negatives in multi-class sentiment analysis

“…The statistical algorithm linear regression attempts to predict Y given X [23]. The data sets are thoroughly examined to find a relationship [24].…”

COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level

“…An example is the work of Rauf et al [ 24 ], which explores the impact of COVID-19 pandemic for the near future in Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. An early attempt for a global pandemic model is the work of Khan et al [ 25 ]. However in their analysis they do not include deep learning schemes but only shallow learning techniques.…”