Comparative Analysis of Classification Techniques on Olympic Games Datasets

Olympic game is a prestigious ceremony that occurs after every four years. However, due to the spread of coronavirus in 2020, the game was held in 2021, which is post-Covid. The main aim of this research is to find out if there was a difference in the performance of nations in Rio 2016 Olympics (pre-Covid) and Tokyo 2020 Olympics (post-Covid). Statistical analysis is carried out to find the correlation between the different variables. One of the highly correlated variables (Gold Tally) is removed while performing the classification analysis. The idea is to see if the classifiers are able to do the comparative analysis without it or not. The classification algorithms utilized in this research are Decision Table, Decision Tree, Naïve Bayes, and Random Forest. The datasets used in this research are imbalanced sets, which were later transformed to balance sets through under-sampling. Random Forest was able to give 100% accuracy in both datasets whereas the True Positive Rate (TPR) was also 100%. After doing the comparative analysis it was found that irrespective of pre and post-Covid, the performance of athletes did not change. This paves the way for other researchers to investigate if Covid had any impact on the performance of the athletes or not. In the future, more vast variables will be investigated to do a more detailed comparative analysis. © 2022 IEEE.

Decision-Making in Mask Disposal Techniques Using Soft Set Theory

In 2003, Maji, Biswas, and Roy developed a method for applying soft set theory to a decision-making problem using Pawlak's rough set approach. Further, research proved that Maji's soft set reductions were inaccurate in 2005, leading to the development of a new method by Chen et al. This article applies soft theory to waste management and disposal decision-making problems. The excessive masks discarded during the COVID-19 era, in particular, must be managed effectively, and the current paper provides a method for better decision-making of the same. The algorithms used are first to compute the reductions and then the reduct soft set is used to choose the ideal objects for decision problems, and then the choice value is calculated. Predefined parameters are sometimes not enough to make precise decisions to solve general or real-time issues. Therefore, additional parameters are added into the existing set, either as a new parameter or generated by the handling of existing ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.