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The Global Innovation Index (GII) aims to rank countries using different innovation factors. This ranking list enables countries to observe their potential status according to the rankings of other countries. The countries are classified under four groups according to the World Bank Income Group Classification on the GII list. The groups are named as; low income (LI), lower-middle income (LM), upper-middle income (UM) and high income (HI). Also, every country has a score in this ranking list. In this study, the ranking scores of 128 countries are estimated using the artificial neural network (ANN). We chose the relevant 27 features on GII 2016 Report, as input data. The significance of this paper is that; it is the first curve fitting and estimation of the score processes on GII 2016 dataset. The low root mean square error (RMSE) value which is obtained in an experimental study shows that the fitting structure is good enough to determine the approximate score of the countries in GII list. The results also show that the selected 27 features are sufficient for obtaining the income score of the countries. Increasing the number of features would lower the RMSE value and enable better approximation in the curve fitting process. The final results can assist the countries in achieving long-term output growth and improving their innovation capabilities.

The COVID-19 is a global disease that occurred at the end of 2019 and it has shown its effects all over the world in a very short time. World Health Organization has mobilized all the countries of the world to survive with minimal damage from this outbreak. The situation in some countries was under control as their health infrastructure is robust enough. On the other hand, many countries suffered significant damage from the outbreak. The countries that have already taken their precautions have suffered less, Turkey is one of the leading countries. Besides taking precautions in advance, countries are guiding each other throughout the outbreak. Therefore, the countries leading the fight against the outbreak should be analyzed and each country should update its precautions to fight the outbreak. In this study, COVID-19 deaths are taken into account and similar countries to Turkey are identified by K-means clustering. Later, by comparing the various characteristics of Turkey with these similar countries, Turkey’s status in fighting the outbreak is revealed. The precautions Turkey took before the outbreak showed that Turkey can fight the COVID-19 outbreak successfully.