In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Human capital, which is a key resource of every organization, is characterized by high sensitivity to social, cultural and other factors that are not necessarily economic in nature. In the process of managing this capital, employee satisfaction becomes key, resulting from various reasons. In this study, we attempted to examine the level of satisfaction of university employees. The aim of this study was to gather information on the level of employee satisfaction with their job positions and to examine the relationships between selected, identified factors influencing their job satisfaction. The paper used multivariate statistical analysis, mainly Wilcoxon tests and Spearman rank correlation. Analysis of the survey results confirmed significant relationships between factors such as work atmosphere, appreciation of work effects, proper division of responsibilities and possible help in the team.