Since the Industrial Revolution, there has been an evolution in the paradigms under which the industrial worker is perceived and dealt with. These paradigms can be briefly listed in the order of their evolutionary stage as: the food-gatherer, the economic man, the social man, the resourceful man, and the enterprising man. Each of them is a combination of two basic paradigms in different proportions, namely, the outsider paradigm and the partnership paradigm. Obviously, the paradigmatic perspectives of management about their workers will have a significant influence on how they treat their workers, which may become especially conspicuous during recessions and other kinds of hard times. It was in this context that we designed a study to understand the human resource strategies of companies during a period of recession. Data for this study was collected through the content analysis of 46 published cases, wherein we developed the ratings of two sets of variables, namely: the external and internal environments of the company and the strategic actions taken by the respective managements. A surprising finding of the study is that the correlations between the environmental factors and the strategy factors were small and non-significant; moreover, the correlations involving the external environment were smaller than those involving the internal environment. Hence, it may be inferred that strategic actions are influenced primarily by the paradigmatic perspectives of management rather than environmental factors. In order to identify the different types of paradigmatic perspectives, we have further carried out a cluster analysis to develop a taxonomy of paradigms. The results showed that there are five sub-paradigms, which are: (1) Pacifiers, constituting 35% of the sample; (2) Modifiers, constituting 22%; (3) Molders, constituting 17%; (4) Enhancers, constituting 15%; and (5) Exploiters, constituting 11%. The limitations of the study and the implications of the findings are discussed in the concluding part.

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.

With the gradual penetration of artificial intelligence technology into various fields of society, it has brought many deeper and broader impacts, gradually improving the status of artificial intelligence in talent cultivation and education to adapt to the current development of social intelligence technology. Therefore, as the core course of artificial intelligence education in universities, machine learning needs to deeply analyze and explore the main factors that affect its development, in order to better mobilize students' learning enthusiasm and teachers' educational innovation, enhance the teaching and learning effectiveness of the course, and maximize the exploration of the educational achievements of artificial intelligence.