COVID-19 pandemic has affected and still affects many countries in the world, reshaping many of the economic and social activities. Based on the results of an online survey, this paper highlights the perceptions of the way the pandemic has affected one of the most vulnerable categories in a society, migrants. We focus our research on the migrants and refugees from Middle East and North Africa (MENA) countries, living in Europe, as in the recent years and mostly after the migrant crisis in 2015, they are in large numbers in European countries. Using ANOVA models, our results show that unemployed migrants, students but also migrants who find it difficult on present income are most worried about the COVID-19 crisis and fell they will be greatly affected in terms of income and employment by this crisis. Also, women are more worried by COVID-19 than men with respect to the health aspect.

The telecommunications industry is representative when it comes to a country’s economy. In this industry, the customer plays a very important role in maintaining a stable income. The churn customer is one of the most important concerns for large companies. This increased attention is due to its direct effect on the revenues of large companies in the telecommunications industry, companies being in a constant search to develop ways to predict this type of customer. The aim of our paper is to identify potential customers at risk of churn using modern data mining techniques, often used in the business world. From the nine techniques tested, we choose as the churn prediction model, the technique with the highest performance. The effectiveness of the model is tested and evaluated by the f1-score. The model developed in the paper uses machine learning techniques on the Python platform, exploring a wide range of algorithms from logistic regression and the method of balancing the analyzed data set (Balanced Random Forest) to supervised learning methods (K-Nearest Neighbors, Naive Bayes) and optimization packages (Ligh GBM, CATBoost, ADABoost, RUSBoost, Stochastic Gradient Descent). The techniques analyzed in this paper cover a diverse range of methods that are compared in terms of performance. RUSBoost proves to be the best churn prediction model for telecom customers in this study. RUSBoost has the lowest loss function of all the tested techniques.

This paper addresses a debatable and challenging issue, which is the employment status of refugees and migrants from third countries in Europe, since Europe is facing an immense refugee flow and the numbers are growing. While refugees are seeking better living conditions, with a secure income and job status, this subject is important to be researched. Our research shows that the job situation of refugees and asylum seekers in Europe had an impact on European economy and society. The methodology relies on descriptive statistical investigations using primary data, Data collection was carried out using a questionnaire, distributed among various groups of individuals from Romania and other European countries. Our results prove that the employment status is rather difficult, due to some factors such as level of education done in home country, last economic status and it can vary from one European country to another.