In this paper, we want to examine how unemployment impacts social life, and, by using datasets from six European countries, we analyze the effect of unemployment on two of the main aspects of social life: social exclusion and life satisfaction. First, we predict unemployment rates using the Auto Regressive Integrated Moving Average (ARIMA) model and the results are further used in a linear regression model alongside social exclusion and life satisfaction data, thus obtaining the hybrid model. With the help of the point prediction method, we use the hybrid model to predict new values for the two aspects of social life for the upcoming three years and we analyze the results obtained in order to better understand their interconnection. The results suggest that unemployment has particularly adverse effects on the subjective perception of life satisfaction, furthermore increasing the social exclusion percentage.

This paper aims to analyze the influence that certain social factors (education and area of residence) have on the most likely scenarios people encounter in their online activities. Among the possible scenarios of using the internet, based on everyday individuals’ activities, we selected seeking information about health, goods, and services, taking online courses, internet banking, and participating in social networks. Using data acquired from international databases over the 2002–2020 period, we proposed five hypotheses and applied a multilinear regression model to the data collected for four European countries, namely, Bulgaria, Greece, Romania, and Slovenia. We have analyzed the degree of confirmation for all five hypotheses. The results provided a better understanding of the influence of the above-mentioned factors on the considered scenarios, allowing stakeholders to define and propose specific development policies.

The rapid technological advancement has led to an increased amount of content and information which needs to be reliably analyzed and correlated in order to make accurate inferences. Data quality is a stringent issue when considering gaps or missing information and various imputation methods, algorithms or statistical techniques have been created to correct missing data. To this purpose we propose an imputation method using the Sliding Window Technique to help infer missing data from datasets, a method that is efficient in terms of accuracy and complexity. In order to illustrate the method, we present a case study that simulates a scenario in which a half-yearly dataset must be analyzed in relation to annual datasets. The obtained results are passed to a statistical descriptive analysis and the conclusions derived from the performed analysis suggest that the estimations are homogeneous enough.

The rapid proliferation of social media platforms has led to a remarkable increase of fake news dissemination, creating a significant challenge for the integrity of online information. To analyze this issue, this study aims to investigate the relationship between user characteristics and behaviors on social media platforms as a prequel for the development of effective fake news detection strategies. For that purpose, we examined open access datasets available for Romanian internet users and selected several internet usage activities which we analyzed in relation to key demographic characteristics that lead to establishing user profiles prone to manipulations. By understanding the underlying factors that contribute to the spread of fake news, we can design targeted interventions to mitigate its impact and promote a more informed and responsible online environment.

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) offers remarkable chances for improving healthcare in smart home environments. In order to anticipate and observe neurological disorders, the study explores the use of IoT devices along with AI analytics. The goal is to identify early signs of any decline in cognitive functions or neurodegenerative conditions such as Alzheimer’s or Parkinson’s. The research emphasises on examining a framework for predictive neurology while it faces challenges associated with real-time data processing and prediction precision. The outcomes should result in a smart home ecosystem development that focuses on healthcare interventions and enhances life quality. This paper explores a multi-layered architecture, including an exploratory phase to analyse user needs, a developmental phase to design and integrate IoT devices, Machine Learning algorithms, and cloud architectures, as well as a validation phase to undergo rigorous testing in simulated environments to evaluate performance metrics.