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.

Economic and social prosperity heavily relies on well‐maintained highways. However, road maintenance faces challenges due to limited funding and resources, with potholes posing significant safety risks. This work introduces a pothole detector designed to detect and estimate pothole areas for timely maintenance. It enhances detection by modifying the YOLO algorithm, using the Xception backbone, and integrating attention mechanisms to improve the prediction of small or clustered objects. Xception’s depthwise separable convolutions enhance feature extraction, outperforming the standard YOLO algorithm in detecting small, irregular potholes and preventing overfitting. The improved YOLO model, along with spatial and channel attention mechanisms, focuses on relevant regions and refines important features specific to pothole areas. Accurate area estimation is achieved through computer vision and traditional segmentation processes. A custom dataset, including the MakeML pothole dataset, a Kaggle dataset, and real‐time footage of Kerala roadways, is used for training and validation. Performance evaluation with mean average precision (mAP) and average precision (AP) metrics shows the pothole detector’s superiority, effectively identifying potholes under various conditions and ensuring safe road infrastructure.

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.

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.