Identifying and reducing customer churn have become a priority for financial institutions seeking to retain clients. Our research focuses on customer churn rate analysis using advanced machine learning (ML) techniques, leveraging a synthetic dataset sourced from the Kaggle platform. The dataset undergoes a preprocessing phase to select variables directly impacting customer churn behavior. SMOTETomek, a hybrid technique that combines oversampling of the minority class (churn) with SMOTE and the removal of noisy or borderline instances through Tomek links, is applied to balance the dataset and improve class separability. Two cutting-edge ML models are applied—random forest (RF) and the Light Gradient-Boosting Machine (LGBM) Classifier. To evaluate the effectiveness of these models, several key performance metrics are utilized, including precision, sensitivity, F1 score, accuracy, and Brier score, which helps assess the calibration of the predicted probabilities. A particular contribution of our research is on calibrating classification probabilities, as many ML models tend to produce uncalibrated probabilities due to the complexity of their internal mechanisms. Probability calibration techniques are employed to adjust the predicted probabilities, enhancing their reliability and interpretability. Furthermore, the Shapley Additive Explanations (SHAP) method, an explainable artificial intelligence (XAI) technique, is further implemented to increase the transparency and credibility of the model’s decision-making process. SHAP provides insights into the importance of individual features in predicting churn, providing knowledge to banking institutions for the development of personalized customer retention strategies.

Climate change is a phenomenon that is sometimes denied or trivialized. However, in recent years, we have faced extreme phenomena such as fires, floods, excessive temperatures, etc. which affect our physical and mental condition and the environment, often leading to significant material damage. To understand these problems and highlight the meteorological and phenomenological changes encountered in the last decade, time series were web-scraped and analyzed from several open data sources: weather news broadcast in Romania, air quality, temperature, etc. The extraction and organization of data recorded between 2009 and 2023 are formulated as a framework that can be reproduced and replicated to continue the monitoring. The exploratory analysis of the categorical and numerical data highlights intricate patterns and correlations within meteorological conditions across regions and seasons. From temperature trends to air quality fluctuations, the study underscores the dynamic interplay of weather phenomena, paving the way for informed forecasting and deeper climate research. At the same time, data processing includes Latent Dirichlet Allocation, K-prototype clustering analysis, in addition to K-means clustering with dimensional reduction techniques, all of which are employed to further reveal the extreme phenomena in news and regions with higher occurrence. Therefore, in this paper, we propose a data processing framework for multiple datasets and analytics, extracting valuable information on climate change and identifying the exposed regions to extreme phenomena.

Climate change is an aspect in our lives that presents urgent challenges requiring innovative approaches and collaborative efforts across diverse fields. Our research investigates the growth and thematic structure of the intersection between climate change research and machine learning (ML). Employing a mixed-methods approach, we analyzed 7521 open-access publications from the Web of Science Core Collection (2004–2024), leveraging both R and Python for data processing and advanced statistical analysis. The results reveal a striking 37.39% annual growth in publications, indicating the rapidly expanding and increasingly significant role of ML in climate research. This growth is accompanied by increased international collaborations, highlighting a global effort to address this urgent challenge. Our approach integrates bibliometrics, text mining (including word clouds, knowledge graphs with Node2Vec and K-Means, factorial analysis, thematic map, and topic modeling via Latent Dirichlet Allocation (LDA)), and visualization techniques to uncover key trends and themes. Thematic analysis using LDA revealed seven key topic areas, reflecting the multidisciplinary nature of this research field: hydrology, agriculture, biodiversity, forestry, oceanography, forecasts, and models. These findings contribute to an in-depth understanding of this rapidly evolving area and inform future research directions and resource allocation strategies by identifying both established and emerging research themes along with areas requiring further investigation.

With agriculture moving to a more performant and autonomous-driven sustainability, advanced digital monitoring is becoming the key to resilient land management. This study investigates hydro-climatic conditions and urban air pollution in 2024 in the Bărăgan Plain, Romania’s primary grain-producing region. Our approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution. To find the most robust approach we compared three machine learning algorithms: Multiple Linear Regression, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), the Enhanced Vegetation Index (EVI) was used as a predictor of the Normalized Difference Vegetation Index (NDVI). Although the models that included the EVI achieved higher accuracy (R2 of 93%), the non-EVI Random Forest model performed better (R ² of 86%) and revealed moisture availability (NDWI) as the primary regulator of crop vigor, with an importance of 81.8%. To isolate the effect of atmospheric chemistry alone, spatial residuals from the optimized RF model were extracted and plotted. Negative residuals that produced anomalies pointed to a unique type of crop stress in which the plants were underperforming even when water was adequate. These anomalies spatially align the urban pollution plume trajectories. Even if the NO2 has a rapid distance-decay effect, the secondary pollutants (O3) reach over the agricultural land through the photochemical titration effect. Our results show that while water determines regional agricultural baselines, atmospheric chemistry from urban sources can independently cause significant crop stress even in the absence of drought. This study provides a robust proof of concept on the separation of climatic and anthropogenic stressors and lays a basic pathway for a multi-sensor diagnostic framework in the domain of remote sensing.