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.

Digitalization advances in many fields due to its clear advantages and facilities especially in pandemic times when crowds are to be avoided. E-voting is gaining importance as voting using laptops, mobile phones or tablets is more practical. Hence, human counting which leads to unintentional errors or fraud could be eliminated. Furthermore, software applications can reduce the costs of the classic election process and physical infrastructure. In this paper, we propose to identify the most critical specifications of an e-voting application, find a solution for elections in universities and compare our solution with others. The goal is to propose a conceptual architecture using encrypted functions and two stages: voting and validation, separating layers and roles, that is based on blockchain tables and innovative interactions between actors (voters and voting committee) and the two software components (web application and database). For replicability, the conceptual architecture is depicted and formalized using several Unified Modeling Language (UML) diagrams. Furthermore, in order to provide proof of concept, the initial steps in implementing the proposed solution are showcased.

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.

Traditional Anti-Money Laundering (AML) systems rely on rule-based approaches, which often fail to adapt to evolving money laundering tactics and produce high false-positive rates, overwhelming compliance teams. This study proposes an innovative machine learning (ML) framework that leverages Conditional Tabular Generative Adversarial Networks (CTGANs) to address severe class imbalance, a common challenge in Suspicious Activity Reporting (SAR). Implemented in Python, CTGAN generates realistic synthetic samples to enhance minority-class representation, improving recall and F1-scores. For instance, the Random Forest (RF) model achieves a recall of 0.991 and an F1-score of 0.528 in oversampled datasets with engineered variables, highlighting the effectiveness of CTGAN in mitigating imbalance. This framework also incorporates SQL-based feature engineering using Oracle Analytics, creating dynamic variables such as cumulative sums, rolling averages, and ranks. The modelling phase and exploratory data analysis are conducted in the SAS programming language, employing Logistic Regression (LR) as baseline, Decision Trees (DT), and RF. Evaluation across undersampled and oversampled datasets, combined with varying probability thresholds, reveals key trade-offs between sensitivity and precision. Among the models, RF consistently achieves the highest ROC-AUC scores, ranging from 0.945 in undersampled datasets to 0.951 in oversampled configurations, demonstrating its robustness and accuracy in SAR detection. By integrating CTGAN and TF-IDF (textual feature transformation in Python) with SQL-engineered variables, this framework provides a comprehensive data-driven approach to AML. It reduces false positives, strengthens the detection of suspicious activities, and ensures scalability, adaptability, and compliance with regulatory standards.