Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision–recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements.

The transition to renewable energy sources is critical for sustainable development, yet integrating these sources into existing power systems poses significant challenges. Energy Storage Systems (ESS) are essential in enhancing the reliability and efficiency of renewable energy systems. Despite growing research, a comprehensive scientometric analysis mapping development and trends in this field is lacking. This study addresses this gap by conducting a detailed scientometric analysis of power systems and new energy research from 2014 to 2023. The novelty of this study lies in its systematic use of advanced bibliometric tools to provide a thorough analysis of the research landscape. Utilizing 425 research articles from the Web of Science database, the study employs CiteSpace to visualize academic networks, identify research hotspots, and outline current trends. Specific methodologies include burst detection to identify significant shifts in research focus, centrality measurement to determine the influence of key studies, and heterogeneous network analysis to map the interconnectedness of various research themes. The analysis reveals extensive international collaborations, with China leading in publication volume (344 articles) and centrality (0.69), followed by the United States (29 articles, centrality 0.53). Significant contributions come from institutions like North China Electric Power University, China Electric Power Research Institute, and Tsinghua University. The findings underscore the importance of international cooperation and the need for broader geographical representation in this research field. This study provides valuable insights into the evolution and current state of power systems and renewable energy research, offering essential guidance for future research and development. The results highlight the critical role of ESS in the transition to renewable energy and suggest directions for future investigations, particularly focusing on enhancing ESS efficiency and reliability and expanding international research collaborations.

This paper proposes a hybrid deep learning model for robust and interpretable sentiment classification of Twitter data. The model integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings, a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom attention mechanism to classify tweets into four sentiment categories: Positive, Negative, Neutral, and Irrelevant. Addressing the challenges of noisy and multilingual social media content, the model incorporates a comprehensive preprocessing pipeline and data augmentation strategies including back-translation and synonym replacement. An ablation study demonstrates that combining BERT with BiLSTM improves the model’s sensitivity to sequence dependencies, while the attention mechanism enhances both classification accuracy and interpretability. Empirical results show that the proposed model outperforms BERT-only and BERT+BiLSTM baselines, achieving F1-scores (F1) above 0.94 across all sentiment classes. Attention weight visualizations further reveal the model’s ability to focus on sentiment-bearing tokens, providing transparency in decision-making. The proposed framework is well-suited for deployment in real-time sentiment monitoring systems and offers a scalable solution for multilingual and multi-class sentiment analysis in dynamic social media environments. We also include a focused characterization of the dataset via an Exploratory Data Analysis in the Methods section.

This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO). The LSTM model, enhanced with an attention mechanism, is used to predict sales volumes, pricing trends, and spoilage rates over a seven-day period. The predictions generated by the LSTM model serve as inputs for the PSO algorithm, which iteratively optimizes pricing and replenishment strategies to maximize profitability while adhering to inventory constraints. The integration of cost-plus pricing allows for dynamic adjustments based on fixed and variable costs, ensuring real-time adaptability to market fluctuations. The framework not only maximizes profits but also reduces food waste, contributing to more sustainable supermarket operations. The attention mechanism enhances the interpretability of the LSTM model by identifying key time points and factors influencing sales, improving decision-making accuracy. This methodology bridges the gap between predictive modeling and optimization, offering a scalable solution for dynamic pricing and inventory management in fresh food retail and other industries dealing with perishable goods.

Bank telemarketing campaigns often experience low subscription rates due to customer heterogeneity and severe class imbalance, which pose challenges for reliable predictive modeling. This study investigates a data-driven approach that integrates synthetic minority oversampling and cost-sensitive learning to improve the prediction of telemarketing outcomes. Experiments are conducted using the Portuguese Bank Marketing dataset, comprising 41,188 instances with a positive response rate of 11.3%. Eight machine learning models are evaluated under a unified preprocessing pipeline and five-fold stratified cross-validation, including Logistic Regression, Decision Tree, Random Forest, and Ensemble methods. The results show that Ensemble models, particularly CatBoost, XGBoost, and LightGBM, achieve improved performance compared with traditional baselines, with notable gains in minority-class recall and overall discrimination ability. The best-performing model attains an F1-score of 0.540, a recall of 0.812 for the positive class, and a ROC–AUC of 0.908. To enhance interpretability, SHAP-based analysis is applied to quantify feature contributions, identifying campaign duration, previous contact outcomes, and selected macroeconomic indicators as key predictors. These findings indicate that combining resampling strategies with cost-sensitive optimization provides a robust and transparent approach for learning from imbalanced telemarketing data, thereby supporting reproducible and data-driven financial decision-making by explicitly addressing difficulty in minority-class identification under imbalance and class imbalance under cross-entropy training in imbalanced banking data.