Carbon nanotube (CNT)-based supercapacitors in H₂SO₄ electrolytes face design optimization challenges due to limited experimental datasets for machine learning model development. This study introduces an integrated framework combining physics-informed data augmentation with advanced machine learning to predict CNT capacitance from structural and electrochemical features. Three augmentation methods—bootstrap, PCA-based, and perturbation—were systematically evaluated across six machine learning algorithms using 69 experimental samples. Bootstrap augmentation with XGBoost achieved superior performance (R² = 0.9518, RMSE = 65.48 F/g), representing a significant improvement over traditional approaches. SHAP analysis identified voltage window and structural defect density as the most critical predictive features, providing actionable insights for CNT electrode design. This framework effectively addresses data scarcity in materials research while reducing experimental costs and accelerating supercapacitor development.

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees’ performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by “KF” with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

Rapid industrialization and urbanization continue to impair global air pollution, making accurate air quality forecasting critical for effective environmental management. The main aim of this paper is to create a hybrid intelligent system that combines improved feature selection optimization to accurately predict applications. A collection of computerized weather stations with the main chemicals, such as CO and hydrogen, is used in the structure. The Crocodile Optimization, Beetle Antenna Search, and Pathfinder Algorithm (CSO–BFA) present superior predictive performance. The bio-inspired methods work concurrently to enhance the feature selection based on learning the most important variable quantity in complex spatial data while filtering out noise and dimensionality. The optimized feature collection is integrated into a strong prediction model for accurate prediction. The performance of the training process was examined by utilizing an improved algorithm that implemented a Multilayer Perceptron Backpropagation Neural Network (MPBPNN) to train and predict air pollution. Based on the practical results, it was determined that the hybrid model showed improved functionality in conditions of classification and prediction. The model's CSO–BFA–MPBPNN framework achieves the highest (0.9029) and the lowest error rates (RMSE = 0.4167, MAE = 0.0976), and the prediction accuracy is 94.05%. In this model, the multiple optimization techniques are integrated across several strategies, resulting in high accuracy. The system delivers a reliable, real‑time tool for early warning and active environmental management. Air pollution-related health and environmental risks can be minimized with informed decision-making.

The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO₂ emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R² = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses.