Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Breast cancer remains a leading cause of female mortality worldwide; hence rapid, highly accurate diagnosis is essential. This study presents an intelligent stacked-ensemble framework that fuses statistical and neural classifiers to distinguish benign from malignant tumors in the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. Two linear feature-selection methods (Pearson Correlation Coefficient (PCC) and Principal Component Analysis (PCA)) and one non-linear method (Back-Propagation Neural Network (BNN)) are first applied to generate three discriminative feature subsets. Six baseline classifiers (SVM, KNN, Decision Tree, Logistic Regression, Naïve Bayes, and ANN) are then trained on each subset. For every feature-selection route, the two best performers feed a meta-learner via stacked generalization. Performance is assessed with accuracy, precision, recall, specificity, and confusion matrices. The proposed stacking strategy boosts every baseline’s accuracy by ≥ 0.2 percentage points. Notably, the M3 ensemble combining SVM, KNN, and ANN attains 100 % accuracy with zero false positives, demonstrating clinical-grade reliability. Results confirm that stacking heterogeneous learners mitigates individual model biases and enhances diagnostic precision. Future work should explore alternative ensemble paradigms (bagging, boosting) and region-specific datasets such as Nigerian breast-cancer cohorts and extend ensembling to feature-selection stages for further dimensionality reduction gains.

Breast cancer continues to be a major global health challenge, necessitating the development of accurate and reliable diagnostic systems. This study presents a comparative evaluation of multiple machine learning classification models aimed at enhancing breast cancer detection. Three feature selection techniques which are Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) were employed to reduce dimensionality and extract relevant features. The performance of six classifiers which are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB), and Artificial Neural Network (ANN) was analyzed based on accuracy, precision, recall, specificity and f1-Score. Results show that among the evaluated classifiers, Random Forest and Support Vector Machine (SVM) consistently delivered the highest performance, with Random Forest achieving up to 98.8% accuracy and SVM up to 98.0%, particularly when trained on features selected through Backpropagation Neural Networks (BNN). K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) also demonstrated strong results, outperforming traditional models like Logistic Regression and Decision Tree in most scenarios. These outcomes underscore the superior classification capabilities of non-linear and ensemble-based models in handling complex feature interactions, affirming their suitability for accurate and robust breast cancer detection.

Breast cancer remains one of the leading causes of mortality worldwide, emphasizing the critical need for accurate and efficient diagnostic tools. This study investigates the effectiveness of combining linear and non-linear feature selection methods-Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) to improve breast cancer classification using machine learning models. We utilized the Wisconsin Breast Cancer Dataset to evaluate the performance of five classifiers-Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB) and Artificial Neural Network (ANN). The results demonstrated that BNN-selected features consistently outperformed PCA and PCC across all classifiers, with SVM achieving the highest classification accuracy. The results showed that BNN-selected features consistently outperformed PCA and PCC, with SVM achieving up to 97.3% accuracy and 98.1% precision, and KNN reaching 97.1% accuracy and 96.9% precision. Stacking ensemble models further improved performance: the non-linear SVM-KNN-ANN ensemble attained perfect classification metrics of 100% accuracy, precision, recall, specificity, and F1-score demonstrating the superior synergy between BNN and ensemble learning. These findings highlight the diagnostic advantage of combining non-linear feature selection with meta-learning approaches and support the development of robust, high-accuracy breast cancer detection systems.