The classification of Capsicum spp. varieties is often hindered by their morphological similarities, making accurate identification a challenging task. To address this issue, this study applies a hybrid computational approach that combines data dimensionality reduction techniques using Principal Component Analysis and Factor Analysis with various supervised Machine Learning algorithms. The dataset, which is unprecedented in the literature and was collected under controlled agricultural conditions, enables a robust evaluation of models including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest, Decision Tree, and Gradient Boosting. Model performance was assessed using Leave-One-Out and K-Fold cross-validation methods. Additionally, the SHapley Additive exPlanations method was applied to assess the importance of the features in species classification, providing greater interpretability that reinforces the relevance of morphological and agronomic descriptors in differentiating pepper varieties. The results show that all models achieved high performance metrics, including accuracy, F1-score, precision, and recall, consistently above 0.89, validating the effectiveness of the proposed approach. These findings highlight the potential of integrated Machine Learning frameworks for species classification in agriculture, contributing to practical applications and advancing intelligent analysis of biological data.

Data preprocessing is a pivotal stage in Machine Learning (ML) workflows, directly influencing both predictive performance and computational efficiency. Although widely acknowledged, the quantitative impact of preprocessing choices, particularly dimensionality reduction strategies, remains insufficiently explored in classification tasks. This study compares three data preparation approaches: raw data, Principal Component Analysis (PCA), and Principal Component Factor Analysis (PCFA). While PCA and Factor Analysis (FA) are well-established techniques, their structured integration as PCFA has received limited attention. By evaluating these alternatives, this study quantifies how dimensionality reduction affects predictive capability, result stability, and processing time across diverse learning scenarios. A factorial Design of Experiments (DOE) framework is employed, enabling the assessment of model performance under multiple attribute configurations and their interaction effects. Ten distinct ML algorithms are applied to ten heterogeneous datasets from different domains, with model training performed using cross-validation and hyperparameter optimization via Grid Search and Optuna. The findings show that PCA and PCFA consistently achieve competitive predictive performance while substantially reducing computational cost compared to raw data. Furthermore, data structure and attribute interactions strongly influence outcomes, indicating that the relationship between preprocessing strategies and model behavior is scenario-dependent. We conclude that combining dimensionality reduction, structured feature design, and optimized training strategies is essential for balancing predictive accuracy and computational cost, a trade-off increasingly critical in modern ML applications.

This study proposes a structured decision-support framework for machining operations involving multiple and conflicting performance objectives. The approach integrates multi-objective optimization using the Varimax Rotated Factor-Normal Boundary Intersection method with a post-Pareto decision stage designed to identify the most appropriate solution among Pareto-optimal alternatives. Principal Component Analysis and Factor Analysis, combined with Varimax rotation, are employed to reorganize eight correlated response variables into three independent and interpretable performance dimensions representing Reliability, Economic Behavior, and Quality. This transformation enhances the stability of the search space and improves the robustness of the optimization process. In the post-optimization stage, the Pareto-optimal solutions are evaluated using the Shannon’s Entropy/Global Percentage Error, allowing a systematic and multi-criteria assessment of the solution set. A sensitivity analysis based on the variation of the weighting parameter indicates that the decision-making process is stable and robust with respect to preference changes. In addition, a comparative analysis was conducted with well-established multi-objective optimization methods, including the Non-dominated Sorting Genetic Algorithm II, the Multi-Objective Evolutionary Algorithm based on Decomposition, the Weighted Sum Method, and the Multi-Objective Lichtenberg Algorithm, using the Hypervolume, Inverted Generational Distance and Spacing metrics. The results show that, while traditional methods exhibit strong capability in exploring the Pareto front, the proposed framework provides more uniformly distributed solutions, along with a structured mechanism for selecting the final solution, in addition to lower computational time and cost. A case study on the hard turning of AISI H13 steel using PCBN 7025W inserts confirms the applicability of the proposed framework, optimizing cutting speed, feed rate, and depth of cut with respect to tool life, economic performance, and surface integrity. The proposed method identifies balanced parameter configurations while reducing reliance on subjective decision-making, thereby supporting decision-making in multi-objective contexts within manufacturing environments.

This dataset comprises 504 observations obtained from numerical simulation using Computational Fluid Dynamics for a real industrial application involving a turbomachine, specifically a centrifugal fan responsible for air recirculation in ovens operating at high angular velocity and temperature. Prior to geometry construction, a Design of Experiments strategy was employed, based on a Central Composite Design to generate a response surface. Four geometric input variables related to the centrifugal fan blades were considered: number of blades, blade entry angle, blade opening angle, and blade length. The design comprised 24 simulation trials, which, combined with 20 replicates generated by small mesh variations, resulted in 504 numerical simulations.