La forma del cuerpo es definida por la genética, la alimentación y el ejercicio diario. La forma del cuerpo es importante para definir y explotar las habilidades en los deportes. Por ejemplo, un corredor requiere un cuerpo Ectomorfo, es decir delgado y con la menor cantidad de grasa corporal para potenciar su velocidad. Por el contrario, un luchador profesional se require sea Endomorfo, el cual tiene mucha grasa y músculo. Por lo tanto, la clasificación de las formas del cuerpo puede ayudar a identificar las áreas idóneas para cada deporte. El método para la obtención del somatotipo con la técnica de Heath-Carter es a través de la medición de: peso, estatura, circunferencia de brazos, piernas, muñecas, tobillos, entre otras medidas. Con las medidas, se aplican cálculos para conocer el somatotipo, obteniendo parámetros de cada somatotipo. En este trabajo, se clasifican los somatotipos: Ectomorfo, Endomorfo y Mesomorfo, con un Dataset con 618 registros de adultos jóvenes. El Dataset se clasificó con la herramienta Orange usando una Red neuronal artificial, Árboles de decisión y Regresión logística obteniendo resultados de un 93% de precisión. Se concluye que, es posible obtener la clasificación de somatotipos con los datos de las medidas de la persona sin hacer los cálculos.

The creation and authentication of real users on social media requires the implementation of artificial intelligence-based technologies that can mitigate malicious behavior from automated accounts. This study presents a machine learning-based approach for detecting social bots on Twitter/X, based on the analysis of user profile features and behavioral attributes. Four classification models were evaluated: a neural network (NN), support vector machines (SVM), a random forest classifier (RF), and Extreme Gradient Boosting (XGBoost), using five-fold stratified cross-validation. To improve the performance and robustness of the classification, additional features and data balancing techniques were incorporated. The experimental results show that the neural network achieved the best overall performance, with an average accuracy of 95.6 ± 0.6%, followed by the random forest (95.0 ± 0.6%), the linear SVM (94.1 ± 1.5%) and XGBoost (94.0 ± 1.1%). These results demonstrate that the proposed methodology improves the automated detection of social bots while maintaining the interpretability of the models, which contributes to the development of more reliable and explainable security mechanisms for social media platforms.

Accurate wheat yield prediction is critical for global food security, yet existing forecasting models often struggle to balance high‐dimensional genomic data with dynamic environmental variables. This study developed an automated framework based on genetic algorithms (GAs) to simultaneously optimize phenotypic selection, climatic feature engineering, and machine learning hyperparameters. The framework was evaluated across two contrasting cultivation environments: irrigated (Mexico) and nonirrigated (Middle East). For the irrigated dataset, the model achieved a peak performance of coefficient of determination ( R ² ) = 0.8363 and root mean squared error (RMSE) = 38.59, demonstrating that the proposed methodology is capable of predicting wheat yield with a R ² exceeding 0.80 under irrigated conditions. Meanwhile, in the nonirrigated environment, the system maintained robust predictive power with R ² = 0.6199 and RMSE = 721.67. To ensure the statistical reliability and reproducibility of these findings, a bootstrapping validation (1000 iterations) was performed on the top‐performing individuals. This process yielded narrow 95% confidence intervals, confirming that while the GA‐optimized features provide higher stability in irrigated systems, the framework effectively captures genotype–environment interactions even under water‐limited conditions. This dual‐environment validation, underpinned by robust resampling techniques, demonstrates the scalability of the proposed soft computing approach for precision breeding across diverse agroclimatic zones.