The precise allocation of weights to criteria plays a fundamental role in multicriteria decision-making, exerting a significant influence on the obtained results. Ensuring an appropriate weighting of criteria is crucial for conducting a fair and accurate evaluation of various alternatives. In this context, we present an innovative solution that addresses the allocation of weights to attributes in datasets, aiming to overcome limitations and challenges associated with expert consultation in multicriteria problems. The proposed method is grounded in an objective approach and adopts a hybrid perspective by integrating the mathematical principles of Principal Component Analysis with the application of the Entropy Method. This method was implemented along with the exponential weighted sum model in a case study related to the classification of neighborhoods in Mexico City based on the level of marginalization. Results were compared with the marginalization index reported in official sources, using evaluation metrics MAE and MAPE with values of 0.24 and 11.3%, respectively. This research demonstrates the efficiency of the proposed method, which integrates techniques used for attribute weighting, providing a robust and reliable tool for decision-making.

Emotion recognition in speech has gained increasing relevance in recent years, enabling more personalized interactions between users and automated systems. This paper presents the development of a dataset of features obtained from RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) to classify emotions in speech. The paper highlights audio processing techniques such as silence removal and framing to extract features from the recordings. The features are extracted from the audio signals using spectral techniques, time-domain analysis, and the discrete wavelet transform. The resulting dataset is used to train a neural network and the support vector machine learning algorithm. Cross-validation is employed for model training. The developed models were optimized using a software package that performs hyperparameter tuning to improve results. Finally, the emotional classification outcomes were compared. The results showed an emotion classification accuracy of 0.654 for the perceptron neural network and 0.724 for the support vector machine algorithm, demonstrating satisfactory performance in emotion classification.

Vehicular transportation is one of the most widely used modes in modern cities for reaching key destinations such as workplaces, healthcare facilities, recreational areas, and educational institutions, among others. However, the performance of vehicular traffic on these roads can vary significantly due to the influence of different environmental variables. In the literature, factors such as traffic incidents, weather conditions, road infrastructure, and driving habits, among others, have been identified as impacting vehicular traffic performance. In this context, predictive models have been developed to anticipate congestion at specific points in cities based on statistics, machine learning, simulation, and complex networks. This study proposes a novel index aimed at assessing the level of performance of vehicular traffic on streets based on the relationship among relevant urban environmental variables. This index is generated through Genetic Programming, considering a set of variables related to traffic, incidents, and services. The case study will focus on the streets of the Tlalpan Municipality in Mexico City.

Accurately modelling and predicting traffic congestion is essential for effective traffic management in dynamic urban environments. This study introduces a hybrid model that combines a deep neural network architecture, designed to capture long-term temporal dependencies, with physics-based approaches to traffic modelling. The deep learning component identifies complex temporal patterns and non-linear behaviours in traffic evolution, while the physics-based component incorporates dynamic constraints that enhance the model’s robustness and ability to generalise. This integration leverages the predictive power of machine learning while maintaining consistency with the fundamental principles governing vehicle flow. Experiments conducted using real-world traffic data demonstrate strong predictive performance, establishing a solid foundation for the development of intelligent transportation management systems and providing advanced tools to support adaptive mobility optimisation in congested urban settings.