Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely related to pattern recognition, where overfitting can occur when there are many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. In addition, the need for portability in hand gesture recognition systems limits the use of deep learning algorithms. In this sense, a study of feature selection and extraction methods is proposed for the use of traditional machine learning algorithms. The feature selection methods analyzed are: maximum relevance and minimum redundancy (MRMR), Sequential, neighbor component analysis without parameters (NCAsp), neighbor component analysis with parameters (NCAp), Relief-F, and decision tree (DT). We also analyze the behavior of feature selection methods using classification and recognition accuracy and processing time. Feature selection methods were fed through seventeen feature extraction functions, which return a score proportional to its importance. The functions are then ranked according to their scores and fed to machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). This work demonstrates that all feature selection methods evaluated on ANN provide better accuracy. In addition, the combination and number of feature extraction functions influence the accuracy and processing time.

Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.

The present study focuses on wind power forecasting (WPF) models based on deep neural networks (DNNs), aiming to evaluate current approaches, identify gaps, and provide insights into their importance for the integration of Renewable Energy Sources (RESs). The systematic review was conducted following the methodology of Kitchenham and Charters, including peer-reviewed articles from 2020 to 2024 that focused on WPF using deep learning (DL) techniques. Searches were conducted in the ACM Digital Library, IEEE Xplore, ScienceDirect, Springer Link, and Wiley Online Library, with the last search updated in April 2024. After the first phase of screening and then filtering using inclusion and exclusion criteria, risk of bias was assessed using a Likert-scale evaluation of methodological quality, validity, and reporting. Data extraction was performed for 120 studies. The synthesis established that the state of the art is dominated by hybrid architectures (e.g., CNN-LSTM) integrated with signal decomposition techniques like VMD and optimization algorithms such as GWO and PSO, demonstrating high predictive accuracy for short-term horizons. Despite these advancements, limitations include the variability in datasets, the heterogeneity of model architectures, and a lack of standardization in performance metrics, which complicate direct comparisons across studies. Overall, WPF models based on DNNs demonstrate substantial promise for renewable energy integration, though future work should prioritize standardization and reproducibility. This review received no external funding and was not prospectively registered.

This research aims to identify the environmental factors that affect economic growth in developed and developing countries using a panel data regression model. In advanced economies, restrictions on international investment, emissions, the use of natural resources, and environmental preservation efforts stand out as influential variables. In contrast, in developing countries, contract enforcement, emissions, and exposure to air pollution are key determinants of economic performance. This study concludes that the natural environment plays a more significant role in the economic growth of developed countries, due to their greater capacity to implement sustainability policies. In developing countries, institutional conditions and environmental management are more relevant, highlighting the need to strengthen legal frameworks and invest in mitigation measures to achieve sustainable economic development.

Currently, gesture recognition is presented as a problem of feature extraction and pattern recognition, in which we label a movement as belonging to a given class. The response of a gesture recognition system can be applied for different problems in different fields, such as medicine, teaching, robotics among others. There are different models proposed in the scientific literature that try to solve the problem of hand gesture recognition. These works do not meet the demand for real-time processing and high recognition accuracy, simultaneously. In this context, the present work describes a project to develop a new model for the recognition of hand gestures using infrared information, acquired with the Leap Motion Controller, and also using machine learning techniques. The proposed model is intended to recognize 5 static and 4 dynamic gestures of the hand in real-time and with high accuracy, simultaneously. The methodology that will be used for developing this work is composed of two phases, the training and testing: In the first phase, we will design, classify, preliminary validation and tune the proposed model. In the second phase, we will test the proposed model by estimating its accuracy of recognition and time of processing.