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A documentary review was carried out on the production and publication of research papers related to the study of the variables INTELLIGENT SYSTEMS, ARTIFICIAL INTELLIGENCE and AUTOMATIC LEARNING. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 196 publications. The information provided by this platform was organized through graphs and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil with 104 publications was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of Intelligent Systems, Artificial Intelligence and Machine Learning was Computer Science with 151 published documents, and the Type of Publication most used during the period indicated above were Conference Articles with 55% of the total scientific production. [ABSTRACT FROM AUTHOR]

Introduction: technological developments in artificial intelligence and health are necessary for Latin American health systems. Objective: to describe the trends in scientific production on artificial intelligence and health in Latin America in Scopus. Method: This is a retrospective bibliometric study of Latin American authors' scientific production on artificial intelligence and health in Scopus between 2012 and 2021. Production, visibility and impact indicators were used. VOSviewer and SciVal were used for data analysis. Results: 2871 articles were published, with a variation between 2012 and 2021 of 94.98%. 2,397 articles were original, and 2,741 were written in English. 58.3% were published in first-quartile journals, the most productive being Sensors (Ndoc=79) and Plos One (Ndoc=66). 64,128 citations were received (mean of 22.3 citations per article). Brazil was the most productive country (Ndoc=1420), and the institution was the University of São Paulo (Ndoc=288). 498 thematic groups were identified, and 1376 themes. 54% of the articles had international collaboration and 3.3% with academic-corporation collaboration. Conclusions: there is a growing scientific production on artificial intelligence and health in Latin America, written mainly in English, medical, engineering and computer science research areas, disseminated in specialized magazines in the first quartiles. Brazil and its institutions were the top producers. The main topics were predictive models and the application of artificial intelligence for classifying, diagnosing and treating diseases. [ABSTRACT FROM AUTHOR]

This work presents a novel investigation on the nowcasting prediction of wind speed for three sites in Bahia, Brazil. For this, it was applied the computational intelligence by supervised machine learning using different artificial neural network technique, which was trained, validated, and tested using time series are derived from measurements that are acquired in towers equipped with anemometers at heights of 100.0, 120.0 and 150.0 m. To define the most efficient ANN, different topologies were tested using MLP and RNN, applying Wavelet packet decomposition (bior, coif, db, dmey, rbior, sym). The best statistical analysis was RNN + discrete Meyer wavelet. A new methodology for improving forecast accuracy of wind speed using artificial neural network (ANN) and Wavelet packet decomposition. Using machine learning and Wavelet packet decomposition to nowcast wind speed (m/s). To predict the wind speed at 100.0 m, 120.0 m and 150.0 m height in tropical region. Performance evaluation of Wavelet packet decomposition applying 48 different mother Wavelet functions. ANN approach for the estimation of nine types of wind speed time series. The proposed hybrid model (ANN + Wavelet packet decomposition) is capable of wind speed forecasting efficiently. [ABSTRACT FROM AUTHOR]