Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.

Automated acoustic recognition of birds is considered an important technology in support of biodiversity monitoring and biodiversity conservation activities. These activities require processing large amounts of soundscape recordings. Typically, recordings are transformed to a number of acoustic features, and a machine learning method is used to build models and recognize the sound events of interest. The main problem is the scalability of data processing, either for developing models or for processing recordings made over long time periods. In those cases, the processing time and resources required might become prohibitive for the average user. To address this problem, we evaluated the applicability of three data reduction methods. These methods were applied to a series of acoustic feature vectors as an additional postprocessing step, which aims to reduce the computational demand during training. The experimental results obtained using Mel-frequency cepstral coefficients (MFCCs) and hidden Markov models (HMMs) support the finding that a reduction in training data by a factor of 10 does not significantly affect the recognition performance.

To create a bird classification model, it is necessary to have training datasets with thousands of samples. Automating this task is possible, but the first step is being able to segment soundscapes by identifying bird vocalizations. In this study, we address this issue by testing four methods for audio segmentation, the Librosa Library, Few-Shot Learning technique: the BirdNET Framework, and a Bird Classification Model called Perch. The results show that the best method for the purpose of this work was BirdNET, achieving the highest values for precision, accuracy, and F1-score.

Resumo Diante da complexidade dos fenômenos que levam à evasão, foi sistematizado um método que simplifica a elaboração de um diagnóstico, permitindo estruturar e agrupar as dificuldades mais relatadas, possibilitando relacionar as estratégias mais adequadas para contribuir com a permanência e a conclusão de estudantes. A pesquisa caracteriza-se como exploratória e descritiva, utilizando a análise documental para subsidiar a elaboração de questionários estruturados e objetivos. Para aplicação, empregou-se um método não probabilístico, no qual obteveram-se 5.041 respondentes de todos os cursos de graduação da UFMT (33% desvinculados, 37% vinculados e 29% egressos), totalizando cerca de 10% da população entre 2013 e 2019. A ponderação e o agrupamento de dificuldades possibilitou capturar diferentes perspectivas dos discentes. As dificuldades relacionadas com o aprendizado, a estrutura do curso e com o corpo docente foram muito citadas, permitindo a priorização de estratégias para gestores dos cursos e da instituição.

This paper presents Mannga (Multiple variables with Artificial Neural Network and Genetic Algorithm), a method designed for gap filling meteorological data. The main approach is to estimate the missing data based on values of other meteorological variables measured at the same time in the same local, since the meteorological variables are strongly related. Experimental tests showed the performance of Mannga compared with other two methods typically used by researches in this area. Good results were achieved, with high accuracy even for sequential failures, which is a big challenge for researchers. The core advantages of Mannga are the flexibility of handling different types of meteorological data, the ability of select the best variables to assist the gap filling and the capacity to deal with sequential failures. Moreover, the method is available to public use with the Java programming language.