Early detection and diagnosis of oral squamous cell carcinoma (OSCC) are essential for improving patient outcomes. This study presents the development of a deep neural network model trained to classify OSCC using hyperspectral FTIR spectroscopy imaging. The network's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model demonstrated high effectiveness in classifying unfolded OSCC hyperspectral images, showing potential as a valuable tool for supporting diagnostic processes in clinical settings.

Knowledge about the seasonal rainfall for some regions of Brazil is essential, due to the dependency of agriculture and for a correct management of water resources. For this, linear and nonlinear models are commonly used for seasonal rainfall prediction, some of them are based on Artificial Neural Networks, which have proved to have a great potential for this purpose. Following this idea, this work presents a seasonal rainfall forecast model based on a neuro-fuzzy technique, called Neo-Fuzzy Neuron Model, that showed a better performance, in terms of root mean square error and correlation coefficient between predicted and real output, when compared with a dynamic downscaling model using the Regional Spectral Model. The experimental results show the effectiveness of the proposed method in predicting the first four trimesters from 2002 up to the current year.