Abstract
This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

Abstract
The techniques of image filtering have undergone an explosive growth in the last years to make new advances and challenges. This is due to the fact, among several other reasons, the increase of the volume of images coming from several sources. Digital images have been used for a variety of purposes, from the storage of souvenirs to accurate medical exams. However, Images may be corrupted due to several factors. The challenge of suppression or noise attenuation has led to the search for improved techniques in order to preserve important characteristics of the image, but, on the other hand, there is no solution available to completely solve the problem, boosting the production of the work proposed here. In this paper proposes a method for noise attenuation in computed tomography images using a hybrid genetic algorithm, the proposed method seeks to optimize the results in the space of solutions composed by a series of techniques of noise filtering. At the end the proposed method is compared statistically with two other competing methods and after the resulting filtered images are shown.

Abstract
This paper presents an innovative and motivating methodology to learn vision systems using a humanoid robot, NAO robot. Vision systems are an area of growing development and interest of engineering students. This approach to learning was applied in students of Master of Electrical Engineering. The goal is to introduce students the main approaches of visual object recognition and human face recognition using computer vision techniques to be embedded in a social robot and therefore he is able to iteract with human beings. NAO robot as an educational platform easy to learn how to program, and it has a high sensory ability and two cameras that can capture the images for processing.

Abstract
This paper describes a group of robots for cleaning a simulated environment and proposes an efficient algorithm for navigation based on Pathfinding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is effective in cleaning. In order to test its efficiency it was compared the combination of the Pathfinding A* algorithm and the decision algorithm proposed in this paper with Pathfinding A* and Euclidean distance, resulted in an improvement in time and distance traveled. © CLAWAR Association.

Abstract
This paper describes the use of NAO robot, a humanoid robot and the use of Arduino in education for final engineering projects in the area of engineering. This learning approach was implemented in the final project of the Biomedical Engineering degree. The objective this project is the sensorial extension of the NAO platform, integrating a set of sensors that allow not only to analyze multiple vital parameters in a passive way but also to obtain a more precise contextual information. © CLAWAR Association.

Abstract
Over the years robotics has made great progress. Nowadays, robots begin to be part of the life of any person, designated social robotic. Humanoid robots are fascinating and have several advantages, such as they can work in places where there is a risk of contamination, risk of health, danger of life, places that are difficult to access. They also are able to access different types of terrain and to climb stairs. NAO robot is currently the humanoid platform with high sensory capacity that it has lower costs in the market. This robot is similar to human in order to have a more real and natural with society. Using the capabilities of the robot and adding other sensors, we can have a more powerful machine in our society. NAO robot is presented in this paper as a domestic robot. © CLAWAR Association.