Abstract
This paper describes the implementation of a simulated bioinspired 3D model, which was moved through segmented gestures using fractional calculation segmentation methods. It was observed that for the increase in the detection of such gestures from this segmentation an improvement in the recognition and standardization of gesture images using the FODPSO method would be obtained. We used the AlexNet neural network for training and recognition of the processed gestures, which resulted in a simulated 3D model of a spider moved by gestures recognized through a webcam, which had an accuracy of approximately 98% recognition through the neural network.

Abstract
This paper compares optimal path planning algorithms based on a Genetic Algorithm and a Particle Swarm Optimization algorithm applied to multiple bioinspired robots in a 2D environment simulation. The planning objectives are related to the harvesting of an apple plantation in which three swarm of butterflies were run, counting the fruits on the ground to optimize the harvest in a cooperative way. Robotic swarms must travel through points on the map to count the fruits. The time for each swarm was also counted for the comparison results. © Springer Nature Switzerland AG 2019.

Abstract
In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.

Abstract
The lack of physical activity and poor nutrition triggers various diseases, among them is diabetes. In this context, several researches seek ways that can mitigate these diseases to provide a better quality of life for people. Therefore, the present work aims to analyze the possible inhibitors of the enzyme Dipeptidil Peptidase 4 that hypotheses will be stipulated for the creation of new drugs through molecular docking techniques, that is, a computational simulation of combinations of drugs of the family of gliptins with other antidiabetics (metformin, glyburide and cucurbitacin). Among the results, it was observed that the antidiabetic cucurbitacin combined with the gliptines obtained greater energy during the process.

Abstract
This paper aims to compare the compression of electro-oculographic signals, based on the (EOG) from MIT / BIH database, and the electromyographic signals, based on the (EMG) from MIT / BIH database, for that purpose, two compression techniques that can be used in electro-oculograms and electromyograms was approached, the two techniques mentioned above, were, the discrete cosine transform and Fast Walsh Hadamard Transform. For statistic the methods used was, the Mean squared error, mean absolute error, signal-to-noise ratio and peak signal-to-noise ratio as well, and for results, the techniques and they performance on each tested signal.

Abstract
This paper shows the advantage of using different sensors such the Microsoft Kinect and Myo Armband to acquire movement description of the plantarflexion and dorsiflexion of the foot with the help of the quaternions and the EMG Myo sensor. For the integration of these devices, it was chosen Python to develop the algorithm and create an interface to aid the signal acquisition. This integration, enabling an accurate motion description as well as a scale of EMG signal, allow the possibility of quantifying the treatment of the people with equinus foot.