Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.

This paper presents the 3D fully convolutional neural network extended by attention gates and deep supervision layers. The model is able to automatically segment the kidney and kidney-tumor from arterial phase abdominal computed tomography (CT) scans. It was trained on the dataset proposed by the Kidney Tumor Segmentation Challange 2019. The best solution reaches the dice score 96, 43 ± 1, 06 and 79, 94 ± 5, 33 for kidney and kidney-tumor labels, respectively. The implementation of the proposed methodology using PyTorch is publicly available at github.com/tureckova/Abdomen-CT-Image-Segmentation.

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This paper is focused on possible utilization of an artificial neural network connected with biometric systems and motion animation for the purpose of training of self-defense techniques. The described experiment was performed in a specialized laboratory of university hospital in Brno with the help of VICON system. The aim was to obtain new inputs for artificial neural networks. Simultaneously, this research proceeds with previous project and research. This paper also contains the most interesting results from the experiment.