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  • Road sign detection based on the YOLO neural network model

    This article presents a research study dedicated to the application of the YOLOv8 neural network model for road sign detection. During the study, a model based on YOLOv8 was developed and trained, which successfully detects road signs in real-time. The article also presents the results of experiments in which the YOLOv8 model is compared to other widely used methods for sign detection. The obtained results have practical significance in the field of road traffic safety, offering an innovative approach to automatic road sign detection, which contributes to improving speed control, attentiveness, and reducing accidents on the roads.

    Keywords: machine learning, road signs, convolutional neural networks, image recognition

  • Using Genetic Algorithms to Increase the Learning Rate of Neural Networks

    Investigation of ways to accelerate the training of neural networks using genetic algorithms and the study of the dependence of the speed of genetic algorithms on the mutation rate. In this study, a program was implemented on the Unity graphics platform using genetic algorithms and mutations to determine their optimal coefficient. The experiment showed that the learning rate really depends on the mutation rate, and the highest learning rate was obtained at 5-7,5%.

    Keywords: machine learning, deep learning, genetic algorithm, optimization, neural network, artificial neuron, mutation, artificial intelligence, non-player character, optimization