A method of measuring cattle parameters using neural network methods of image processing was proposed. To this end, several neural network models were used: a convolutional artificial neural network and a multilayer perceptron. The first is used to recognize a cow in a photograph and identify its breed followed by determining its body dimensions using the stereopsis method. The perceptron was used to estimate the cow's weight based on its breed and size information. Mask RCNN (Mask Regions with CNNs) convolutional network was chosen as an artificial neural network. To clarify information on the physical parameters of animals, a 3D camera (Intel RealSense D435i) was used. Images of cows taken from different angles were used to determine the parameters of their bodies using the photogrammetric method. The cow body dimensions were determined by analyzing animal images taken with synchronized cameras from different angles. First, a cow was identified in the photograph and its breed was determined using the Mask RCNN convolutional neural network. Next, the animal parameters were determined using the stereopsis method. The resulting breed and size data were fed to a predictive model to determine the estimated weight of the animal. When modeling, Ayrshire, Holstein, Jersey, Krasnaya Stepnaya breeds were considered as cow breeds to be recognized. The use of a pre-trained network with its subsequent training applying the SGD algorithm and Nvidia GeForce 2080 video card has made it possible to significantly speed up the learning process compared to training in a CPU. The results obtained confirm the effectiveness of the proposed method in solving practical problems.

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched. Hyperparameters affect model selection. They have the greatest impact on the number and size of hidden layers. Effective selection of hyperparameters improves the speed and quality of the learning algorithm. It is also necessary to pay attention to the fact that the hyperparameters of the convolutional neural network are interconnected. That is why it is very difficult to manually select the effective values of hyperparameters, which will ensure the maximum efficiency of the convolutional neural network. It is necessary to automate the process of selecting hyperparameters, to implement a software mechanism for setting hyperparameters of a convolutional neural network. The author has successfully implemented the specified task. Objective. The purpose of the paper is to develop a technology for selecting hyperparameters of a convolutional neural network to improve the quality of segmentation of medical images.. Methods. Selection of a convolutional neural network model that will enable effective segmentation of medical images, modification of the Keras Tuner library by developing an additional function, use of convolutional neural network optimization methods and hyperparameters, compilation of the constructed model and its settings, selection of the model with the best hyperparameters. Results. A comparative analysis of U-Net and FCN-32 convolutional neural networks was carried out. U-Net was selected as the tuning network due to its higher quality and accuracy of image segmentation. Modified the Keras Tuner library by developing an additional function for tuning hyperparameters. To optimize hyperparameters, the use of the Hyperband method is justified. The optimal number of epochs was selected - 20. In the process of setting hyperparameters, the best model with an accuracy index of 0.9665 was selected. The hyperparameter start_neurons is set to 80, the hyperparameter net_depth is 5, the activation function is Mish, the hyperparameter dropout is set to False, and the hyperparameter bn_after_act is set to True. Conclusions. The convolutional neural network U-Net, which is configured with the specified parameters, has a significant potential in solving the problems of segmentation of medical images. The prospect of further research is the use of a modified network for the diagnosis of symptoms of the coronavirus disease COVID-19, pneumonia, cancer and other complex medical diseases.

The article considers new approaches to assessing student achievement in higher education. Modern priorities in education are considered, one of which is quality control of knowledge of future specialists. In general, mastering professional disciplines during training involves studying issues of application: designing tests of different levels of application, control material for use, interpretation of test results, interpretation of its results and drawing conclusions about its compliance with the purpose, objectives , understanding of the main quality characteristics of control material and the possibility of using computer technology in creating, conducting and processing test results. The article experimentally proves that assessing the quality of knowledge of futur e professionals using criterion-based testing based on the use of computers is one of the most effective means of pedagogical control.