The article discusses the developed model for recognizing a clothing brand by image. The model not only predicts the type and brand of clothing, but can also determine their similarity. At the initial stage, a dataset was collected containing images of clothing from various brands with a total volume of 9,000 images. In this work, the ViT (Vision Transformer) neural network architecture was used, a model for working with images, which was presented by experts from Google Brain. The vit-base-patch16-224 model acted as a representative of the transformer architecture. Before training, all images were converted to black and white, and data augmentation was also used: image rotation by a random angle, mirror transformation. All photos have been normalized – pixel coordinates have been adjusted to the interval [0,1].

Keywords: neural network, model, machine learning, Vision Transformer, fashion industry, clothing brand prediction, clothing type prediction, brand similarity determination

The results of clinical trials are the main source of information in the implementation of medical activities in accordance with the principles of evidence-based medicine. At the moment, there are no information systems that would allow a doctor to select clinical studies within the framework of nosology that best match the profile of a particular patient, in order to further analyze their results and select therapy. The aim of the study was to improve the existing process of searching for clinical trials by using the prioritization method according to the inclusion criteria set by the doctor during the selection. To achieve this goal, the following tasks were implemented, namely, the process of selecting and searching for clinical trials by doctors was studied and the method of searching for clinical trials by doctors and the allocation of the necessary criteria was worked out. The team of authors proposed an algorithm for searching for clinical trials according to inclusion criteria, which in turn will significantly increase the effectiveness and reduce the time for searching and choosing therapy.

Keywords: clinical studies, criteria search algorithms, criteria search methods, including factors, search for the nearest class, services

The article describes a method for translating pictogram messages into text in Russian language based on machine learning. The text of the article contains the concept of alternative communication systems, a description of the training data preparing process, a description of the neural network architecture and the results of training.

Keywords: alternative communication systems, machine translation, machine learning, neural network, transformer architecture, Python, software