The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.
Актуальность в разработке интеллектуальных систем классификации сложноструктурируемых изображений возникает при обработке снимков с видеокамер беспилотных летательных аппаратов, используемых в навигационных целях при отсутствии связи с искусственными спутниками Земли или при анализе снимков оператором в режиме реального времени. Разработанный метод обеспечивает высокие требования к качеству классификации объектов на снимках, а также быстродействию выделения и классификации исследуемых сегментов изображения. Для классификации таких изображений предложены компьютерные технологии, основанные на методологии бустинга. Пространство информативных признаков формируется посредством спектральных окон, полученных в результате сканирования исходного изображения. Спектральные окна, принадлежащие к различным классам, располагаются в виде кластеров на плоскости Кохонена. Для формирования кластеров применяются правила коррекции векторов весов, позволяющие снизить величины незначащих компонент векторов, и определяются координаты центров кластеров. На основе кластерной структуры плоскости Кохонена строятся сильные классификаторы. Разработана и приведена структура сильного классификатора на нейронных сетях прямого распространения блочного типа, реализованная для задачи классификации рентгенограмм грудной клетки.
Relevance. The pandemic served as a catalyst for the digitalization of the economy, accelerated the processes of automation and digitalization in all spheres of the economy and in the social life of society. The key problem faced by IT companies in connection with the widespread introduction of information technology is the increased need for qualified programmers and analysts. The current situation has led to the need to assess the labor market of the IT sector in order to identify trends in meeting the demand for IT specialists. The purpose of the study: to assess the current situation and trends in the development of the labor market of IT specialists in our country after a one-year pandemic. The objectives: the number of IT specialists in the country's economy; the job market, requests and offers from employers; the structure of vacancies; activity and portrait of applicants. The research methodology is based on a systematic approach. The research is based on a resource approach. Traditional methods for theoretical and applied research were used - statistical analysis, synthesis, comparison, generalization. The research material was statistical data of Rosstat and the All-Russian federal database of vacancies and resumes, analytical materials of labor market research of IT specialists. Results. The pandemic accelerated the processes of digitalization of the economy and social life of society, caused a new round of competition for IT specialists. Research has revealed a growing need for IT specialists in all areas of the economy. There was an imbalance of supply and demand in the industrial labor market. Programmers are one of the most needed and paid specialists, the need for which is observed in all sectors of the economy. Conclusions. There was a need to change the paradigm of staff search and recruitment. How well the employer company will be represented on the market and how well it will behave with applicants in the process of coordinating the interests of the employer and the employee, so the success of hiring the right specialist will be ensured.
The method for classification of x-ray images, which is implemented in this article, improves the differential diagnosis of socially considerable diseases. The novelty of this method is that the input digital x-ray image is supplemented by a transparency mask which is found by image segmentation, and the classified sign vector is formed by pixels which aren’t masked by the transparency mask. The original x-ray image is divided into segments to classify morphological structures with pathological formings. The classification of the selected segment is implemented by a modified convolutional neural network which uses pooling layers and layers of a fully-connected neural network. The proposed classifier allows dividing patients into two groups: patients without found pathology and patients with disorders of health condition. The found morphological pathological formings help clinicians to find the most promising way for prevention of the disease development at the patients, saving money and time.
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