The development of image recognition technologies has allowed to automate many activities, previously largely based not on the capabilities of technical means and mathematical apparatus, and the experience and skills of people involved in this activity.
The subject of this work is the application of image recognition technologies in the processing of photographs of the earth's surface, namely, forests.
This article describes a possible approach to the analysis of a complex indicator of the innovation potential of a region. The definition of the concept is given, the components included in its structure are singled out, the model of a complex indicator of the innovation potential of a region is offered. The calculation of a complex indicator of the innovation potential on the basis of the proposed methodology is made and the conclusions on the obtained result are formulated. The article also offers a method of analysis of integrated indicators – the components of the regional innovation potential on the basis of the used technique. The summary table of the normalized values of the integrated indicators - components of the innovation potential of the regions of the Central Federal district and their estimated values on the basis of which it is possible to draw conclusions about the level of the regional innovation development is presented. The approach used is acceptable for assessing the innovation potential of various organizational systems, for ranking regions relative to each other and for making recommendations to improve the level of the innovation development of regions.
The article discusses the features of using Kohonen neural network in the problems of classification and clustering of environmental media. Special attention is paid to data normalization, entering mathematical relationships, and geometric interpretation.
Предложен метод экспертных оценок профессиональных компетенций при подготовке специалистов разных направлений, уровней и профилей подготовки с использованием метода весовых коэффициентов важности. Проанализированы способы выбора траектории тестирования. Предложен алгоритм проведения адаптивного тестирования с использованием искусственных нейронных сетей. Ключевые слова: весовые коэффициенты важности, дисперсия, адаптивное тестирование, искусственная нейронная сеть, многослойный персептрон.
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