The problem of inverse correlation filters design to recognize a set of objects is considered as the problem of regression parameters estimation on the base of input data arrays and desirable response. The data and response should be processes with zero mean to consider this problem as evaluation of regression parameters. The problem is solved using the least squares method with regularization. The regularization is optimized to achieve high resolution of the filters in conjunction with capture’ broad band of objects given by a set of templates. The least squares method is using in the terms of singular value decomposition that made it possible to linearize the nonlinear ridge regression optimization problem. The methods to false recognitions elimination are considered, It was shown that the regression approach gives additional condition to recognize classes of objects. This allows to have more high accuracy in recognition of desired objects on a foreign background in comparison with other correlation filters types.
For many years, the medical community has used experimental and clinical observation methods to study the course of various diseases. This approach has a high degree of validity, but it also has some limitations, such as high costs and difficulty in controlling all influencing factors and individual variability. In this regard, there is a need to create new methods for studying and predicting the course of diseases. One of these tools is a mathematical model. This study considers the possibility of mathematical modeling of the effectiveness of anti-tuberculosis treatment, based on the levels of biochemical markers of tuberculosis, namely Human-beta-defensin-1 (HBD-1), ferritin and interleukin-6 (IL-6). Based on a study of the relationships between the effectiveness of anti-tuberculosis treatment and the levels of Humanbeta-defensin-1, ferritin and interleukin-6, a mathematical model was created that allows predicting the effectiveness of anti-tuberculosis treatment based on determining biochemical markers at the beginning of treatment and after 60 days of anti-tuberculosis therapy with sensitivity and specificity of at least 88%.
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