The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.
The approach to solving the problems of diagnosis and prognosis of diseases of agricultural crops using machine learning methods is described. To solve the problem of forecasting diseases of agricultural crops, it is proposed to use a genetic algorithm in the work. The analysis of the effectiveness of the proposed method is carried out depending on the convergence rate of such parameters as the mutation coefficient and population size. To solve the problem of diagnostics of agricultural crops, it is proposed to use a recurrent type of neural network. A software modelling complex has been developed that allows solving the problems of plant diseases diagnostics and making forecasts. The results obtained can reduce the costs of agricultural enterprises by reducing the cost of diagnosing agricultural diseases.
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