Estimates calculations algorithms in condition of huge dimensions of features' space

Kamilov, Mirzoyan; Fazilov, Shavkat; Mirzaev, N. M.; Radjabov, S. S.

doi:10.1109/icpci.2012.6486314

Cited by 9 publications

(2 citation statements)

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“…An analysis of scientific sources on pattern recognition, in particular [7][8][9][10][11][12][13][14][15][16][17][18][19][20]32], shows that by now a number of recognition models have been created and rather well studied. Among them, the following most common recognition models can be distinguished: models based on the use of the separation principle; models based on methods of mathematical statistics; models built on the basis of potential functions; models based on calculation of estimates, etc.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for plant disease diagnostics by leaf image

Mirzaev,

Radjabov,

Nurmukhamedov

et al. 2024

BIO Web Conf.

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This article discusses the task of detecting diseases of cultivated plants. When determining the phytosanitary status of cultivated plants, images of their leaves are considered as initial data. To solve the problem under consideration, a model of diagnostic algorithms based on two-dimensional threshold functions is proposed. The main idea of the proposed algorithms is to form a set of preferred diagnostic features and make decisions aimed at making a diagnosis based on a comparison of these features. The classification stages of the diagnostic algorithm model are presented. An assessment of the applicability of the proposed model is demonstrated using the example of solving the problem of diagnosing wheat diseases by leaf images. Keywords: diagnostic algorithms, basic image slices, diagnostic features, preferred features, calculation of the overall score.

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Section: Introductionmentioning

confidence: 99%

Algorithms for plant disease diagnostics by leaf image

Mirzaev,

Radjabov,

Nurmukhamedov

et al. 2024

BIO Web Conf.

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“…At present, a number of models of recognition algorithms (RA) were developed and studied in detail [1,4]. These include well-known RA models based on 1) discriminant functions [6][7][8][9][10]; 2) mathematical statistics [8,[11][12][13][14]; 3) potential functions [8,[15][16][17][18][19][20]; 4) mathematical logic [21][22][23][24][25][26]; 5) estimate calculation [1,4,[27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Recognition algorithms based on the construction of threshold rules using two-dimensional representative pseudo-objects

Fazilov,

Mirzaev,

Radjabov

et al. 2023

E3S Web Conf.

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The development of recognition algorithms is discussed in the article; they are built using threshold rules based on representative pseudo-objects that provide a solution to the recognition problem in conditions of high dimensionality of feature space. A new approach is proposed, based on the formation of a set of two-dimensional base pseudo-objects and the determination of a relevant set of two-dimensional threshold proximity functions when constructing an extreme recognition algorithm. A parametric description of the proposed recognition algorithms is given, presented in the form of a sequence of computational procedures, the main of which are procedures for determining: 1) groups of tightly coupled features; 2) a set of representative features (RF); 3) groups of tightly coupled pseudo-objects in the RF subspace; 4) difference functions between objects in the two-dimensional subspace of RF; 5) groups of tightly coupled pseudo-objects in the RF subspace; 6) a set of basic pseudo-objects; 7) difference functions between the basic and simple pseudo-object in the two-dimensional RF subspace; 8) functions that differentiate between a pseudo-object and a class; 9) discriminant functions in the two-dimensional subspace of RF; 10) groups of tightly coupled separating functions; 11) basic separating functions in each group and 12) integral recognition operator for basic discriminant proximity functions. The results of a comparative analysis of the proposed and known recognition algorithms are presented. The main conclusion is that the implementation of the approach proposed in this study allows us to move from a given feature space to a space of RFs of lesser dimension.

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