Examination and Processing of the Images of Biomedical Microobjects

Popova, Gergana; Stepanov, V. N.

doi:10.1023/b:aurc.0000011695.79084.34

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…Mechanisms are proposed that use the generalized properties of fuzzy sets, fuzzy logic algorithms, five-layer NN, which are effective tools in conditions of uncertainty and non-stationarity of identification processes [30,31,32].…”

Section: Mechanisms For Identification Of Non-stationary Objects Base...mentioning

confidence: 99%

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Jumanov

Kholmonov²,

Djumanov³

2023

E3S Web Conf.

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Researched and developed mechanisms for optimizing the identification of random time series based on mechanisms for searching for unknown knowledge, hidden properties, patterns, relationships, features of non-stationary objects under the condition of limited a priori information, uncertainty, non-stationary processes. A generalized model for optimizing the identification of RTS based on the use of neural networks, neuro-fuzzy networks of dynamic models, as well as fuzzy logic algorithms is implemented. Instruments for data reliability control are obtained based on statistical, dynamic, intelligent approaches suitable for the conditions of transmission of incomplete, heterogeneous, partially given information with large parametric uncertainty. The principles and methods of data reliability control in a fuzzy environment are developed based on the generalization and use of the properties of neural networks, fuzzy logic and statistical modeling methods. Algorithms for searching for correlations, trends, interrelations and regularities in data, forming training, control and test sets for solving problems of recognition, classifying images of micro-objects and predicting power supply indicators are synthesized. The results of the research are implemented in the form of a software package for identifying non-stationary objects, which ensures the adaptability of model variables, high data processing performance and accuracy of results.

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Section: Mechanisms For Identification Of Non-stationary Objects Base...mentioning

confidence: 99%

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Jumanov

Kholmonov²,

Djumanov³

2023

E3S Web Conf.

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“…The mechanism for setting the parameters of the NN structural components was developed on the basis of the conjugate gradient optimization method. Various NN models, methods for calculating the weights of neurons, interneuronal connections, choosing an appropriate activation function, NN architecture, and searching for global and local extrema have been studied [35][36][37][38][39][40].…”

Section: The Weight Coefficientsmentioning

confidence: 99%

“…In automated production and technological complexes for various purposes (industrial, agricultural, water resources management, power consumption), information on technical and economic indicators when solving problems for identification is considered as non-stationary objects. The proposed approach allows, in turn, the widespread use of methods and algorithms for data mining (DM) based on neural networks, optimization tools for dynamic identification of random time series (RTS) in substitutions, differential, recurrent equations, and other complex dependencies [1,2,3,4]. Existing technologies for solving problems of identification, search for extrema, optimization, are determined by labor-intensive calculations and the use of highly iterative algorithms.…”

Section: Introductionmentioning

confidence: 99%

Identification of Non-Stationary Objects Based on Training a Neural Network and Tuning the Parameters of a Generalized Model

Jumanov,

Kholmonov

2023

E3S Web Conf.

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Methods and algorithms have been developed for identifying non-stationary objects of various types using statistical, dynamic, neural network models, which are taken into account when solving problems of conditions of a priori insufficiency, uncertainty, low reliability of data. Mechanisms are proposed that provide effective identification based on combining the features of dynamic models with the properties of random time series. The possibilities of algorithms based on mechanisms that use statistical, dynamic, specific data characteristics, as well as the properties of self-adaptation, approximation, organization, self-learning of neural networks have been expanded. A generalized function identification algorithm has been developed and its functions have been expanded by adaptive segmentation of time series, setting the informative interval of element values, the size of the training set, training multilayer neural networks, database, and knowledge base. The training algorithms for a three-layer neural network are modified based on the mechanisms for regulating interneuronal connections in layers, weight coefficients of neurons, variable activation functions, network architecture, and superposition of continuous input-output dependencies. A software package for identifying random time series in the C++ language in the CUDA parallel computing environment has been developed to predict the annual power consumption of the industrial zone of the Samarkand region using software tools for data preprocessing, filtering, smoothing; determining the boundaries of the informative interval of time series elements.

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“…The study is related to the accounting, recognition, classification and systematization of various microobjects, for example, pollen grains, unicellular organisms, etc [3,4]. Methods, models, algorithms for the identification of micro-objects are created, which are implemented in the form of software and hardware, information processing complexes that differ from analogues in their functionality, specialization, and level of automation [5,6].…”

Section: Intoductionmentioning

confidence: 99%

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Jumanov¹,

Safarov²

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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Constructive approaches, principles and methods of identification, recognition and classification of micro-objects based on the use of neural networks and mechanisms for extracting morphometric characteristics of images have been developed. Information processing technology based on obtaining images of micro-objects from a photo, video camera, digital microscope is proposed. A technique has been developed for interactive measurement of the size of micro-objects, counting, determining the structure, conducting statistical analysis, isolating and segmenting fragments, selecting informative points, recognizing and classifying images. A computational scheme for preliminary processing of images is built, including mechanisms for texture, contour segmentation, detection, and regulation of variables. Algorithms for learning neural networks with setting variables within the limits of permissible values, taking into account the properties of nonstationarity of image points, have been built. The effectiveness of learning algorithms combined with neural network dynamic models with mechanisms for regulating linear, nonlinear, compositional connections of neurons between network layers has been investigated. The study was carried out according to the criterion of the percentage of correct recognition. A software package for visualization, recognition, classification of images of pollen grains has been developed and implemented, which has been tested under conditions of a priori insufficiency, uncertainty and nonstationarity.

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Examination and Processing of the Images of Biomedical Microobjects

Cited by 5 publications

References 2 publications

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Identification of Non-Stationary Objects Based on Training a Neural Network and Tuning the Parameters of a Generalized Model

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

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