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Your search keyword '"Holida Primova"' showing total 13 results
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13 results on '"Holida Primova"'

1. Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset

Author

Abror Shavkatovich Buriboev, Dilnoz Muhamediyeva, Holida Primova, Djamshid Sultanov, Komil Tashev, and Heung Seok Jeon

Subjects
pneumonia detection, concatenated CNN, image enhancement, chest X-ray image, Chemical technology, TP1-1185
Abstract

Pneumonia is a form of acute respiratory infection affecting the lungs. Symptoms of viral and bacterial pneumonia are similar. Rapid diagnosis of the disease is difficult, since polymerase chain reaction-based methods, which have the greatest reliability, provide results in a few hours, while ensuring high requirements for compliance with the analysis technology and professionalism of the personnel. This study proposed a Concatenated CNN model for pneumonia detection combined with a fuzzy logic-based image improvement method. The fuzzy logic-based image enhancement process is based on a new fuzzification refinement algorithm, with significantly improved image quality and feature extraction for the CCNN model. Four datasets, original and upgraded images utilizing fuzzy entropy, standard deviation, and histogram equalization, were utilized to train the algorithm. The CCNN’s performance was demonstrated to be significantly improved by the upgraded datasets, with the fuzzy entropy-added dataset producing the best results. The suggested CCNN attained remarkable classification metrics, including 98.9% accuracy, 99.3% precision, 99.8% F1-score, and 99.6% recall. Experimental comparisons showed that the fuzzy logic-based enhancement worked significantly better than traditional image enhancement methods, resulting in higher diagnostic precision. This study demonstrates how well deep learning models and sophisticated image enhancement techniques work together to analyze medical images.

Published
2024
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2. Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Author

Dilmurod Turimov Mustapoevich, Dilnoz Muhamediyeva Tulkunovna, Lola Safarova Ulmasovna, Holida Primova, and Wooseong Kim

Subjects
osteodystrophy, secondary osteodystrophy, ketosis, hypomicroselementosis, decision making, expert systems, Chemical technology, TP1-1185
Abstract

The health and productivity of animals, as well as farmers’ financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno’s fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

Published
2023
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5. Main Problems and Tasks of Intellectualisation of Information Processing System

Author

Holida Primova and Lola Safarova

Subjects
General Computer Science, Mechanics of Materials, Electrical and Electronic Engineering, Civil and Structural Engineering
Abstract

Semistructured decision-making systems (SDMS) are an important class of intelligent systems (IS) in conditions of different types of indeterminacy and, fuzzy indeterminacy in particular. A dominating issue in projection of indistinct logical system is a selection of intelligent database, or rather a rational number of rules and effective values of their membership function. As a result of it the main problems and tasks of information processing system intellectualisation and their ways of solution are presented in this article.

Published
2019

6. The predictive model of disease diagnosis osteodystrophy cows using fuzzy logic mechanisms

Author

Holida Primova and Lola Safarova

Subjects
business.industry, Fuzzy set, Disease, medicine.disease, Diagnostic system, Machine learning, computer.software_genre, Fuzzy logic, medicine, Prognostic model, Osteodystrophy, Artificial intelligence, business, computer
Abstract

It is considered important to use the prognostic tasks of the mathematical method using modern information and communication technology. Noticeable changes have been made in the development of the computer diagnostic system, for studying types of diseases in the field of veterinary medicine, the occurrence of the causes of diseases, and improving the quality of targeted treatment methods.A prognostic model using a fuzzy set has been created to study clinical factors and the likelihood of secondary osteodystrophy in cows.

Published
2021

7. Accounting Experience Between Fuzzy Integral and Z-numbers

Author

Holida Primova, F. N. Iskandarova, and Q. M. Gaybulov

Subjects
Mathematical optimization, Work (electrical), Integral theory, Z number, Fuzzy logic, Selection (genetic algorithm), Mathematics
Abstract

In this paper, computational experiments using the fuzzy integral theory and the Z-number combination have been conducted using fuzzy theory. The article was considered for the selection of the best biological and technologically-based selection breeds of cotton in the case of poor decision-making, and the results of the computational work using these two methods were theoretically justified.

Published
2021

8. Selection of Construction Materials on Fuzzy Inference Rules

Author

Qodir Gaybulov, Holida Primova, and Feruza Iskandarova

Subjects
Nonlinear system, Mathematical optimization, Material selection, Linear programming, Computer science, Regression analysis, System of linear equations, Fuzzy logic, Nonlinear regression, Selection (genetic algorithm)
Abstract

Properties and material selection are an important factor in the design of an object. When choosing a material, it is necessary to take into account not only technical and economic factors but also environmental factors should be taken into account. The article dedicated a method for optimizing the choice of building material using a nonlinear rule derivation to the algorithm and nonlinear regression equations. This article presents the choice of parameters based on a fuzzy () inference system and a nonnegative regression model, and solving a system of linear equations by optimizing this parameter.

Published
2020

9. Solution of the multi-criterial routing problem in telecommunication networks

Author

O. Yalgashev, Lola Safarova, and Holida Primova

Subjects
business.industry, Computer science, Component (UML), Fuzzy set, Throughput, Routing (electronic design automation), Telecommunications, business, Telecommunications network, Membership function, Data transmission, Communication channel
Abstract

In this paper, one of the methods for solving the multi-criteria routing problem in multiservice telecommunication networks is presented. This paper proposes a multi-criteria approach that solves the problem of routing in multiservice communication networks and that takes into account not only the technical requirements for resources but also the economic component. To the restrictions imposed on the route - the throughput and the time delay of data transmission in the channel. This approach allows you to build an optimal route for almost any type of service.

Published
2019

10. Development of medical information systems

Author

Holida Primova, Sevara Nabieva, and Temur Sakiev

Subjects
Theoretical computer science, Computer science, business.industry, media_common.quotation_subject, Measure (physics), Local area network, Information technology, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), Fuzzy logic, Convolution, Nonlinear system, Development (topology), Function (engineering), business, media_common
Abstract

The fuzzy integral of a function with a fuzzy measure is a general estimate in the form of a nonlinear convolution of particular estimates of information elements, and the possibility of interconnection of information elements is not excluded. Here is an approach that al-lows the expert to best formalize his fuzzy ideas, trans-forming the language of words into a language of quantitative estimates.

Published
2019

11. Formation of informative signs for predicting the disease of highly productive cows with non-communicable diseases

Author

Holida Primova and Lola Safarova

Subjects
History, medicine.medical_specialty, business.industry, Internal medicine, Medicine, Disease, business, Computer Science Applications, Education
Abstract

In this article, fuzzy set membership functions have been developed based on the main factors (clinical, morphochemical, rumen contents) to predict the following non-communicable diseases such as ketosis, osteodystrophy, secondary osteodystrophy and hypomicroelementosis in high-yielding cows. The results of the study show the high efficiency of the proposed decision-making algorithm for forecasting, classifying and measuring poorly formalized processes, that are described by fuzzy models. The available knowledge about the existing experimental data makes it possible to increase the adequacy of the fuzzy expert system.

Published
2021

12. COMPUTING FUZZY INTEGRAL OF THE BASIS OF FUZZY MESURE

Author

X. Bobabekova, R. T. Raximov, Holida Primova, and D. M. Sotvoldiyev

Subjects
History, Mathematical optimization, Basis (linear algebra), Computer science, Fuzzy logic, Computer Science Applications, Education
Abstract

The fuzzy integral of a function with a fuzzy measure is a general estimate in the form of a nonlinear convolution of particular estimates of information elements, and the possibility of interconnection of information elements is not excluded. Here is an approach that allows the expert to best formalize his fuzzy ideas, transforming the language of words into a language of quantitative estimates.

Published
2020

13. Approaches to solving the problem of risk assessment with fuzzy initial information

Author

Lola Safarova, Holida Primova, and Dilshodbek Sotvoldiev

Subjects
Risk analysis (engineering), business.industry, Computer science, business, Risk assessment, Fuzzy logic, Risk management
Abstract

An analysis of existing methods for solving the problems of risk assessment showed that they are based on the lack of computational capabilities and the lack of necessary information about the conditions of the problem. Therefore, in such cases it is advisable to use fuzzy mathematical methods. In this paper, we consider approaches to solving the problem of risk assessment with fuzzy source information.

Published
2018

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