Modified Recognition Algorithms Based on the Construction of Models of Elementary Transformations

Fazilov, Shavkat; Mirzaev, N. M.; Mirzaeva, G. R.

doi:10.1016/j.procs.2019.02.037

Cited by 16 publications

(2 citation statements)

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“…The course "Information technologies and mathematical modeling of processes" describes the modeling methods proposed in [11][12][13][14], modeling methods used in geodynamics [15][16][17], the original methods of pattern recognition [18][19][20][21][22][23], methods of reducing the dimension of the initial feature space for the problem of pattern recognition in relation to solving medical and other problems [24][25][26][27][28][29][30]. Also included in the course are: results of studies of the spectra of electron paramagnetic resonance and infrared rays in order to consider the possibility of changing the interfacial layer of the nanocomposite [31]; the results of electron microscopic studies of morphology and X-ray diffraction analysis of the phase composition of the nanocomposite, where it is shown that the dispersed phase mainly consists of nanoparticles of metallic copper with a size of 33.94 nm [32]; the results of studies of the frequency dependence of the conductivity of compositions based on glass and cadmium oxide [33].…”

Section: Discussionmentioning

confidence: 99%

The effectiveness of digital technologies use in higher education: a modern approach to training

Baydullaev

Tayrov

Narzullaev

et al. 2023

International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023)

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This article is devoted to issues of digitalization of the educational system, organization and development of educational processes using digital technologies, which is one of the current problems in the educational system. The need to use digital technologies in providing distance education to students, the need to use the field of distance education and introduce new generation systems of distance education, that it is faster, easier and cheaper to learn new skills or materials related to the subject through the distance education system, is covered in detail. At the same time, the problems hindering the effective implementation of distance education using digital technologies in our country and suggestions and recommendations for their solution are given. Management of higher education processes at the Tashkent pharmaceutical institute, based on the information platform "HEMIS" and distance learning systems "Moodle LMS", a brief description of the principles of organization of online education in teaching all modules of students is presented, and its main capabilities and advantages are indicated. By studying, systematizing and summarizing the existing practice in drawing up scientifically-based conclusions on the introduction of digital technologies in higher education, suggestions are made not only on how they will take an important place in the field of education and in what form they will be introduced.

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Section: Discussionmentioning

confidence: 99%

The effectiveness of digital technologies use in higher education: a modern approach to training

Baydullaev

Tayrov

Narzullaev

et al. 2023

International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023)

View full text Add to dashboard Cite

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“…However, there are some datasets that attempt to solve multiple problems and are taken under real-world conditions [9]. Fazilov, Mirzaev, and Mirzaeva [10] examined an algorithm to enhance the classification of objects in higher dimensions. e proposed algorithm formed a subset of correlated images, and then, a feature representation was elected to build elementary transformation models in the representative features' subspace.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Implementation of Optimization Techniques for Facial Recognition

Appati

Abu

Owusu

et al. 2021

Applied Computational Intelligence and Soft Computing

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Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.

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