Methods for Constructing Artificial Neural Networks for Data Classification
The features of the organization of distance learning of students in a higher educational institution, as well as the information and educational technologies necessary for this, are considered. A system of automatic assessment of students’ knowledge is proposed. It is based on a model in the form o...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center
2022-06-01
|
| Series: | Цифровая трансформация |
| Subjects: | |
| Online Access: | https://dt.bsuir.by/jour/article/view/657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832543816987443200 |
|---|---|
| author | L. V. Serebryanaya |
| author_facet | L. V. Serebryanaya |
| author_sort | L. V. Serebryanaya |
| collection | DOAJ |
| description | The features of the organization of distance learning of students in a higher educational institution, as well as the information and educational technologies necessary for this, are considered. A system of automatic assessment of students’ knowledge is proposed. It is based on a model in the form of an artificial neural network. The features of such a model are given. The two implemented methods for constructing artificial neural networks have been used in the software module for testing students’ knowledge. The choice of the type of network, its structure, and parameters has been substantiated. The first method is related to the construction of an artificial neural network in the manual mode. An algorithm is presented that reflects the iterative process of its training. In the second case, the network is built automatically by applying a genetic algorithm. At the beginning of the work, a set of randomly generated initial data arrives at the input of the algorithm. In the course of its work, the genetic algorithm determines the architecture and parameters of the neural network, which ensure the successful solution of the assigned applied problem. Trained networks are used to classify data. Both networks showed acceptable classification accuracy of the results obtained in the course of the students’ knowledge testing. |
| format | Article |
| id | doaj-art-ebeff3eb20b54686962ede3019fdc8c8 |
| institution | Kabale University |
| issn | 2522-9613 2524-2822 |
| language | Russian |
| publishDate | 2022-06-01 |
| publisher | Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center |
| record_format | Article |
| series | Цифровая трансформация |
| spelling | doaj-art-ebeff3eb20b54686962ede3019fdc8c82025-02-03T11:26:35ZrusMinistry of Education of the Republic of Belarus, Establishment The Main Information and Analytical CenterЦифровая трансформация2522-96132524-28222022-06-01281202610.35596/2522-9613-2022-28-1-20-26259Methods for Constructing Artificial Neural Networks for Data ClassificationL. V. Serebryanaya0BIP – University of Law and Social Information Technologies; Belarusian State University of Informatics and RadioelectronicsThe features of the organization of distance learning of students in a higher educational institution, as well as the information and educational technologies necessary for this, are considered. A system of automatic assessment of students’ knowledge is proposed. It is based on a model in the form of an artificial neural network. The features of such a model are given. The two implemented methods for constructing artificial neural networks have been used in the software module for testing students’ knowledge. The choice of the type of network, its structure, and parameters has been substantiated. The first method is related to the construction of an artificial neural network in the manual mode. An algorithm is presented that reflects the iterative process of its training. In the second case, the network is built automatically by applying a genetic algorithm. At the beginning of the work, a set of randomly generated initial data arrives at the input of the algorithm. In the course of its work, the genetic algorithm determines the architecture and parameters of the neural network, which ensure the successful solution of the assigned applied problem. Trained networks are used to classify data. Both networks showed acceptable classification accuracy of the results obtained in the course of the students’ knowledge testing.https://dt.bsuir.by/jour/article/view/657distance learningartificial neural networkmultilayer perceptrongenetic algorithmdata classification |
| spellingShingle | L. V. Serebryanaya Methods for Constructing Artificial Neural Networks for Data Classification Цифровая трансформация distance learning artificial neural network multilayer perceptron genetic algorithm data classification |
| title | Methods for Constructing Artificial Neural Networks for Data Classification |
| title_full | Methods for Constructing Artificial Neural Networks for Data Classification |
| title_fullStr | Methods for Constructing Artificial Neural Networks for Data Classification |
| title_full_unstemmed | Methods for Constructing Artificial Neural Networks for Data Classification |
| title_short | Methods for Constructing Artificial Neural Networks for Data Classification |
| title_sort | methods for constructing artificial neural networks for data classification |
| topic | distance learning artificial neural network multilayer perceptron genetic algorithm data classification |
| url | https://dt.bsuir.by/jour/article/view/657 |
| work_keys_str_mv | AT lvserebryanaya methodsforconstructingartificialneuralnetworksfordataclassification |