Galois Field Augmentation Model for Training of Artificial Neural Network in Dentistry

Кривенко, С. А.; Безрук, В. М.

Публікація:
Galois Field Augmentation Model for Training of Artificial Neural Network in Dentistry

dc.contributor.author	Кривенко, С. А.
dc.contributor.author	Безрук, В. М.
dc.date.accessioned	2020-08-13T14:53:16Z
dc.date.available	2020-08-13T14:53:16Z
dc.date.issued	2021
dc.description.abstract	In work entitled “Galois Field Augmentation Model for Training of Artificial Neural Network in Dentistry,” the authors consider how to label and save a large number of images that should be predicted in a single file. The technique of automatic labeling the dataset with the finite element model for training of artificial neural network in tomography is proposed. A simple transparent example of thirty-two images to be predicted in a single HDF5 file training of artificial neural network in tomography shows accuracy of 100% for training set as well for the test set. Then this technique is able to build an information model of salivary immune and periodontal status and to evaluate the correlation between salivary immunoglobulin level, inflammation in periodontal tissues and orthodontic pathology. The results showed changes in the antioxidant balance in children with atopy that were expressed in an increase in malondialdehyde level, a decrease in superoxide dismutase activity and a level of reduced glutathione. These indicators can be considered as biological markers of the development of gingivitis at the preclinical stage in children against atopic diseases.	uk_UA
dc.identifier.citation	Valeryi M. Bezruk, Stanislav A. Krivenko, and Liudmyla S. Kryvenko, "Galois Field Augmentation Model for Training of Artificial NeuralNetwork in Dentistry," Springer, 2021, pp. 339-271	uk_UA
dc.identifier.issn	2367-4512
dc.identifier.uri	http://openarchive.nure.ua/handle/document/12685
dc.language.iso	en_US	uk_UA
dc.publisher	Springer	uk_UA
dc.relation.ispartofseries	Lecture Notes on Data Engineering and Communications Technologies;
dc.subject	regression analyses	uk_UA
dc.subject	atopic disease	uk_UA
dc.subject	gingivitis	uk_UA
dc.subject	convolutional neural networks (CNN)	uk_UA
dc.title	Galois Field Augmentation Model for Training of Artificial Neural Network in Dentistry	uk_UA
dc.type	Book chapter	uk_UA
dspace.entity.type	Publication