Many-To-Many Linear-Feedback Shift Model for Training of Artificial Neural Network in Dentistry

Кривенко, С. А.; Pulavskyi, A. A.; Krivenko, S. S.; Kryvenko, L. S.

Публікація:
Many-To-Many Linear-Feedback Shift Model for Training of Artificial Neural Network in Dentistry

dc.contributor.author	Кривенко, С. А.
dc.contributor.author	Pulavskyi, A. A.
dc.contributor.author	Krivenko, S. S.
dc.contributor.author	Kryvenko, L. S.
dc.date.accessioned	2020-08-14T09:38:21Z
dc.date.available	2020-08-14T09:38:21Z
dc.date.issued	2019
dc.description.abstract	In this paper, the authors consider how to label and save a large number of images that should be predict in a single file. Technique of automatic labeling the data set with finite element model for training of artificial neural network in tomography are proposed. Simple transparent example of sixteen images for predict in a single HDF5 file training of artificial neural network in tomography show accuracy 100% for training set as well for test set. Then this technique is able to build 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 study was conducted on 139 subjects, which were in the age group of 12-18 years reporting to the Department of Pediatric Dentistry of Kharkiv National Medical University. The atopic group consisted of 103 patients with the following conditions: 76 patients of atopic diseases and gingivitis (Group 1) and 27 patients of atopic diseases, gingivitis and orthodontic pathology (Group 2). Among the 139 subjects, 36 healthy controls formed Group 3. The obtained data prove that there is an immune misbalance in children with atopy and in children with combined atopic and orthodontic pathology. Level of sIgA and IgG is decreased in group of patients with atopy and in group of children with atopic and orthodontic pathology. The information model of salivary immune and periodontal status was built and regression analysis showed that there was strong correlation between inflammation in periodontal tissues and level of immunoglobulins.	uk_UA
dc.identifier.citation	S. A. Krivenko, A. A. Pulavskyi, S. S. Krivenko and L. S. Kryvenko, "Many-To-Many Linear-Feedback Shift Model for Training of Artificial Neural Network in Dentistry," 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, 2019, pp. 429-434, doi: 10.1109/ELNANO.2019.8783543.	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/12689
dc.language.iso	en_US	uk_UA
dc.publisher	IEEE	uk_UA
dc.relation.ispartofseries	;doi: 10.1109/ELNANO.2019.8783543.
dc.subject	artificial neural network	uk_UA
dc.subject	orthodontic pathology	uk_UA
dc.subject	atopy	uk_UA
dc.subject	artificial neural network	uk_UA
dc.subject	regression analysis	uk_UA
dc.subject	artificial neural network , electrical impedance tomogralinear-feedback shift register	uk_UA
dc.subject	finite field	uk_UA
dc.subject	artelectrical impedance tomography	uk_UA
dc.subject	linear-feedback simmune
dc.subject	tomography
dc.subject	electrical impedance tomography
dc.title	Many-To-Many Linear-Feedback Shift Model for Training of Artificial Neural Network in Dentistry	uk_UA
dc.type	Conference proceedings	uk_UA
dspace.entity.type	Publication