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@article{IZKAB_2023_5_a3, author = {M. A. Shereuzheva and M. A. Shereuzhev and Z. M. Albekova}, title = {The use of convolutional neural networks}, journal = {News of the Kabardin-Balkar scientific center of RAS}, pages = {41--51}, publisher = {mathdoc}, number = {5}, year = {2023}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/IZKAB_2023_5_a3/} }
TY - JOUR AU - M. A. Shereuzheva AU - M. A. Shereuzhev AU - Z. M. Albekova TI - The use of convolutional neural networks JO - News of the Kabardin-Balkar scientific center of RAS PY - 2023 SP - 41 EP - 51 IS - 5 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IZKAB_2023_5_a3/ LA - ru ID - IZKAB_2023_5_a3 ER -
M. A. Shereuzheva; M. A. Shereuzhev; Z. M. Albekova. The use of convolutional neural networks. News of the Kabardin-Balkar scientific center of RAS, no. 5 (2023), pp. 41-51. http://geodesic.mathdoc.fr/item/IZKAB_2023_5_a3/
[1] Digital agriculture, https://en.wikipedia.org/wiki/Digital_ agriculture
[2] M. A. Shereuzheva, M. A. Shereuzhev, “Development of expert systems to improve the efficiency of growing plants in agriculture”, News of the Kabardino-Balkarian Scientific Center of RAS, 2022, no. 5 (109), 93–104 (In Russian) | DOI | DOI
[3] Z. V. Nagoev, V. M. Shuganov, K. Ch. Bzhikhatlov et al., “Prospects for increasing the productivity and efficiency of agricultural production with the use of an intelligent integrated environment”, News of the Kabardino-Balkarian Scientific Center of RAS, 2021, no. 6 (104), 155–165 (In Russian) | DOI | DOI
[4] A. Krizhevsky, I. Sutskever, G. E. Hinton, “ImageNet classification with deep convolutional neural networks”, Communications of the ACM, 60:6 (2012), 84–90
[5] A. Pushkarev, O. Yakubailik, “A web application for visualization, analysis, and processing of agricultural monitoring spatial-temporal data”, CEUR Workshop Proceedings, 3006, 2021, 231–237 pp. http://ceur-ws.org/Vol-3006/27_short_paper.pdf
[6] E. A. Skvortsov, E. G. Skvortsova, I. S. Sandu et al., “Transition of Agriculture to Digital, Intellectual and Robotics Technologies”, Economy of Region, 14:3 (2018), 1014–1028 (In Russian)
[7] K. He et al, “Deep residual learning for image recognition”, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770–778 pp.
[8] F. Iandola et al., Densenet: Implementing efficient convnet descriptor pyramids, 2014, arXiv: 1404.1869
[9] G. Huang, Z. Liu, Van Der Maaten et al., “Connected convolutional networks”, Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 4700–4708 | MR
[10] M. Sandler, A. Howard, M. Zhu et al., “MobileNetV2. Inverted residuals and linear bottlenecks”, Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, 4510–4520 | Zbl
[11] C. Szegedy et al., “Inception-v4, inception-resnet and the impact of residual connections on learning”, Thirty-first AAAI conference on artificial intelligence, 2017
[12] M. Tan, Le Q. Efficientnet, “Rethinking model scaling for convolutional neural networks”, International conference on machine learning. PMLR, 2019, 6105–6114