Modelling of Complex Project Management System in the Field of Information Technologies

Uspenskiy, Mikhail B.; Smirnov, Sergey V.; Loginova, Aleksandra V.; Shirokova, Svetlana

doi:10.1109/cts48763.2019.8973245

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…The formation of a dataset without source data is a more difficult task from a technical perspective, but presumably allows getting almost unlimited possibilities in generating a dataset. However, the created data will differ significantly from the real ones, as a result of which there is a risk of obtaining data of unacceptable quality [9].…”

Section: Methodsmentioning

confidence: 99%

Research and application of neural network approaches to solving image recognition problems

Bolsunovskaya

Kuzmichev²,

Shirokova³

et al. 2022

E3S Web Conf.

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The paper investigates neural network approaches to solving number recognition problems and develops an algorithm for creating authentic datasets. In the course of the work, research and development of an algorithm for creating authentic datasets for solving the problem of number recognition is carried out. When solving image recognition problems, it is advisable to use neural network technologies, but often there is a problem of lack of data to form a full-fledged training sample during recognition. An algorithm has been developed to create a set of artificial data appropriate for use in training neural networks. The recognition of number plates and wagon numbers is assumed to be the scope of application. An algorithm that forms a set of synthetic images marked up for training has been created. The result of the algorithm application is a dataset appropriate for supplementing the training sample when training neural networks in the field of recognition of number plates and wagon numbers.

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Section: Methodsmentioning

confidence: 99%