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- W4317914623 abstract "The actual problem of digital logistics of recognition and classification of objects – vehicles from video cameras of the road network is considered. The purpose of recognition and classification is to detect trucks and cars for solving the logistic problem of analyzing the density of freight traffic from video cameras. To solve the problem in the work, we analyzed the classical methods of machine learning: “neural differential equations”, “decision tree”, “gradient amplification trees”, “logistic regression”, “Markov model”, “naive Bayesian”, “nearest neighbors”, “random forest”, “machine support vectors”. The authors proposed a new method of autonomization of neural differential equations, which allows to increase the accuracy and productivity of recognition and classification. For recognition and classification methods, the accuracy, computation time, training time, memory size for training, and computation speed are examined. Comparative analysis of the methods is carried out based on a computational experiment for datasets of nine hundred objects. The computational experiment shows an increase in the speed of learning and computations for neural differential equations using the autonomization method. The “neural differential equations” method, using the autonomization method presented by the authors, can reduce the training time of the neural network and the memory size for the classifier compared to other traditional methods. Application of the autonomization method for the “neural differential equations” in recognition and classification problems is optimal from the point of view of speed and accuracy of calculations in digital logistics." @default.
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- W4317914623 date "2023-01-01" @default.
- W4317914623 modified "2023-09-26" @default.
- W4317914623 title "Methods of Recognition and Classification of Objects in Digital Logistics" @default.
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- W4317914623 doi "https://doi.org/10.1007/978-3-031-24434-6_1" @default.
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