On-device facial verification using NUF-Net model of deep learning

Termritthikun, Chakkrit; Jamtsho, Yeshi; Muneesawang, Paisarn

doi:10.1016/j.engappai.2019.07.018

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…In recent years, convolutional neural networks have been used in many diverse areas, e.g., DNA–Protein binding sites prediction [ 33 ], magnetic resonance image reconstruction [ 34 ], automatic road segmentation [ 32 ], facial verification [ 35 ], music generation [ 36 ], relation extraction (extracting the semantic relationship between the pairs from plain text) [ 37 ], database intrusion detection [ 38 ], arc detection in pantograph–catenary systems [ 39 ] and the time-series classification [ 40 ]. This versatility of the CNNs makes them a reasonable choice to use while building a solution to the considered problem.…”

Section: Preliminaries and Definitions/backgroundmentioning

confidence: 99%

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

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The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

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