Machine learning based KNN classifier: towards robust, efficient DTMF tone detection for a Noisy environment

Maity, Arunit; Periasamy, P.; Bhargava, Sarthak

doi:10.1007/s11042-021-11194-3

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…To verify the accuracy and timeliness of the SAE-LVQ situation assessment model, five classifiers are selected for comparison, including hierarchical support vector machine (HSVM) [46], crow search algorithm optimized support vector machine (CSA-SVM) [47], K-nearest neighbor (KNN) [48], stacked autoencoder-hierarchical support vector machine (SAE-HSVM), and LVQ network. The specific parameter settings are shown in Table 5.…”

Section: Situation Assessment Performance Testmentioning

confidence: 99%

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

Xie

Tang

Wei

et al. 2023

J. of Syst. Eng. Electron.

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The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into twodimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

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Section: Situation Assessment Performance Testmentioning

confidence: 99%