A CNN model embedded with local feature knowledge and its application to time-varying signal classification

Yang, Ruiping; Zha, Xianyu; Liu, Kun; Xu, Shengli

doi:10.1016/j.neunet.2021.07.018

Cited by 19 publications

(5 citation statements)

References 16 publications

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“…Siamese network was first proposed for signature and image matching [ 32 ], in which different inputs are processed by the same network. Unlike other structures, the weights between the same networks are shared, and the parameters of the two networks are updated simultaneously.…”

Section: Methodsmentioning

confidence: 99%

“…CNN has advantages in local perception, which can ensure that the convolution kernel can fully consider local features [32]. 1D convolution is majorly used where the input is sequential such as text or audio [33], which is very suitable for protein sequences.…”

Section: Mcn Layermentioning

confidence: 99%

“…Siamese network was first proposed for signature and image matching [32], in which different inputs are (12)…”

Section: Siamese Networkmentioning

confidence: 99%

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DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

et al. 2022

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Background Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to predict PPI in biology, they are time-consuming and lack accuracy, so it is necessary to build an efficiently and accurately computational model in the field of PPI prediction. Results We present a novel sequence-based computational approach called DCSE (Double-Channel-Siamese-Ensemble) to predict potential PPI. In the encoding layer, we treat each amino acid as a word, and map it into an N-dimensional vector. In the feature extraction layer, we extract features from local and global perspectives by Multilayer Convolutional Neural Network (MCN) and Multilayer Bidirectional Gated Recurrent Unit with Convolutional Neural Networks (MBC). Finally, the output of the feature extraction layer is then fed into the prediction layer to output whether the input protein pair will interact each other. The MCN and MBC are siamese and ensemble based network, which can effectively improve the performance of the model. In order to demonstrate our model’s performance, we compare it with four machine learning based and three deep learning based models. The results show that our method outperforms other models in all evaluation criteria. The Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC of our model are 0.9303, 0.9091, 0.9268, 0.9452, 0.8609. For the other seven models, the highest Accuracy, Precision, $$F_{1}$$ F 1 , Recall and MCC are 0.9288, 0.9243, 0.9246, 0.9250, 0.8572. We also test our model in the imbalanced dataset and transfer our model to another species. The results show our model is excellent. Conclusion Our model achieves the best performance by comparing it with seven other models. NLP-based coding method has a good effect on PPI prediction task. MCN and MBC extract protein sequence features from local and global perspectives and these two feature extraction layers are based on siamese and ensemble network structures. Siamese-based network structure can keep the features consistent and ensemble based network structure can effectively improve the accuracy of the model.

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Section: Methodsmentioning

confidence: 99%

Section: Mcn Layermentioning

confidence: 99%

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DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

et al. 2022

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“…However, the question of the stability of the operation of such a model with an unbalanced sample of training data remains unresolved. The solution to this issue is proposed in [2], where knowledge about local features is built into the developed convolutional model. Using a model with built-in knowledge of local features has the advantage of greater control over the learning process of an artificial neural network, but complicates the calculation process and, accordingly, requires a significant amount of time to train the model.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The models in works [1,2] are proposed to be used for signals of very low frequencies, such as electrocardiograms. For higher-frequency audio signals, work [3] suggests combining a discrete sinusoidal transformation network and a recurrent neural network for the classification of the type of audio signal.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Approach to processing radio signals with amplitude modulation of many components using one-dimensional convolutional neural network

Tsymbaliuk,

Horbatyi

2023

EEJET

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The object of research is the methods of using one-dimensional convolutional neural networks in radio receiving systems in order to increase their interference resistance. The task of the research is to test the hypothesis about the likely higher efficiency of radio signal recognition under conditions of high noise (or weak signals) by neural network models of radio signal reception in comparison with trivial reception systems. With the use of one-dimensional convolutional neural networks, a higher efficiency of extracting useful information from a signal-noise mixture at sufficiently high noise levels and, accordingly, a higher accuracy of radio signal recognition accuracy has been achieved. This result was achieved due to the specific architecture of convolutional neural networks, the ability to automatically detect important patterns in the data and analyze radio signals more deeply and informatively. Hierarchical representation of data with the selection of more complex and abstract features of the signal as the convolutional neural models become more complicated is one of the main advantages of using the proposed methods and algorithms under complex conditions of radio signal transmission. The comparison with trivial methods of radio signal processing is performed on the basis of the symbol error probability parameter at different signal-to-noise ratios of the investigated signals and demonstrates a stable decrease in the symbol error probability at signal-to-noise ratios of less than 4 dB. The results could be used in real radio communication systems, especially under conditions where it is necessary to quickly and reliably recognize radio signals among noise, under conditions of interference or with weak signals. They could also be useful in military applications, Earth remote sensing systems, mobile communication networks, etc.

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Classification of ECG signals based on local fractal feature

Jiang,

Wang

2023

Multimed Tools Appl

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A CNN model embedded with local feature knowledge and its application to time-varying signal classification

Cited by 19 publications

References 16 publications

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction

Approach to processing radio signals with amplitude modulation of many components using one-dimensional convolutional neural network

Classification of ECG signals based on local fractal feature

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