ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

Wang, Nianbin; He, Ming; Sun, Jianguo; Wang, Hongbin; Zhou, Lianke; Chu, Constance R.; Chen, Lei

doi:10.32604/cmc.2019.03709

Cited by 27 publications

(12 citation statements)

References 7 publications

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“…Sruthy et al [45] proposed a CNN-based hand gesture recognition framework for capturing various hand gestures. The deep convolutional neural network [25,26,50] used in this work to classify the gestures and train the two spectrograms of the Doppler radar capable. The proposed model got trained by CNN, and testing was done in two phases in producing the quadrature components.…”

Section: Literature Surveymentioning

confidence: 99%

“…Considering the same concept, the Convolutional neural network (CNN) has been used based on inter-and intra-parallel processing of the sequences in "hand-skeletal joints" for the classification of hand gestures. RGB image sequences and 3D skeletal data sequences both have been used for image processing purposes [10,50]. It is also evident that deep learning based models can be used effectively for image processing.…”

Section: Introductionmentioning

confidence: 99%

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Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu

Alazab

Kaluri

et al. 2021

Complex Intell. Syst.

137

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Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu

Alazab

Kaluri

et al. 2021

Complex Intell. Syst.

137

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“…It is obtained by the residual convolutional neural network, so the self-joining mapping problem can be transformed into the residual mapping problem. Researches on image enhancement and noise processing based on convolutional neural networks have proposed related methods [30,31]. Research on text detection and system recognition is also introduced in [32,33].…”

Section: Residual Learningmentioning

confidence: 99%

Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

Wang

2020

Complexity

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This paper mainly analyzes the current situation of e-commerce in domestic SMEs and points out that there are limited initial investment and difficulty in financing in China’s SMEs; e-commerce control is not scientific; e-commerce personnel of SMEs are not of high quality, in the case of improper setting of the e-commerce sector and shortage of talents, rigid management model, and outdated management concepts. By using the loss function and the value chain management theory of the deep learning in the stationary wavelet domain residual learning model, the e-commerce model of SMEs is newly constructed, and the e-commerce department as the core department of the enterprise is proposed. By training the optimal parameters of the deep residual network and comparing the results with other models, the method of this paper has a good effect against the sample. The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. Finally, based on the wavelet residual depth residual evaluation method, this paper evaluates the application effect of this model and proposes relevant suggestions for improving this model, including rationalizing and perfecting the external value chain coordination mechanism, establishing the e-commerce value chain sharing center, and promoting integration of e-commerce business, strengthening measures and recommendations in various aspects of e-commerce information construction. At last, taking the business activities of a company as an example, applying the theory described in this paper to specific practice proves the feasibility and practical value of the theory.

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“…Feature extraction is the process of extracting various features from radiated noise signals. MFCC [8][9][10], wavelet feature [11][12][13], and Hilbert Huang feature [14][15][16] are often used in traditional radiated noise feature extraction. How to extract features and what features to extract suitable for the recognition and classification of radiated noise has always been a topic of research by researchers.…”

Section: Introductionmentioning

confidence: 99%

Ship Radiated Noise Recognition Technology Based on ML‐DS Decision Fusion

Wang

Zhu

et al. 2021

Computational Intelligence and Neuroscience

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Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.

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ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

Cited by 27 publications

References 7 publications

Hand gesture classification using a novel CNN-crow search algorithm

Hand gesture classification using a novel CNN-crow search algorithm

Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

Ship Radiated Noise Recognition Technology Based on ML‐DS Decision Fusion

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