deep neural network-based fault diagnosis model Search Results


deep neural networkebased model  (Aiforia Technologies)
90
Aiforia Technologies deep neural networkebased model
Deep Neural Networkebased Model, supplied by Aiforia Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep multilayer perceptron neural network based clonal selective optimization (dmlpnn-cso) technique  (MathWorks Inc)
90
MathWorks Inc deep multilayer perceptron neural network based clonal selective optimization (dmlpnn-cso) technique
Deep Multilayer Perceptron Neural Network Based Clonal Selective Optimization (Dmlpnn Cso) Technique, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep multilayer perceptron neural network based clonal selective optimization (dmlpnn-cso) technique - by Bioz Stars, 2026-03
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motion prediction based on deep artificial neural networks  (Respiratory Motion)
90
Respiratory Motion motion prediction based on deep artificial neural networks
Motion Prediction Based On Deep Artificial Neural Networks, supplied by Respiratory Motion, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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& electromagnetics research symposium  (Photonics Inc)
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Photonics Inc & electromagnetics research symposium
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2d residual neural network  (MathWorks Inc)
96
MathWorks Inc 2d residual neural network
2d Residual Neural Network, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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neural network-based closed-loop deep brain stimulation  (IEEE Access)
90
IEEE Access neural network-based closed-loop deep brain stimulation
Neural Network Based Closed Loop Deep Brain Stimulation, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep neural networks  (Takeda)
90
Takeda deep neural networks
Deep Neural Networks, supplied by Takeda, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep neural networks  (Mitsufuji Corporation)
90
Mitsufuji Corporation deep neural networks
Deep Neural Networks, supplied by Mitsufuji Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural network (cnn)  (Kaggle Inc)
90
Kaggle Inc deep convolutional neural network (cnn)
Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes <t>convolutional</t> layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.
Deep Convolutional Neural Network (Cnn), supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep neural networks  (Pattern Recognition Systems (PRS) as)
90
Pattern Recognition Systems (PRS) as deep neural networks
Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes <t>convolutional</t> layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.
Deep Neural Networks, supplied by Pattern Recognition Systems (PRS) as, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep neural networks - by Bioz Stars, 2026-03
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learning based convolutional neural network cnn  (Addgene inc)
91
Addgene inc learning based convolutional neural network cnn
Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes <t>convolutional</t> layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.
Learning Based Convolutional Neural Network Cnn, supplied by Addgene inc, used in various techniques. Bioz Stars score: 91/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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learning based convolutional neural network cnn - by Bioz Stars, 2026-03
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neural network  (MathWorks Inc)
90
MathWorks Inc neural network
Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes <t>convolutional</t> layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.
Neural Network, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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neural network - by Bioz Stars, 2026-03
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Image Search Results


Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes convolutional layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.

Journal: Physics in medicine and biology

Article Title: Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features

doi: 10.1088/1361-6560/ab326a

Figure Lengend Snippet: Illustration of the proposed SS-OLHF algorithm by fusing the highest level CNN representation learned at the output layer (OL) of a 3D CNN into the handcrafted features (HF). In the CNN architecture, Stage I mainly includes convolutional layers and pooling layers with tensor output; Stage Ⅱ mainly includes hidden fully connected layers with vector output; Stage Ⅲ is the output layer with vector output.

Article Snippet: In a recent lung cancer detection challenge organized by Kaggle, most top-scored models were based on a deep convolutional neural network (CNN).

Techniques: Plasmid Preparation

2D illustration by unfolding into the columns for three randomly selected feature maps learned in the last convolutional layers from: (a) Alex Net (a), (b) VGG16 Net and (c) Multi-crop Net. In the (a), (b) and (c), all left three columns are corresponding to the malignant nodule showed in left Fig. 6 (a) and all right three columns are corresponding to the benign nodule showed in right Fig. 6 (a). The white circles show the major discriminative information between the malignant nodule and benign nodule in the feature maps of each CNN: (a) Alex Net (a), (b) VGG16 Net and (c) Multi-crop Net.

Journal: Physics in medicine and biology

Article Title: Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features

doi: 10.1088/1361-6560/ab326a

Figure Lengend Snippet: 2D illustration by unfolding into the columns for three randomly selected feature maps learned in the last convolutional layers from: (a) Alex Net (a), (b) VGG16 Net and (c) Multi-crop Net. In the (a), (b) and (c), all left three columns are corresponding to the malignant nodule showed in left Fig. 6 (a) and all right three columns are corresponding to the benign nodule showed in right Fig. 6 (a). The white circles show the major discriminative information between the malignant nodule and benign nodule in the feature maps of each CNN: (a) Alex Net (a), (b) VGG16 Net and (c) Multi-crop Net.

Article Snippet: In a recent lung cancer detection challenge organized by Kaggle, most top-scored models were based on a deep convolutional neural network (CNN).

Techniques: