artificial neural network (ann) machine learning toolbox Search Results


multilayer artificial neural network classifiers  (MathWorks Inc)
90
MathWorks Inc multilayer artificial neural network classifiers
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Multilayer Artificial Neural Network Classifiers, 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 model  (MathWorks Inc)
96
MathWorks Inc neural network model
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Neural Network Model, 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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artificial neural networks  (Pattern Recognition Systems (PRS) as)
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Pattern Recognition Systems (PRS) as artificial neural networks
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Artificial 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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pattern recognition artificial neural network tool matlab 2019a  (MathWorks Inc)
90
MathWorks Inc pattern recognition artificial neural network tool matlab 2019a
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Pattern Recognition Artificial Neural Network Tool Matlab 2019a, 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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machine learning algorithm created in  (MathWorks Inc)
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MathWorks Inc machine learning algorithm created in
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Machine Learning Algorithm Created In, 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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artificial neural network (ann) machine learning model  (MathWorks Inc)
90
MathWorks Inc artificial neural network (ann) machine learning model
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Artificial Neural Network (Ann) Machine Learning Model, 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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artificial intelligent quantum machine system  (Nano Technology Inc)
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Nano Technology Inc artificial intelligent quantum machine system
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Artificial Intelligent Quantum Machine System, supplied by Nano Technology 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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convolutional neural network (cnn) artificial intelligence (ai) software  (Aiforia Technologies)
90
Aiforia Technologies convolutional neural network (cnn) artificial intelligence (ai) software
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Convolutional Neural Network (Cnn) Artificial Intelligence (Ai) Software, 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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artificial neural network (ann) machine learning toolbox  (MathWorks Inc)
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MathWorks Inc artificial neural network (ann) machine learning toolbox
Performance of the artificial neural network <t>(ANN)</t> model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and <t>the</t> <t>Bayesian</t> Regularization training algorithm.
Artificial Neural Network (Ann) Machine Learning Toolbox, 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 implants  (Nanosensors Inc)
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Nanosensors Inc neural implants
Performance of the artificial neural network <t>(ANN)</t> model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and <t>the</t> <t>Bayesian</t> Regularization training algorithm.
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neuralnet library  (RStudio)
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RStudio neuralnet library
Performance of the artificial neural network <t>(ANN)</t> model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and <t>the</t> <t>Bayesian</t> Regularization training algorithm.
Neuralnet Library, supplied by RStudio, 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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convolution neural network i - cnn (densenet from halo, indica v3.2)  (Indica Labs)
90
Indica Labs convolution neural network i - cnn (densenet from halo, indica v3.2)
Performance of the artificial neural network <t>(ANN)</t> model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and <t>the</t> <t>Bayesian</t> Regularization training algorithm.
Convolution Neural Network I Cnn (Densenet From Halo, Indica V3.2), supplied by Indica Labs, 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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Image Search Results


The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

Performance of the artificial neural network (ANN) model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and the Bayesian Regularization training algorithm.

Journal: Polymers

Article Title: Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete

doi: 10.3390/polym13060900

Figure Lengend Snippet: Performance of the artificial neural network (ANN) model based on ( a ) the training dataset; ( b ) test dataset; ( c ) all datasets; and ( d ) mean squared error for 8 hidden neurons and the Bayesian Regularization training algorithm.

Article Snippet: This paper uses an artificial neural network (ANN) machine learning toolbox in a MATLAB programming environment together with a Bayesian regularization algorithm, the Levenberg-Marquardt algorithm and a scaled conjugate gradient algorithm to attain a specified target compressive strength at 28 days.

Techniques: