feedforward, fully connected neural network for regression Search Results


fitcnet function  (MathWorks Inc)
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2-d u-net fully convolutional neural network  (MathWorks Inc)
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MathWorks Inc 2-d u-net fully convolutional neural network
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fully convolutional neural network  (SoftMax Inc)
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SoftMax Inc fully convolutional neural network
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regression neural networks  (MathWorks Inc)
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MathWorks Inc regression neural networks
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fully connected neural network fc  (SoftMax Inc)
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SoftMax Inc fully connected neural network fc
Fully Connected Neural Network Fc, supplied by SoftMax 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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full-band neural activity  (Intan Technologies Llc)
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neural network  (Beuth Verlag GmbH)
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Neural Network, supplied by Beuth Verlag GmbH, 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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fully convolutional residual neural network (fcr-nn)  (MathWorks Inc)
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MathWorks Inc fully convolutional residual neural network (fcr-nn)
Overview of papers using deep learning for brain tumor classification.
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artificial neural network (ann)  (National Institute of Standards and Technology)
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National Institute of Standards and Technology artificial neural network (ann)
Overview of papers using deep learning for brain tumor classification.
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neural recording chip  (Neuronix Inc)
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Neuronix Inc neural recording chip
Experimental paradigm and hardware components. (A) Diagram of the experiment setup including the Scorpius front-end <t>neural</t> <t>recording</t> system, implanted FAST-LIFE microelectrode arrays, a computer for data acquisition and back-end AI models implementation, and a data glove for obtaining ground-truth movements during mirrored bilateral training. (B) Photo of the transradial amputee during an experiment session (C) The Scorpius is a miniaturized prototype designed toward implantable applications. The head-piece contains a fully-integrated <t>Neuronix</t> neural recording <t>chip,</t> an electrode connector, and passive components while the auxiliary-piece includes voltage regulators and data relaying circuits. (D) Microphotograph of the Neuronix chip that has ten fully-integrated, high-performance neural recording channels based on the frequency-shaping (FS) architecture.
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stacked auto-encoders (sae) and fully-connected neural network (fnn)  (MathWorks Inc)
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MathWorks Inc stacked auto-encoders (sae) and fully-connected neural network (fnn)
Deep Learning (DL) applications in hyperspectral image analysis of food products.
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fully connected neural network architectures  (MathWorks Inc)
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MathWorks Inc fully connected neural network architectures
Deep Learning (DL) applications in hyperspectral image analysis of food products.
Fully Connected Neural Network Architectures, 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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Image Search Results


Overview of papers using deep learning for brain tumor classification.

Journal: Brain Sciences

Article Title: Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

doi: 10.3390/brainsci10020118

Figure Lengend Snippet: Overview of papers using deep learning for brain tumor classification.

Article Snippet: Peter D. Chang [ ]. , Fully Convolutional Neural Networks , Fully Convolutional Residual Neural Network (FCR-NN) , MATLAB R2016a , Dice Scores Complete = 0.87 Core Tumor = 0.81 Enhancing = 0.72.

Techniques: Software, Activation Assay

Experimental paradigm and hardware components. (A) Diagram of the experiment setup including the Scorpius front-end neural recording system, implanted FAST-LIFE microelectrode arrays, a computer for data acquisition and back-end AI models implementation, and a data glove for obtaining ground-truth movements during mirrored bilateral training. (B) Photo of the transradial amputee during an experiment session (C) The Scorpius is a miniaturized prototype designed toward implantable applications. The head-piece contains a fully-integrated Neuronix neural recording chip, an electrode connector, and passive components while the auxiliary-piece includes voltage regulators and data relaying circuits. (D) Microphotograph of the Neuronix chip that has ten fully-integrated, high-performance neural recording channels based on the frequency-shaping (FS) architecture.

Journal: bioRxiv

Article Title: A bioelectric neural interface towards intuitive prosthetic control for amputees

doi: 10.1101/2020.09.17.301663

Figure Lengend Snippet: Experimental paradigm and hardware components. (A) Diagram of the experiment setup including the Scorpius front-end neural recording system, implanted FAST-LIFE microelectrode arrays, a computer for data acquisition and back-end AI models implementation, and a data glove for obtaining ground-truth movements during mirrored bilateral training. (B) Photo of the transradial amputee during an experiment session (C) The Scorpius is a miniaturized prototype designed toward implantable applications. The head-piece contains a fully-integrated Neuronix neural recording chip, an electrode connector, and passive components while the auxiliary-piece includes voltage regulators and data relaying circuits. (D) Microphotograph of the Neuronix chip that has ten fully-integrated, high-performance neural recording channels based on the frequency-shaping (FS) architecture.

Article Snippet: shows the overview of the Scorpius system – a miniaturized front-end recorder equipped with the fully-integrated Neuronix neural recording chip.

Techniques:

Deep Learning (DL) applications in hyperspectral image analysis of food products.

Journal: Current Research in Food Science

Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

doi: 10.1016/j.crfs.2021.01.002

Figure Lengend Snippet: Deep Learning (DL) applications in hyperspectral image analysis of food products.

Article Snippet: Detection and quantification of nitrogen content in rapeseed leaf , 400–1000 , – , Image thresholding , Stacked auto-encoders (SAE) and fully-connected neural network (FNN) , Feature extraction from hyperspectral data through SAE , Input for FNN: SAE extracted features , 80:20 , MATLAB 8.1; PYTHON , 90% , .

Techniques: Biomarker Discovery, Software, Activation Assay, Control, Extraction