artificial neural network (ann) model-development tool Search Results


neuralworks professional ii plus  (NeuralWare Inc)
90
NeuralWare Inc neuralworks professional ii plus
Neuralworks Professional Ii Plus, supplied by NeuralWare 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) models  (MicroFluidic Systems)
90
MicroFluidic Systems artificial neural network (ann) models
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Artificial Neural Network (Ann) Models, supplied by MicroFluidic Systems, 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 model  (Deepmind Technologies Ltd)
86
Deepmind Technologies Ltd artificial neural network ann model
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Artificial Neural Network Ann Model, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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neurosolution ® software  (NeuroDimension Inc)
90
NeuroDimension Inc neurosolution ® software
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Neurosolution ® Software, supplied by NeuroDimension 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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model development procedure  (DataRobot Inc)
90
DataRobot Inc model development procedure
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Model Development Procedure, supplied by DataRobot 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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r studio version 1.4.1717  (RStudio)
90
RStudio r studio version 1.4.1717
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
R Studio Version 1.4.1717, 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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logistic regression sas/statt proc logistic  (SAS institute)
90
SAS institute logistic regression sas/statt proc logistic
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Logistic Regression Sas/Statt Proc Logistic, supplied by SAS institute, 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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model development, scenario selection and simulations, and the preparation of this article  (Bayer AG)
90
Bayer AG model development, scenario selection and simulations, and the preparation of this article
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Model Development, Scenario Selection And Simulations, And The Preparation Of This Article, supplied by Bayer AG, 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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sas v.9.3  (SAS institute)
90
SAS institute sas v.9.3
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Sas V.9.3, supplied by SAS institute, 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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data collection and model development  (Parsons Brinckerhoff)
90
Parsons Brinckerhoff data collection and model development
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Data Collection And Model Development, supplied by Parsons Brinckerhoff, 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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model development and necessary analyses  (Wing Tech Inc)
90
Wing Tech Inc model development and necessary analyses
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Model Development And Necessary Analyses, supplied by Wing Tech 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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cellml/opencell environments  (OpenCell Technologies Inc)
90
OpenCell Technologies Inc cellml/opencell environments
Schematic illustration of the generation of <t>monodisperse</t> <t>PLGA</t> droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train <t>ANN</t> models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.
Cellml/Opencell Environments, supplied by OpenCell Technologies 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


Schematic illustration of the generation of monodisperse PLGA droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train ANN models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.

Journal: Scientific Reports

Article Title: Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

doi: 10.1038/s41598-020-76477-5

Figure Lengend Snippet: Schematic illustration of the generation of monodisperse PLGA droplets either in a single emulsion format by single junction devices ( A ) or microfluidic devices with seven parallel junctions ( B ), or in a multiple emulsion format ( C ) by two sequentially coupled devices. Generated droplets were imaged at the orifice of the flow-focusing region in the microfluidic chips. Data generated were thereafter used to train ANN models. Schematic of one of the developed models (ANN-ABC) ( D ). AP aqueous phase, FR flow rate.

Article Snippet: These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged.

Techniques: Emulsion, Generated

Correlation between observed and predicted droplet/particle size generated by 3D flow focusing droplet chip with a single junction. Results obtained from the developed ANN-A model implemented by Statistica v13.3. Results shown for the developed ANN-A model for the training, test, validation, and external datasets. The reported r 2 values were calculated for each dataset including droplets and microparticles data.

Journal: Scientific Reports

Article Title: Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

doi: 10.1038/s41598-020-76477-5

Figure Lengend Snippet: Correlation between observed and predicted droplet/particle size generated by 3D flow focusing droplet chip with a single junction. Results obtained from the developed ANN-A model implemented by Statistica v13.3. Results shown for the developed ANN-A model for the training, test, validation, and external datasets. The reported r 2 values were calculated for each dataset including droplets and microparticles data.

Article Snippet: These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged.

Techniques: Generated, Biomarker Discovery

Correlation between experimentally observed and predicted droplet/particle size generated by the flow focusing droplet chip with 7 junctions operating in parallel. Results obtained from ANN-B model implemented by Statistica v13.3. Data shown for Training, test, validation, and external datasets. The reported r 2 values were calculated for each dataset including droplets and microparticles data.

Journal: Scientific Reports

Article Title: Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

doi: 10.1038/s41598-020-76477-5

Figure Lengend Snippet: Correlation between experimentally observed and predicted droplet/particle size generated by the flow focusing droplet chip with 7 junctions operating in parallel. Results obtained from ANN-B model implemented by Statistica v13.3. Data shown for Training, test, validation, and external datasets. The reported r 2 values were calculated for each dataset including droplets and microparticles data.

Article Snippet: These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged.

Techniques: Generated, Biomarker Discovery

Correlation between observed and predicted droplet/particle size generated by two consecutive cross-junctions featuring flow focusing microfluidic chips placed in series obtained from ANN-C model implemented by Statistica v13.3. Data are shown for training, test, validation, and external datasets.

Journal: Scientific Reports

Article Title: Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

doi: 10.1038/s41598-020-76477-5

Figure Lengend Snippet: Correlation between observed and predicted droplet/particle size generated by two consecutive cross-junctions featuring flow focusing microfluidic chips placed in series obtained from ANN-C model implemented by Statistica v13.3. Data are shown for training, test, validation, and external datasets.

Article Snippet: These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged.

Techniques: Generated, Biomarker Discovery

Correlation between observed and predicted droplet/particle sizes with the corresponding r 2 values obtained from the developed ANN-ABC model implemented by Statistica v13.3. Correlations are shown for the training, test, validation and, external datasets for three types of microfluidic systems: MFS A, MFS B, and MFS C. The reported r 2 values were calculated for each the dataset including droplets and microparticles data.

Journal: Scientific Reports

Article Title: Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

doi: 10.1038/s41598-020-76477-5

Figure Lengend Snippet: Correlation between observed and predicted droplet/particle sizes with the corresponding r 2 values obtained from the developed ANN-ABC model implemented by Statistica v13.3. Correlations are shown for the training, test, validation and, external datasets for three types of microfluidic systems: MFS A, MFS B, and MFS C. The reported r 2 values were calculated for each the dataset including droplets and microparticles data.

Article Snippet: These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged.

Techniques: Biomarker Discovery