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Existing research and program history was used to determine the model architecture and determinant variables.
This approach assumes therefore minimal constraints on the model architecture and cell characteristics.
As a consequence, there is currently large heterogeneity in terms of interface, data model, architecture and even terminology across NoSQL databases [7, 12].
Background information gleaned from clinical and medical research literature is used to develop model architecture and processes.
We focus in the conceptual model, architecture and necessary elements we believe are required for a real world implementation.
The ANN model architecture and training parameters were optimized, with inputs being selected using correlation analysis and principal component analysis.
Similar(38)
Rather than adhering to the minimal modelling approach [ 33, 98], we have not considered multiple model architectures, and the architecture used is fairly complex, so we accept that some parameters will not be uniquely determined.
It comes equipped with key ingredients including hyperparameters, data-sets, model architectures and learning rate decay schemes.
We suggest a combination of graph theory, Big Data and Uncertainty Quantification are foundational tools that can model architectures and more importantly the movement of data across architecture interfaces.
To capitalize on this void, new solutions – built with fundamentally different models, architecture, and DNA – are rapidly emerging.
In order to improve the network performance, fuzzy subtractive clustering was used to identify model architecture, extract and optimize fuzzy rule of the model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com