In this article, we introduce a novel machine learning scheme that overcomes several disadvantages associated with existing methods.
This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision.
In this paper, the proposed model is intended to employ a novel evolutionary computing-based artificial intelligence or machine learning scheme for regression tests to be used for reusability estimation.
Given a query mutation of SRPM we follow the traditional machine learning scheme.
Our method can be used to support other, e.g. structure based, methods for the discovery of (specific) ncRNA classes by incorporating our similarity scores into the respective probabilistic model or machine learning scheme.
In this paper, a data-driven approach using a machine learning (ML) scheme is proposed to model VIV response based on a database of field-measured VIVs for a full-scale long-span suspension bridge over six recent years.
To reach this goal we started from the well known "70-genes signature" and proceeded with the application of several machine learning schemes, in order to perform a comparison between them.
