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Results indicate that the proposed method is better in line with engineering practice and more flexible in decision making and it can predict the reliability of aero engine turbine blades to be an interval by utilizing the proposed linear fusion algorithm.
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A large substation load dataset with annual interval is utilized and firstly preprocessed by the proposed linear clustering method to prepare for modelling.
The proposed linear neutron source will be operated both in the continuous and pulse modes and it will be utilized for a few near term applications namely fusion reactor material studies and explosive detection.
We utilize the proposed scheme on some initial value problems and approximate numerical solutions of linear and nonlinear time fractional PDEs are obtained, which shows that the method is highly accurate and simple to apply.
We utilize the proposed method for two real-world datasets, selected from the Stanford Microarray Database.
We utilize the proposed method in analyzing two microarray datasets with different statistical characteristics.
Can we reduce the running time of the proposed algorithms by utilizing the techniques proposed in [ 20- 22]?
We propose utilizing the diagonal linear discriminant analysis (DLDA) algorithm to classify patients into g+ and g− subgroups (Methods).
A TPS is analyzed by utilizing the linear diffraction theory as well as the linear part of the Morison equation.
In this paper, we propose a solution methodology for designing the infrastructure of the reverse production system by utilizing the mixed integer linear programming (MILP) model.
This paper proposes a novel ensemble model of Extreme Learning Machine (ELM) in the prediction of reservoir properties while utilizing the non-linear approximation capability of Functional Networks to select the optimal input features.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com