Neural network ensembles and an improved Differential Evolutionary Algorithm (DEA) with a non-inferior sorting scheme and niche technology are employed for problem solving.
Since its invention, the Kalman filtering approach has been successfully and widely employed for many problems in scientific and engineering fields, e.g. target tracking, satellite systems, control, communications, etc.
A hybridization temperature of 50°C results in comparably high signal intensities in the sample and can thus be employed for background problems when the activity of fluorescent proteins is not of importance.
In the present research, the suggested model was employed for the problem of supplier selection.
Different methods of swift analyses are employed for various problem formulations.
For this purpose a committee of competitive and back propagation neural networks has been employed for this problem, which uses both unsupervised and supervised learning algorithms.
In this paper the method of multipole re-expansion is employed for the problem of two spherical particles consisting of a core, coated with a conducting layer which in turn is surrounded by an outer dielectric coating of arbitrary size.
Since most machine learning algorithms can be used to find an approximate solution for the optimization problem, they can be employed for most data analysis problems if the data analysis problems can be formulated as an optimization problem.
In the proposed system, multi-class support vector machine (SVM) is employed for solving classification problems.
