The proposed meta-models incorporate a three-layered advantage over the conventional ones, which are, global refinement of the basis functions, enabled with compressive sampling based methods based on ℓ0- and ℓ1-norms (p-adaptivity) and integrated with an optimal sequential experimental design scheme (h-adaptivity).
Therefore, we set forth to explore machine-learning based methods based on various protein features for the prediction of CDKI proteins.
In contrast, several genetically based methods based on the identification of the wciN gene or sizes of specific PCR fragments have been reported before [ 2, 7, 10, 6].
The message based method, bases its expression recognition on finding what underlies the displayed expression.
Inspired by the field of reinforcement learning (RL), we give two methods, a model-based method based on Gaussian processes (GPs) and a model-free method based on temporal difference learning (TD).
This paper develops a shape-based method based on a Fourier series approximation that is capable of representing trajectories defined in spherical coordinates and that enforces thrust constraints.
In this work we present a cluster-based method based on Voronoi diagrams and covering radius criterion in order to avoid unnecessary interaction between individuals.
In the previous study (Yazdani Sarvestani, submitted for publication), a new simple-input displacement-based method (based on layerwise formulation) was developed for the stress analysis of thick laminated composite straight tubes subjected to cantilever loading.
In Section 3, we discussed the rank-based method based on the optimal score function for a specified shape parameter α.
For abrupt shot boundaries, they proposed a correlation-based method, based on which localized edge gradients are then used for detecting gradual shot transitions.
