In this paper, we propose a gene expression based approach for the prediction of Parkinson's disease (PD) using 'projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)'.
Within single lap joints, presence of material and geometric discontinuities restrict the applicability of strength of material based approach for failure load prediction.
In particular, we present a novel state space based approach for the link quality prediction, and demonstrate that it is possible to integrate this model into operating system interfaces, so that higher layer data aggregation protocols can directly exploit these interfaces to improve their performance.
The paper critically examines the advantages and limitations of empirical and constitutive equation based approaches for creep strain prediction.
The continuum mechanics based approach is used for the prediction of the fatigue process initiation phase, where the basic fatigue parameters of the materials are taken into account.
Two modeling strategies, namely a non-parametric nearest neighbor and a parametric regression based approach, were tested for obtaining the prediction models.
Since experimental material testing of individual masonry components (e.g., masonry unit and mortar joints) often produces highly variable results, this paper presents a numerical modelling based approach to address the associated uncertainty for the prediction of the maximum compressive load of masonry prisms.
In this paper, a right failure prediction criterion is identified that can be used for the Fiber-Tear-Failure load prediction using zone based approach.
Recently machine learning based approaches have been implemented for the prediction of ΔΔG in proteins upon residue substitution taking as input either the protein structure or sequence (for a recent review see [ 20]).
The computational approaches for in silico prediction can be roughly categorized into genome based approaches, network topology based approaches, literature knowledge based methods, and structure based approaches [ 11].
