Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm - specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm - in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.
Kidney allograft pathology assessment has been traditionally
based on clinical and histological criteria. Despite improvements
in Banff histological classification, the diagnostics in particular
cases is problematic reflecting a complex pathogenesis of graft
injuries. With the advent of molecular techniques, polymerasechain reaction, oligo- and microarray technologies allowed to
study molecular phenotypes of graft injuries, especially acute and
chronic rejections. Moreover, development of the molecular
microscope diagnostic system (MMDx) to assess kidney graft
biopsies represents the first clinical application of a microarraybased method in transplantation. Whether MMDx may replace
conventional pathology is the subject of ongoing research,
however this platform is particularly useful in complex histological
findings and may help clinicians to guide the therapy.