Jen-Feng Wang

TL;DR: The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm, substituting a QR algorithm for the traditional Gram-Schmidt algorithm, to find the connected weight of the hidden layer neurons.

TL;DR: Experimental results show that the tested ridge density features alone are not very effective for gender determination, but the proposed ridge count and finger size features of left little fingers are useful, achieving a classification accuracy of 75% and 79% respectively.

TL;DR: Two dermatoglyphic asymmetry measures are proposed that draw on the orientation field of homologous fingers that suggest that the proposed measures are promising for detecting the dermatoglyPHic patterns that can differentiate the patient and control groups.

TL;DR: This work develops a very efficient semi-automatic procedure to segment entire nodules by combining morphometry and perfusion characteristics by perfusion CT, which greatly reduces the amount of radiation exposure to patients and the data processing time.

TL;DR: This work proposes a design method for a two‐stage committee machine that combines a neural network approach for the base classifier and a computationally more intensive bagging ensemble employed in the second stage.