Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fi...

The accurate and effective classification of household solid waste (HSW) is an indispensable component in the current procedure disposal. In this paper, a novel ensemble learning model called EnCNN-UPMWS, which based on convolutional neural networks (CNNs) unequal precision measurement weighting strategy (UPMWS), proposed for HSW via images. First, three state-of-the-art CNNs, namely GoogLeNet,...

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This me...

Elucidating the complex interplay between protein structure and dynamics is a prerequisite to an understanding of both function and adaptation in proteins. Unfortunately, it has been difficult to experimentally decouple these effects because it is challenging to rationally design mutations that will either affect the structure but not the dynamics, or that will affect the dynamics but not the s...

Affect detection from physiological signals has received considerable attention. One challenge is that physiological measures exhibit considerable variations over time, making classification of future data difficult. The present study addresses this issue by providing insights on how diagnostic physiological features of affect change over time. Affective physiological data (Electrocardiogram, E...