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Appl Soft Comput 2015 Feb;27:148-157

School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK.

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. Read More

Appl Soft Comput 2012 Mar;12(3):1227-1230

River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Penang, Malaysia.

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). Read More

Appl Soft Comput 2008 Jan;8(1):599-608

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

OBJECTIVE: To classify patients by age based upon information extracted from their electro-cardiograms (ECGs). To develop and compare the performance of Bayesian classifiers. METHODS AND MATERIAL: We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Read More

Appl Soft Comput 2007 Jan;7(1):471-479

Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, 519 Light Hall, Nashville, TN 37232.

The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Read More

Appl Soft Comput 2004 Feb;4(1):79-86

Program in Human Genetics, Department of Molecular Physiology and Biophysics, 519 Light Hall, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA.

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. Read More