Home // ADVCOMP 2013, The Seventh International Conference on Advanced Engineering Computing and Applications in Sciences // View article
Authors:
Tung-Kuang Wu
Shian-Chang Huang
Ying-Ru Meng
Hsiu-Ting Kao
Hsu Chang
Keywords: learning disabilities; MapReduce; neural network; virtualization; cloud computing
Abstract:
Diagnosis of students with learning disabilities (LD) is a difficult procedure that requires extensive man power and takes a long time. Fortunately, through genetic-based (GA) parameters optimization, artificial neural network (ANN) classifier may be a good alternative to the above procedure. However, GA-based ANN model construction is computation-intensive and may take quite a while to process. Accordingly, parallel processing such as multi-core programming and grid computing have been used to speedup the process. In this study, we setup a Hadoop min-cloud environment with virtualized hosts so that we may take full advantage of the current multi-core CPU technology. The GA-based ANN LD classifier is then re-programmed based on the MapReduce programming model and ported to this mini-cloud environment. Some implementation issues and considerations regarding the process will be discussed in the paper. Although the preliminary results may not show significant breakthrough over our previous studies, yet we do gain some experience through this process and see the potential of the MapReduce model in our future applications.
Pages: 30 to 35
Copyright: Copyright (c) IARIA, 2013
Publication date: September 29, 2013
Published in: conference
ISSN: 2308-4499
ISBN: 978-1-61208-290-5
Location: Porto, Portugal
Dates: from September 29, 2013 to October 3, 2013