文章基本信息

标题：P-CC-NN: PARALLEL CASCADE CORRELATION NEURAL NETWORK METHODS FOR PATTERN RECOGNITION APPLICATIONS USING MULTICORE TECHNIQUES
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作者：KHALID MOHAMMAD JABER ; SOKYNA M. ALQATAWNEH
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：93
期号：1
出版社：Journal of Theoretical and Applied
摘要：This paper presents a multi-core programming model that implements the cascade correlation neural networks technique, to enhance the classification phase of any pattern recognition system. It is based on combining the strengths of both approaches in order to construct an efficient Parallel Cascade Correlation Neural Network (P-CC-NN) system. In this work a complex case of pattern recognition system which is a 3D facial data has been used to examine the proposed system and ensure its effectiveness, experimental results are presented using 360 3D facial images, each image contains 96 distinguishable features. Results show significant improvement in execution time about 31 minutes (4.6 times speedup) in comparison with 146.5 minutes for serial time, this topology generated an accuracy of 94 %. This work is the first approach to handle the classification challenges for different pattern recognition applications using multi-core techniques.
关键词：Parallel; Cascade Correlation Neural Network; Pattern Recognition; Facial Image.