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A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems

1. Zhengzhou University, Zhengzhou, Henan, China;
2. Zhongyuan University of Technology, Zhengzhou, Henan, China

Vein recognition is a new identity authentication technology. It attracts many researchers' attention due to its good security and reliability. This paper proposes a wrist vein recognition system. The proposed system identifies people according to the characteristics of their wrist veins. A special camera is reformed to obtain the wrist vein images and an image dataset is established. Principal component analysis (PCA) is adopted to eliminate the redundant information in the images and extract their global features. The global features are classified by Two-hidden-layer Extreme Learning Machine (TELM). TELM is compared with original Extreme Learning Machine (ELM) and other two algorithms Support Vector Machine (SVM) and Naive Bayes (NB). Experiment results show that the accuracy of the proposed system is higher than the other three algorithms. Though the speed of TELM is not the fastest, it is able to recognize images within satisfactory time.
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Keywords Vein recognition; PCA; Extreme Learning Machine; TELM

Citation: Cai-Tong Yue, Jing Liang, Bo-Fei Lang, Bo-Yang Qu. A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems. Big Data and Information Analytics, 2017, 2(1): 59-68. doi: 10.3934/bdia.2017008

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Copyright Info: 2017, Jing Liang, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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