An ensemble approach of improved quantum inspired gravitational search algorithm and hybrid deep neural networks for computational optimization

被引:7
作者
Kumar, Yogesh [1 ]
Verma, Shashi Kant [2 ]
Sharma, Sandeep [3 ]
机构
[1] Uttarakhand Tech Univ, Dept Comp Sci & Engn, Dehra Dun 248007, Uttarakhand, India
[2] Govind Ballabh Pant Inst Engn & Technol, Dept Comp Sci & Engn, Pauri Garhwal 246194, Uttarakhand, India
[3] Chang Gung Univ, Dept Elect Engn, Ctr Reliabil Sci & Technol, Taoyuan 33302, Taiwan
来源
INTERNATIONAL JOURNAL OF MODERN PHYSICS C | 2021年 / 32卷 / 08期
关键词
Gravitational search algorithm; quantum computing; deep neural network; convolutional neural network; recurrent neural network; facial expression recognition; FACIAL EXPRESSION RECOGNITION;
D O I
10.1142/S012918312150100X
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, an autonomous ensemble approach of improved quantum inspired gravitational search algorithm (IQI-GSA) and hybrid deep neural networks (HDNN) is proposed for the optimization of computational problems. The IQI-GSA is a combinational variant of gravitational search algorithm (GSA) and quantum computing (QC). The improved variant enhances the diversity of mass collection for retaining the stochastic attributes and handling the local trapping of mass agents. Further, the hybrid deep neural network encompasses the convolutional and recurrent neural networks (HDCR-NN) which analyze the relational & temporal dependencies among the different computational components for optimization. The proposed ensemble approach is evaluated for the application of facial expression recognition by experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets. The experimentation evaluations evidently exhibit the outperformed recognition rate of the proposed ensemble approach in comparison with state-of-the-art techniques.
引用
收藏
页数:16
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