scholarly journals Corrigendum to “Adaptive Digital Watermarking Scheme Based on Support Vector Machines and Optimized Genetic Algorithm”

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Xiaoyi Zhou ◽  
Chunjie Cao ◽  
Jixin Ma ◽  
Longjuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Digital Watermarking ◽  
Support Vector ◽  
Vector Machines ◽  
Watermarking Scheme

scholarly journals Adaptive Digital Watermarking Scheme Based on Support Vector Machines and Optimized Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoyi Zhou ◽  
Chunjie Cao ◽  
Jixin Ma ◽  
Longjuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Digital Watermarking ◽  
Image Watermarking ◽  
Histogram Equalization ◽  
Support Vector ◽  
Digital Products ◽  
Vector Machines ◽  
Low Pass ◽  
Watermarking Scheme

Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3,0.5), histogram equalization, and contrast increasing (0.5,0.6)) on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines.

scholarly journals Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Information ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 212-227 ◽  
Author(s):  
Fang Zong ◽  
Yu Bai ◽  
Xiao Wang ◽  
Yixin Yuan ◽  
Yanan He
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gps Data ◽  
Travel Mode ◽  
Vector Machines

Patent Innovation Factors Evolution Based on P-SVM and GCCA

2010 ◽  
Vol 39 ◽  
pp. 247-252
Author(s):  
Sheng Xu ◽  
Zhi Juan Wang ◽  
Hui Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Network Architecture ◽  
Innovation Management ◽  
Model Development ◽  
Real Data ◽  
Support Vector ◽  
Vector Machines ◽  
Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel

2018 ◽  
Vol 32 (5) ◽  
pp. 1239-1248 ◽  
Author(s):  
Eva María Artime Ríos ◽  
Ana Suárez Sánchez ◽  
Fernando Sánchez Lasheras ◽  
María del Mar Seguí Crespo
Keyword(s):  
Genetic Algorithm ◽  
Computer Vision ◽  
Support Vector Machines ◽  
Health Personnel ◽  
Support Vector ◽  
Computer Vision Syndrome ◽  
Vector Machines ◽  
Syndrome Classification
Sign in / Sign up

Export Citation Format

Share Document