J
Jeng-Shyang Pan
Researcher at Shandong University of Science and Technology
Publications - 889
Citations - 14887
Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.
Papers
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Proceedings ArticleDOI
New Anti-phishing Method with Two Types of Passwords in OpenID System
TL;DR: A model of two types of passwords for anti-phishing is proposed and through analysis, this method can effectively avoid phishing.
Journal ArticleDOI
An Improved Integer Transform Combining with an Irregular Block Partition
TL;DR: The irregular block partition method which makes full use of high correlation between two neighboring pixels is proposed to increase the embedding performance and the mean value of an image block in Alattar’s integer transform has embedding invariance property, and therefore, it can be used for increasing the estimation performance of a block's local complexity.
Book ChapterDOI
A Novel Hybrid GWO-FPA Algorithm for Optimization Applications
TL;DR: A new approach hybrid Grey Wolf Optimizer-Flower Pollination Algorithm is proposed based on the combination of exploitation phase in GWO and exploration stage in FPA that improves movement directions and speed of the grey wolves in updating positions of FPA.
Proceedings ArticleDOI
A New Vector Particle Swarm Optimization for Constrained Optimization Problems
TL;DR: A new vector Particle Swarm Optimization (NVPSO) is proposed to solve constrained optimization problems and introduces a shrinkage coefficient to ensure that all dimensions of a particle are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region.
Journal ArticleDOI
Adaptively weighted sub-directional two-dimensional linear discriminant analysis for face recognition
TL;DR: A novel image classification algorithm named Adaptively Weighted Sub-directional Two-Dimensional Linear Discriminant Analysis (AWS2DLDA) can extract the directional features of images in the frequency domain, and it is applied to face recognition.