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.
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BookDOI
Information Hiding and Applications
TL;DR: This work presents genetic watermarking for Copyright Protection, a Secure Data Embedding Scheme using Gray-Code Computation and SMVQ Encoding, and a Semi-fragile Image Authentication Method for Robust to JPEG, JPEG2000 Compressed and Scaled Images.
Journal ArticleDOI
Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP
TL;DR: Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms.
Journal ArticleDOI
A Novel Improved Bat Algorithm Based on Hybrid Parallel and Compact for Balancing an Energy Consumption Problem
TL;DR: An improved Bat algorithm based on hybridizing a parallel and compact method (namely pcBA) for a class of saving variables in optimization problems achieves a practical method of reducing the number of stored memory variables, and the running time consumption.
Journal ArticleDOI
Reversible watermarking based on multiple prediction modes and adaptive watermark embedding
Shaowei Weng,Jeng-Shyang Pan +1 more
TL;DR: A new reversible watermark scheme based on multiple prediction modes and adaptive watermark embedding is presented and can accurately evaluate the classification of watermarked pixels by analyzing the local complexity of their corresponding neighborhoods on the decoding side.
Journal ArticleDOI
A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm
TL;DR: This study suggests a new process improving MFO by hybridizing Lévy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm.