期刊：
Advances in Intelligent Systems and Computing,2021年 1274: 128-137 ISSN：2194-5357
通讯作者：
Zheng, G.
作者机构：
[Zheng G.] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;[Xu Y.] College of Computer Science, Changsha Normal University, Changsha, 410199, China
通讯机构：
[Zheng, G.] C;College of Computer Science and Technology, China
会议名称：
10th International Conference on Computer Engineering and Networks, CENet 2020
会议时间：
16 October 2020 through 18 October 2020
会议论文集名称：
The 10th International Conference on Computer Engineering and Networks
关键词：
Chemical reaction optimization;Hybrid;Molecule;N-queens
摘要：
This paper proposes a high-capacity reversible watermarking algorithm for medical image analysis based on the difference block histogram, which will benefit the medical image authentication and doctor–patient confidentiality. By dividing the original medical image into blocks, the method displaces the peak point of a block's histogram of difference and embeds multi-bit information at 1 pixel point. In so doing, secret communication and storage of large-capacity invisible medical diagnoses and patients personal confidential data can be achieved. Once the watermark is extracted, not only the image integrity is authenticated, but also the original image and personal data of the patient can be recovered in a nondestructive way. With low computational complexity, a high embedding capacity and little demand for auxiliary information, the proposed algorithm is highly secure and practical.
通讯机构：
[Xu, Yuming] H;Hengyang Normal Univ, Dept Comp Sci, Hengyang 421008, Hunan, Peoples R China.
关键词：
Artificial Chemical Reaction Optimization;Job Scheduling;Makespan;NP-Hard Problem
摘要：
The non-deterministic polynomial-time-hard job scheduling problem can be regarded as the optimal assignment of a set of jobs to a set of computing nodes to minimize the completion time. Such problems can be efficiently addressed through a meta-heuristic optimization approach, such as the new artificial chemical reaction optimization method. This approach mimics a chemical reaction process in which reactants interact with one another to reach the minimum enthalpy (potential energy) state. Therefore, this study proposes a novel approach of artificial chemical reaction optimization for job scheduling (ACROAJS) in grid computing environments based on the recently proposed chemical reaction-inspired meta-heuristic. Software simulation results show that the proposed ACROAJS algorithm significantly improves job schedule quality (makespan) in grid computing environments compared with two existing solutions [genetic algorithm and heterogeneous earliest finish time algorithm] over a set of randomly generated graphs and over graphs for real-world problems with various characteristics. With this algorithm, makespan was reduced by approximately 5.06% on average.
