An improved cultural algorithm and its application in image matching

Yan, Xuesong; Song, Tao

doi:10.1007/s11042-016-4313-2

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…Despite the fame of BDA, insufficient empirical research has investigated factors that can influence BDA adoption in healthcare [21,49]. Empirical evidence from Pakistan's healthcare organizations represents a big gap in the literature from both dimensions (i.e., knowledge about BDA and adoption of BDA) [50].…”

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confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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“…CA was developed based on the bio-cultural evolutionary mechanism [11], which is computationally different from the conventional EAs. The algorithm has found interesting applications in different research areas, ranging from computer science to different branches of engineering [12,[55][56][57][58][59].…”

Section: Cultural Algorithms (Cas)mentioning

confidence: 99%

Performance assessment of meta-heuristics for composite layup optimisation

Jalili

Khani

Maheri

et al. 2021

Neural Comput & Applic

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This paper investigates the performance of several meta-heuristic algorithms, including Particle Swarm Optimisation (PSO), different variants of Differential Evolution (DE), Biogeography-Based Optimisation (BBO), Cultural Algorithm (CA), Optics-Inspired Optimisation (OIO), and League Championship Algorithm (LCA), for optimum layup of laminated composite plates. The study provides detailed Pseudo codes for different algorithms. The buckling capacity maximisation of a 64-layer laminated composite plate under various load scenarios has been considered as the benchmark problem, in which the design variables are the stacking sequences of layers. A Deep Statistical Comparison (DSC) method is employed to rank the performance of different algorithms. The DSC uses a nonparametric two-sample Kolmogorov-Smirnov test to conduct the performance comparisons between the algorithms. The overall performance rankings obtained from the DSC suggest that the LCA, OIO, and PSO algorithms perform remarkably better in comparison to other algorithms. The comparisons provide some interesting conclusions on the performance of different algorithms.

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“…Computational intelligence-based algorithms were also employed in the area of image matching. Yan et al [30] introduced the isolation niche technology into the traditional Cultural Algorithm (CA) and applied it to the image matching problem to improve stability and convergence precision. Liu et al [31] proposed a Chaotic Quantum-behaved Particle Swarm Optimization Based on Lateral Inhibition (LI-CQPSO), which utilized the Chaos theory to ensure the PSO avoids premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Rao-NM Algorithm for Image Template Matching

Liu

Wang

et al. 2021

Entropy

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This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.

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An improved cultural algorithm and its application in image matching

Cited by 24 publications

References 26 publications

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Performance assessment of meta-heuristics for composite layup optimisation

A Hybrid Rao-NM Algorithm for Image Template Matching

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