Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Zheng, Zhigao; Saxena, Nitin Kumar; Mishra, K. K.; Sangaiah, Arun Kumar

doi:10.1016/j.future.2018.05.027

Cited by 42 publications

(23 citation statements)

References 21 publications

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“… Pay per use: Users can access the cloud services only when they use it and cloud just charge them for that specific service [6,7].…”

Section: Characteristics Of Cloud Computingmentioning

confidence: 99%

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

2019

International Journal of Research Studies in Computer Science a

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Cloud computing has found its widespread use because of the several provisions it has enabled and made easier in technology sector. The research paper is intended to analyze and implement the three prominent task scheduling algorithms (FCFS, SJF, RR) which plays critical role in cloud computing. The more efficiently the processes are organized for execution on remote servers, the more optimal gets the performance of cloud setup. The paper elaborated the performance of three task scheduling algorithms under different scenarios and an effective conclusion has been reached.

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“… Pay per use: Users can access the cloud services only when they use it and cloud just charge them for that specific service [6,7].…”

Section: Characteristics Of Cloud Computingmentioning

confidence: 99%

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

2019

International Journal of Research Studies in Computer Science a

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“…10 Common data standardization methods include simple scaling, unit length scaling, sample-by-sample mean subtraction, and feature standardization. 1 Simple scaling Simple scaling has important implications for data processing. Many parameters (such as the parameter £ in whitening) are valued based on the premise that the data are scaled into a reasonable range.…”

Section: Multi-feature Ct Image Preprocessingmentioning

confidence: 99%

“…Suppose Θ represents a recognition framework, the probability function derived from the basic probability assignment function on Θ is defined as Bel: 2 Θ → [0,1] inEquation 17, where Bel(A) represents the degree of overall probability given to proposition A, that is, the sum of basic probability values corresponding to all subsets in A Bel(Θ) = 0, Bel(Θ) = 1Bel (A) = ∑ B⊆A m (B) , ∀A ⊆ Θ. (17)Definition 3.…”

mentioning

confidence: 99%

Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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Recognition method of CT images based on color, morphology, and texture is inaccurate and unreliable recently. Therefore, a medical image recognition method based on data mining is proposed with a multi-feature fusion in this paper. First, image acquisition method is determined by analyzing the environment of CT image recognition. Then, acquired CT image is standardized and whitened to reduce redundant information. Moreover, based on feature of the color, texture, and height in preprocessed CT images, a deep neural network is trained by using a large amount of image data in normal scene. The deep learning classifier is fine-tuned by using the marked multi-feature CT image data. Finally, output recognition result is obtained according to the classification decision threshold. Experimental results show that the correct recognition rate of the proposed method can reach more than 98%. The accuracy rate is higher and the stability of proposed method is better by comparing with traditional CT image recognition methods. KEYWORDSCT images, data mining, deep learning, image recognition, multiple feature INTRODUCTIONIn recent years, with the rapid development of medical imaging technology, it improved to big medical image analysis. Today, many big database are constructed. How we can extract useful information from massive medical image data in the database has brought great challenges to medical image recognition since traditional database technology cannot provide enough service for the large database. Data mining is used to obtain effective information in medical image database. CT image recognition is a multidisciplinary cross-domain of comprehensive medical imaging, mathematical modeling, and computer technology. 1,2 Today, there are two new issues that are brought because of large scale of multi-feature CT image. One issue is CT image data that has a higher dimension and requires a model with stronger learning adaptability. The other is big CT image data that is more fragmented with more complex data structure, which requires integration of huge different information. 3 Such requirements cannot be solved by traditional data analysis methods. Therefore, how we extract useful information from massive CT image data has become a research hotspot. With the rapid development and popularization of CT imaging equipment, CT image data have increased explosively. How we can efficiently and accurately analyze CT image has become a major challenge. 4 Furthermore, computer-assisted CT medical image analysis improves the efficiency and accuracy of the analysis and diagnosis of CT image. Therefore, computer-aided detection and diagnosis are becoming a cross-sectional research area that receives more and more attention.A multi-feature CT medical image recognition method was proposed based on polarization spectrum. 5 The method used indoor imaging spectrometer FISS-P equipped with polarizers to collect the polarization spectrum images, including general images and five kinds of CT medical images indoors. They recognized CT images by ...

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“…Kennedy and Eberhart [5] proposed a particle swarm optimization (PSO) algorithm derived from the simulation of bird foraging behaviors. However, the PSO often faces premature convergence problem, especially in multimodal problems as it may get stuck in specific point [6]. Wu and Yang [7] presented an elitist transposon quantum-based PSO to solve economic dispatch problems.…”

Section: Introductionmentioning

confidence: 99%

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Cited by 42 publications

References 21 publications

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

Implementing and Evaluating Impact of Task Scheduling in Cloud Environment

Multi‐feature data mining for CT image recognition

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

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