An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar, M.; Anitha, T N; Mallikarjunaswamy, S

doi:10.17485/ijst/v14i3.2318

Cited by 20 publications

(15 citation statements)

References 48 publications

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“…e simulated annealing algorithm starts from a certain higher temperature, and after a large number of solution transformations, it can obtain the relatively optimal solution of the combinatorial optimization problem with a given control parameter value, then reduce the value of the control parameter, and repeatedly execute the Metropolis algorithm. When the control parameter t tends to 0, the overall optimal solution of the combinatorial optimization problem can be finally obtained [8].…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

2021

Security and Communication Networks

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The human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm. Applying the simulated annealing algorithm, using the Metropolis algorithm to generate the sequence of solutions to the combinatorial optimization problem, finding the overall optimal solution of the combinatorial optimization problem, using big data directional mining and analysis to help companies establish and find a “radar” system suitable for talents, the maximum tree method is adopted; that is, a special graph is constructed to realize the effective application of data mining technology in enterprise human resource management. The optimization of nurse scheduling in a hospital was used for case analysis. The results show that the target value of the nurse scheduling model is 43.43% lower than the actual manual scheduling target value, the salary cost is reduced by 10.8%, and the nurse’s satisfaction with the shift is increased by 35.24%. After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. Then, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

2021

Security and Communication Networks

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“…At present, the multilayer feedforward network is based on back propagation (BP), but most of the learning algorithms of this network must be based on some nonlinear optimization technology. erefore, its application is limited due to the large amount of computation and slow learning speed [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Yang

2021

International Journal of Antennas and Propagation

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In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks. Based on RBF, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund. RBF neural network is used to build the risk warning model of social security fund operation. Then, put forward the corresponding risk treatment scheme to the warning signal. Finally, the RBF neural network is used for comprehensive risk warning. In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal. This paper uses an improved ant colony algorithm to optimize the parameters of the RBF neural network, which overcomes the shortcomings of the traditional RBF neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the RBF neural network. It has the characteristics of good output stability and fast convergence speed. On this basis, the prediction model based on the improved ANT colony-RBF neural network is established, and the MATLAB software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers. The results show that the network has small calculation error, fast convergence, and good generalization ability.

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“…In which A (ω), B(ω) , H f (ω) represents the frequency domain equivalent of the restored image, captured blurred image and the blur function (PSF) respectively (28)(29)(30)(31)(32) . As the L2 regularization will not conduct any denoising, in order to eliminate noise more frequencies in a blurred picture can be penalized by using Sobolev prior.…”

Section: Tikhonov and Sobolev Priorsmentioning

confidence: 99%

Adaptive Sparsity through Hybrid Regularization for Effective Image Deblurring

S¹,

Mallikarjunaswamy²,

Sharmila³

2021

IJST

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Objectives: To develop image deblurring algorithm to effectively recover the original image from the captured blurry and noisy image. Methods: A novel image deblurring algorithm using adaptive priors is proposed. The adaptive priors vary the sparsity induced based on factors such as the type of blur affecting the image, image region, statistical parameters regarding the blur kernel and/or statistical predictions on the blur kernel. The proposed algorithm using adaptive priors improves the quality of the deblurred result, when compared with various recent image deblurring algorithms. Finding: the work contains a case study with regard to certain standard parameters. It is observed that proposed method is better in terms of frequency response, Peak signal-tonoise ratio (PSNR) and Structural Similarity Index (SSIM) values in comparison with other priors. Novelty: the proposed priors lead to the most effective results for image deblurring using the Bayesian framework. The proposed method improves the performance by 30% in PSNR and 45% in SSIM values in dB with uniform kernel size 12 * 12 and improves performance by 30 % in PSNR and 32% SSIM with a standard deviation of 3.5. The proposed method enhances the frequency response of the real-time image restoration process. Applications: Some important applications include the restoration of medical images such as MRI images, CT images where the intensity of these radiations is maintained to avoid damage to human organs. Remote sensing images captured through drones at a specified time cannot be retaken and hence restoring such images is very important. Similarly restoring images from CCTV footage, astronomical images.

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An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Cited by 20 publications

References 48 publications

Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

Adaptive Sparsity through Hybrid Regularization for Effective Image Deblurring

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