A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Zheng, Weimin; Pang, Shengyong; Liu, Ning; Chai, Qing-Wei; Li, Xu

doi:10.3390/s23146282

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…This algorithm is inspired by the mating behavior of snakes in nature. Compared with other algorithms, SO has higher precision and faster iteration speed [50]. The algorithm includes the following four stages: initialization stage, selection stage, exploration stage and development stage (see Figure 4).…”

Section: Somentioning

confidence: 99%

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

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Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

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Section: Somentioning

confidence: 99%

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

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“…Zheng applies the compact strategy to the snake optimization algorithm (SO). The compact snake optimization algorithm (cSO) is proposed, which effectively reduces the use of memory resources [46]. Wang proposes the adaptive Bat algorithm (ABA), which can dynamically and adaptively adjust the flight speed and direction, significantly improving the global convergence accuracy of the BA [47].…”

Section: Equilibrium Optimizer Algorithmmentioning

confidence: 99%

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Zheng,

Meng,

Liu

et al. 2023

Sensors

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Wireless sensors networks (WSNs) play an important role in life. With the development of 5G, its security issues have also raised concerns. Therefore, it is an important topic to study the offense and defense confrontation in WSNs. A complete information static game model is established to analyze the offense and defense confrontation problem of WSNs in 5G. An adaptive equilibrium optimizer algorithm (AEO) based on parameter adaptive strategy is proposed, which can jump out of the local optimal solution better. Experiments show that the optimization ability of AEO outperforms other algorithms on at least 80% of the 23 classical test functions of CEC. The convergence speed of AEO is better in the early stage of population iteration. The optimal offensive and defensive strategy under different offense and defense resources through simulation experiments is analyzed. The conclusion shows that when the offensive resources are large, the offender takes an indiscriminate attack. When the defense resources are small, the defender should defend the most important elements, and when the defense resources are large, the defender should allocate the same resources to defend each element to obtain the maximum benefit. This paper provides new solution ideas for the security problems under the offense and defense game in WSNs.

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“…Abirami et al 10 have described crypto-deep neural network cloud security (CDNNCS)as superior to an encrypted proportional mathematical solution technique for increasing the degree of credibility amongst cloud consumers. The suggested paradigm improves connectivity while presenting node-level information.…”

Section: Literature Surveymentioning

confidence: 99%

“…The white shark movement phase of WSO algorithm performance is boosted up by maintaining the minimal memory usage of compact strategy 10 and the new model is an adaptive WSO algorithm. The winner and loser-based perturbation iterative is updated as, From this, , and are the random numbers with the target distance.…”

Section: Selecting Featuresmentioning

confidence: 99%

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

Ramu,

Ramamoorthy,

Ranganathan

2023

Yanbu Journal of Engineering and Science

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The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.

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A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Cited by 10 publications

References 39 publications

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

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