RETRACTED ARTICLE: Optimized virtual network function provisioning technique for mobile edge cloud computing

Ponmagal, R.S.; Karthick, S.; Dhiyanesh, B.; Balakrishnan, S.; Venkatachalam, K.

doi:10.1007/s12652-020-02122-8

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…This wastage contains soluble and insoluble content and chemicals. In this proposed work, we implement the hybrid of Voting-Based Extreme Learning Machine with sine-cosine algorithm (VELM-SCA) to improve the efficiency in the prediction of wastewater treatment plants [20][21][22][23][24][25][26][27][28][29]. The architecture of the proposed work (VELM-SCA) is given in Figure 1.…”

Section: Proposed Methodologymentioning

confidence: 99%

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Alsolai

Asiri

Alabdan

et al. 2022

Adsorption Science & Technology

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Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97.

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Section: Proposed Methodologymentioning

confidence: 99%

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Alsolai

Asiri

Alabdan

et al. 2022

Adsorption Science & Technology

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“…Fragrance of butterflies attracts the other butterflies while searching for food and mating [17,18]. Search operators of BOA are butterflies that optimize solutions within the search spaces in an organized manner [19][20][21][22][23][24][25]. Butterflies are equipped with sense receptors which identify the fragrance for their food discoveries.…”

Section: Asmbo Algorithmmentioning

confidence: 99%

Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

Karthikeyan¹,

Raj²,

Senthilkumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The number of attacks is growing tremendously in tandem with the growth of internet technologies. As a result, protecting the private data from prying eyes has become a critical and tough undertaking. Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks. For attack detection, the prior system has created an SMSRPF (Stacking Model Significant Rule Power Factor) classifier. To provide creative instance detection, the SMSRPF combines the detection of trained classifiers such as DT (Decision Tree) and RF (Random Forest). Nevertheless, it does not generate any accurate findings that are adequate. The suggested system has built an EWF (Ensemble Wrapper Filter) feature selection with SMSRPF classifier for attack detection so as to overcome this problem. The UNSW-NB15 dataset is used as an input in this proposed research project. Specifically, min-max normalization approach is used to pre-process the incoming data. The feature selection is then carried out using EWF. Based on the selected features, SMSRPF classifiers are utilized to detect the attacks. The SMSRPF is integrated with the trained classifiers such as DT and RF to create creative instance detection. After that, the testing data is classified using MCAR (Multi-Class Classification based on Association Rules). The SRPF judges the rules correctly even when the confidence and the lift measures fail. Regarding accuracy, precision, recall, f-measure, computation time, and error, the experimental findings suggest that the new system outperforms the prior systems.

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“…In the process of human body intelligent monitoring, it is necessary to use image monitoring technology to transmit human body intelligent monitoring signal. As the scene change has obvious interference effect on the monitoring effect, this effect will cause the attenuation of image visual signal, thus reducing the accuracy of monitoring [31,32]. According to Equation (3), the human body intelligent monitoring and tracking coefficient obtained under the influence of rapid regional change, such as rapid running, can be calculated.…”

Section: Information Security Communication Protocolmentioning

confidence: 99%

Intelligent Monitoring System for Prison Perimeter Based on Cloud Intelligence Technology

Pan

2022

Wireless Communications and Mobile Computing

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In order to realize the security of prison perimeter, a prison perimeter intelligent monitoring system based on cloud intelligence technology is designed. Based on the multisupply chain collaboration framework, the prison network interface submodule and sniffer monitoring structure are connected on demand to complete the hardware execution environment of the prison perimeter intelligent monitoring system. Analyze the specific service mode of the cloud smart technology host, set the necessary cloud smart technology service monitoring protocol, judge the processing information parameters, select the prison perimeter remote communication port number, set the prison perimeter network IP address and password program, complete the construction of the software execution environment of the monitoring system, and intelligently monitor and track the abnormal conditions around the prison; combined with the relevant hardware and software structure, the design of prison perimeter intelligent monitoring system based on cloud intelligence technology is realized. The experimental results show that the DPI index of the designed system has an obvious upward trend, and the processing results are close to the original image results to avoid the impact caused by the change of monitoring scene.

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RETRACTED ARTICLE: Optimized virtual network function provisioning technique for mobile edge cloud computing

Cited by 14 publications

References 20 publications

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

Intelligent Monitoring System for Prison Perimeter Based on Cloud Intelligence Technology

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