A Cuckoo Search-Support Vector Machine Model for Predicting Dynamic Measurement Errors of Sensors

Jiang, Minlan; Luo, Jingyuan; Jiang, Dingde; Xiong, Jiang; Song, Houbing; Shen, Jing

doi:10.1109/access.2016.2605041

Cited by 57 publications

(20 citation statements)

References 14 publications

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“…To carry out the different experiments, the PSO and CS algorithms were used. They were chosen mainly because they are simple to parameterize, both have successfully solved a large number of optimization problems [2,5,[80][81][82], and there are simplified convergence models for CS [83] and PSO [56].…”

Section: Resultsmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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Section: Resultsmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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“…Thus, many researches use the naturally inspired optimisation-based searching techniques such as GAbased optimisation [22], PSO [23], differential evolutionbased optimisation [24], artificial immune system-based optimisation [25] and ant-colony optimisation [26] in the feature-selection process. The cuckoo search also is a nature-inspired optimisation algorithm that is used in many applications to get optimal solutions [27][28][29], and this algorithm is used for feature selection. Kulshestha et al presented a cuckoo search-based feature selection [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

Singh¹,

Priyadharshini²,

Leavline³

2018

IJCNIS

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In the digital era, cloud computing plays a significant role in scalable resource sharing to carry out seamless computing and information sharing. Securing the data, resources, applications and infrastructure of the cloud is a challenging task among the researchers. To secure the cloud, cloud security controls are deployed in the cloud computing environment. The cloud security controls are roughly classified as deterrent controls, preventive controls, detective controls and corrective controls. Among these, detective controls are significantly contributing for cloud security by detecting the possible intrusions to prevent the cloud environment from the possible attacks. This detective control mechanism is established using intrusion detection system (IDS). The detecting accuracy of the IDS greatly depends on the network traffic data that is employed to develop the IDS using machine-learning algorithm. Hence, this paper proposed a cuckoo optimisation-based method to preprocess the network traffic data for improving the detection accuracy of the IDS for cloud security. The performance of the proposed algorithm is compared with the existing algorithms, and it is identified that the proposed algorithm performs better than the other algorithms compared.

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“…The two datasets both are one-dimensional data, in order to achieve the better predict results and get more information from the data, these two one-dimensional data must be converted to multidimensional data [16]. Assuming p is the dimension of the input vector, the reconstructed samples are listed in Table 1.…”

Section: Preprocessingmentioning

confidence: 99%

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

Jiang¹,

Lan²,

Jiang³

et al. 2017

Preprint

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Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

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A Cuckoo Search-Support Vector Machine Model for Predicting Dynamic Measurement Errors of Sensors

Cited by 57 publications

References 14 publications

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

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