Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm

This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.

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“…27 This algorithm was coupled with SVM method to tune its hyper-parameters. 28 3 Results and discussion…”

Section: Methodsmentioning

confidence: 99%

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

et al. 2021

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“…As reference measurement device we used a Fidas® 200 with an IADS dryer (Palas GmbH) without the weather station. The Fidas® 200 devices are used in various publications as a certified measuring device for fine dusts 19–21.…”

Section: Methodsmentioning

confidence: 99%

Detection and Differentiation of Liquid and Solid Particles in Ambient Air

Westphal

Teumer

Schäfer

et al. 2021

Chemie Ingenieur Technik

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Aerosols contain liquid and/or solid particles, which can pose a significant risk to humans. In order to detect these potential hazards at an early stage, this paper shows the possibility of measuring aerosols with an adapted particle sensor system (ProxiCube®) and displaying the liquid and solid components separately. To demonstrate the reproducibility of the measurements, samples are monitored simultaneously with two ProxiCubes and a well‐established reference system.

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“…DA has been used for the training of a multi-layer perceptron (MLP) in [57,58], which are then used for analysing the bearing capacity of a two-layered soil and for the classification of sonar target with high accuracy, respectively. It has also been applied for optimization of the parameters of support vector machines (SVM) in [59][60][61][62], which are then used for classification, regression, prediction and prediction, respectively. Moreover, DA has been used for the training of artificial neural networks (ANN).…”

Section: Machine Learningmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm

Cited by 29 publications

References 59 publications

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

Detection and Differentiation of Liquid and Solid Particles in Ambient Air

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

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