Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour, Narjes; Mosavi, Amir; Baghban, Alireza Akbarzadeh; Shamshirband, Shahaboddin; Felde, Imre

doi:10.20944/preprints202001.0010.v1

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…William's plot was utilized for determining the outliers of the model [ 45 , 46 ]. Figure 4 represents the standardized residuals versus hat values.…”

Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.

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“…William's plot was utilized for determining the outliers of the model [ 45 , 46 ]. Figure 4 represents the standardized residuals versus hat values.…”

Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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“…Then, these input combinations were used in data mining methods to estimate evaporation at three stations of Gonbad-e Kavus, Gorgan and Bandar Torkaman. There is no straightforward guideline for splitting the training and testing data in machine learning modeling [38][39][40][41][42][43][44][45][46]. For instance, the study of Choubin [47] used a total of 63% of their data for model development, whereas Qasem et al, [48] utilized 67% of data, Asadi et al, [41], Samadianfard et al, [49,50], and Dodangeh et al, [51] used 70%, and Zounemat-Kermani et al, [52] implemented 80% of total data to develop their models.…”

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

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Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

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“…e orthogonal projection method is possible in the following two ways: for times when HH T is nonsingular and H † � H T (HH T ) −1 or for times H T H and H † � H T (H T H) −1 is nonsingular. is solution works more accurately and its generalized performance is better as well [45].…”

Section: Extended Learning Machinementioning

confidence: 98%

Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel

Wang¹,

Heydari

2021

International Journal of Chemical Engineering

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Nowadays, the high consumption of fossil fuels has caused many pollutants and environmental problems. Biodiesel has recently been considered as a clean and renewable alternative to fossil fuels. They are found in some molecular structures including fatty acid ethyl esters (FAEEs) and also fatty methyl esters (FAMEs), having various thermophysical characteristics. Thus, it appears essential to select the suitable methods for a particular diesel engine to estimate the ester characteristics. The current research sets out to develop a new and robust method predicting isothermal compressibility of long-chain fatty acid methyl and ethyl esters directly from several basic efficient parameters (pressure, temperature, normal melting point, and molecular weight). Therefore, as a novel and prevailing mathematical method in this field, an extreme learning machine was implemented for isothermal compressibility on the massive dataset. According to statistical evaluations, this novel established model had high accuracy and applicability (R2 = 1 and RMSE = 0.0018714) which is more accurate than previous models presented by former researchers. Among various factors of the sensitivity analysis, temperature and pressure had the greatest effect on the output values, so that the output parameter has a direct relationship with temperature and an inverse relationship with pressure with relevancy factors of 22.44% and −79.81%.

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Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Cited by 11 publications

References 40 publications

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel

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