Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Hossain, S. M. Zakir; Sultana, Nahid; Mohammed, M. Ezzudin; Razzak, Shaikh A.; Hossain, Mohammad M.

doi:10.1016/j.jenvman.2021.113783

Cited by 18 publications

(5 citation statements)

References 51 publications

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“…In [42], the authors studied the space design method-based support vector regression modelling of MFC − A 2 /O equipment, where both the forward and inverse SVR models were investigated using a quadratic kernel function. The authors in [43] combined support vector regression and a crow search algorithm for modelling and optimization of a microbial fuel cell process based on microalgal wastewater treatment.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Optimal Data-Driven Modelling of a Microbial Fuel Cell

et al. 2023

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Microbial fuel cells (MFCs) are biocells that use microorganisms as biocatalysts to break down organic matter and convert chemical energy into electrical energy. Presently, the application of MFCs as alternative energy sources is limited by their low power attribute. Optimization of MFCs is very important to harness optimum energy. In this study, we develop optimal data-driven models for a typical MFC synthesized from polymethylmethacrylate and two graphite plates using machine learning algorithms including support vector regression (SVR), artificial neural networks (ANNs), Gaussian process regression (GPR), and ensemble learners. Power density and output voltage were modeled from two different datasets; the first dataset has current density and anolyte concentration as features, while the second dataset considers current density and chemical oxygen demand as features. Hyperparameter optimization was carried out on each of the considered machine learning-based models using Bayesian optimization, grid search, and random search to arrive at the best possible models for the MFC. A model was derived for power density and output voltage having 99% accuracy on testing set evaluations.

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Section: Support Vector Regressionmentioning

confidence: 99%

Optimal Data-Driven Modelling of a Microbial Fuel Cell

et al. 2023

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“…By adopting this method, it is possible to accurately distribute air pollution at different times in locations with no data. The radial basis functions (RBF), empirical Bayesian kriging (EBK), global polynomial interpolation (GPI), local polynomial interpolation (LPI), inverse distance weighting (IDW), kernel smoothing estimation (KSE), and diffusion kernel interpolation (DKI) are applied here according to validation, and the output is selected for each studied time period (Hossain et al 2022 ; Azizsafaei et al 2021 ).…”

Section: Phase Ii: Geostatistics and Spatial Analysis Of Aqi Datamentioning

confidence: 99%

Analysis of changes in air pollution quality and impact of COVID-19 on environmental health in Iran: application of interpolation models and spatial autocorrelation

Keshtkar

heidari

Moazzeni

et al. 2022

Environ Sci Pollut Res

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In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO 2 ), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

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“…Gupta et al [65] introduced a novel boosted CSA to predict Parkinson's disease with an accuracy of 100% and helped patients obtain proper treatment. Hossain et al [66] developed a hybrid support vector regression and CSA to handle the multi-objective optimization of microalgae-based wastewater treatment. Ke et al [67] proposed an enhanced CSA to deal with energy optimization problems, and the results showed that the approach can better obtain proper solutions with lower calculation times.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Cited by 18 publications

References 51 publications

Optimal Data-Driven Modelling of a Microbial Fuel Cell

Optimal Data-Driven Modelling of a Microbial Fuel Cell

Analysis of changes in air pollution quality and impact of COVID-19 on environmental health in Iran: application of interpolation models and spatial autocorrelation

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

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