Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach

Oyehan, Tajudeen A.; Alade, Ibrahim Olanrewaju; Bagudu, Aliyu; Sulaiman, Kazeem O.; Olatunji, Sunday O.; Saleh, Tawfik A.

doi:10.1016/j.compbiomed.2018.04.024

Cited by 38 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To keep this work concise, we have not detailed the underlying mathematical formulation for SVR. This has been presented in the literature which interested readers can refer to [4, 23, 29, 30, 31].…”

Section: Methodsmentioning

confidence: 99%

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

Alade

Rahman

Bagudu³

et al. 2019

Heliyon

Self Cite

View full text Add to dashboard Cite

The specific heat capacity of nanofluids is a fundamental thermophysical property that measures the heat storage capacity of the nanofluids. is usually determined through experimental measurement. As it is known, experimental procedures are characterised with some complexities, which include, the challenge of preparing stable nanofluids and relatively long periods to conduct experiments. So far, two correlations have been developed to estimate the The accuracies of these models are still subject to further improvement for many nanofluid compositions. This study presents a four-input support vector regression (SVR) model hybridized with a Bayesian algorithm to predict the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. The bayesian algorithm was used to obtain the optimum SVR hyperparameters. 189 experimental data collected from published literature was used for the model development. The proposed model exhibits low average absolute relative deviation (AARD) and a high correlation coefficient (r) of 0.40 and 99.53 %, respectively. In addition, we analysed the accuracies of the existing analytical models on the considered nanofluid compositions. The model based on the thermal equilibrium between the nanoparticles and base fluid (model II) show good agreement with experimental results while the model based on simple mixing rule (model I) overestimated the specific heat capacity of the nanofluids. To further validate the superiority of the proposed technique over the existing analytical models, we compared various statistical errors for the three models. The AARD for the BSVR, model II, and model I are 0.40, 0.82 and 4.97, respectively. This clearly shows that the model developed has much better prediction accuracy than existing models in predicting the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids. We believe the presented model will be important in the design of nanofluid-based applications due to its improved accuracy.

show abstract

Section: Methodsmentioning

confidence: 99%

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

Alade

Rahman

Bagudu³

et al. 2019

Heliyon

Self Cite

View full text Add to dashboard Cite

show abstract

“…SVR is an extension of the work of Vapnik and co-workers on support vector machine (SVM) [26], which is a tool that is obtained from statistical learning theory for carrying out classification tasks. Several implementations of SVM have been achieved in different areas of research shortly after its proposal [27,28]. SVM is therefore a universal term that can be grouped into classification and regression algorithms based on the nature of the problem under consideration [29].…”

Section: Support Vector Regression Based Algorithmmentioning

confidence: 99%

Energy Band Gap Modeling of Doped Bismuth Ferrite Multifunctional Material Using Gravitational Search Algorithm Optimized Support Vector Regression

Owolabi

Rahman

2021

Crystals

View full text Add to dashboard Cite

Bismuth ferrite (BiFeO3) is a promising multiferroic and multifunctional inorganic chemical compound with many fascinating application potentials in sensors, photo-catalysis, optical devices, spintronics, and information storage, among others. This class of material has special advantages in the photocatalytic field due to its narrow energy band gap as well as the possibility of the internal polarization suppression of the electron-hole recombination rate. However, the narrow light absorption range, which results in a low degradation efficiency, limits the practical application of the compound. Experimental chemical doping through which the energy band gap of bismuth ferrite compound is tailored to the desired value suitable for a particular application is frequently accompanied by the lattice distortion of the rhombohedral crystal structure. The energy band gap of doped bismuth ferrite is modeled in this contribution through the fusion of a support vector regression (SVR) algorithm with a gravitational search algorithm (GSA) using crystal lattice distortion as a predictor. The proposed hybrid gravitational search based support vector regression HGS-SVR model was evaluated by its mean squared error (MSE), correlation coefficient (CC), and root mean square error (RMSE). The proposed HGS-SVR has an estimation capacity with an up to 98.06% accuracy, as obtained from the correlation coefficient on the testing dataset. The proposed hybrid model has a low MSE and RMSE of 0.0092 ev and 0.0958 ev, respectively. The hybridized algorithm further models the impact of several doping materials on the energy band gap of bismuth ferrite, and the predicted energy gaps are in excellent agreement with the measured values. The precision and robustness exhibited by the developed model substantiate its significance in predicting the energy band gap of doped bismuth ferrite at a relatively low cost while the experimental stress is circumvented.

show abstract

“…The conceptual framework of Support Vector Machine (SVM) was founded by Vapnik [36]. Since its introduction, there have been increasing interests in the use of SVM across diverse fields of studies [37][38][39][40][41][42]. Originally, SVM was developed for classification tasks but was later extended to regression problems [43].…”

Section: Brief Descriptions Of Svm Theorymentioning

confidence: 99%

“…Hence, the wide dispersions are expected. Nevertheless, normally distributed datasets have better prediction ability than otherwise [40]. We, therefore, normalized the datasets to manage the wide dispersion using the equation;…”

Section: Sources and Description Of The Datamentioning

confidence: 99%

Modeling the efficiency of TiO2 photocatalytic degradation of MTBE in contaminated water: a support vector regression approach

2019

View full text Add to dashboard Cite

Methyl tert-Butyl Ether (MTBE) is one of the common environmental contaminants that pose severe risks to human health and the environment. Due to the detrimental effects of MTBE, many efforts have been devoted towards decontamination of wastewater containing MTBE. Most of the reports on the treatment of MTBE are experimental based, which requires studying the effect of several factors such as dosage of the photocatalyst, pH of the medium, the initial concentration of contaminant and the contact time on degradation efficiency in MTBE contaminated waters. Although, this approach is highly dependable and often leads to new insights. However, because the degradation efficiency is influenced by multiple factors, performing experiments to investigate the effect of these factors often increases the experimental burden, thus requiring more time and materials consumption to achieve desirable results. Herein, we propose a computational intelligent strategy to mitigate these challenges. In this contribution, the degradation efficiencies of MTBE in the presence of TiO 2 /UV were modeled under various experimental conditions using the support vector regression model. The model was built using experimental data comprising of inputs such as TiO 2 dose, initial MTBE concentration, UV wavelength and contact time. Remarkably, the developed model exhibits significant accuracy as determined from the values of correlation coefficient (98.27%) and root means square error (5.53). In addition, it was determined that the achievable optimum conditions for degradation of 0.5-100 ppm MTBE-contaminated water were 2.4 g/L of TiO 2 dose with UV radiation, a solution pH of 3 and treatment time of 2 h. This study will be useful in the experimental design of treatment for MTBE, consequently reducing the time spent on running experiments and at the same time ensuring efficient use of resources for treatment of MTBE contaminated water.

show abstract

Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach

Cited by 38 publications

References 30 publications

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

Development of a predictive model for estimating the specific heat capacity of metallic oxides/ethylene glycol-based nanofluids using support vector regression

Energy Band Gap Modeling of Doped Bismuth Ferrite Multifunctional Material Using Gravitational Search Algorithm Optimized Support Vector Regression

Modeling the efficiency of TiO2 photocatalytic degradation of MTBE in contaminated water: a support vector regression approach

Contact Info

Product

Resources

About