Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm

Alade, Ibrahim Olanrewaju; Rahman, Mohd Amiruddin Abd; Saleh, Tawfik A.

doi:10.1016/j.solener.2019.02.060

Cited by 126 publications

(37 citation statements)

References 47 publications

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“…Most recently, studies have focused on the use of artificial intelligence to predict this fluid property. Alade et al [82] developed a support vector regression (SVR) model optimized with (21)…”

Section: Nanofluid Specific Heat Capacity (C P )mentioning

confidence: 99%

Nanofluids in Solar Thermal Collectors: Review and Limitations

et al. 2020

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Solar thermal collectors are systems that allow for the use of solar energy in thermal applications. These collectors utilize a heat transfer fluid to transport absorbed solar radiation to applications where they are needed. Scientists in a bid to improve the conversion efficiency of solar collectors have suggested different collector designs and improved collector materials. Over the last 25 years, the study of nanofluids and their applications have revolutionized material science, and nanotechnology has found applications in improving solar collector materials. This article reviews the impact of different nanomaterials on the efficiency of solar collectors. The study also outlines the limitations of applying nanofluids and discusses the long-term challenges of their application to solar collectors. Nanofluids have the potential to improve the overall efficiency of most solar collectors, however, the full potential of nanofluids in heat transfer applications cannot be completely achieved until some of the questions regarding hysteresis, stability, and the overall predictability of nanofluids are answered.

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Section: Nanofluid Specific Heat Capacity (C P )mentioning

confidence: 99%

Nanofluids in Solar Thermal Collectors: Review and Limitations

et al. 2020

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“…The objective function converged at iterations of around 50 with a value of 0.0454 for the BO technique, indicating an excellent efficiency in searching for the optimal hyperparameter for the SVR model. This is because the BO technique makes full use of information from previous iterations to find the next possible combination of hyperparameters [41,42]. The results of the objective function and hypermeters after optimization (training process) are also listed in Table 5.…”

Section: Prediction Results Of Mass Changementioning

confidence: 99%

“…Bayesian optimization makes full use of all the information from previous iterations to find the next data point. Herein, the BO is able to find the global optimal solution with relatively fewer iterations [41,42].…”

Section: Implementation Of Optimization In Svr Modelsmentioning

confidence: 99%

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Hu²,

Liu

2020

SN Appl. Sci.

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“…Each model in ML techniques includes different hyperparameters that need to be set to obtain an excellent result. For example, parameters such as box constraint (C), kernel parameter (γ) and epsilon (ε) play an important role in the success of the SVM algorithm [28]. In another example, the proper selection of number of terminal nodes in the ensemble trees and the regularization parameter is important for stochastic gradient boosting algorithm performance.…”

Section: Hyperparameter Optimization With Bayesian Optimizationmentioning

confidence: 99%

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

Ceylan¹

2020

ijisae

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Breast cancer is one of the most common types of cancer and is the second main cause of cancer death in females. Early detection of breast cancer is crucial for the survival of a patient as well as for the quality of life throughout cancer treatment. The aim of this study is to develop improved machine learning models for early diagnosis of breast cancer with high accuracy. In this context, a performance comparison of machine learning algorithms including Support Vector Machines, Decision Trees, Naive Bayes, K-Nearest Neighbor, and Ensemble Classifiers was performed on a dataset consisting of routine blood analysis combined with anthropometric measurements to diagnose breast cancer. Neighborhood component analysis was applied as a feature selection method to reveal relevant biomarkers that can be used in breast cancer prediction. In order to assess the performance of each proposed classifier model, two different data division procedures such as hold-out and 10-fold cross-validation were employed. Bayesian Optimization algorithm was applied to all classifiers for the maximizing the prediction accuracy. Different performance criteria such as accuracy, precision, sensitivity, specificity, and Fmeasure were used to measure the success of each classifier. Experimental results showed that the Bayesian optimization-based K-Nearest Neighbor performs better than other machine learning algorithms under the hold-out data division protocol with an accuracy of 95.833%. The results obtained in this study may provide a new perspective on the application of improved machine learning techniques for the early detection of breast cancer.

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Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm

Cited by 126 publications

References 47 publications

Nanofluids in Solar Thermal Collectors: Review and Limitations

Nanofluids in Solar Thermal Collectors: Review and Limitations

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

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