Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations

Eyecioğlu, Önder; Karabul, Yaşar; Kılıç, Mehmet; Özdemir, Zeynep Güven

doi:10.35378/gujs.810948

Cited by 1 publication

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References 22 publications

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“…In References 16 and 17, the ML models are used to investigate dielectric properties of PANI composites. Supervised ML regression algorithm are applied to predict the dielectric properties of polymer composites for energy implementations by Reference 18. Also there are many studies in the literature that use the ML algorithms for the prediction of the dielectric properties of polymer composites and nanomaterials 19–21 …”

Section: Introductionmentioning

confidence: 99%

Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites

Eyecioğlu

2022

J of Applied Polymer Sci

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In this study, the detailed temperature and frequency-dependent (ac) conductivity analyzes of Polypyrrole/Edirne Kufeki Stone (PPy/EKS) composites have been realized by considering both experimental and Machine Learning (ML) algorithms predicted data. In this respect, the experimental ac conductivity data of pure PPy, PPy/5% EKS, PPy/10% EKS, and PPy/20% EKS composites between 1 Hz and 40 MHz at 296, 313, and 333 K temperatures have been usedfor the data set of ML. First, a benchmark study has been done for applied ML algorithms to obtain an eligible model. It is found that the Gaussian process regression (GPR) algorithm provided the best prediction performance. Since it has been observed a good conformity between the experimental and prediction data of GPR model, the ac conductivity (σ ac ) versus angular frequency (w) curves of the composites produced experimentally have been estimated for new temperature values, which were not treated experimentally. Then, the σ ac ¼ f w ð Þ curves of at temperature values have been estimated by GPR which is for the EKS composites at various contributions that have not been experimentally produced. Ultimately, the GPR algorithm developed in the present work enables us to determine the optimum EKS additive percentage, working temperature, and frequency band for the PPy polymer matrix.

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Section: Introductionmentioning

confidence: 99%

Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites

Eyecioğlu

2022

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations

Cited by 1 publication

References 22 publications

Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites

Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites

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