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Dergiler / Gazi University Journal of Science / 2026 / Volume: 35 Issue: 1

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

Önder Eyecioglu(NİŞANTAŞI ÜNİVERSİTESİ)0000-0001-7770-2826
Yaşar Karabul(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0002-0789-556X
Mehmet Kılıç(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0003-1882-0405
Zeynep Güven Özdemir(Yildiz Technical University)0000-0001-5085-5814
Sayfa
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Yayın Tarihi
2022-03-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In the light of experimental data and the estimations made by the GPR algorithm, some specific KS additive percentage, working temperature, and frequency ranges have been suggested for the capacitor applications of PPy.

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Amaç & Hipotez:The present study deals with the application of the supervised machine learning regression algorithms known as Linear Regression (LR), Support Vector Machine (SVM), and Gaussian process regression (GPR) to the frequency and temperature-dependent dielectric parameters of polymer/inorganic film composites.
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Temel Bulgular & Veri:ML models were compared based on their model performance and the most suitable was chosen.
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Sonuç & Katkı:In the light of experimental data and the estimations made by the GPR algorithm, some specific KS additive percentage, working temperature, and frequency ranges have been suggested for the capacitor applications of PPy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Machine learning · Supervised regression algorithms · Gaussian process regression

Abstract

The present study deals with the application of the supervised machine learning regression algorithms known as Linear Regression (LR), Support Vector Machine (SVM), and Gaussian process regression (GPR) to the frequency and temperature-dependent dielectric parameters of polymer/inorganic film composites. The frequency and temperature-dependent experimental data set of the dielectric parameters (ε^' and ε^'') of Polypyrrole/Kufeki Stone (PPy/KS) has been utilized. ML models were compared based on their model performance and the most suitable was chosen. After choosing the most suitable ML model, at first, the predictions of the same dielectric parameters of the same samples for different temperatures have been made. Then, the predictions of temperature and frequency-dependent ε^' and ε^'' have been performed for the new PPy based composites consisting of different KS additives that were not produced experimentally. As a result of machine learning, the saturation for KS reinforcing material weight % for dielectric parameters has been determined for capacitor applications. In the light of experimental data and the estimations made by the GPR algorithm, some specific KS additive percentage, working temperature, and frequency ranges have been suggested for the capacitor applications of PPy.

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Bu Makaleye Atıf Yap

Eyecioglu, Ö., Karabul, Y., Kılıç, M., Özdemir, Z. G. (2022). Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations. Gazi University Journal of Science, Volume: 35 Issue: 1. https://doi.org/10.35378/gujs.810948

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