Nas, Abdurrahim

Profile URL
https://hdl.handle.net/20.500.14627/506
Name Variants
Nas, Abdurrahim
Job Title
Araştırma Görevlisi
Email Address
abdurrahim.nas@fbu.edu.tr
Main Affiliation
Elektrik-elektronik Mühendisliği Bölümü
Status
Website
ORCID ID
0009-0008-8804-2075
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Files
photo_410975.jpg.JPG (2.18 MB)

Sustainable Development Goals

SDG data could not be loaded because of an error. Please refresh the page or try again later.
No records found in other affiliations.
Bibliometrics data could not be loaded because of an error. Please refresh the page or try again later.

Filters

20252

Settings

Author of Publication: search.filters.isAuthorOfPublication.0f3be39e-1128-4047-95bc-8e62707b295c

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Conference Object
    Reflection Coefficient Prediction in Triple-Layer Microwave Absorbers: A Machine Learning Perspective
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nas, Abdurrahim; Kankilic, Sueda; Karpat, Esin
    Electromagnetic absorbers prevent the reflection and transmission of electromagnetic waves. Electromagnetic absorbers have a wide range of applications from military to medical applications. In these areas, absorber designs have different importance in terms of parameters such as reflection coefficient, selected material and thickness. Many difficulties are encountered to achieve the optimal design. In this paper, we propose a machine learning regression method for three-layer microwave absorber architecture to obtain the optimum parameters, overcome the difficulties and speed up the process. The material and thickness of each layer are used as parameters to feed the models and the reflection coefficient is estimated using these parameters. Predictions are made with various regression algorithms. These algorithms are KNeighbors Regression, Random Forest Regressor, XGBoost Regression, CatBoost Regressor, AdaBoost Regressor which uses similarities between observations, Gradient Boosting Regressor which is tree based or boosted tree based algorithms, Linear Regression which uses a linear model, Partial Least Squares Regression which uses cross decomposition, Gaussian Process Regressor which uses statistical distribution, Stochastic Gradient Descent Regressor which uses a linear model to reduce empirical loss to predict an output. Mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), R-squared (R2) are used with the predictions of each model to obtain the metrics for the analysis of the results. The predicted values and actual values of the metrics are used to compare the regression algorithms used in the research. After the comparison, our observations show that in most cases CatBoost Regressor is better than other models used in the research. In general, it is observed that most of the machine learning regression algorithms used in this paper can be used to predict the reflection coefficient of three-layer microwave absorbers as output and input parameters used in the research. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Low Error-Rate Temperature Prediction with Machine Learning Using LoRaWAN Data
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nas, Abdurrahim; Yildiz, Omer; Karlik, Sait Eser