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Article Citation - WoS: 4Citation - Scopus: 5Neural Network Based Resonant Frequency Solver for Rectangular-Shaped Shorting Pin-Loaded Antennas(Wiley, 2013) Can, Sultan; Kapusuz, Kamil Yavuz; Aydin, ElifThis study presents an artificial neural network (ANN) estimation of the operating frequencies of shorting pin-loaded rectangular microstrip patch antennas. A feed forward back propagation multilayer perceptron neural network structure is applied in the study. The results are compared with the ones in the literature and the FEM based simulation results. The results of the operating frequencies obtained by using this method are in very good agreement with the experimental results presented in the literature. Several antennas are also simulated by a finite element method based solver and these results are also compared with the results of the proposed neural network model. The average error of the lower frequency obtained by this study has a decrement of 2.025% when compared to the FEM based simulation software and for the upper frequency this difference is 6.835%. The effects of permittivity of the antenna, size of the dimensions of the rectangular patch, and the shorting pin position are also evaluated. In the light of the ANN model and the relations obtained two antennas in the same shape are produced and the results of these antennas are presented as well. (c) 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:3025-3028, 2013Article Citation - WoS: 9Citation - Scopus: 11Using Deep Learning Approaches for Coloring Silicone Maxillofacial Prostheses: a Comparison of Two Approaches(Wolters Kluwer Medknow Publications, 2023) Kurt, Meral; Kurt, Zuhal; Isik, SahinAim: This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. Settings and Design: This was an in vitro study. Materials and Methods: A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the LFNx01, aFNx01, and bFNx01 values were recorded. The relationship between the LFNx01, aFNx01, and bFNx01 values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. LFNx01, aFNx01, and bFNx01 values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Statistical Analysis Used: Data were analyzed with the Student t-test (alpha=0.05). Results: The mean RMSE values and MAE values for the ANN algorithm (0.029 & PLUSMN; 0.0152 and 0.045 & PLUSMN; 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 & PLUSMN; 0.0005 and 0.002 & PLUSMN; 0.0008, respectively) (P < 0.001). Conclusions: Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.
