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7 results on '"Cizmeci, Huseyin"'

3. Prediction of Gender and Age Period from Periorbital Region with VGG16.

Author

Akmese, Omer Faruk, Cizmeci, Huseyin, Ozdem, Selim, Ozdemir, Fikri, Deniz, Emre, Mazman, Rabia, Erdogan, Murat, and Erdogan, Esma

Subjects
GENDER, AGE groups, DEEP learning, AGE
Abstract

Using deep learning methods, age and gender estimation from people's facial area has become popular. Recently, with the increase in the use of masks due to Covid-19, only the eye area of people is seen. The periorbital region can give an idea about the person's characteristics, such as age and gender. This study it is aimed to predict gender and age from images obtained by cutting the eye area from facial photographs of people using Visual Geometry Group-16 (VGG16). With the transfer learning method for age group (male, female) and gender group (child, youth, adults, and old) classification, 5714 images in the data set were used for the age group, and 3280 images were used for the gender group. As a result of this study, 99.41% success in age estimation and 95.73% in gender estimation was achieved. [ABSTRACT FROM AUTHOR]

Published
2023
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6. AN ESTIMATION OF IN-CYLINDER PRESSURE BASED ON LAMBDA AND ENGINE SPEED IN HCCI ENGINE USING ARTIFICIAL NEURAL NETWORKS

Author

Polat, Seyfi, Ozdilli, Ozgur, Cizmeci, Huseyin, and [Belirlenecek]

Subjects
Artificial neural network, engine, HCCI, ANN, lambda, in-cylinder pressure
Abstract

In this study, the in-cylinder pressure predicted based on lambda and engine speed with the ANN method for HCCI engine. In-cylinder pressures obtained at different lambda and engine speeds, constant inlet air temperature (80 degrees C), RON40 (40% iso-octane/60% n-heptane) fuel in a single cylinder, four-stroke, naturally aspirated, port injection HCCI engine. MATLAB ANN program was used for training, validation and testing ofinputs. The crank angle, engine speed, and lambda were used as input values and the in-cylinder pressure was used as the target value. The Levenberg-Marquardt training algorithm was used for the training of inputs. Also, three layers and 10 neurons were used for the training process. The best validation performance was obtained at epoch 535 as 0.000043691 MSE value. The correlation factor of training, validation, and testing between the targets to outputs were obtained at 0.99912, 0.99905 and 0.99893 respectively. The total correlation factor was found at 0.99908. It is observed that there is a high degree of accuracy between the estimation of results and experimental data using the developed ANN model. Hitit University Scientific Research Project [MYOT19003.16.001] This study was supported by Hitit University Scientific Research Project which is numbered as MYOT19003.16.001. WOS:000467668200072

Published
2019

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