Browsing by Author "Yildiz, Osman"

Now showing 1 - 3 of 3

Citation - Scopus: 3
Beef Quality Assesment with Electronic Nose Based on an Application Specific Convolution Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2021) Parvin Bulucu; Nalan Ǒzkurt; Cuneyt Guzels; Osman Yıldız; Guzels, Cuneyt; Bulucu, Pervin; Yildiz, Osman; Ozkurt, Nalan
This paper presents a convolutional neural network algorithm for the classification of beef samples electronic nose dataset. Proposed algorithm was tested and results were compared to other works that used the same dataset. Overall proposed algorithm showed high performance results without any pre-processing steps. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 23
Citation - Scopus: 33
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Mert Nakip; Cuneyt Guzelis; Osman Yildiz; Guzelis, Cuneyt; Yildiz, Osman; Nakip, Mert
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR) Nonlinear Perceptron (NP) Multi-Layer Perceptron (MLP) Kendall-tau combined with MLP Probabilistic Bayesian Neural Network (PBNN) Long-Short Term Memory (LSTM) and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire where this duration is 22 s for the second-best model. Moreover we present that the execution time of rTPNN is acceptable for real-time applications.
Citation - Scopus: 1
Wine Quality Assessment with Application Specific 2D Single Channel Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Parvin Bulucu; Nalan Ǒzkurt; Cüneyt Güzeliş; Osman Yıldız; Bulucu, Pervin; Guzelis, Cüneyt; Yildiz, Osman; Özkurt, Nalan
Electronic nose is becoming a popular tool for various application areas. The data of an electronic nose is collected with various chemical sensor arrays and then odors are classified with suitable pattern recognition methods. This paper proposes a convolutional neural network for the the classification task of a wine quality electronic nose dataset. Method was tested on different portions of the dataset and compared with two previous studies. Proposed method managed to obtain high accuracy results within the relatively short time period. Additionally method was tested by using portions of the sensor responses hence allowing the user to assess wine quality earlier. Each training was repeated ten times in order to minimize the effects of random data selection. © 2022 Elsevier B.V. All rights reserved.