Browsing by Author "Bulucu, Pervin"

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: 5
Citation - Scopus: 8
Multi-Sensor E-Nose Based on Online Transfer Learning Trend Predictive Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2024) Parvin Bulucu; Mert Nakıp; Cüneyt Güzeliş; Guzelis, Cuneyt; Bulucu, Pervin; Nakip, Mert
Electronic Nose (E-Nose) systems widely applied across diverse fields have revolutionized quality control disease diagnostics and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However despite their potential offline training efforts often prove intensive and may still fall short in achieving high generalization ability and specialization for considered application. To address these challenges this paper introduces the e-rTPNN decision system which leverages the Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. The recurrent architecture of the e-rTPNN system effectively captures temporal dependencies and hidden sequential patterns within E-Nose sensor data enabling accurate estimation of trends and levels. Notably the system demonstrates the ability to adapt quickly to new data during online operation requiring only a small offline dataset for initial learning. We evaluate the performance of the e-rTPNN decision system in two domains: beverage quality assessment and medical diagnosis using publicly available wine quality and Chronic Obstructive Pulmonary Disease (COPD) datasets respectively. Our evaluation indicates that the proposed e-rTPNN achieves decision accuracy exceeding 97% while maintaining low execution times. Furthermore comparative analysis against established Machine Learning (ML) models reveals that the e-rTPNN decision system consistently outperforms these models by a significant margin in terms of accuracy. © 2024 Elsevier B.V. All rights reserved.
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.