Bulanık mantık yöntemi ve yapay sinir ağları kullanılarak farklı kokuların sınıflandırılması

Öz

Bu çalışmada 8 adet metal oksit gaz sensörü kullanılarak gerçekleştirilen bir elektronik burun devresi kullanılarak 9 farklı maddenin (yumurta, çürük yumurta, nane, naftalin, limon, melek otu kökü, aseton, oje ve gül suyu) ayırt edilmesi sağlanmıştır. Çalışmada veri sınıflandırma için bulanık mantık yöntemi ve yapay sinir ağları kullanılmıştır. Yapay sinir ağları kullanılarak gerçekleştirilen veri sınıflandırma işleminde farklı ağ mimarileri kullanılarak sınıflandırma performansları incelenmiştir. Bulanık mantık yöntemiyle gerçekleştirilen sınıflandırma işleminde ise kullanılan farklı üyelik fonksiyonları ile sınıflandırma doğruluğu artırılmaya çalışılmıştır. Yapay sinir ağları ile yapılan sınıflandırma işleminde gizli katman ve çıktı katmanında sırasıyla logaritmik sigmoid ve hiperbolik tanjant fonksiyonları kullanılarak %96.41 sınıflandırma doğruluğu elde edilmiştir. En yüksek sınıflandırma doğruluğunun elde edildiği bu yapay sinir ağının gizli katmanında 8 adet yapay sinir hücresi kullanılmıştır. Bulanık mantık yöntemi ile yapılan sınıflandırma işleminde ise çan üyelik fonksiyonunun kullanılmasıyla sınıflandırma doğruluğu %95.55 olarak elde edilmiştir.

Anahtar Kelimeler

• Elektronik burun
• Veri boyutu indirgeme
• Yapay sinir ağları
• Bulanık mantık yöntemi
• Veri sınıflandırma

Classification of different odors using fuzzy logic method and artificial neural network

Abstract

In this study, 9 different substances (egg, rotten egg, mint, naphthalene, lemon, angelica root, acetone, nail polish and rose water) were distinguished by using an electronic nose using 8 metal oxide gas sensors. In the study, fuzzy logic method and artificial neural networks were used for data classification. In the data classification process performed using artificial neural networks, the classification performances were examined using different network architectures. In the classification process carried out with the fuzzy logic method, an attempt was made to increase the classification accuracy with the different membership functions used. In the classification process performed with artificial neural networks, 96.41% classification accuracy was achieved by using logarithmic sigmoid and hyperbolic tangent functions in the hidden layer and output layer, respectively. 8 artificial nerve cells were used in the hidden layer of this artificial neural network, where the highest accuracy value was achieved. In the classification process carried out with the fuzzy logic method, the classification accuracy was achieved as 95.55% by using the bell membership function.

Keywords

• Electronic nose
• Data dimension reduction
• Artificial neural network
• Fuzzy logic method
• Data classification

References

1. [1] Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JBB. “Electronic noses for food quality: A review”. Journal of Food Engineering, 144, 103-111, 2015.
2. [2] Timsorn K, Thoopboochagorn T, Lertwattanasakul N, Wongchoosuk C. “Evaluation of bacterial population on chicken meats using a briefcase electronic nose”. Biosystems Engineering, 151, 116-125, 2016.
3. [3] Pearce TC, Schiffman SS, Nagle HT, Gardner JW. Handbook of Machine Olfaction: Electronic Nose Technology. Weinheim, Germany, Wiley, 2003.
4. [4] Saraoğlu HM. “Elektronik burun teknolojisi ve uygulama alanları”. Akademik Bilişim’08 10. Akademik Bilişim Konferansı, Çanakkale, Türkiye, 30 Ocak - 1 Şubat 2008.
5. [5] Valdez LF, Gutierrez JM. “Chocolate classification by an electronic nose with pressure controlled generated stimulation”. Sensors, 6(10), 1745, 2016.
6. [6] Viejo CG, Fuentes S, Godbole A, Widdicombe B, Unnithan RR. “Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality”. Sensors and Actuators B: Chemical, 308, 127688, 2020.
7. [7] Lin SS, Zhang XM. “A rapid and novel method for pre-dicting nicotine alkaloids in tobacco through electronic nose and partial least-squares regression analysis”. Analytical Methods, 8(7), 1609-1617, 2016.
8. [8] Zhang M, Guo, D, Wang H, Wu G, Ding N, Shi Y, Zhou J, Zhao E, Li X. “Integrated characterization of filler tobacco leaves: HS-SPME-GC-MS, E-nose, and microbiome analysis across different origins”. Bioresources and Bioprocessing, 11(1), 11, 2024.

9. [9] Özsandıkçıoğlu Ü, Atasoy A. "Breath analysis for detection of lung cancer with hybrid sensor-based electronic nose," Turkish Journal of Electrical Engineering and Computer Sciences, 31, 550-565, 2023.
10. [10] Voss A, Witt K, Kaschowitz T, Poitz W, Ebert A, Roser P, Bär KJ. “Detecting cannabis use on the human skin surface via an electronic nose system”. Sensors, 14(7), 13256-13272, 2014.
11. [11] Wilson AD. “Review of electronic-nose technologies and algorithms to detect hazardous chemicals in the environment”. Procedia Technology, 1, 453-463, 2012.
12. [12] Munoz R, Sivret EC, Parcsi G, Lebrero R, Wang XG, Suffet IH, Stuetz RM. “Monitoring techniques for odour abatement assessment”. Water Research, 44(18), 5129-5149, 2010.
13. [13] Mumyakmaz B, Karabacak K. “An E-nose-based indoor air quality monitoring system: Prediction of combustible and toxic gas concentrations”. Turkish Journal of Electrical Engineering & Computer Sciences, 23(3), 729-740, 2015.
14. [14] Bax C, Sironi S, Capelli L. “Definition and Application of a Protocol for Electronic Nose Field Performance Testing: Example of Odor Monitoring from a Tire Storage Area”. Atmosphere, 11(4), 426, 2020.
15. [15] Bakiler H, Güney S, “Estimation of concentration values of different gases based on long short-term memory by using electronic nose”. Biomedical Signal Processing and Control, 69, 102908, 2021.
16. [16] Wu Z, Wang H, Wang X, Zheng H, Chen Z, Meng C. “Development of electronic nose for qualitative and quantitative monitoring of volatile flammable liquids”. Sensors, 20(7), 1817, 2020.
17. [17] Serdaroğlu M, Turp Y. “Recent techniques for evaluation of meat quality”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 111-117, 2004.
18. [18] Bakar MAA, Abdullah AH, Saad FSA, Shukor SAA, Razak AAA, Mustafa MH. “Electronic nose calibration process for monitoring atmospheric hazards in confined space applications”. Journal of Telecommunication, Electronic and Computer Engineering, 10, 15-19, 2018.
19. [19] Sundic T, Marco S, Samitier J, Wide P. “Electronic tongue and electronic nose data fusion in classification with neural networks and fuzzy logic based models”. 17th IEEE Instrumentation and Measurement Technology Conference, Maryland, USA, 1-4 May 2000.
20. [20] Güney S. Karma Yapılı İkili Karar Ağacı tabanlı Koku Tanıma Sistemi. Doktora Tezi, Karadeniz Teknik Üniversitesi, Trabzon, Türkiye, 2013.
21. [21] Guney S, Atasoy A. "Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure". International Journal of Pattern Recognition and Artficial Intelligence, 34(03), 2050003, 2020.
22. [22] Guney S, Atasoy A. "Study of fish species discrimination via electronic nose". Computers and Electronics in Agriculture, 119, 83-91, 2015.
23. [23] Sathyavani R, JaganMohan K, Kalaavathi B. “Classification of nutrient deficiencies in rice crop using denseNet-BC”. Materials Today: Proceedings, 56, 1783-1789, 2022.
24. [24] Osuna RG, Nagle HT, Schiffman SS. “Transient Response Analysis of an Electronic Nose Using Multi-Exponential Models”. Sensors and Actuators B: Chemical, 61, 170-182, 1999.
25. [25] Fornacon-Wood I, Mistry H, Ackermann CJ, Blackhall F, McPartlin A, Faivre-Finn C, O’Connor JP. “Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform”. European radiology, 30, 6241-6250, 2020.
26. [26] Gutierrez-Osuna R, Nagle HT. “A Method for Evaluating Data-Preprocessing Techniques for Odor Classification with An Array of Gas Sensors”. IEEE Transactions on Systems, Man, and, Cybernetics-Part B, 29(5), 626-632, 1999. Timm NH. Applied multivariate analysis. New York, USA, Springer, 2002.
27. [27] King JR, Jackson DA. “Variable selection in large environmental data sets using principal components analysis”. Environmetrics: The official journal of the International Environmetrics Society, 10(1), 67-77, 1999.
28. [28] Bilginer O, Tunga B, Demirer RM. “Deri lezyonlarının evrişimsel yapay sinir ağları ile sınıflandırılması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 208-214, 2022.
29. [29] Ekersular MN, Alkan A. “Detection of COVID-19 disease with machine learning algorithms from CT images”. Gazi University Journal of Science, 37(1), 169-181, 2024.
30. [30] Alkan A. EEG işaretlerinin klasik ve modern yöntemlerle önişlenmesi ve sınıflandırılması. Doktora Tezi, Sakarya Üniversitesi, Sakarya, 2005.
31. [31] Alkan A, Koklukaya E, Subasi A. “Automatic seizure detection in EEG using logistic regression and artificial neural network”. Journal of neuroscience methods, 148(2), 167-176, 2005.
32. [32] Öztürk E. “Yapay Sinir Ağları (YS)”. https://academy.patika.dev/blogs/detail/yapay-sinir-aglariys (15.02.2024).
33. [33] Yılmaz E. “Yapay Sinir Ağları ile Araç Satışlarının Tahmin Edilmesi”. https://www.veribilimiokulu.com/yapay-sinir-aglari-ile-arac-satislarinin-tahmin-edilmesi/ (15.02.2024).
34. [34] Altas IH. Fuzzy Logic Control in Energy Systems with Design Applicati-ons in MatLab/Simulink. 1st ed. London, England, The Institution of Engineering and Technology, 2017.
35. [35] Altas IH, Sharaf AM. “A generalized direct approach for designing fuzzy logic controllers in Matlab/Simulink GUI environment”. International journal of information technology and intelligent computing, 1(4), 1-27, 2007.
36. [36] Altas IH, Sharaf AM. “A novel maximum power fuzzy logic controller for photovoltaic solar energy systems”. Renewable Energy, 33(3), 388-399, 2008.
37. [37] Maiers J, Sherif YS. “Applications of fuzzy set theory”. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 175-189, 1985.
38. [38] Jain A, Sharma A. “Membership function formulation methods for fuzzy logic systems: A comprehensive review”. Journal of Critical Reviews, 7(19), 8717-8733, 2020.
39. [39] Altaş İH. “Bulanık Mantık: Bulanıklık Kavramı”. Enerji, Elektrik, Elektromekanik-3e, 62, 80-85, 1999.
40. [40] Cheng L, Meng QH, Lilienthal AJ, Qi PF. “Development of compact electronic noses: A review”. Measurement Science and Technology, 32(06), 062002, 2021.
41. [41] Chen H, Huo D, Zhang J. “Gas recognition in E-nose system: A review”. IEEE Transactions on Biomedical Circuits and Systems, 16(2), 169-184, 2022.
42. [42] Ali MM, Hashim N, Abd Aziz S, Lasekan O. “Principles and recent advances in electronic nose for quality inspection of agricultural and food products”. Trends in Food Science & Technology, 99, 1-10, 2020.
43. [43] Karakaya D, Ulucan O, Turkan M. “Electronic nose and its applications: A survey”. International journal of Automation and Computing, 17(2), 179-209, 2020.