Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids
Abstract
Keywords
References
- International Diabetes Federation, “IDF Diabetes Atlas 2021 _ IDF Diabetes Atlas,” IDF official website. 2021.
- Gonzales WV, Mobashsher AT, and Abbosh A, “The progress of glucose monitoring—A review of invasive to minimally and non-invasive techniques, devices and sensors,” Sensors (Switzerland), vol. 19, no. 4. 2019, doi: 10.3390/s19040800.
- Mahnashi Y, Qureshi KK, Al-Shehri A, and Attia H, “Microwave-Based Technique for Measuring Glucose Levels in Aqueous Solutions,” in 2023 International Microwave and Antenna Symposium, IMAS 2023, 2023, pp. 1–4, doi: 10.1109/IMAS55807.2023.10066913.
- Ermeydan EŞ, Değirmenci A, Çankaya İ, and Erdoğan F, “Patolojik Görüntülerin Sıkıştırılmış Algılamasında Ölçüm Matrisi ve Geri Çatma Algoritmalarının Etkileri,” Düzce Üniversitesi Bilim ve Teknol. Derg., vol. 8, no. 1, 2020, doi: 10.29130/dubited.626880.
- Degirmenci A, “Performance Comparison of kNN, Random Forest and SVM in the Prediction of Cervical Cancer from Behavioral Risk,” Int. J. Innov. Sci. Res. Technol., vol. 7, no. 10, 2022.
- Değirmenci A, Çankaya İ, Gümüşkaya Öcal B, and Karal Ö, “TCGA Verilerinden H&E ile Boyanmış Örneklerden Mesane Kanseri Derecelendirmesi,” Gazi Üniversitesi Fen Bilim. Derg. Part C Tasarım ve Teknol., vol. 11, no. 2, 2023, doi: 10.29109/gujsc.1232028.
- Zhang J, Hodge W, Hutnick C, and Wang X, “Noninvasive diagnostic devices for diabetes through measuring tear glucose,” Journal of Diabetes Science and Technology, vol. 5, no. 1. 2011, doi: 10.1177/193229681100500123.
- Malik S, Gupta S, Khadgawat R, and Anand S, “A novel non-invasive blood glucose monitoring approach using saliva,” 2015, doi: 10.1109/SPICES.2015.7091562.
Details
Primary Language
English
Subjects
Electronics
Journal Section
Research Article
Authors
İlyas Çankaya
0000-0002-6072-3097
Türkiye
Publication Date
March 28, 2024
Submission Date
September 29, 2023
Acceptance Date
October 30, 2023
Published in Issue
Year 2024 Volume: 19 Number: 1
Cited By
Machine Learning Models for Accurate Prediction of Obesity: A Data-Driven Approach
Turkish Journal of Science and Technology
https://doi.org/10.55525/tjst.1572382