American Journal of Data Mining and Knowledge Discovery
Volume 4, Issue 2, December 2019, Pages: 57-62
Received: May 31, 2019;
Accepted: Jul. 10, 2019;
Published: Oct. 23, 2019
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Nafise Masomi, Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
Elham Ghanbari, Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
Mohammad Taghi Adl, Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
Data mining, also referred to as knowledge extraction from databases, is one of the most important analytical methods for identifying the relationships between the various elements of the information collected in order to discover the useful knowledge and support of strategic decision-making and sustainable development systems in various industries. Mathematical modeling, quantitative analysis of data and new algorithms can identify new relationships between different data, which in turn leads to competitive advantage. Olive oil is one of the most important agricultural crops due to its digestive properties and economic status. However, olive oil production is a costly process which causes an expensive price of the final product. The most jobbery ways during olive oil production consist of mixing other oils such as maize, sunflower, Canola and corn into the olive oil. So, the aim of this study was to develop a dielectric-based system to Authenticate in olive oil using cylindrical capacitive sensor. For categorizing of fake olive oil by using frequency specification, Support vector machine, linear regression, Ensemble Trees and Gaussian was developed. A set of 16 samples of olive oil, sunflower, canola and corn oil which mixed with different ratio of Authentication, were used for calibration and evaluation of developed system.
Mohammad Taghi Adl,
Data Mining Technique Used in Order to Analysis the Capacitive Sensor, American Journal of Data Mining and Knowledge Discovery.
Vol. 4, No. 2,
2019, pp. 57-62.
Ramesh, D., & Vardhan, B. V. (2015). Analysis of crop yield prediction using data mining techniques. I nternational Journal of Research in Engineering and Technology, 4 (1), 47-473.
Patel, H., & Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computer Applications, 95 (9).
Bharadi, V. A., Abhyankar, P. P., Patil, R. S., Patade, S. S., Nate, T. U., & Joshi, A. M. (2017). Analysis and Prediction in Agricultural Data using Data Mining Techniques. International Journal of Research in Science and Engineering, 386-393.
Tayyebi, A., & Pijanowski, B. C. (2014). Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools. International Journal of Applied Earth Observation and Geoinformation, 28, 102-116.
Sahu, V., Pandey, A., Khan, M. Z., Mishra, V., & Shrivastava, A. K. (2018). Role of Dielectric Behaviour of Soil in Agriculture with Reference to Pond Area. Journal of Pure Applied and Industrial Physics, 8 (6), 62-65.
Mahani, R., Atia, F., Al Neklawy, M. M., & Fahem, A. (2016). Dielectric spectroscopic studies on the water hyacinth plant collected from agriculture drainage. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 162, 81-85.
Bertrand, S. (2018). Dielectric Properties of a Protein Probed by Reversal of a Buried Ion Pair. Journal of physical chemistry.
Gaikwad, S. V., Gaikwad, A. N., Harsh, R., & Gupta, A. (2015). Simulation modeling and implementation of RF and MW system to control the insect pests in agriculture. In India Conference (INDICON), pp. 1-4. IEEE.
Rodriguez-Morato, J., Xicota, L., Fito, M., Farre, M., Dierssen, M., & de la Torre, R. (2015). Potential role of olive oil phenolic compounds in the prevention of neurodegenerative diseases. Molecules, 20 (3), 4655-4680.
García, A., Rodríguez-Juan, E., Rodríguez-Gutiérrez, G., Rios, J. J., & Fernández-Bolaños, J. (2016). Extraction of phenolic compounds from virgin olive oil by deep eutectic solvents (DESs). Food chemistry, 197, 554-561.
Guasch-Ferré, M., Hu, F. B., Martínez-González, M. A., Fitó, M., Bulló, M., Estruch, R.,. & Fiol, M. (2014). Olive oil intake and risk of cardiovascular disease and mortality in the PREDIMED Study. BMC medicine, 12 (1), 78.
Lizhi, H., Toyoda, K., & Ihara, I. (2010). Discrimination of olive oil adulterated with vegetable oils using dielectric spectroscopy. Journal of Food Engineering, 96 (2), 167-171.
Soltani, M., Alimardani, R., & Omid, M. (2010). A new mathematical modeling of banana fruit and comparison with actual values of dimensional properties. Modern Applied Science, 4 (8), 104.
Ragni. L, P. Gradari, A. Berardinelli, A. Giunchi, and A. Guarnieri. (2006). Predicting quality parameters of shell eggs using a simple technique based on the dielectric properties. Biosystems Engineering, 94, 255–262.
Soltani. M, M. Omid. (2105). Detection of poultry egg freshness by dielectric spectroscopy and machine learning technique. LWT-Food Science and Technology. 6210164-0142.