American Journal of Data Mining and Knowledge Discovery

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Data Mining Technique Used in Order to Analysis the Capacitive Sensor

Received: 31 May 2019    Accepted: 10 July 2019    Published: 23 October 2019
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Abstract

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.

DOI 10.11648/j.ajdmkd.20190402.11
Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 2, December 2019)
Page(s) 57-62
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data Mining, Regression, Olive Oil, Authentication

References
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[2] Patel, H., & Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computer Applications, 95 (9).
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] Bertrand, S. (2018). Dielectric Properties of a Protein Probed by Reversal of a Buried Ion Pair. Journal of physical chemistry.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] Soltani. M, M. Omid. (2105). Detection of poultry egg freshness by dielectric spectroscopy and machine learning technique. LWT-Food Science and Technology. 6210164-0142.
Author Information
  • Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran

  • Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran

  • Department of Engineering, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran

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  • APA Style

    Nafise Masomi, Elham Ghanbari, Mohammad Taghi Adl. (2019). Data Mining Technique Used in Order to Analysis the Capacitive Sensor. American Journal of Data Mining and Knowledge Discovery, 4(2), 57-62. https://doi.org/10.11648/j.ajdmkd.20190402.11

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    ACS Style

    Nafise Masomi; Elham Ghanbari; Mohammad Taghi Adl. Data Mining Technique Used in Order to Analysis the Capacitive Sensor. Am. J. Data Min. Knowl. Discov. 2019, 4(2), 57-62. doi: 10.11648/j.ajdmkd.20190402.11

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    AMA Style

    Nafise Masomi, Elham Ghanbari, Mohammad Taghi Adl. Data Mining Technique Used in Order to Analysis the Capacitive Sensor. Am J Data Min Knowl Discov. 2019;4(2):57-62. doi: 10.11648/j.ajdmkd.20190402.11

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  • @article{10.11648/j.ajdmkd.20190402.11,
      author = {Nafise Masomi and Elham Ghanbari and Mohammad Taghi Adl},
      title = {Data Mining Technique Used in Order to Analysis the Capacitive Sensor},
      journal = {American Journal of Data Mining and Knowledge Discovery},
      volume = {4},
      number = {2},
      pages = {57-62},
      doi = {10.11648/j.ajdmkd.20190402.11},
      url = {https://doi.org/10.11648/j.ajdmkd.20190402.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajdmkd.20190402.11},
      abstract = {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.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Data Mining Technique Used in Order to Analysis the Capacitive Sensor
    AU  - Nafise Masomi
    AU  - Elham Ghanbari
    AU  - Mohammad Taghi Adl
    Y1  - 2019/10/23
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajdmkd.20190402.11
    DO  - 10.11648/j.ajdmkd.20190402.11
    T2  - American Journal of Data Mining and Knowledge Discovery
    JF  - American Journal of Data Mining and Knowledge Discovery
    JO  - American Journal of Data Mining and Knowledge Discovery
    SP  - 57
    EP  - 62
    PB  - Science Publishing Group
    SN  - 2578-7837
    UR  - https://doi.org/10.11648/j.ajdmkd.20190402.11
    AB  - 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.
    VL  - 4
    IS  - 2
    ER  - 

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