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Classification of Different Wheat Varieties by Using Data Mining Algorithms

Year 2016, , 40 - 44, 27.05.2016
https://doi.org/10.18201/ijisae.62843

Abstract

There are various applications using computer-aided quality controlling system. In this study, seed data set acquired from UCI machine learning database was used. The purpose of the study is to perform the operations for separation of seed species from each other in the seed data set. Three different seed whose data was acquired from the UCI machine learning database was used. Later it was classified by applying the methods of KNN, Naive Bayes, J48 and multilayer perceptron to the dataset. While wheat seed data received from the UCI machine learning database was classified, WEKA program was used. Depending on the number of neurons the highest classification success came in 7-layer neurons. Our success rate for the number of 7-layer neurons came to 97.17% When the classification success rate was calculated according to KNN for the values of different neighbour, the highest success rate for neighbour was set at 95.71% for 4. Neighbour. With this method, classification of seeds depending on their properties was provided more quickly and effectively.

 

References

  • Gökhan Silahtaroğlu (2013). Data Mining Concepts And Algorithms, Papatya Education Publishing.
  • T. C. Sharma and M. Jain (2013). “WEKA Approach For Comparative Study Of Classification Algorithm”, International Journal Of Advanced Research In Computer And Communication Engineering Vol. 2, Issue 4.
  • Geoffrey Holmes, Andrew Donkin, and Ian H. Witten (2002). Weka: A Machine Learning Workbench “Department Of Computer Science University Of Waikato, Hamilton, New Zealand”.
  • Omid Mahmoud (2011). Design Of An Expert System For Sorting Pistachio Nuts Through Decision Tree And Fuzzy Logic Classifier, Expert Systems With Applications 38, 4339-4347.
  • G.Silvia Ceballos-Magana, De Pablos Fernando, Jurado Jose Marcos, Martin Jesus Maria, Alcazar Angela, Valencia-Muniz Roberto and Lumbreras Gonzalo Raquel (2011). Hornillos Izquierdo Roberto, Characterisation Of Tequila According To Their Major Volatile Composition Using Multilayer Perceptron Neural Networks, Food Chemistry Volume 136, Issues 3–4, 1–15, Pages 1309–1315 ASSET .
  • Emanuelle Morais De Oliveira, Dimas Samid Leme,
  • Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27.
  • T. Karthikeyan and P. Thangaraju (2015). Best First and Greedy Search Based CFS- Naïve Bayes Classification Algorithms For Hepatitis Diagnosis, Biosciences Biotechnology Research Asia, Vol. 12(1), 983-990.
  • Nowakowski, K. (2009). Inst. Of Agric. Eng., Poznah Univ. Of Life Sci., Poznan, Poland ; Boniecki, P. ; Dach, J. The Identification Of Mechanical Damages Of Kernels Basis On Neural Image Analysis, Digital Image Processing, 2009 International Conference On, 978-0-7695-3565-4, Page 412 – 415.
  • Daniel Alves Aguiar, Marcos Adami, Wagner Fernando Silva, Bernardo Friedrich Theodor Rudorff, Marcio Pupin Mello and João Dos Santos Vila Da Silva (2010), Modis Time Series To Assess Pasture Land, Geoscience And Remote Sensing Symposium (IGARSS), 2010 IEEE International, Page 2123 – 2126, 25-30.
  • UCI,Https://Archive.Ics.Uci.Edu/Ml/Datasets.Html Las Access: 22.12.2015
  • M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik and S. Zak (2010). 'A Complete Gradient Clustering Algorithm For Features Analysis Of X-Ray Images', In: Information Technologies In Biomedicine, Ewa Pietka, Jacek Kawa (Eds.), Springer-Verlag, Berlin-Heidelberg, Pp. 15-24.
  • WEKA, Http://Www.Cs.Waikato.Ac.Nz/~Ml/Weka/ Last Access: 19.12.2015.
  • Roberto Muniz-Valencia, Jose M.Jurado, Silvia G. Ceballos-Magana and Angela Alcazar (2014). Characterization Of Mexicon Coffee According To Mineral Contents By Means Of Multilayer Perceptrons Artificial Neural Networks, Journal Of Food Composition And Analysis 34, 7-11.
  • Sibel K. Çalışkan and İbrahim Soğukpınar (2012). Knn: K-Means And Methods K Nearest Neighbor Determination Of The Adoption Network.
  • Cengiz Coşkun (2010). Data Mining Algorithm And Classification, Master’ S Thesis.
  • Emanuelle Morais De Oliveira, Dimas Samid Leme,
  • Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27 .
Year 2016, , 40 - 44, 27.05.2016
https://doi.org/10.18201/ijisae.62843

Abstract

References

  • Gökhan Silahtaroğlu (2013). Data Mining Concepts And Algorithms, Papatya Education Publishing.
  • T. C. Sharma and M. Jain (2013). “WEKA Approach For Comparative Study Of Classification Algorithm”, International Journal Of Advanced Research In Computer And Communication Engineering Vol. 2, Issue 4.
  • Geoffrey Holmes, Andrew Donkin, and Ian H. Witten (2002). Weka: A Machine Learning Workbench “Department Of Computer Science University Of Waikato, Hamilton, New Zealand”.
  • Omid Mahmoud (2011). Design Of An Expert System For Sorting Pistachio Nuts Through Decision Tree And Fuzzy Logic Classifier, Expert Systems With Applications 38, 4339-4347.
  • G.Silvia Ceballos-Magana, De Pablos Fernando, Jurado Jose Marcos, Martin Jesus Maria, Alcazar Angela, Valencia-Muniz Roberto and Lumbreras Gonzalo Raquel (2011). Hornillos Izquierdo Roberto, Characterisation Of Tequila According To Their Major Volatile Composition Using Multilayer Perceptron Neural Networks, Food Chemistry Volume 136, Issues 3–4, 1–15, Pages 1309–1315 ASSET .
  • Emanuelle Morais De Oliveira, Dimas Samid Leme,
  • Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27.
  • T. Karthikeyan and P. Thangaraju (2015). Best First and Greedy Search Based CFS- Naïve Bayes Classification Algorithms For Hepatitis Diagnosis, Biosciences Biotechnology Research Asia, Vol. 12(1), 983-990.
  • Nowakowski, K. (2009). Inst. Of Agric. Eng., Poznah Univ. Of Life Sci., Poznan, Poland ; Boniecki, P. ; Dach, J. The Identification Of Mechanical Damages Of Kernels Basis On Neural Image Analysis, Digital Image Processing, 2009 International Conference On, 978-0-7695-3565-4, Page 412 – 415.
  • Daniel Alves Aguiar, Marcos Adami, Wagner Fernando Silva, Bernardo Friedrich Theodor Rudorff, Marcio Pupin Mello and João Dos Santos Vila Da Silva (2010), Modis Time Series To Assess Pasture Land, Geoscience And Remote Sensing Symposium (IGARSS), 2010 IEEE International, Page 2123 – 2126, 25-30.
  • UCI,Https://Archive.Ics.Uci.Edu/Ml/Datasets.Html Las Access: 22.12.2015
  • M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik and S. Zak (2010). 'A Complete Gradient Clustering Algorithm For Features Analysis Of X-Ray Images', In: Information Technologies In Biomedicine, Ewa Pietka, Jacek Kawa (Eds.), Springer-Verlag, Berlin-Heidelberg, Pp. 15-24.
  • WEKA, Http://Www.Cs.Waikato.Ac.Nz/~Ml/Weka/ Last Access: 19.12.2015.
  • Roberto Muniz-Valencia, Jose M.Jurado, Silvia G. Ceballos-Magana and Angela Alcazar (2014). Characterization Of Mexicon Coffee According To Mineral Contents By Means Of Multilayer Perceptrons Artificial Neural Networks, Journal Of Food Composition And Analysis 34, 7-11.
  • Sibel K. Çalışkan and İbrahim Soğukpınar (2012). Knn: K-Means And Methods K Nearest Neighbor Determination Of The Adoption Network.
  • Cengiz Coşkun (2010). Data Mining Algorithm And Classification, Master’ S Thesis.
  • Emanuelle Morais De Oliveira, Dimas Samid Leme,
  • Bruno Henrique Groenner Barbosa Mirian Pereira Rodarte and Rosemary Gualberto Fonseca Alvarenga Pereira (2016). A Computer Vision System For Coffee Beans Classification Based On Computational Intelligence Techniques, Journal Of Food Engineering Volume 171, Pages 22–27 .
There are 18 citations in total.

Details

Journal Section Research Article
Authors

Kadir Sabancı

Mustafa Akkaya

Publication Date May 27, 2016
Published in Issue Year 2016

Cite

APA Sabancı, K., & Akkaya, M. (2016). Classification of Different Wheat Varieties by Using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 4(2), 40-44. https://doi.org/10.18201/ijisae.62843
AMA Sabancı K, Akkaya M. Classification of Different Wheat Varieties by Using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering. May 2016;4(2):40-44. doi:10.18201/ijisae.62843
Chicago Sabancı, Kadir, and Mustafa Akkaya. “Classification of Different Wheat Varieties by Using Data Mining Algorithms”. International Journal of Intelligent Systems and Applications in Engineering 4, no. 2 (May 2016): 40-44. https://doi.org/10.18201/ijisae.62843.
EndNote Sabancı K, Akkaya M (May 1, 2016) Classification of Different Wheat Varieties by Using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering 4 2 40–44.
IEEE K. Sabancı and M. Akkaya, “Classification of Different Wheat Varieties by Using Data Mining Algorithms”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 2, pp. 40–44, 2016, doi: 10.18201/ijisae.62843.
ISNAD Sabancı, Kadir - Akkaya, Mustafa. “Classification of Different Wheat Varieties by Using Data Mining Algorithms”. International Journal of Intelligent Systems and Applications in Engineering 4/2 (May 2016), 40-44. https://doi.org/10.18201/ijisae.62843.
JAMA Sabancı K, Akkaya M. Classification of Different Wheat Varieties by Using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:40–44.
MLA Sabancı, Kadir and Mustafa Akkaya. “Classification of Different Wheat Varieties by Using Data Mining Algorithms”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 2, 2016, pp. 40-44, doi:10.18201/ijisae.62843.
Vancouver Sabancı K, Akkaya M. Classification of Different Wheat Varieties by Using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(2):40-4.

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