695
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Grade Level of Lignite Coal datas in the different areas with Decison Tree, Random Forest, and Discriminant Analysis Methods

References

  • Aki, M. O., 0000. Sürücü uykululuğunun gerçek Zamanlı Görüntü İşleme ve Makine Öğrenmesi Teknikleri ile Tespitine Yönelik Bir Sistem Tasarımı ve Uygulaması, Trakya Üniversitesi, Fen Bilimleri Enstitüsü.
  • Cheng, Y., L. Xu, X. Li, and Z. Guo (2012, July). Online coal calorific value prediction from multiband coal/air combustion radiation characteristics. In Instrumentation and Control Technology (ISICT), 2012 8th IEEE International Symposium on,309–13, London, UK: IEEE.
  • Chelgani S C, Mesroghli S H, and J. C. Hower. 2010. Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network. International Journal of Coal Geology 83 (1):31–34. doi:10.1016/j.coal.2010.03.004.
  • Feng, Q., Zhang, J., Zhang, X., & Wen, S. 2015. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Processing Technology 129:120–29. doi:10.1016/j.fuproc.2014.09.001.
  • Fırat, M., F. Dikbaş, A. C. Koç, and M. Ve Güngör. 2012. K-Ortalamalar Yöntemi ile Yıllık Yağışların Sınıflandırılması ve Homojen Bölgelerin Belirlenmesi. IMO Teknik Dergi 383:6037–50.
  • Galetakis M J, K. Theodoridis, and O. Kouridou. 2002. Lignite quality estimation using ANN and adaptive neuro-fuzzy inference systems (ANFIS). In APCOM 2002: 30 th International Symposium on the Application of Computers and Operations Research in the Mineral Industry (pp. 425-431).
  • Iakovidis, D. K., Maroulis, D. E., Karkanis, S. A., & Brokos, A. “A comparative study of texture features for the discrimination of gastric polyps in endoscopic video.” Computer-based medical systems, 2005. Proceedings. 18th IEEE Symposium on. Dublin, Ireland: IEEE, 2005.
  • IEA. 2000. International energy annual. France. International Journal of Coal Science & Engineering (China) Energy AgencyTutmez, B., Hozatli, B., & Cengiz, A. K. (2013). An overview of Turkish lignite qualities by logistic analysis. Journal of Coal Science and Engineering (China), 19(2), 113-118.
  • Karakitsos, P., T. M. Megalopoulou, A. Pouliakis, M. Tzivras, A. Archimandritis, and A. Kyroudes. 2004. Application of discriminant analysis and quantitative cytologic examination to gastric lesions. Analytical and Quantitative Cytology and Histology/the International Academy of Cytology [And] American Society of Cytology 26 (6):314–22.
  • Karhan, Z., and B. Ergen (2016). Content based medical image classification using discrete wavelet and cosine transforms. 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, (pp:1445–48).
  • Korkmaz, S. A., and H. Binol. 2018. Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection. Journal of Molecular Structure, 1156, 255-263.
  • Korkmaz, S. A., and F. Esmeray (2018). Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method. 2018 6th International Symposium on Digital Forensic and Security (ISDFS,1-6), Antalya, Turkey: IEEE.
  • Korkmaz, S. A., and M. Poyraz. 2014. A new method based for diagnosis of breast cancer cells from microscopic images: DWEE—JHT. Journal of Medical Systems 38 (9):92. doi:10.1007/s10916-014-0092-3.
  • Leśniak, A., and Z. Isakow. 2009. Space–time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland. Poland. International Journal of Rock Mechanics and Mining Sciences 46 (5):918–28. doi:10.1016/j.ijrmms.2008.12.003.
  • Moon C J, Whateley M K G, and Evans A M. 2006. Introduction to mineral exploration. India: Blackwell Publishing.
  • Matin, S. S., and S. Chehreh Chelgani. 2016. Estimation of coal gross calorific value based on various analyses by random forest method. Fuel 177:274–78. doi:10.1016/j.fuel.2016.03.031.
  • MTA. 2010. Lignite inventory of Turkey, general directorate of mineral research and exploration (MTA) in Turkey. Ankara (in Turkish).
  • Muda, Z., W. Yassin, M. N. Sulaiman, and N. I. Udzir (2011, July). Intrusion detection based on K-Means clustering and Naïve Bayes classification. In Information Technology in Asia (CITA 11), 2011 7th International Conference on (pp. 1–6). Kuching, Sarawak, Malaysia: IEEE.
  • Ozcift, A., and A. Gulten. 2008. Assessing effects of pre-processing mass spectrometry data on classification performance. European Journal of Mass Spectrometry 14 (5):267–73. doi:10.1255/ejms.938.
  • Özkan, Y. 2008. Veri madenciliği yöntemleri. Turkey/Istanbul: Papatya Yayıncılık Eğitim.
  • Pandit, Y. P., Y. P. Badhe, B. K. Sharma, S. S. Tambe, and B. D. Kulkarni. 2011. Classification of Indian power coals using K-means clustering and self organizing map neural network. Fuel 90 (1):339–47. doi:10.1016/j.fuel.2010.09.012.
  • Petropoulos, G. P., P. Partsinevelos, and Z. Mitraka. 2013. Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery. Geocarto International 28 (4):323–42. doi:10.1080/10106049.2012.706648.
  • Sahu, H. B., S. S. Mahapatra, and D. C. Panigrahi. 2012. Fuzzy c-means clustering approach for classification of Indian coal seams with respect to their spontaneous combustion susceptibility. Fuel Processing Technology 104:115–20. doi:10.1016/j.fuproc.2012.03.017.
  • Sahu, H. B., S. S. Mahapatra, K. Sirikasemsuk, and D. C. Panigrahi. 2011. A discrete particle swarm optimization approach for classification of Indian coal seams with respect to their spontaneous combustion susceptibility. Fuel Processing Technology 92 (3):479–85. doi:10.1016/j.fuproc.2010.10.015.
  • Senguler, I., (2010). Lignite explorations in Turkey: New projects and new reserves. 17th Annual International Pittsburgh Coal Conference, İstanbul, Turkey.
  • Sengur, A., I. Turkoglu, and M. C. Ince. 2007. Wavelet packet neural networks for texture classification. Expert Systems with Applications 32 (2):527–33. doi:10.1016/j.eswa.2005.12.013.
  • Sengur, A., I. Turkoglu, and M. C. Ince. 2008. Wavelet oscillator neural networks for texture segmentation. Neural Network World 18 (4):275.
  • Khorami, M. T., Chelgani, S. C., Hower, J. C., & Jorjani, E. 2011. Studies of relationships between free swelling index (FSI) and coal quality by regression and adaptive neuro fuzzy inference system. International Journal of Coal Geology. 85 (1):65–71. doi:10.1016/j.coal.2010.09.011.
  • Tercan, A. E., and A. I. Karayiğit. 2001. Estimation of lignite reserve in the Kalburcayiri field, Kangal basin, Sivas, Turkey. International Journal of Coal Geology 47 (2):91–100. doi:10.1016/S0166-5162(01)00033-7.
  • Tutmez, B., B. Hozatli, and A. K. Cengiz. 2013. An overview of Turkish lignite qualities by logistic analysis. Journal of Coal Science& Engineering China 19 (2):113–18. doi:10.1007/s12404-013-0201-9.
  • Tutmez, B., and U. Kaymak. 2013. Hybrid least-squares regression modelling using confidence bounds. In Towards advanced data analysis by combining soft computing and statistics (pp. 53-63). Springer, Berlin, Heidelberg.
  • Yang, X. L., F. Wang, W. C. Wang, Y. X. Chen, and J. S. Chen. 2014. DWT-PLS regression on near-infrared spectra for moisture determination of coal. In Xianzhang Feng, Qi Luo and Tianbiao Zhang (Eds.), Advanced materials research, vol. 827, 209–12. Switzerland: Trans Tech Publications.
  • Yilmaz, I., N. Y. Erik, and O. Kaynar. 2010. Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals. Scientific Research and Essays 5 (16):2242–49.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.