2026-10-29T20:44:10Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/37892026-10-28T15:31:07Zopenairecom_munzur_1col_munzur_2com_munzur_11col_munzur_162

Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering methodKorkmaz, Sevcan AytacEsmeray, FurkanVarol, A.Karabatak, M.Varol, C.Artifical Neural NetworkDiscrete Fourier TransformDiscrete Wavelet Transformk-means clusteringIn this article, the lignite coal datas in the Kalburcayi area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburcayi area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT-ANN, DWT-ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT-ANN method.Institute of Electrical and Electronics Engineers Inc.2018Conference Objecthttps://rdlopen.rawdatalibrary.net/r/munzur/3789https://doi.org/10.1109/isdfs.2018.8355326978-153863449-3en6th International Symposium on Digital Forensic and Security, ISDFS 2018 - Proceedinginfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/3789