Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A mineral is a natural, homogeneous solid with a definite chemical composition and a highly ordered atomic arrangement. Recently, fast and accurate mineral identification/classification became a necessity. Energy Dispersive X-ray Spectrometers integrated with Scanning Electron Microscopes (SEM) are used to obtain rapid and reliable elemental analysis or chemical characterization of a solid. Hover, mineral identification is challenging since there is wide range of spectral dataset for natural minerals. The more mineralogical data acquired, time required for classification procedures increases. Moreover, applied instrumental conditions on a SEM-EDS differ for various applications, affecting the produced X-ray patterns even for the same mineral. This study aims to test whether C5.0 Decision Tree is a rapid and reliable method algorithm for classification and identification of various natural magmatic minerals.Ten distinct mineral groups (olivine, orthopyroxene, clinopyroxene, apatite, amphibole, plagioclase, K-feldspar, zircon, magnetite, biotite) from different igneous rocks have been analyzed on SEM-EDS. 4601 elemental X-ray intensity data have been collected under various instrumental conditions. 2400 elemental data have been used to train and the remaining 2201 data have been tested to identify the minerals. The vast majority of the test data have been classified accurately. Additionally, high accuracy has been reached on the minerals with similar chemical composition, such as olivine ((Mg,Fe)2SiO4]) and orthopyroxene ((Mg,Fe)2SiO6]). Furthermore, two members from amphibole group (magnesiohastingsite, tschermakite) and two from clinopyroxene group (diopside, hedenbergite) have been accurately identified by the Decision Tree Algorithm. These results demonstrate that C5.0 Decision Tree Algorithm is an efficient method for mineral group classification and the identification of mineral members. C5.0 Algorithm is tested as a method for mineral identification using EDS data.Selected minerals have been classified accurately using C5.0 Algorithm.Effects of instrumental conditions have been minimized by applied methods.C5.0 Decision Tree stands as an effective tool for mineral classification using EDS.
['Efe Akkaş', 'Lutfiye Akin', 'H. Evren Çubukçu', 'Harun Artuner']
I am writing a research paper on a idea Application of Decision Tree Algorithm for classification and identification of natural minerals using SEM-EDS
[{"from": "human", "value": "I am writing a research paper on a idea Application of Decision Tree Algorithm for classification and identification of natural minerals using SEM-EDS"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A mineral is a natural, homogeneous solid with a definite chemical composition and a highly ordered atomic arrangement. Recently, fast and accurate mineral identification/classification became a necessity. Energy Dispersive X-ray Spectrometers integrated with Scanning Electron Microscopes (SEM) are used to obtain rapid and reliable elemental analysis or chemical characterization of a solid. Hover, mineral identification is challenging since there is wide range of spectral dataset for natural minerals. The more mineralogical data acquired, time required for classification procedures increases. Moreover, applied instrumental conditions on a SEM-EDS differ for various applications, affecting the produced X-ray patterns even for the same mineral. This study aims to test whether C5.0 Decision Tree is a rapid and reliable method algorithm for classification and identification of various natural magmatic minerals.Ten distinct mineral groups (olivine, orthopyroxene, clinopyroxene, apatite, amphibole, plagioclase, K-feldspar, zircon, magnetite, biotite) from different igneous rocks have been analyzed on SEM-EDS. 4601 elemental X-ray intensity data have been collected under various instrumental conditions. 2400 elemental data have been used to train and the remaining 2201 data have been tested to identify the minerals. The vast majority of the test data have been classified accurately. Additionally, high accuracy has been reached on the minerals with similar chemical composition, such as olivine ((Mg,Fe)2SiO4]) and orthopyroxene ((Mg,Fe)2SiO6]). Furthermore, two members from amphibole group (magnesiohastingsite, tschermakite) and two from clinopyroxene group (diopside, hedenbergite) have been accurately identified by the Decision Tree Algorithm. These results demonstrate that C5.0 Decision Tree Algorithm is an efficient method for mineral group classification and the identification of mineral members. C5.0 Algorithm is tested as a method for mineral identification using EDS data.Selected minerals have been classified accurately using C5.0 Algorithm.Effects of instrumental conditions have been minimized by applied methods.C5.0 Decision Tree stands as an effective tool for mineral classification using EDS."}]