Name
Title | ||
|---|---|---|
An intelligent technique for the characterization of coal microscopic images using ensemble learning |
Abstract | ||
|---|---|---|
Coal is a primary natural resource of fuel that is efficiently used for electricity generation, steel or iron production, and household usage. Characterization is needed for industries to understand the quality of coal before shipping. Currently, industries follow chemical, microscopical, and machine-based analysis as the gold standard for coal characterization. These conventional analyses of coal are an indispensable method over the years and have tested by high skilled petrologists. Though, these types of optical or machine-dependent recognition of coal category samples are quite slow, expensive, and restricted by subjective analyses with less accuracy. The main aim of this research is to propose an accurate, time, and cost-effective machine learning-based automated characterization system by analyzing coal color and textural features. This paper comes up with a quantitative approach toward the characterization of dissimilar types of coal for better utilization in industries. The proposed ensemble learning coal characterization method provides an accuracy of around 97% and takes less computational time than conventional methods. Hence, the proposed automated coal characterization system provides support to industries in the development of computer-aided assessment of coal category samples. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/JIFS-179707 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
Coal,HSV,GLCM,image processing,machine learning | Journal | 38 |
Issue | ISSN | Citations |
5 | 1064-1246 | 1 |
PageRank | References | Authors |
0.40 | 0 | 2 |