Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data
The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet in...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2890996 |
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| author | Seema Khanum M. Gunasekaran S. V. Rajiga A. Firos Kafui Tsoeke Agbevanu |
| author_facet | Seema Khanum M. Gunasekaran S. V. Rajiga A. Firos Kafui Tsoeke Agbevanu |
| author_sort | Seema Khanum |
| collection | DOAJ |
| description | The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements. |
| format | Article |
| id | doaj-art-a7262e707dc140f794745724d74ab4f8 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-a7262e707dc140f794745724d74ab4f82025-02-03T07:24:27ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/2890996Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite DataSeema Khanum0M. Gunasekaran1S. V. Rajiga2A. Firos3Kafui Tsoeke Agbevanu4Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer Science and EngineeringDepartment of Computer ScienceThe normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.http://dx.doi.org/10.1155/2022/2890996 |
| spellingShingle | Seema Khanum M. Gunasekaran S. V. Rajiga A. Firos Kafui Tsoeke Agbevanu Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data Advances in Materials Science and Engineering |
| title | Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data |
| title_full | Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data |
| title_fullStr | Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data |
| title_full_unstemmed | Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data |
| title_short | Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data |
| title_sort | intuitionistic fuzzy hamacher generalized shapley choquet integral operators based decision making model for feature extraction and automatic material classification in mining area using satellite data |
| url | http://dx.doi.org/10.1155/2022/2890996 |
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