Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model
Following the progress made in the construction of high-speed processors, the limiting effect of the way data is entered into the computer on the speed of information transfer has become more apparent. By using processing devices, in addition to achieving higher speed in the data retrieval stage, it...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
levent
2023-12-01
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| Series: | International Journal of Pioneering Technology and Engineering |
| Subjects: | |
| Online Access: | https://ijpte.com/index.php/ijpte/article/view/51 |
| _version_ | 1797248246532276224 |
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| author | Bahareh Tadayoni Ehsan Amiri Faezeh Soorani |
| author_facet | Bahareh Tadayoni Ehsan Amiri Faezeh Soorani |
| author_sort | Bahareh Tadayoni |
| collection | DOAJ |
| description | Following the progress made in the construction of high-speed processors, the limiting effect of the way data is entered into the computer on the speed of information transfer has become more apparent. By using processing devices, in addition to achieving higher speed in the data retrieval stage, it is also possible to use their pre-processing capabilities and change the data format. Considering the importance of the topic and the work that has been done in this field, the need to discover the knowledge of the existing features with the help of choosing the appropriate feature for the classification of texts is well felt. In this article, genetic algorithm and fuzzy logic have been used to present a method for classifying texts in insurance booklets. This system is based on several stages. These steps include the learning phase that examines a set of educational texts to extract the characteristics of the categories to be the characteristics of each category; the test phase of the system is used to classify uncategorized texts. The proposed method's accuracy has been assessed using a collection of patient notebooks, and the outcomes indicate an accuracy of around 98% for the classification. |
| first_indexed | 2024-04-24T20:11:32Z |
| format | Article |
| id | doaj.art-f2be41a2e2244fcebcdf38fdaf4e8a81 |
| institution | Directory Open Access Journal |
| issn | 2822-454X |
| language | English |
| last_indexed | 2024-04-24T20:11:32Z |
| publishDate | 2023-12-01 |
| publisher | levent |
| record_format | Article |
| series | International Journal of Pioneering Technology and Engineering |
| spelling | doaj.art-f2be41a2e2244fcebcdf38fdaf4e8a812024-03-23T09:04:17ZengleventInternational Journal of Pioneering Technology and Engineering2822-454X2023-12-0120214214610.56158/jpte.2023.51.2.0251Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid ModelBahareh Tadayoni0https://orcid.org/0009-0001-0345-9277Ehsan Amiri1https://orcid.org/0000-0001-6058-7083Faezeh Soorani2https://orcid.org/0009-0008-2735-2635Department of Computer Engineering, Lar University, Lar, IranDepartment of Computer Engineering, Jahrom Branch, Jahrom University, Jahrom, Iran,Department of Computer Engineering, Jahrom Branch, Jahrom University, Jahrom, Iran,Following the progress made in the construction of high-speed processors, the limiting effect of the way data is entered into the computer on the speed of information transfer has become more apparent. By using processing devices, in addition to achieving higher speed in the data retrieval stage, it is also possible to use their pre-processing capabilities and change the data format. Considering the importance of the topic and the work that has been done in this field, the need to discover the knowledge of the existing features with the help of choosing the appropriate feature for the classification of texts is well felt. In this article, genetic algorithm and fuzzy logic have been used to present a method for classifying texts in insurance booklets. This system is based on several stages. These steps include the learning phase that examines a set of educational texts to extract the characteristics of the categories to be the characteristics of each category; the test phase of the system is used to classify uncategorized texts. The proposed method's accuracy has been assessed using a collection of patient notebooks, and the outcomes indicate an accuracy of around 98% for the classification.https://ijpte.com/index.php/ijpte/article/view/51knowledge discoverytext classificationgenetic algorithmfuzzy logic |
| spellingShingle | Bahareh Tadayoni Ehsan Amiri Faezeh Soorani Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model International Journal of Pioneering Technology and Engineering knowledge discovery text classification genetic algorithm fuzzy logic |
| title | Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model |
| title_full | Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model |
| title_fullStr | Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model |
| title_full_unstemmed | Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model |
| title_short | Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model |
| title_sort | knowledge in medical prescriptions with the help of genetic fuzzy hybrid model |
| topic | knowledge discovery text classification genetic algorithm fuzzy logic |
| url | https://ijpte.com/index.php/ijpte/article/view/51 |
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