A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts
This paper presents a new approach called MultiNeutrosophic Offset Structures (MN-OS) to improve students’ Mental Health Literacy (MHL). The method combines ideas from psychology, education, sociology, and computer science to provide a more personalized and accurate way of understanding and improvin...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-07-01
|
| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | http://fs.unm.edu/NSS/42MultiNeutrosophic.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233664748027904 |
|---|---|
| author | Yan Li |
| author_facet | Yan Li |
| author_sort | Yan Li |
| collection | DOAJ |
| description | This paper presents a new approach called MultiNeutrosophic Offset Structures (MN-OS) to improve students’ Mental Health Literacy (MHL). The method combines ideas from psychology, education, sociology, and computer science to provide a more personalized and accurate way of understanding and improving students’ mental health knowledge. Unlike traditional models, MN-OS allows the levels of truth (T), uncertainty (I), and falsehood (F) to go beyond normal limits (above 1 or below 0), which helps capture extreme cases like strong misconceptions or outstanding understanding. The model represents students’ knowledge as points within a Multiple space and uses new mathematical tools such as custom operators, matrices, and clustering algorithms to group students with similar MHL profiles. Based on these groups, targeted interventions can be designed to address specific needs. To test the model, a simulation was conducted with 300 students. The results showed 94% accuracy in clustering and an 87% improvement in MHL outcomes after the interventions. This demonstrates that the MNOS framework is effective, flexible, and scalable for improving mental health education in diverse student populations. |
| format | Article |
| id | doaj-art-2bc0113378564aacaa523297e2d8a769 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-2bc0113378564aacaa523297e2d8a7692025-08-20T04:03:27ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-07-018767868810.5281/zenodo.15708537 A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary ContextsYan LiThis paper presents a new approach called MultiNeutrosophic Offset Structures (MN-OS) to improve students’ Mental Health Literacy (MHL). The method combines ideas from psychology, education, sociology, and computer science to provide a more personalized and accurate way of understanding and improving students’ mental health knowledge. Unlike traditional models, MN-OS allows the levels of truth (T), uncertainty (I), and falsehood (F) to go beyond normal limits (above 1 or below 0), which helps capture extreme cases like strong misconceptions or outstanding understanding. The model represents students’ knowledge as points within a Multiple space and uses new mathematical tools such as custom operators, matrices, and clustering algorithms to group students with similar MHL profiles. Based on these groups, targeted interventions can be designed to address specific needs. To test the model, a simulation was conducted with 300 students. The results showed 94% accuracy in clustering and an 87% improvement in MHL outcomes after the interventions. This demonstrates that the MNOS framework is effective, flexible, and scalable for improving mental health education in diverse student populations. http://fs.unm.edu/NSS/42MultiNeutrosophic.pdfmultineutrosophic offsetrefined neutrosophic setmental health literacyinterdisciplinary educationknowledge clusteringneutrosophic logic |
| spellingShingle | Yan Li A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts Neutrosophic Sets and Systems multineutrosophic offset refined neutrosophic set mental health literacy interdisciplinary education knowledge clustering neutrosophic logic |
| title | A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts |
| title_full | A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts |
| title_fullStr | A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts |
| title_full_unstemmed | A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts |
| title_short | A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts |
| title_sort | multineutrosophic offset model for clustering and optimizing college students mental health literacy in interdisciplinary contexts |
| topic | multineutrosophic offset refined neutrosophic set mental health literacy interdisciplinary education knowledge clustering neutrosophic logic |
| url | http://fs.unm.edu/NSS/42MultiNeutrosophic.pdf |
| work_keys_str_mv | AT yanli amultineutrosophicoffsetmodelforclusteringandoptimizingcollegestudentsmentalhealthliteracyininterdisciplinarycontexts AT yanli multineutrosophicoffsetmodelforclusteringandoptimizingcollegestudentsmentalhealthliteracyininterdisciplinarycontexts |