| _version_ | 1866901744387096576 |
|---|---|
| author | Mogili Ravinder Ramidi Srinivas Adepu Sindhuja Konda Srija Syed Azmath Joginipally Adithya Rao |
| author_facet | Mogili Ravinder Ramidi Srinivas Adepu Sindhuja Konda Srija Syed Azmath Joginipally Adithya Rao |
| contents | The issue of psychological instability has become a significant factor on a global scale, which has a significant impact on the mental state of a person. Early signs of psychological instability need to be addressed to avoid the development of severe mental health issues, including depression, anxiety disorders, and suicidal tendencies. The traditional methods of identifying psychological instability rely on questionnaires or interviews by mental health professionals. Even though these methods are effective, they are considered to be time-consuming. Therefore, to overcome these issues, a research study has been proposed to identify psychological instability using an automated system based on Artificial Intelligence techniques. The proposed model is based on Machine Learning techniques, which process text data collected from user inputs or social media sites. The text data is analyzed based on various linguistic, emotional, and behavioral features available in the text to classify a person based on psychological stability. The experimental results of the proposed model indicate that psychological instability can be effectively identified using an automated system based on Artificial Intelligence techniques. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19205094 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Automated Identification of Psychological Instability using AI Mogili Ravinder Ramidi Srinivas Adepu Sindhuja Konda Srija Syed Azmath Joginipally Adithya Rao Mental Health Health Monitoring Machine Learning Behavioral Patterns Early Detection Artificial Intelligence The issue of psychological instability has become a significant factor on a global scale, which has a significant impact on the mental state of a person. Early signs of psychological instability need to be addressed to avoid the development of severe mental health issues, including depression, anxiety disorders, and suicidal tendencies. The traditional methods of identifying psychological instability rely on questionnaires or interviews by mental health professionals. Even though these methods are effective, they are considered to be time-consuming. Therefore, to overcome these issues, a research study has been proposed to identify psychological instability using an automated system based on Artificial Intelligence techniques. The proposed model is based on Machine Learning techniques, which process text data collected from user inputs or social media sites. The text data is analyzed based on various linguistic, emotional, and behavioral features available in the text to classify a person based on psychological stability. The experimental results of the proposed model indicate that psychological instability can be effectively identified using an automated system based on Artificial Intelligence techniques. |
| title | Automated Identification of Psychological Instability using AI |
| topic | Mental Health Health Monitoring Machine Learning Behavioral Patterns Early Detection Artificial Intelligence |
| url | https://doi.org/10.5281/zenodo.19205094 |