Exploration of despair eccentricities based on scale metrics with feature sampling using a deep learning algorithm
The majority of people in the modern biosphere struggle with depression as a result ofthe coronavirus pandemic’s impact, which has adversely impacted mental health without warning.Even though the majority of individuals are still protected, it is crucial to check for post-corona virussymptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptomsand attacks that are present in the human body, the recommended approach is included. When aharmful virus spreads inside a human body, the post-diagnosis symptoms are considerably moredangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally,if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order toprevent someone from succumbing to depression, the technology of audio prediction is employedto recognise all the symptoms and potentially dangerous signs. Different choral characters areused to combine machine-learning algorithms to determine each person’s mental state. Designconsiderations are made for a separate device that detects audio attribute outputs in order to evaluatethe effectiveness of the suggested technique; compared to the previous method, the performancemetric is substantially better by roughly 67%.
- VoR (Version of Record)
CitationHasanin, T., Kshirsagar, P.R., Manoharan, H., Sengar, S.S., Selvarajan, S. and Satapathy, S.C. (2022) 'Exploration of despair eccentricities based on scale metrics with feature sampling using a deep learning algorithm', Diagnostics, 12(11), p.2844. doi: 10.3390/diagnostics12112844
Cardiff Met Affiliation
- Cardiff School of Technologies
Cardiff Met AuthorsSandeep Singh Sengar
- © The Authors