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 of the 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 virus symptoms if...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/11/2844 |
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| author | Tawfiq Hasanin Pravin R. Kshirsagar Hariprasath Manoharan Sandeep Singh Sengar Shitharth Selvarajan Suresh Chandra Satapathy |
| author_facet | Tawfiq Hasanin Pravin R. Kshirsagar Hariprasath Manoharan Sandeep Singh Sengar Shitharth Selvarajan Suresh Chandra Satapathy |
| author_sort | Tawfiq Hasanin |
| collection | DOAJ |
| description | The majority of people in the modern biosphere struggle with depression as a result of the 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 virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, 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 to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%. |
| first_indexed | 2024-03-09T18:24:00Z |
| format | Article |
| id | doaj.art-ef78acb1b3e2443a925e577ff508955c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T18:24:00Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ef78acb1b3e2443a925e577ff508955c2023-11-24T08:05:10ZengMDPI AGDiagnostics2075-44182022-11-011211284410.3390/diagnostics12112844Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning AlgorithmTawfiq Hasanin0Pravin R. Kshirsagar1Hariprasath Manoharan2Sandeep Singh Sengar3Shitharth Selvarajan4Suresh Chandra Satapathy5Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Artificial Intelligence, G.H Raisoni College of Engineering, Nagpur 440016, IndiaDepartment of Electronics and Communication Engineering, Panimalar Engineering College, Chennai 600123, IndiaDepartment of Computer Science, Cardiff Metropolitan University, Cardiff CF5 2YB, UKDepartment of Computer Science, Kebri Dehar University, Kebri Dehar 001, EthiopiaSchool of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, IndiaThe majority of people in the modern biosphere struggle with depression as a result of the 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 virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, 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 to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.https://www.mdpi.com/2075-4418/12/11/2844audio featuresmental imbalancedepression predictiondeep learning |
| spellingShingle | Tawfiq Hasanin Pravin R. Kshirsagar Hariprasath Manoharan Sandeep Singh Sengar Shitharth Selvarajan Suresh Chandra Satapathy Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm Diagnostics audio features mental imbalance depression prediction deep learning |
| title | Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm |
| title_full | Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm |
| title_fullStr | Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm |
| title_full_unstemmed | Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm |
| title_short | Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm |
| title_sort | exploration of despair eccentricities based on scale metrics with feature sampling using a deep learning algorithm |
| topic | audio features mental imbalance depression prediction deep learning |
| url | https://www.mdpi.com/2075-4418/12/11/2844 |
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