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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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Autores principales: Hasanin, Tawfiq, Kshirsagar, Pravin R., Manoharan, Hariprasath, Sengar, Sandeep Singh, Selvarajan, Shitharth, Satapathy, Suresh Chandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689169/
https://www.ncbi.nlm.nih.gov/pubmed/36428903
http://dx.doi.org/10.3390/diagnostics12112844
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author Hasanin, Tawfiq
Kshirsagar, Pravin R.
Manoharan, Hariprasath
Sengar, Sandeep Singh
Selvarajan, Shitharth
Satapathy, Suresh Chandra
author_facet Hasanin, Tawfiq
Kshirsagar, Pravin R.
Manoharan, Hariprasath
Sengar, Sandeep Singh
Selvarajan, Shitharth
Satapathy, Suresh Chandra
author_sort Hasanin, Tawfiq
collection PubMed
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%.
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spelling pubmed-96891692022-11-25 Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm Hasanin, Tawfiq Kshirsagar, Pravin R. Manoharan, Hariprasath Sengar, Sandeep Singh Selvarajan, Shitharth Satapathy, Suresh Chandra Diagnostics (Basel) Article 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%. MDPI 2022-11-17 /pmc/articles/PMC9689169/ /pubmed/36428903 http://dx.doi.org/10.3390/diagnostics12112844 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hasanin, Tawfiq
Kshirsagar, Pravin R.
Manoharan, Hariprasath
Sengar, Sandeep Singh
Selvarajan, Shitharth
Satapathy, Suresh Chandra
Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689169/
https://www.ncbi.nlm.nih.gov/pubmed/36428903
http://dx.doi.org/10.3390/diagnostics12112844
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