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 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%.

[1]  Hassan A. Alterazi,et al.  Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm , 2022, Frontiers in public health.

[2]  Hassan A. Alterazi,et al.  Deep Conviction Systems for Biomedical Applications Using Intuiting Procedures With Cross Point Approach , 2022, Frontiers in Public Health.

[3]  Radha Raman Chandan,et al.  Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor , 2022, INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL.

[4]  Venkatesa Prabhu Sundramurthy,et al.  Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach , 2022, Journal of healthcare engineering.

[5]  Jeff S. Shamma,et al.  An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing , 2021, IEEE Access.

[6]  Bin Hu,et al.  A Gait Assessment Framework for Depression Detection Using Kinect Sensors , 2021, IEEE Sensors Journal.

[7]  Hariprasath Manoharan,et al.  An operative constellation rate for smart safety units using Internet of Things , 2020, Concurr. Comput. Pract. Exp..

[8]  Shreya Patel,et al.  Emotion and Depression Detection from Speech , 2020, ICT Analysis and Applications.

[9]  Ascensión Gallardo-Antolín,et al.  Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks , 2020, Entropy.

[10]  José Balbuena,et al.  Depression Detection Using Audio-Visual Data and Artificial Intelligence: A Systematic Mapping Study , 2020 .

[11]  Shruti Telang,et al.  EMOTION DETECTION USING AUDIO DATA SAMPLES , 2019 .

[12]  David C. Atkins,et al.  Depression Screening from Voice Samples of Patients Affected by Parkinson’s Disease , 2019, Digital Biomarkers.

[13]  Mark Swainson,et al.  Deep Bayesian Self-Training , 2018, Neural Computing and Applications.

[14]  Michael Riegler,et al.  Mental health monitoring with multimodal sensing and machine learning: A survey , 2018, Pervasive Mob. Comput..

[15]  Gang Wang,et al.  Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features , 2018, Comput. Math. Methods Medicine.

[16]  Mandar Deshpande,et al.  Depression detection using emotion artificial intelligence , 2017, 2017 International Conference on Intelligent Sustainable Systems (ICISS).

[17]  Fan Yang,et al.  Facial geometry and speech analysis for depression detection , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Alex Kendall,et al.  What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.

[19]  M. Jagannath,et al.  Advances in biomedical signal and image processing – A systematic review , 2017 .

[20]  Bin Hu,et al.  Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram , 2017 .

[21]  Vijay M. Wadhai,et al.  Clinical Depression Analysis Using Speech Features , 2013, 2013 6th International Conference on Emerging Trends in Engineering and Technology.

[22]  Thomas F. Quatieri,et al.  On the relative importance of vocal source, system, and prosody in human depression , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[23]  Nicholas B. Allen,et al.  Detection of Clinical Depression in Adolescents’ Speech During Family Interactions , 2011, IEEE Transactions on Biomedical Engineering.