Optimized DEC : An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19
© 2023 Elsevier Ltd. All rights reserved..
Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
|---|---|
| Enthalten in: |
Biomedical signal processing and control - (2023) vom: 15. Mai, Seite 105026 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Awais, Muhammad [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Revised 01.07.2023 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1016/j.bspc.2023.105026 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM358625548 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM358625548 | ||
| 003 | DE-627 | ||
| 005 | 20231226075139.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.bspc.2023.105026 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1195.xml |
| 035 | |a (DE-627)NLM358625548 | ||
| 035 | |a (NLM)37361196 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Awais, Muhammad |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Optimized DEC |b An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19 |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 01.07.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2023 Elsevier Ltd. All rights reserved. | ||
| 520 | |a Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Corona Virus Disease 2019 | |
| 650 | 4 | |a Cough audio signal | |
| 650 | 4 | |a Empirical Mean Curve Decomposition | |
| 650 | 4 | |a Modified Cat and Mouse Based Optimizer | |
| 650 | 4 | |a Optimal Weighted Feature Selection | |
| 650 | 4 | |a Optimized Deep Ensemble Classifier | |
| 700 | 1 | |a Bhuva, Abhishek |e verfasserin |4 aut | |
| 700 | 1 | |a Bhuva, Dipen |e verfasserin |4 aut | |
| 700 | 1 | |a Fatima, Saman |e verfasserin |4 aut | |
| 700 | 1 | |a Sadiq, Touseef |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biomedical signal processing and control |d 2007 |g (2023) vom: 15. Mai, Seite 105026 |w (DE-627)NLM178615463 |x 1746-8094 |7 nnns |
| 773 | 1 | 8 | |g year:2023 |g day:15 |g month:05 |g pages:105026 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.bspc.2023.105026 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 15 |c 05 |h 105026 | ||