Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes ...Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.展开更多
文摘Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which classifies the feature vector as Covid-19 or non-Covid 19.Moreover,the DNFN is trained by devised JHB0 approach,which is introduced by combining HBA and Jaya algorithm.
