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Article
Publication date: 1 March 2003

Sandhya D. Srivastava

120

Abstract

Details

Collection Building, vol. 22 no. 1
Type: Research Article
ISSN: 0160-4953

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Content available
Article
Publication date: 1 June 2004

Sandhya Srivastava

101

Abstract

Details

Collection Building, vol. 23 no. 2
Type: Research Article
ISSN: 0160-4953

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Content available
Article
Publication date: 1 June 2005

Sandhya Srivastava

249

Abstract

Details

Collection Building, vol. 24 no. 2
Type: Research Article
ISSN: 0160-4953

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Content available

Abstract

Details

Collection Building, vol. 23 no. 2
Type: Research Article
ISSN: 0160-4953

Keywords

Abstract

Details

Collection Building, vol. 21 no. 4
Type: Research Article
ISSN: 0160-4953

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Content available
59

Abstract

Details

Collection Building, vol. 21 no. 2
Type: Research Article
ISSN: 0160-4953

Keywords

Content available
79

Abstract

Details

Collection Building, vol. 22 no. 1
Type: Research Article
ISSN: 0160-4953

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Abstract

Details

Collection Building, vol. 21 no. 2
Type: Research Article
ISSN: 0160-4953

Keywords

Content available
Article
Publication date: 23 January 2007

Bradford Lee Eden

246

Abstract

Details

Collection Building, vol. 26 no. 1
Type: Research Article
ISSN: 0160-4953

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Article
Publication date: 24 May 2022

Jawad Ahmad Dar, Kamal Kr Srivastava and Sajaad Ahmad Lone

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more…

Abstract

Purpose

The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult 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 design an effectual Covid-19 detection model using devised JHBO-based 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 roll-off, 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 designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.

Findings

The performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

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 JHBO approach, which is introduced by combining HBA and Jaya algorithm.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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