The Covid-19 virus is fast spreading disease in globally, which threateness billions of human begins. In this paper, Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is introduced for Covid-19 prediction by audio signal. Here, Covid-19 prediction is done using DNFN, and it is trained by developed JHBO algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. 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. An effective Covid-19 detection technique is introduced based on hybrid optimization driven deep learning model. The Deep Neuro Fuzzy network (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 Jaya Honey Badger Optimization (JHBO) approach, which is introduced by combining Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. An effectual Covid-19 detection model using devised Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is developed in this paper. 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. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy persons sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. This paper explicates the Covid-19 detection approach using designed Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) with audio sample. The series of steps followed for introduced Covid-19 diagnosis model are pre-processing, feature extraction, and classification. The input audio sample is acquired from a Coswara dataset and gaussian filter is applied. The gaussian filter effectively reduces the salt and pepper noise with minimal duration. Feature extraction process is most significant for precise detection of Covid-19, where spectral bandwidth, spectral roll off, Spectral flatness, Mel frequency cepstral coefficients (MFCC), spectral centroid, root mean square energy, spectral contract, and zero crossing rate are extracted. The Deep learning approach is effectual for disease detection and classification process in medical field. Here, DNFN is utilized for detecting the Covid-19 disease. Moreover, DNFN is trained by developed JHBO approach for obtaining better performance. The proposed JHBO algorithm is newly devised by combining Jaya algorithm and HBA. Here, Jaya algorithm is incorporated with HBA for obtaining improved performance with better convergence speed. The performance of DNFN is estimated with three performance metrics, namely specificity, testing accuracy and sensitivity. The proposed JHBO-based DNFN achieved improved performance testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Proposed Methodology The main aim of this research is to design and develop COVID-19 Diagnosis model using respiratory sounds. The series of steps followed for the COVID-19 Diagnosis are pre-processing, feature extraction, and classification. Initially, the input audio samples will be fed into the pre-processing module wherein the noise and artifacts contained in the audio samples will be discarded using filtering technique. Then, the pre-processed audio samples will be fed into the feature extraction module. Here, the features, like spectral contrast, Mel frequency cepstral coefficients (MFCC) [21], spectral roll-off, spectral centroid, mean square energy, zero-crossing rate, spectral bandwidth, and spectral flatness will be extracted. Finally, the classification will be done using deep neuro fuzzy network [11] wherein the training of deep neuro fuzzy network will be done using Jaya honey badger optimization (JHBO) algorithm. The proposed JHBO algorithm will be newly devised by combining Jaya algorithm [12] and honey badger optimization (HBA) [10]. The proposed JHBO-based deep neuro fuzzy network will be implemented in Matlab tool using Coswara-data [9]. The performance analysis of the proposed JHBO-based deep neuro fuzzy network will be done based on the metrics, like accuracy, sensitivity and specificity, and the results of proposed method will be compared with the other existing techniques. Specifically, the proposed method of COVID-19 diagnosis model using JHBO-based deep neuro fuzzy network will be compared with the existing methods [4], [2], and [5] in order to reveal the performance of the proposed method. As per discussion with guide,he told me that you should verify results after thed deduction with k fold validation method. Figure 1 shows the block diagram of COVID-19 diagnosis model using developed JHBO-based deep neuro fuzzy network.