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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.

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.

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.

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.

The proposed Covid-19 detection method is useful in various applications, like medical and so on.

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.

The purpose of this study is to investigate the impact of Corona Virus Disease 2019 (COVID-19) on the outcome of construction projects and explore the moderating effects of emerging technologies on the relationship between COVID-19 and construction project outcomes.

Data for the study was collected through a Web-based, semistructured questionnaire. The responses of 62 construction practitioners were analyzed using a hierarchical linear regression model. The model consists of 16 independent variables, three control variables (organization size, organization type and project size), one moderator (adoption level of emerging technologies) and three dependent variables (project time, project cost and project quality).

The study confirms the negative significant impact of the COVID-19 pandemic on the performance of construction projects. It also identifies the significant moderating effects of emerging technologies in mitigating the impact of COVID-19 on construction projects. Further, it shows a significant increase in the application of emerging technologies in construction projects during the COVID-19 pandemic. Based on the findings related to the moderating impact of the technology, this study provides a clear set of recommendations for construction firms, public sector and research community in combating the unavoidable situation similar to the COVID-19 pandemic in the future.

To the best of the authors’ knowledge, this is the first study to identify the moderating role of technology on the impact of COVID-19 on the performance of the construction sector in Pakistan. The findings can also be used for the construction sectors of other developing countries.