Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects

ABSTRACT

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.

KEYWORDS:

• GMM-ubm
• deep neural network (DNN)
• bottleneck feature
• voice signal
• COVID-19 detection

Acknowledgements

This work was supported by the Science and Engineering Research Board (SERB) under the Department of Science and Technology (SRG/2020/002193).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Science and Engineering Research Board [SRG/2020/002193].