A Novel Smart City Based Framework on Perspectives for application of Machine Learning in combatting COVID-19

Abstract

The spread of COVID-19 across the world continues as efforts are being made from multi-dimension to curtail its spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent its spread. COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day. In this paper, we propose a framework integrated with machine learning to curtail the spread of COVID-19 in smart cities. A novel mathematical model is created to show the spread of the COVID-19 in smart cities. The proposed solution framework can generate, capture, store and analyze data using machine learning algorithms to detect, prevent the spread of COVID-19, forecast next epidemic, effective contact tracing, diagnose cases, monitor COVID-19 patient, COVID-19 vaccine development, track potential COVID-19 patients, aid in COVID-19 drug discovery and provide better understanding of the virus in smart cities. The study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals in smart cities across the world. The framework can provide a guide for real world execution in smart cities. The proposed framework has the potential for helping national healthcare systems in curtailing the COVID-19 pandemic in smart cities.

Applications of artificial intelligence in battling against covid-19: A literature review

“…The application automatically records interactions between people and offers a self-assessment tool for monitoring the symptoms. In order to detect and prevent the spread of the pandemic and forecast the next epidemic and effective contact tracing, a machine learning modeling algorithm is proposed in [507] .…”

Future Smart Cities: Requirements, Emerging Technologies, Applications, Challenges, and Future Aspects

“…Rather their applications are extensively found in the field of medicine and safety precaution systems as well. The authors in [132] proposed a ML approach to detect the COVID-19 spread across the smart cities. Their study also covers the research areas of predicting the next epidemic, effective contact tracing, diagnose COVID-19 cases, monitor COVID-19 patients, COVID-19 vaccine development, tracking potential COVID-19 patients, and aiding in COVID-19 drug discovery.…”

Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN

“…Moreover, the CAD deep learning approach showed greater reliability in assisting health care systems, patients, and physicians to deliver their practical validations. For a comprehensive review of existing machine learning models for COVID-19, interested readers are referred to the following references [30, 31].…”

“…Although different artificial intelligence approaches also exist, like the case-based reasoning (CBR) [25] which have been applied to the detection of this disease, CNN methods however have shown to be more effective and promising. Several studies [4, 5, 6, 78, 26, 30] and reviews which have adapted CNN to the task of detection and classification of COVID-19 have proven that the deep learning model is one of the most popular and effective approaches in the diagnosis of COVD-19 from digitized images. This outstanding performance of CNN is due to its ability to learn features automatically from digital images as has been applied to diagnoses of COVID-19 based on clinical images, CT scans, and X-rays of the chest by researchers.…”