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

Area of Science:

• Computer Science
• Public Health
• Artificial Intelligence

Background:

• The COVID-19 pandemic necessitates innovative strategies for containment and treatment.
• Global efforts, including lockdowns, have been implemented to curb the spread of COVID-19.
• Smart cities require integrated technological solutions for effective pandemic management.

Purpose of the Study:

• To present a novel framework combining machine learning (ML) and the Internet of Things (IoT) for combating COVID-19 in smart cities.
• To enhance the interoperability of ML algorithms with IoT technology for real-time pandemic response.
• To explore data generation, capture, storage, and analysis using ML for COVID-19 detection, prevention, and tracing.

Main Methods:

• Development of an intelligent framework integrating ML models and IoT technology.
• Utilizing ML algorithms for data analysis to detect, prevent, and trace COVID-19.
• Reviewing case studies on ML applications in hospitals for COVID-19 management.
• Proposing a comprehensive integration of ML with other AI-based solutions.

Main Results:

• The proposed framework facilitates the interaction between populations, environments, and technology for pandemic control.
• ML algorithms demonstrate potential in generating, capturing, storing, and analyzing data for COVID-19 insights.
• Case studies highlight the successful application of ML in hospital settings for fighting COVID-19.
• The framework provides a blueprint for integrating ML and AI for smart city pandemic response.

Conclusions:

• The integrated ML and IoT framework offers a robust approach to curtailing COVID-19 in smart cities.
• This framework can significantly support national healthcare systems in pandemic management.
• The study serves as a foundation for future research and development of improved pandemic response frameworks.