This activity aims to identify the state-of-the-art in the application of ML algorithms in specific areas:

• In the smart city health area and, more specifically, in the context of the spread of infectious diseases; and

• In smart city energy efficiency with 5G/6G network slicing.

This activity uses machine learning techniques to identify and assess health problems in cities and regions with urban clusters.

Intelligent identification and evaluation of the impact of health problems in urban regions will focus mainly on cases of the spread of infectious diseases.

This activity aims to identify and analyze the innovation and impact of new engineering and architectural approaches for smart cities.

This activity aims to identify and analyze the performance of network slicing strategies as a smart city infrastructure enabler that observes sustainable energy efficiency criteria.

The research's primary focus is on investigating and deploying 5G and 6G mobile networks in the context of the SFI2 (Slicing Future Internet Infrastructures) network slicing reference architecture for smart cities.