Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies

Researchers have presented two distinct machine learning frameworks: one for longitudinal analysis of major economies through the Global Ease of Living Index, and another for UAV trajectory optimization in 6G cellular systems.

A team of researchers has developed a machine learning framework to quantify the quality of life in major economies using the Global Ease of Living Index, which combines socio-economic and infrastructural factors into a single composite score^[1]. The index, created for major economies since 1970, utilises economic indicators to define living standards and addresses missing data for certain indicators in specific countries. This framework provides a practical tool for policymakers to identify areas needing improvement. Meanwhile, another study introduced a novel UAV trajectory optimization framework, integrating enhanced continual transfer learning within the O-RAN architecture^[2]. The framework leverages real-world city maps and ray tracing techniques to enhance learning reliability and improve trajectory planning. According to the study, the proposed model selection-based transfer learning approach reduces convergence time by 44% to 56% compared to retraining from scratch^[2].