2UrbanGirls on MSN

6 no-code AI app builders that survived real testing

I spent three weeks testing no-code AI app builders after watching demos that promised perfect apps from single sentences.
IEEE

Machine-Learning-Based Urban Path Loss Prediction at 900 MHz: Principal Component Analysis, Clustering, Feature Importance and Regression

Abstract: In this work, the possibility of applying machine learning (ML) techniques to analyze and predict radio wave propagation losses in urban environments is explored. Thus, from a measurement ...

Need a new path in midlife? There's a school for that and a quiz to kickstart it

Schools across the country are offering courses and retreats for people 50+ who want to reinvent themselves and embrace ...
GitHub

Rethinking Multi-Instance Learning through Graph-Driven Fusion: A Dual-Path Approach to Adaptive Representation

GDF-MIL (Graph-Driven Fusion Multiple Instance Learning) is a novel graph-driven multi-instance learning framework that adaptively balances topology modeling and semantic feature preservation through ...
IEEE

SimPRL: A Simple Contrastive Learning for Path Representation Learning by Joint GPS Trajectories and Road Paths

Abstract: Path representations are widely applied in smart-city tasks, such as travel time estimation, road classification, and trajectory search. Existing approaches effectively learn these ...