POLITECNICO DI BARI - Catalogo dei prodotti della Ricerca

Sviluppo di una pipeline di machine learning per la modellazione prognostica della malattia di Alzheimer mediante dati multimodali

La malattia di Alzheimer rappresenta una delle principali sfide in ambito neurologico e sanitario a livello globale. Una delle difficoltà maggiori risiede nella diagnosi precoce e nella previsione dell’evoluzione clinica, che può variare significativamente tra individui. Negli ultimi anni, l’uso del machine learning (ML) ha mostrato grande potenziale, ma molti studi in letteratura si focalizzano sull’utilizzo di soli dati di imaging o di biomarcatori, non utilizzando metodi di selezione delle...

AI-Assisted Scan-to-BIM for Masonry Arch Bridges: From Point Cloud Segmentation to Parametric Heritage BIM Reconstruction

This paper shows an AI-assisted workflow supported by a Large Language Model for the geometric and informative reconstruction of masonry arch bridges from multi-source input data. The proposed methodology combines point cloud preprocessing, vegetation filtering, AI-based or semi-automatic segmentation, geometric feature extraction, 3D mesh generation, and parametric Heritage Building Information Modeling integration. The study specifically aims to examine how the quantity and type of input in...

A survey of information retrieval and recommender systems in the era of large language models: Two sides of the same task

Field-level spectral support auditing of legacy land-use inventories using PRISMA hyperspectral data

Regional land-use inventories are frequently used as reference information in agricultural remote sensing, although their labels may be temporally displaced relative to the satellite observations being analysed. This is problematic when legacy polygon labels are interpreted as contemporaneous pixel-level ground truth. We propose a field-level spectral-support audit for the 2016 SIT land-use inventory of Puglia, Southern Italy, using PRISMA Level-2D hyperspectral acquisitions from 2019–2025. T...