853 citations · 1.5k across the 31 of their papers we have counts for
33 papers
GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes
Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato +1
Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes. Th…
Subject-Conditioned Glucose Forecasting in Type-1 Diabetes
Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli +1
Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely thera…
Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Models
Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli +1
Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption…
The ISLab Solution to the Algonauts Challenge 2025: A Multimodal Deep Learning Approach to Brain Response Prediction
Andrea Corsico, Giorgia Rigamonti, Simone Zini +2
In this work, we present a network-specific approach for predicting brain responses to complex multimodal movies, leveraging the Yeo 7-network parcellation of the Schaefer atlas. R…
On using AI for EEG-based BCI applications: problems, current challenges and future trends
Thomas Barbera, Jacopo Burger, Alessandro D'Amelio +6
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how…
Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes
Mirko Paolo Barbato, Giorgia Rigamonti, Davide Marelli +1
Type 1 Diabetes (T1D) affects millions worldwide, requiring continuous monitoring to prevent severe hypo- and hyperglycemic events. While continuous glucose monitoring has improved…
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
Luigi Celona, Simone Bianco, Paolo Napoletano
The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver…
Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding
Hamza Amrani, Daniela Micucci, Paolo Napoletano
Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invas…
Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset
Simone Zini, Mirko Paolo Barbato, Flavio Piccoli +1
In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the a…
Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion
Luigi Celona, Simone Bianco, Marco Donzella +1
State-of-The-Art (SoTA) image captioning models are often trained on the MicroSoft Common Objects in Context (MS-COCO) dataset, which contains human-annotated captions with an aver…
Unsupervised Deep Learning-based clustering for Human Activity Recognition
Hamza Amrani, Daniela Micucci, Paolo Napoletano
One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled d…
A unified platform for GIS data management and analytics
Flavio Piccoli, Simone Locatelli, Paolo Napoletano +1
In this work we propose a software platform for the collection, visualization, management and analysis of heterogeneous and multisource data for soil characteristics estimation. Th…
A deep scalable neural architecture for soil properties estimation from spectral information
Flavio Piccoli, Micol Rossini, Roberto Colombo +2
In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method…
A health telemonitoring platform based on data integration from different sources
Gianluigi Ciocca, Paolo Napoletano, Matteo Romanato +1
The management of people with long-term or chronic illness is one of the biggest challenges for national health systems. In fact, these diseases are among the leading causes of hos…
Semi-supervised cross-lingual speech emotion recognition
Mirko Agarla, Simone Bianco, Luigi Celona +5
Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingu…
Unified Framework for Identity and Imagined Action Recognition from EEG patterns
Marco Buzzelli, Simone Bianco, Paolo Napoletano
We present a unified deep learning framework for the recognition of user identity and the recognition of imagined actions, based on electroencephalography (EEG) signals, for applic…
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels
Mirko Paolo Barbato, Paolo Napoletano, Flavio Piccoli +1
In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a…
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition
Hamza Amrani, Daniela Micucci, Marco Mobilio +1
Several techniques have been proposed to address the problem of recognizing activities of daily living from signals. Deep learning techniques applied to inertial signals have prove…
Composition and Style Attributes Guided Image Aesthetic Assessment
Luigi Celona, Marco Leonardi, Paolo Napoletano +1
The aesthetic quality of an image is defined as the measure or appreciation of the beauty of an image. Aesthetics is inherently a subjective property but there are certain factors…
Personalization in Human Activity Recognition
Anna Ferrari, Daniela Micucci, Marco Mobilio +1
In the recent years there has been a growing interest in techniques able to automatically recognize activities performed by people. This field is known as Human Activity recognitio…
Disentangling Image Distortions in Deep Feature Space
Simone Bianco, Luigi Celona, Paolo Napoletano
Previous literature suggests that perceptual similarity is an emergent property shared across deep visual representations. Experiments conducted on a dataset of human-judged image…
A Platform to Collect, Unify, and Distribute Inertial Labeled Signals for Human Activity Recognition
Anna Ferrari, Daniela Micucci, Marco Mobilio +1
Human activity recognition (HAR) is a very active research field. Recently, deep learning techniques are being exploited to recognize human activities from inertial signals. Howeve…
Multitask Painting Categorization by Deep Multibranch Neural Network
Simone Bianco, Davide Mazzini, Paolo Napoletano +1
In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation. In order to gather clues fro…
Benchmark Analysis of Representative Deep Neural Network Architectures
Simone Bianco, Remi Cadene, Luigi Celona +1
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance…