56 citations · 233 across the 22 of their papers we have counts for
5 papers · 1 filter
Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
Diana Magalhães, Eva Maia, João Vitorino +1
Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU res…
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
Tomás Pereira, João Vitorino, Eva Maia +1
Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable. An e…
SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards
João Vitorino, Eva Maia, Isabel Praça +1
Due to the susceptibility of Artificial Intelligence (AI) to data perturbations and adversarial examples, it is crucial to perform a thorough robustness evaluation before any Machi…
From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities
Tiago Dias, Tiago Fonseca, João Vitorino +3
The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential t…
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
João Vitorino, Lourenço Rodrigues, Eva Maia +2
Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairmen…