Search Results for author: Stefano Cagnoni

Found 4 papers, 0 papers with code

Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems

no code implementations12 Jun 2024 Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu

Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Anomaly detection in laser-guided vehicles' batteries: a case study

no code implementations27 Dec 2022 Gianfranco Lombardo, Stefano Cagnoni, Stefano Cavalli, Juan José Contreras Gonzáles, Francesco Monica, Monica Mordonini, Michele Tomaiuolo

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e. g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants.

Anomaly Detection Scheduling +2

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

no code implementations14 Sep 2022 Ying Bi, Bing Xue, Pablo Mesejo, Stefano Cagnoni, Mengjie Zhang

This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis.

Edge Detection Image Classification +4

Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval

no code implementations19 Aug 2019 Federico Magliani, Laura Sani, Stefano Cagnoni, Andrea Prati

We propose to use genetic algorithms to find the optimal setting of all the diffusion parameters with respect to retrieval performance for each different dataset.

Content-Based Image Retrieval Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.