Sara Silva
YOU?
Author Swipe
THE USE OF INFORMATION TECHNOLOGY IN EDUCATION: THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ONLINE SYSTEMS Open
In a Brazilian educational context, it is not uncommon to see tools that improve and stimulate learning. This article aims to investigate the possible applications of Information Technology in education, focusing on the hypothesis that Art…
Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study Open
A multimodal machine learning model combining radiomic, radiologic, and clinical variables reduced unnecessary prostate biopsies while maintaining high sensitivity for clinically significant cancer detection.
Evolving Financial Trading Strategies with Vectorial Genetic Programming Open
Establishing profitable trading strategies in financial markets is a challenging task. While traditional methods like technical analysis have long served as foundational tools for traders to recognize and act upon market patterns, the evol…
Graph Contrastive Learning for Connectome Classification Open
With recent advancements in non-invasive techniques for measuring brain activity, such as magnetic resonance imaging (MRI), the study of structural and functional brain networks through graph signal processing (GSP) has gained notable prom…
Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning–assisted Prostate Cancer Classification Using Multiparametric MRI Open
Aggressiveness of prostate cancer could be predicted using biparametric MRI and deep learning with negligible expert input, but performance was affected by scanner manufacturer and scanning protocol.
Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction Open
PURPOSE Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing…
Benchmark datasets for biomedical knowledge graphs with negative statements Open
We present a collection of datasets for three relation prediction tasks - protein-protein interaction prediction, gene-disease association prediction and disease prediction - that aim at circumventing the difficulties in building benchmark…
Explaining protein–protein interactions with knowledge graph-based semantic similarity Open
The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a ke…
Kawasaki disease with atypical initial symptoms: a case report Open
Introduction: Kawasaki disease (KD) is an acute, self-limited, systemic vasculitis of unknown etiology. The diagnosis is essentially clinical and the treatment is based on the use of intravenous human immunoglobulin (IVIG) and acetylsalicy…
Testing the Segment Anything Model on radiology data Open
Deep learning models trained with large amounts of data have become a recent and effective approach to predictive problem solving -- these have become known as "foundation models" as they can be used as fundamental tools for other applicat…
Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming Open
We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving …
Biomedical Knowledge Graph Embeddings with Negative Statements Open
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, espec…
IoT based Plant Growth and Health Monitoring System for Mushrooms Open
This research paper proposes the development of an IoT-based monitoring system using a robot camera for mushroom cultivation in Sri Lanka. The system aims to address several sub-objectives, including harvest time prediction, disease detect…
Explainable Representations for Relation Prediction in Knowledge Graphs Open
Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent…
Benchmark datasets for biomedical knowledge graphs with negative statements Open
Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, they have been demonst…
A Comparison of Structural Complexity Metrics for Explainable Genetic Programming Open
Rebuli, K. B., Giacobini, M., Silva, S., & Vanneschi, L. (2023). A Comparison of Structural Complexity Metrics for Explainable Genetic Programming [Poster]. In S. Silva, & L. Paquete (Eds.), GECCO '23 Companion: Proceedings of the Companio…
Evolutionary Algorithms for Segment Optimization in Vectorial GP Open
875441 Vektor-basierte Genetische Programmierung für Symbolische Regression und Klassifikation mit Zeitreihen (SymRegZeit), funded by the Austrian Research Promotion Agency FFG. It was also partially supported by FCT, Portugal, through fun…