Carlos Ferreira

Domains

Artificial Intelligence and Decision Support Artificial Intelligence

Projects

NanoStima-RL5

The goal of CAD-RL5 is to develop advanced capabilities for computer-aided detection and diagnosis (CAD). This requires research on innovative methodologies for CAD development, that will make it possible to go from ad hoc engineering approaches, driven by direct expert knowledge, to more automated approaches, driven by the intrinsic structure of data, knowledge discovery and expert supervision. Problems tackled will be generic in the sense that appropriate outcomes can be applied universally to medical imaging practices. The developed method will enable lab demonstrations of several clinical problems where the research team has relevant experience (e.g. radiology, ophthalmology and ultrasound imaging). Project Information Sheet (PT)

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

AgWearCare

Publications

Map-matching methods in agriculture

Silva, A;Mendes Moreira, J;Ferreira, C;Costa, N;Dias, D;

2024

COMPUTERS AND ELECTRONICS IN AGRICULTURE

Modeling the Ink Tuning Process Using Machine Learning

Costa, C;Ferreira, CA;

2023

Intelligent Data Engineering and Automated Learning - IDEAL 2023 - 24th International Conference, Évora, Portugal, November 22-24, 2023, Proceedings

Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study

Pinho, C;Kaliontzopoulou, A;Ferreira, CA;Gama, J;

2022

ZOOLOGICAL JOURNAL OF THE LINNEAN SOCIETY

Development and testing of improved statistical wind power forecasting methods.

Mendes, J;Bessa, R;Keko, H;Sumaili, J;Miranda, V;Ferreira, C;Gama, J;Botterud, A;Zhou, Z;Wang, J;

2011

View all publications

Supervised theses

Verificação e descoberta de modelos probabilísticos no Alloy Analyser.

Pedro Faria Durães da Silva

D - 2023

UM-EEUM

Centres

Artificial Intelligence and Decision Support

Our Laboratory of Artificial Intelligence and Decision Support (LIAAD) conducts research in the fields of Artificial Intelligence, Machine Learning, Data Science, and Modelling. These areas are cross-cutting and apply to all sectors of society and the economy. The vast amounts of data being collected, alongside the ubiquity of digitalisation and sensorisation, are increasingly creating opportunities and challenges for automating decision support. The combination of Machine Learning and complex models is transforming the economy, healthcare, justice, industry, science, public administration, and education. This encourages us to invest in diverse technological and scientific approaches and perspectives. Our overarching strategy is to explore the flow and diversification of data, and to invest in research lines that will lead to the development of applied Artificial Intelligence foundations and models that are responsible and human centred.