Publications

Scientific publications produced within the research lines of the PRAIA group.
PRAIA Publications

The most relevant research lines within the PRAIA group are Health (AI/HEALTH), Industrial 3D Inspection (AI/3DII), and Strong Component (AI/SC). In each of them, the aim is to provide a solution, through Artificial Intelligence, to health, industrial, and fundamental research challenges respectively.

Healthcare related scientific publications

This line of work is framed in the area of Bioinformatics and aims to provide novel solutions to problems related to precision medicine and offer help in the investigation of the mechanisms inherent to diseases. In this way, the application of AI techniques, more specifically Machine Learning (ML), and existing computing capabilities to the field of health will make it possible to generate support systems for physicians that facilitate their work and the quality of the services provided, resulting in a better quality of life for patients. Thus, the aim is to apply techniques that have proven their validity and effectiveness in other fields, such as biometrics, handwritten text recognition, and automatic translation to healthcare.

3D Industrial Inspection scientific publications

This line focuses on applying AI in industrial environments for this strategic line, especially for quality control through 3D inspection. It aims to improve industrial processes such as in-line inspection, early detection of defects in the production environment, increasing product quality, and reducing material waste. Machine learning, pattern recognition, and metrology techniques are applied to achieve these objectives, ensuring quality and accuracy standards beyond the reach of traditional industry.

Strong AI component scientific publications

PRAIA has acquired knowledge and conducts research using different techniques in the fields mentioned above. These techniques range from:

  1. Data preprocessing techniques, including normalizations, typifications, missing value management, outlier management, imperfect supervision, principal component, independent component extraction, etc.
  2. Traditional feature extraction techniques based on a priori knowledge, including representation spaces for geometric, statistical, or decision-theoretic based models, metric and pseudo-metric spaces, and syntactic and structural model-oriented encodings.
  3. Feature extraction techniques using ensembles.