Renato Cerceau

A computational approach for screening dyslexia

This paper aims to present a computational tool based on neural networks to detect people with possible learning disabilities (dyslexia). Participants were aged from nine to 18 years old with or without the diagnosis of dyslexia. Neural networks technique showed consistency in dealing with the problems of pattern recognition in the screening of dyslexia.

Data Provenance in Agriculture

Soils are probably the most critical natural resource in Agriculture, and soils security represents a critical growing global issue. Soils experiments require vast amounts of high-quality data, are very hard to be reproduced, and there are few studies about data provenance of such tests. We present OpenSoils; it shares knowledge about data-centric soils experiments. OpenSoils is a provenance-oriented and lightweight e-infrastructure that collects, stores, describes, curates and, harmonizes various soil datasets.

Computational Neuroscience

Understanding the core function of the brain is one the major challenges of our times. In the areas of neuroscience and education, several new studies try to correlate the learning difficulties faced by children and youth with behavioral and social problems. This work aims to present the challenges and opportunities of computational neuroscience research, with the aim of detecting people with learning disorders. We present a line of investigation based on the key areas: neuroscience, cognitive sciences and computer science, which considers young people between nine and eighteen years of age, with or without a learning disorder. The adoption of neural networks reveals consistency in dealing with pattern recognition problems and they are shown to be effective for early detection in patients with these disorders. We argue that computational neuroscience can be used for identifying and analyzing young Brazilian people with several cognitive disorders.