São Paulo Federal Institute of Education, Science and Technology

About: São Paulo Federal Institute of Education, Science and Technology is a education organization based out in São Paulo, Brazil. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 1707 authors who have published 2374 publications receiving 11333 citations.

Epileptic Seizure Prediction from EEG Signals Using Unsupervised Learning and a Polling-Based Decision Process

Abstract: Epilepsy is a central nervous system disorder defined by spontaneous seizures and may present a risk to the physical integrity of patients due to the unpredictability of the seizures. It affects millions of people worldwide and about 30% of them do not respond to anti-epileptic drugs (AEDs) treatment. Therefore, a better seizure control with seizures prediction methods can improve their quality of life. This paper presents a patient-specific method for seizure prediction using a preprocessing wavelet transform associated to the Self-Organizing Maps (SOM) unsupervised learning algorithm and a polling-based method. Only 20 min of 23 channels scalp electroencephalogram (EEG) has been selected for the training phase for each of nine patients for EEG signals from the CHB-MIT public database. The proposed method has achieved up to 98% of sensitivity, 88% of specificity and 91% of accuracy. For each subsequence of EEG data received, the system takes less than one second to estimate the patient state, regarding the possibility of an impending seizure.

Association between exposure to particulate matter and hospital admissions for respiratory disease in children

Abstract: O objetivo desse estudo foi estimar a associacao entre exposicao a material particulado com menos de 2,5 micra de diâmetro aerodinâmico e internacoes por doencas respiratorias em criancas. Foi realizado estudo ecologico de series temporais com indicadores diarios de internacao por doencas respiratorias, em criancas de zero a dez anos de idade, residentes em Piracicaba, SP, entre 1o de agosto de 2011 e 31 de julho de 2012. Utilizou-se modelo aditivo generalizado da regressao de Poisson. Os riscos relativos foram RR = 1,008; IC95% 1,001;1,016 para o lag 1 e RR = 1,009; IC95% 1,001;1,017 para o lag 3. O incremento de 10 μg/m 3 de material particulado com menos de 2,5 micra de diâmetro implicou aumento no risco relativo entre 7,9 e 8,6 pontos percentuais. Concluiu-se que a exposicao ao material particulado com menos de 2,5 micra de diâmetro aerodinâmico esteve associada as internacoes por doencas respiratorias em criancas.

Analysis of ISO 9001 certification benefits in Brazilian companies

Abstract: This study aims to analyse the main benefits observed due to ISO 9001:2015 implementation and certification in Brazilian companies. A survey with market professionals was conducted in order to perf...

Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

Abstract: Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.