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Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.

Students' dropout is certainly one of the major problems that afflict educational institutions, the losses caused by the student's abandonment are social, academic and economic waste. The quest for its causes has been subject of work and educational research around the world. Several organizations seek strategic decisions to control the dropout rate. This work's goal is to evaluate the effectiveness of the most used data mining algorithms in the education area. An "in vivo" controlled experiment was planned and performed to compare the efficacy selected classifiers. The Random Forest and SVM algorithms have stood out in this context, having, statistically similar accuracy (80.36%, 81.18%), precision (80.79%, 80.25%), recall (76.50%, 77.51%) and f-measure (78.86%, 78.81%) averages. The results showed evidence of significant differences between the algorithms, and also showed that, although the SVM had the best metric of accuracy and recall, it results were statistically similar with Random Forest results. 1 www.educationaldatamining.org Communication Papers of the Federated Conference on Computer Science and Information Systems pp. 3-10