TCC - Bacharelado em Ciência da Computação (UAG)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/2952
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Item Previsão de resultados de jogos do campeonato brasileiro de futebol utilizando aprendizagem de máquina(2019-02-05) Almeida, Luiz Alberes Bispo de; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500Over the past few years, the demand for sports betting has grown, and several people started living off this market. Using Machine Learning with the goal of making game analysis for betting more comfortable as well as measuring profits, a database for the Brazilian Soccer Championship Série A 2017 was created, encompassing features from two opposing teams and the match. For testing in an evaluation model, the Naive Bayes technique was chosen, simulating a Brazilian Championship round, which contains ten matches. The evaluation model was executed both with and without correct probability restrictions, with the goal of reducing errors. Three scenarios were used on the evaluation model, the first having two classes that consider goals by both teams, the second regarding the total match goals (over or below 2.5 goals) and the third considering all three possible match results (a victory for one side, a tie, or a victory for the other side). The results without probability restriction reached its higher value at 5.51% of average and total profit. However, regarding the results with probability restriction, the best average gain was 36.05%, and the best total profit was 39.13%, both using 99% correct probability restriction.Item Sistema de gestão para localização indoor utilizando Wifi Fingerprints e Machine Learning(2019-07-17) Silva, José Kellison de Almeida; Santana, Alixandre Thiago Ferreira de; Pereira, Luis Filipe Alves; http://lattes.cnpq.br/7320714889983490; http://lattes.cnpq.br/3283294973534606; http://lattes.cnpq.br/6271011367469780Software solutions that are dependent on the Global Positioning System (GPS) signal may not display good accuracy in indoor spaces (malls, airports, commercial complexes, etc.), as the signal variations are likely to make it unreliable. This work aims to implement an indoor location management solution using signals from local wireless networks to predict the position of a user. To do so, the solution uses WiFi fingerprints, captured by mobile devices with Android operating system, to build bases of instances representing real positions of a user collected in one of the Federal Rural University of Pernambuco - Academic Unit of Garanhuns (UFRPE - UAG) buildings. The mobile application determines the user’s position and a product location management web tool allows the insertion of user points of interest on the indoor map. To complete the process, the mobile application plots a vector from the predicted position of the user to the position of a specific target object, thus composing a complete and functional solution of indoor routes. The algorithms used to predict the position of the user were random forest, multi layer perceptron and Adaboost. The best indoor localization result was obtained with Adaboost, presenting an average error just under 1 meter from the actual position and 98.64% of the results were under an acceptable margin of error (up to 2 meters).
