The increase in the availability of computational resources gave rise to new technologies to estimate the amount of people in a given area. In this context, algorithm-based solutions for crowd counting can be grouped into image-based and non-image based approaches, the latter considering any other feature that is not visual. Currently, due to the popularization of smartphones and mobile devices, several researchers have been using Wi-Fi request packets for crowd counting estimation. Assuming that, on average, each person in a given place carries a Wi-Fi device, the number of unique MAC addresses can be associated with the number of people. However, since the probe may capture all Wi-Fi traffic-which may include broadcast messages from other access points or packets from notebooks and desktop devices-some strategy must be applied in order to identify only personal mobile devices, thus improving the method accuracy. In this work, we trained classifiers to segment mobile from static devices through its Wi-Fi behavior pattern. Therefore, using data collected from different devices and in different environments, we evaluated the proposed methodology by using several machine learning algorithms. Best results were achieved with logistic regression and neural network (MLP). The results of this study suggest the feasibility of the proposed method for crowd counting in high-density Wi-Fi zones.
The evolution of the computational capacity has been helping financial markets to increase the success in their operational running strategies on its investment portfolios. After stock market evolved to make all its operations electronically a new approach called algorithmic trading has gained attention from academic researches. This paper presents a novel method of the dynamic optimization to improve the profit of the algorithmic trading. Combining two genetic algorithms, the proposed approach seek to finding the best optimization and trading window for a trading strategy. The performance of this approach was evaluated with data of the last five years of two stocks traded at the Brazilian Stock Exchange. Comparing the results obtained with classical moving averages indicators, the proposed method performed better in all cases using the complete dataset and using year by year, all experiments using shares of PETR4. These results suggest that the discovery of the optimal trading and optimization window we can improve the system trading strategy and lead to increased profits.
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