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Gesture recognition technology provides multiple opportunities for direct human-computer interaction, without the use of additional external devices. As such, it had been an appealing research area in the field of computer vision. Many of its challenges are related to the complexity of human gestures, which may produce nonlinear distributions under different viewpoints. In this paper, we introduce...
Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose...
In this paper, we propose a novel deep neural network based on learning subspaces and convolutional neural network with applications in image classification. Recently, multistage PCA based filter banks have been successfully adopted in convolutional neural networks architectures in many applications including texture classification, face recognition and scene understanding. These approaches have shown...
In this paper, we present a novel supervised learning algorithm for object recognition from sets of images, where the sets describe most of the variation in an object's appearance caused by lighting, pose and view angle. In this scenario, generalized mutual subspace method (gMSM) has attracted attention for image-set matching due to its advantages in accuracy and robustness. However, gMSM employs...
The Amazon Rainforest degradation is a worldwide concern. The rainforest has been endangered by the illegal wood extraction without control even in the preservation areas. Due to the large geography extension prevent these crimes with an unmanned aerial vehicle (UAV) is not always possible. The Wireless Acoustics Sensor Network (WASNs) technology can alleviate this problem. Here, we present an acoustical...
With the popularization of Internet access, institutions and parents have encountered serious problems to prevent access by employers and children to inappropriate content such as pornographic pages. The detection mechanisms try to circumvent or at least mitigate this problem by using of filters or mechanisms that enable the nudity detection in digital images. This paper proposes ANDImage, an adaptative...
Machine Learning solutions for concept drift detection problems try to decide to what extent a particular set of examples still represents the current concept rather than treating all data equally. Monitoring the set of relevant features used to generate the classification model may be an effective strategy for concept drift detection. This paper focuses on analyzing the possibility of detecting drifts...
The structure of dynamic websites comprised of a set of objects such as HTML tags, script functions, hyperlinks and advanced features in browsers lead to numerous resources and interactiveness in services currently provided on the Internet. However, these features have also increased security risks and attacks since they allow malicious codes injection or XSS (Cross-Site Scripting). XSS remains at...
Automatic nudity detection strategies play an important role in solutions focusing on controlling access to inappropriate content. These strategies usually apply filter or similar approaches in order to detect nudity in digital images. In this paper we propose a strategy for nudity detection based on applying image zoning. Moreover, we perform feature extraction using color and texture information,...
Anurans (frogs or toads) are commonly used by biologists as early indicators of ecological stress. The reason is that anurans are closely related to the ecosystem. Although several sources of data may be used for monitoring these animals, anuran calls lead to a non-intrusive data acquisition strategy. Moreover, wireless sensor networks (WSNs) may be used for such a task, resulting in more accurate...
Dynamic classifier ensemble selection is focused on selecting the most confident classifier ensemble to predict the class of a particular test pattern. The overproduce-and-choose strategy is a dynamic classifier ensemble selection method which is divided into optimization and dynamic selection phases. The first phase involves the test of different candidate ensembles in order to produce a population...
Information fusion research has recently focused on the characteristics of the decision profiles of ensemble members in order to optimize performance. These characteristics are particularly important in the selection of ensemble members. However, even though the control of overfitting is a challenge in machine learning problems, much less work has been devoted to the control of overfitting in selection...
The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose...
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