With multimedia applications, the Internet traffic increased a lot in volume, but also suffered major alterations in its nature, requesting other network services besides the current best-effort service. To satisJL this new situation, network architectures QoS-oriented such as the Integrated Services and Differentiated Services architectures have been discussed in several IETF groups. In this work we propose a methodology to implement packet schedulers, an element of fundamental importance to the new services, based on f u u y control.The main innovation of this proposal is the employment of rule induction algorithms as C4.5 to infer the fuzzy controller's main rules. The approach has several advantages over the current ones, specially flexibility, efficiency and the possibility of being applied in several environments.
Sentiment analysis of texts posted on Twitter is a natural language processing task whose importance has grown along with the increase in the number of users of the platform and the interest of organizations on the opinions of their employees, customers and users.Although Brazil is the sixth country in the world with most active users of Tweeter and Portuguese is the seventh most spoken language in the world, with 221 million speakers (200 million of them living in Brazil), the number of articles that discuss sentiment analysis approaches for Brazilian Portuguese is a small fraction of those that focus on the English language. On the other hand, few works use deep learning for this task when compared with other machine learning and lexical based methods. In this context, the work described in this article addresses the problem using Convolutional Neural Networks (CNN). The paper presents the results of an experimental evaluation that shows that a CNN with a relatively simple architecture can perform much better than a previous approach that uses ensembles of other machine learning classifiers combined with text preprocessing heuristics
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