Autor(es): Adalbery R. Castro, Lilian C. Freitas, Claudomir C. Cardoso, João C.W.A. Costa and Aldebaro B.R. Klautau.
Local de publicação: Foundation of Cognitive Radio Systems, ISBN: 978-953-51-0268-7. Publisher: InTech, Published: March 16, 2012 under CC BY 3.0 license, in subject Electrical and Electronic Engineering.
Autor(es): Ádamo Lima Santana; Carlos Renato Francês; João Weyl Costa
Abstract:
One of the main factors for the success of data mining is related to the comprehensibility of the patterns discovered by the computational intelligence techniques; with Bayesian networks standing as one of the most prominent, when considering the easiness of knowledge interpretation achieved. Its quantitative and qualitative semantics, allied to the comprehensibility of the patterns discovered, motivates its application in the knowledge discovery process. Bayesian networks, however, like any computational intelligence technique, presents limitations and disadvantages regarding its use; amongst which we can point the learning of the structure from large datasets and the provision of inferences throughout time. This book will show extensions for the improvement of Bayesian networks, presenting strategies to improve its properties, treating aspects such as performance, as well as interpretability and use of its results; incorporating models of multiple regression for structure learning, and temporal aspects using Markov chains. The models should help users extending the range of applicability of this versatile model for new domains and tasks.
Autor(es): Lilian Freitas, Yomara Pires, Jefferson Morais, João Costa and Aldebaro Klautau
Local de publicação: Advances in Data Mining Knowledge Discovery and Applications, chapter 9, ISBN 978-953-51-0748-4, Published: September 12, 2012 under CC BY 3.0 license
Autor(es): Silvana Rossy de Brito, Aleksandra do Socorro da Silva, Dalton Lopes Martins, Cláudio Alex Jorge da Rocha, João Crisóstomo Weyl Albuquerque Costa and Carlos Renato Lisboa Francês.
Abstract:
This chapter summarizes several previous studies on the analysis of social networks and presents some challenges in monitoring and evaluating large-scale training programs that make use of social networks. The main objective is to understand the dynamics and identify how information is shared among the participating agents of the training program. In this regard, the authors present various algorithms that apply metrics to social network analysis to assess the evolution of networks throughout the training process, and specifically, to discuss the application of these metrics in the evaluation of large-scale training programs for digital inclusion.
Autor(es): Antonio F. L. Jacob, Eulália C. da Mata, Ádamo L. Santana, Carlos R. L. Francês, João C. W. A. Costa and Flávia de A. Barros.
Abstract:
The Web is providing greater freedom for users to create and obtain information in a more dynamic and appropriate way. One means of obtaining information on this platform, which complements or replaces other forms, is the use of conversation robots or Chatterbots. Several factors must be taken into account for the effective use of this technology; the first of which is the need to employ a team of professionals from various fields to build the knowledge base of the system and be provided with a wide range of responses, i.e. interactions. It is a multidisciplinary task to ensure that the use of this system can be targeted to children. In this context, this chapter carries out a study of the technology of Chatterbots and shows some of the changes that have been implemented for the effective use of this technology for children. It also highlights the need for a shift away from traditional methods of interaction so that an affective computing model can be implemented.