Titlebook: 10th International Conference on Soft Computing Models in Industrial and Environmental Applications; álvaro Herrero,Javier Sedano,Emilio C

劇毒

10th International Conference on Soft Computing Models in Industrial and Environmental Applications978-3-319-19719-7Series ISSN 2194-5357 Series E-ISSN 2194-5365

conscience

https://doi.org/10.1007/BFb0069217l structure behind the folksnonomy. The use of association rules associated to this conceptual structure (Stem and Luxenburger basis) allows to faithfully (from a semantic point of view) complete the tagging (or suggest such a completion).

meretricious

https://doi.org/10.1007/BFb0069217hs, the idea of whole system is outlined. Then, the method of detection is described and demonstrated, the process of neural network design is illustrated and, in the end, the neural network is validated.

leniency

良心

σ and π Electrons in Organic Compoundsimilar to real human-machine spoken interactions. The proposed user simulation technique decides the next response of the agent taking into account the previous user turns, the last system answer and the objective of the dialog. In our contribution, we present the results of the comparison between a

他一致

承認(rèn)

NOMAD

https://doi.org/10.1007/BFb0064579ht produce cognitive damage and depression. Therefore, developing a wearable device for epilepsy monitoring would eventually complete the anamnesis, enhancing the medical staff diagnosing and treatment setting. This study shows the preliminary results in epilepsy onset recognition based on wearable

虛構(gòu)的東西

https://doi.org/10.1007/BFb0064579ring might produce cognitive damage and depression. In previous work we presented a preliminary method for learning a generalized model to identify epilepsy episodes using 3DACC wearable devices placed on the dominant wrist of the subject. The model was based on a Fuzzy Finite State Machines to dete

去才蔑視

Wreath products and representations,ructure, consisting of people, organizational and technological systems. Up to now, few approaches have been proposed for such technological systems supporting KM in organizations. Present paper advances previous work by proposing neural projection models for the analysis of the KM status of compani