By Sigeru Omatu, Qutaibah M. Malluhi, Sara Rodríguez Gonzalez, Grzegorz Bocewicz, Edgardo Bucciarelli, Gianfranco Giulioni, Farkhund Iqba
The twelfth overseas Symposium on allotted Computing and synthetic Intelligence 2015 (DCAI 2015) is a discussion board to give functions of leading edge recommendations for learning and fixing complicated difficulties. The trade of principles among scientists and technicians from either the educational and commercial area is vital to facilitate the improvement of platforms that could meet the ever-increasing calls for of today’s society. the current version brings jointly prior event, present paintings and promising destiny developments linked to disbursed computing, synthetic intelligence and their software in an effort to supply effective ideas to genuine problems.
This symposium is geared up by way of the Osaka Institute of know-how, Qatar college and the college of Salamanca.
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Additional resources for Distributed Computing and Artificial Intelligence, 12th International Conference
The TPR and FPR are calculated by α × 100 α+β γ × 100 FPR = α+γ TPR = (11) (12) where α means the number of relevant rice ﬁeld extracted, β means the number of relevant rice ﬁeld not extracted, and γmeans the number of irrelevant rice ﬁeld extracted. Extraction rice ﬁeld of SPOT image by supervised MLH was used as reference rice ﬁeld image. Fig. 3. Classiﬁcation result of SPOT image by SOM (White: rice, Light gray: forest, Dark gray: urban) Table 6 shows the results of TPR and FPR by SOM and MLH classiﬁcation for SPOT data and RADARSAT data.
Open systems exist in dynamic operating environments, in which new components may continuously join the system, or already existing components may leave it, and in which the operation conditions might unpredictability change. Therefore, open systems are characterized by the heterogeneity of their participants, their limited confidence, possible individual goals in conflict and a great probability of nonaccordance with specifications. These large systems can quite naturally be seen in terms of the services offered or demanded, and consequently, in terms of the entities (agents) providing or consuming services.
This performance was slightly worse than the Base RCAM case, because the relationship network in our dataset was incomplete. The advance of the indirectly relations, such as followers of followers, was not demonstrated as we expected in retweeting prediction. In the case of Mention RCAM, we appended the mention relationship between users into our dataset. With this feature, we obtained the prediction results with better performance. 9% respectively, as shown in table 2. The result was better than Base RCAM with the introduction of the mention relationship.