By Hiroshi Sakai, Mao Wu, Michinori Nakata, Dominik Ślęzak (auth.), Aboul Ella Hassanien, Abdel-Badeeh M. Salem, Rabie Ramadan, Tai-hoon Kim (eds.)
This booklet constitutes the refereed complaints of the 1st foreign convention on complex computer studying applied sciences and functions, AMLTA 2012, held in Cairo, Egypt, in December 2012. The fifty eight complete papers provided have been rigorously reviewed and chosen from ninety nine intial submissions. The papers are prepared in topical sections on tough units and functions, computer studying in development popularity and picture processing, desktop studying in multimedia computing, bioinformatics and cheminformatics, facts category and clustering, cloud computing and recommender systems.
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Additional info for Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012. Proceedings
Example 2. Consider a parameterised fuzzy Petri net in Figure 3(a). For the net we have: the set of places P , the set of transitions T , the input function I, the output function O, the set of statements S, the binding function α, the truth degree function β, the threshold function γ and the initial marking M 0 described analogously to Example 1. )) with a relevant parameter value v. (a) (b) Fig. 3. (a) A parameterised fuzzy Petri net with the initial marking before ﬁring the enabled transitions t1 and t2 .
33–42, 2012. c Springer-Verlag Berlin Heidelberg 2012 34 Z. Suraj s-norms. The new model is more ﬂexible than the GF P N one as in the former class the user has the chance to deﬁne the parameterised input/output operators. There has been intensive research in the ﬁeld of logical operators carried out for the last three decades, which involves the development of parameterised families of sums and products . The preliminary results of real-life data experiments using the proposed model are promising.
Usually, this number is interpreted as a truth degree of an implication corresponding to a given transition t ,. The meaning of the threshold function γ is explained below. The operator binding function δ connects transitions with triples of operators (opIn , opOut1 , opOut2 ). The ﬁrst operator appearing in this triple is called the input operator, and two remaining ones are called the output operators. The input operator opIn belongs to one of the classes: t-norms or s-norms. It concerns the way in which all input places are connected to a given transition t (more precisely, statements corresponding to those places).