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Hard and Soft Computing for Artificial Intelligence, by Shin-ya Kobayashi, Andrzej Piegat, Jerzy Pejaś, Imed El

By Shin-ya Kobayashi, Andrzej Piegat, Jerzy Pejaś, Imed El Fray, Janusz Kacprzyk

This booklet gathers the court cases of the twentieth foreign convention on complex computers 2016, held in Międzyzdroje (Poland) on October 19–21, 2016. Addressing issues that come with synthetic intelligence (AI), software program applied sciences, multimedia structures, IT protection and layout of data structures, the most goal of the convention and the booklet is to create a chance to switch major insights in this zone among technological know-how and enterprise. particularly, this services matters using challenging and gentle computational equipment for man made intelligence, photograph and information processing, and eventually, the layout of knowledge and defense platforms. The booklet encompasses a choice of rigorously chosen, peer-reviewed papers, combining top of the range unique unpublished learn, case reviews, and implementation experiences.

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This example shows that to cover all states occurrence in M order HMM at least SÁM more training data is required. Combination of both M-order HMM with N-gram observation results in (OÁN)Á (SÁM) more training data requirements. Both approaches modify the HMM model in terms of maximizing the probability of the data and from this point of view greater model complexity and higher demanding on training data possibly pays off with better HMM fitting to the data. 4 HMM in the Field of Syntactic Classification The process of determining a syntactic class for each word in the analyzed text can be achieved by using HMM, where the discreet observation represent the word while the state of the Markov Model will represent the corresponding part of speech/sentence.

X(t) = −X(t) + W cos t X(0) = (−1, 0, 1) (49) where W = (−1, 0, 1). The solution of Eq. (49) for t ≥ 0 expressed in the form of μ-solution sets [1], for μ ∈ [0, 1], is given by (50). 5[W ]μ ) exp(−t) (50) The solution (50) obtained by standard fuzzy (SF-) arithmetic for [W ]μ = [μ − 1, 1 − μ] and [X(0)]μ = [μ − 1, 1 − μ] is given by (51). This standard solution exists in 3D-space. 5[μ − 1, 1 − μ]) exp(−t) μ (51) In this case fuzzy numbers [W ]μ = [X(0)]μ = [μ − 1, 1 − μ] are equal. Figure 11(a) presents in 2D-space border values of the SFA solution (51).

2008 12th International Conference on Information Visualisation, IV 2008, pp. 373–380 (2008) 10. : A Field Guide to Digital Color. K. Peters, Natick (2003) 11. : Color in information display. In: Tutorial, IEEE Visualization Conference, Sacramento, USA, October 2007 12. : A Review of the Evaluation of Pain Using a Variety of Pain Scales.

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