Download Hard and Soft Computing for Artificial Intelligence, by Shin-ya Kobayashi, Andrzej Piegat, Jerzy Pejaś, Imed El PDF
By Shin-ya Kobayashi, Andrzej Piegat, Jerzy Pejaś, Imed El Fray, Janusz Kacprzyk
This publication gathers the lawsuits of the twentieth overseas convention on complex desktops 2016, held in Międzyzdroje (Poland) on October 19–21, 2016. Addressing issues that come with man made intelligence (AI), software program applied sciences, multimedia platforms, IT protection and layout of knowledge platforms, the most objective of the convention and the publication is to create a chance to switch major insights in this sector among technological know-how and company. specifically, this services matters using demanding and gentle computational equipment for synthetic intelligence, snapshot and knowledge processing, and eventually, the layout of data and defense platforms. The publication encompasses a selection of rigorously chosen, peer-reviewed papers, combining fine quality unique unpublished learn, case stories, and implementation experiences.
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Additional resources for Hard and Soft Computing for Artificial Intelligence, Multimedia and Security
Example text
Landowski coordinate. A fuzzy interval (a, b, c, d) is shown in Fig. 1. For such intervals vertical MFs can be used. Vertical models express the vertical dependence μ = f (x). Vertical MF of fuzzy interval (a, b, c, d) is given by (15). ⎧ (x − a)/(b − a) for x ∈ [a, b) ⎪ ⎪ ⎪ ⎨1 for x ∈ [b, c] (15) μ(x) = ⎪ (d − x)/(d − c) for x ∈ (c, d] ⎪ ⎪ ⎩ 0 otherwise The model (15) is a model of the fuzzy interval borders only. It does not model the interval interior. This fact limits usefulness of the vertical model in calculations.
We selected and compared color coded scales contained from 15 to 25 colors from violet to red. All the scales were center concentrated with green center point (See Figs. 1, 2, 3, 4, 5 and 6). We calculated the effectiveness of these scales on the basis of the response time and the cognitive load (mental effort). The second experiment aimed to compare the effectiveness of numeric −10 + 10 scale, numeric 1–21 scale and 21-word scale with the effectiveness of color scale contains optimal number of colors, obtained in previous experiment.
The differences in the observation number may reach decimal of percents. This is due to combinatorial limitations of the model where a smaller number of letters is subject of observation. The biggest advantage of HMMs with simple and small set of observations is the computation time. For many applications (where computation latency/time is essential) complex HMM compute to slow and despite the higher accuracy more useful is a smaller model even with some classification deficiency. Table 3. Classification results for affix based HMM in the field of syntactic analysis.