By Renato A. Krohling, Daniel Rigo (auth.), Jörn Mehnen, Mario Köppen, Ashraf Saad, Ashutosh Tiwari (eds.)
The global gentle Computing (WSC) convention is an annual overseas on-line convention on utilized and theoretical delicate computing know-how. the themes coated by means of the convention diversity from utilized to theoretical points of fuzzy, neuro-fuzzy and tough units over to neural networks to unmarried and multi-objective optimisation. Contributions approximately particle swarm optimisation, gene expression programming, clustering, classi cation, help vector machines, quantum evolution and agent structures have additionally been bought. One complete consultation was once dedicated to gentle computing ideas in special effects, imaging, imaginative and prescient and sign processing.
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In: Proceedings of the IEEE International Workshop on Expert Systems Applications, pp. C. D. Bendtsen 5. : Distinctive Image Features from Scale-Invariant Key points. International Journal of Computer Vision 60, 91–110 (2004) 6. : Introductory techniques for 3-D Computer Vision. Prentice Hall, Englewood Cliffs (1998) 7. : Real-time map building and navigation for autonomous robots in unknown environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28, 316–333 (1998) 8. : Fuzzy Logic with Engineering Applications.
Fig. 3 Membership functions for the linguistic variable oil at the coast (OC) and for the linguistic variable oil intercepted (OI), each with 5 fuzzy sets This type of membership function is very used due to its overlapping characteristics. The fuzzy sets VL, L, M, H, VH stands for ”Very Low”, ”Low”, ”Medium”, ”High” and ”Very High” impact, respectively. The three participants represented by an environmental agency, an oil company, and a NGO express their preferences as given in Table 3. The application of the fuzzy evaluation method allows calculating the total impact for each alternative n taking into account the preference of the decision agent s, as shown in Table 4.
E. Hassanien et al. g. extracted feature samples) approximate another set of objects of interest. Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. Based on rough set theory it is possible to construct a set of simple if-then rules from information tables. Often, these rules can reveal previously undiscovered patterns in sample data. Rough set methods can also be used to classify unknown data based on already gained knowledge.
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