By Yong Soo Kim, Young J. Ryoo, Moon-soo Jang, Young-Chul Bae
Intelligent structures were initiated with the try to imitate the human mind. humans desire to enable machines practice clever works. Many innovations of clever structures are in accordance with man made intelligence. based on altering and novel standards, the complex clever platforms disguise a large spectrum: gigantic info processing, clever keep watch over, complicated robotics, man made intelligence and computing device studying. This booklet makes a speciality of coordinating clever structures with hugely built-in and foundationally practical parts. The e-book includes 19 contributions that includes social network-based recommender structures, software of fuzzy enforcement, strength visualization, ultrasonic muscular thickness size, neighborhood research and predictive modeling, research of 3D polygon information, blood strain estimation process, fuzzy human version, fuzzy ultrasonic imaging technique, ultrasonic cellular clever know-how, pseudo-normal snapshot synthesis, subspace classifier, cellular item monitoring, standing-up movement tips approach, popularity constitution, multi-CAM and multi-viewer, powerful Gaussian Kernel, multi human circulation trajectory extraction and type coordination. This version is released in unique, peer reviewed contributions overlaying from preliminary layout to ultimate prototypes and authorization.
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The basic idea is the similarity between two samples is computed with Gaussian kernel function. Therefore Gaussian kernel induces a fuzzy relation satisfying the properties of reflexivity and symmetry. Moreover, it can introduce Gaussian kernel for computing fuzzy T-equivalence relations in fuzzy rough sets and thus approximate arbitrary fuzzy subsets with kernel induced fuzzy granules. The similarity between two samples is computed with Gaussian kernel function k ( xi , x j ) = exp( − xi − x j where xi − x j 2 / 2δ 2 ) (6) is the Euclidean distance between samples xi and x j .
1 Shape Characteristic Extraction of the Numeral The system recognizes the extracted numeral region based on fuzzy inference. The system extracts number of endpoints, number of holes, similarity of direction histogram and a similarity between a template images and thinning image as numeral characteristics. Firstly, the system applies thinning processing to numeral region to extract the shape characteristic. Figure 8 shows an example of thinning image of “6”. From the thinning image, the system extracts and counts endpoints and holes.
To address this issue, as pointed out in , some attributes can be omitted, which will not seriously affect the resulting classification accuracy. Though the concept of symbolic data has been studied extensively in clustering, its inherent capabilities in the problem of Symbolic Data Selection (SDS) have not been sufficiently explored. The rough set theory proposed by Pawlak  is a mathematical theory dealing with uncertainty in data. The concepts of attributes reduction and rule extraction can be viewed as the strongest and most important results in rough sets theory to distinguish itself from other theories.
Advanced Intelligent Systems by Yong Soo Kim, Young J. Ryoo, Moon-soo Jang, Young-Chul Bae