An Improved Continuous-Action Extended Classifier Systems for Function Approximation

AbstractDue to their structural simplicity and superior generalization capability, Extended Classifier Systems (XCSs) are gaining popularity within the Artificial Intelligence community. In this study an improved XCS with continuous actions is introduced for function approximation purposes. The proposed XCSF uses “prediction zones,” rather than distinct “prediction values,” to enable multi-member match sets that would allow multiple rules to be evaluated per training step. It is shown that this would accelerate the training procedure and reduce the computational cost associated with the training phase. The improved XCSF is also shown to produce more accurate rules than the classical classifier system when it comes to approximating complex nonlinear functions

Intelligent Detection of Intrusion into Databases Using Extended Classifier System (XCS)

With increasing tendency of users to distributed computer systems in comparison with concentrat-ed systems, intrusion into such systems has emerged as a serious challenge. Since techniques of intrusion into systems are being intelligent, it seems necessary to use intelligent methods to en-counter them. Success of the intrusion systems depends on the strategy employed in these sys-tems for attack detection. Application of eXtended Classifier Systems (XCS) is proposed in this paper for detection of intrusions to databases. The extended classifier systems which are known as one of the most successful types of learning agents create a set of stochastic rules and com-plete them based on the methods inspired from human learning process. Thereby, they can grad-ually get a comprehensive understanding of the environment under study which enables them to predict the correct answer at an acceptable accuracy once encountered with new issues. The method suggested in this paper an improved version of extended classifier systems is “trained” using a set of existing examples in order to identify and avoid attempts to intrude computer sys-tems during phases of application and encountering these attempts. The proposed method has been tested on several problems to demonstrate its performance while its results indicate a 91% detection of various known intrusions to the databases.DOI:http://dx.doi.org/10.11591/ijece.v3i5.403

Finite element analysis of biomechanical interactions of a subcutaneous suspension suture and human face soft-tissue: a cadaver study

In order to study the local interactions between facial soft-tissues and a Silhouette Soft suspension suture, a CE marked medical device designed for the repositioning of soft tissues in the face and the neck, Finite element simulations were run, in which a model of the suture was embedded in a three-layer Finite Element structure that accounts for the local mechanical organization of human facial soft tissues. A 2D axisymmetric model of the local interactions was designed in ANSYS, in which the geometry of the tissue, the boundary conditions and the applied loadings were considered to locally mimic those of human face soft tissue constrained by the suture in facial tissue repositioning. The Silhouette Soft suture is composed of a knotted thread and sliding cones that are anchored in the tissue. Hence, simulating these interactions requires special attention for an accurate modelling of contact mechanics. As tissue is modelled as a hyper-elastic material, the displacement of the facial soft tissue changes in a nonlinear way with the intensity of stress induced by the suture and the number of the cones. Our simulations show that for a 4-cone suture a displacement of 4.35mm for a 2.0N external loading and of 7.6mm for 4.0N. Increasing the number of cones led to the decrease in the equivalent local strain (around 20%) and stress (around 60%) applied to the tissue. The simulated displacements are in general agreement with experimental observations

Novel modified triply periodic minimal surfaces (MTPMS) developed using genetic algorithm

The desirable properties of natural porous materials have inspired humans to design cellular materials with remarkable properties such as high energy absorption, acoustic and thermal insulation, and high specific strength structures. Triply Periodic Minimal Surface (TPMS) structures are particularly important among the architected materials. In this research, we have developed a novel approach based on a genetic algorithm for geometry modification of TPMS structures. The approach is exploited to enhance the effective thermal and mechanical properties of TPMS structures, namely Primitive, IWP, Gyroid, and GPrime. New modified TMPS structures (MTPMS) have much higher Young's modulus and thermal conductivity than their base structures, and some of them have properties very close to the upper Hashin-Shtrikman bounds. Unlike previous studies conducted to improve the properties of TPMS structures, MTPMS can be expressed by simple equations. Although MTPMS are not generally considered minimal surfaces, they nonetheless possess the desirable geometric qualities of TPMS structures, such as being smooth and splitting space into two non-intersecting and continuous domains, etc