As automated face recognition applications tend towards ubiquity, there is a growing need to secure the sensitive data used within these systems. This paper presents survey of biometric template protection (BTP) methods proposed for securing “templates” (images/features) in neural-network-based The BTP are categorised into two types: Non-NN and NN-learned. use neural network (NN) as feature ext...

Non-parametric decision rules, such as the nearest neighbor (NN) rule, are attractive because no a priori knowledge is required concerning the underlying distributions of the data. Two traditional criticisms directed at the NN-rule concern the large amounts of storage and computation involved due to the apparent necessity to store all the sample (training) data. Thus there has been considerable...

A self-organizing neural network model for mechanism of pattern recognition unaected by shift in position. of Japanese Kanji using principal component analysis as a preprocessor to an articial neural etwork. 10 necessary, only one initial guess was used. These results show that for character classication accuracy NN methods and statistical methods have comparable accuracies conrming the COCR re...

In this work, a new methodology for inverse estimation of thermal constitutive models is introduced. This new methodology combines computational intelligence with finite element analysis for solving inverse heat transfer problems. A neural network (NN) representation of thermal constitutive behavior and its implementation in non-linear finite element analysis are presented. The self-learning me...

This paper is devoted to application of neural network with a specific restricted connectivity structure for identification and control of a real process. Parameters of the identified model are used to design a controller based on dynamic feedback linearization. The designed neural network based controller is verified on mathematical model within MATLAB/Simulink environment and applied to the r...

this paper presents an accurate differential global positioning system (dgps) using multi-layered neural networks (nns) based on the back propagation (bp) and imperialistic competition algorithm (ica) in order to predict the dgps corrections for accurate positioning. simulation results allowed us to optimize the nn performance in term of residual mean square error. we compare results obtained b...

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately presented. There are two conflicting requirements in the implementation of TTR. On the one hand, a super-real-time model i...

We apply Artificial Immune Systems(AIS) [4] for credit card fraud detection and we compare it to other methods such as Neural Nets(NN) [8] and Bayesian Nets(BN) [2], Naive Bayes(NB) and Decision Trees(DT) [13]. Exhaustive search and Genetic Algorithm(GA) [7] are used to select optimized parameters sets, which minimizes the fraud cost for a credit card database provided by a Brazilian card issue...

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The pur...

Non-parametric decision rules, such as the nearest neighbor (NN) rule, are attractive because no a priori knowledge is required concerning the underlying distributions of the data. Two traditional criticisms directed at the NN-rule concern the large amounts of storage and computation involved due to the apparent necessity to store all the sample (training) data. Thus there has been considerable...