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Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. However, it is a big challenge for efficient(More)
An intrusion is defined as a violation of the security policy of the system, and, hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Current intrusion detection systems ~IDS! examine all data features to detect intrusion or misuse patterns. Some of the features may be redundant or(More)
This paper proposes a new face recognition approach by using the discrete cosine transform (DCT) and hybrid flexible neural tree (FNT) classification model. The DCT is employed to extract the input features to build a face recognition system, and the flexible neural tree is used to identify the faces. Based on the pre-defined instruction/operator sets, a(More)
Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer(More)
A local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of particle swarm optimization (PSO) with(More)
The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using Flexible Neural Tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock Market SM and the S&P CNX NIFTY stock index. We analyzed(More)
A flexible neural network (FNN) is a multilayer feedforward neural network with the characteristics of: (1) overlayer connections; (2) variable activation functions for different nodes and (3) sparse connections between the nodes. A new approach for designing the FNN based on neural tree encoding is proposed in this paper. The approach employs the ant(More)
The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the(More)