Network Intrusion Detection (NID) is the process of identifying network activity that can lead to the compromise of a security policy. In this paper, we will look at four intrusion detection approaches, which include ANN or Artificial Neural Network, SOM, Fuzzy Logic and SVM. ANN is one of the oldest systems that have been used for Intrusion Detection System (IDS), which presents supervised lea...

this paper shows that the use the optimal topology of an Artificial Neural Network (ANN) for a particular application is one of the difficult tasks. Neural Network is optimized by a Genetic Algorithm (GA), in a hybrid technique, to calculate the multiplicity distribution of the charged shower particles on Larger Hadron Collider (LHC). Moreover, ANN, as a machine learning technique, is usually u...

This paper investigates an approach that maximizes the joint posterior probability of the pronounced word and the speaker identity given the observed data. This probability can be expressed as a product of the posterior probability of the pronounced word estimated through an artificial neural network (ANN), and the likelihood of the data estimated through a Gaussian mixture model (GMM). We show...

Predicting Tra c flow in the busiest cities has become a popular research area in the past decades. The rapid development of intelligent tra c management system attracts the software industry to come up with e cient tools for tra c prediction over the roads. In this study, Discrete Wavelet Transformation (DWT) is employed with Artificial Neural Network (ANN) to forecast the tra c flow over the ...

In this paper we investigate the error criteria that are optimized during the training of artificial neural networks (ANN). We compare the bounds of the squared error (SE) and the crossentropy (CE) criteria being the most popular choices in stateof-the art implementations. The evaluation is performed on automatic speech recognition (ASR) and handwriting recognition (HWR) tasks using a hybrid HM...

The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs) and the imperialist competitive algorithm (ICA). The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT) metho...

Ž . A hybrid expert system prototype using artificial neural networks ANN and classical rules has been developed for predicting toxicology of compounds. Modularity was a must for the architecture of the system. The study of chemicals was approached by establishing classes. When appropriate descriptors are calculated for the molecule, the ANN classifier assigns the chemical class to the compound...

In this study, to cope with the needs of the predictive maintenance for complex systems, a hybrid dynamic Artificial Neural Network (ANN) based fault and degradation diagnosis and tolerance method is designed. The multi-layer feed forward ANN and recurrent ANN are combined, so as to be able to form a dynamic identification model for the nonlinear time-varying system. It has three work modes, an...

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion app...

The applicability and performance of the so-called M5 model tree machine learning technique is investigated in a flood forecasting problem for the upper reach of the Huai River in China. In one of configurations this technique is compared to multilayer perceptron artificial neural network (ANN). It is shown that model trees, being analogous to piecewise linear functions, have certain advantages...