Physical Sciences and Mathematics Commons^™

Min–Max Hyperellipsoidal Clustering For Anomaly Detection In Network Security, Suseela T. Sarasamma, Qiuming Zhu

Computer Science Faculty Publications

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is …

Learning As A Nonlinear Line Of Attraction For Pattern Association, Classification And Recognition, Ming-Jung Seow

Electrical & Computer Engineering Theses & Dissertations

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns.

It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter …

Whole Word Phonetic Displays For Speech Articulation Training, Fansheng Meng

Electrical & Computer Engineering Theses & Dissertations

The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children's pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators …

A Neural Network Model For Classification Of Coastal Wetlands Vegetation Structure With Moderate Resolution Imaging Spectro-Radiometer (Modis) Data, Evaristo Joseph Liwa

LSU Doctoral Dissertations

Mapping coastal marshes is an important component in the management of coastal environments. Classification of marshes using remote sensing data has traditionally been performed by employing either parametric supervised classification algorithms or unsupervised classification algorithms. The implementation of these conversional classification methods is based on the underlying distributions concerning the probability density functions (PDF). Neural networks provide a practical approach to this classification because they are essentially non-parametric data transformations that are not restricted by any underlying assumptions. The major objective of this study was to evaluate the ability of neural networks using Moderate Resolution Imaging Spectro-radiometer (MODIS) data to …

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …