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The problem of blind estimation of the room acoustic clarity index C50 from single-channel reverberant speech signals is presented in this paper. We analyze the performance of several machine learning methods for a regression task using 309 features derived from the speech signal and modeled with a Deep Belief Network (DBN), Classification And Regression Tree (CART) and Linear Regression (LR). These...
Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the...
One of the important problems in medical imaging is two-class classification, for example determination of benign from malignant cases in breast cancer treatment. In this paper we present a new support vector machine method for two-class medical image classification. The key idea of this method is to construct an optimal hypersphere such that both the interior margin between the surface of this sphere...
This paper presents a new speaker classification scheme based on Australian accents which are broad, general and cultivated. Speakers are classified in to speaker groups according to their accents, ages and genders. Mel-frequency cepstral coefficients extracted after speech processing were used to build Gaussian speaker group mixture models. Fusion of speaker group classifiers is then performed. Experiments...
The paper presents a novel biometric authentication approach using principal component analysis (PCA), regularized-linear discriminant analysis (R-LDA) and supervised neural networks. Low dimensional feature vectors of human face images are required to drive neural networks effectively. After histogram equalization process each image is presented to PCA or R-LDA for normalization and dimension reduction...
Predicting the outcome of a graft transplant with high level of accuracy is a challenging task. To answer the challenge, data mining can play a significant role. The goal of this study is to compare the performances and features of an artificially intelligent (AI)-based data mining technique namely artificial neural network with logistic regression as a standard statistical data mining method to predict...
In this paper we discussed and implemented Morphological method for face recognition using fiducial points. A new technique for extracting facial features is suggested here. This method is independent of the face expressions. In recognition process, these fiducial point are fed as inputs to a Back propagation neural network for learning and identifying a person. So with the help of this technique,...
Over the past few years, data mining and multi-agent approach has been used successfully in the development of large complex systems. Such a hybrid approach can be considered as an effective approach for the development of predictive modeling in complex e-health systems. We propose a real time Data Mining and Multi-Agent System called DMMAS, modeling chronic disease data. DMMAS approach employs data...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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