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We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes...
Automatic Speech Recognition (ASR) is one of the advanced fields of Natural Language Processing (NLP). Recent past has witnessed valuable research activities in ASR in English, European and East Asian languages. But unfortunately South Asian Languages in general and ??Urdu?? in particular have received very less attention. In this paper we present an approach to develop an ASR system for Urdu language...
Text Categorization aims to assign an electronic document to one or more categories based on its contents. Due to the rapid growth of the number of online Arabic documents, the information libraries and Arabic document corpus, automatic Arabic document classification becomes an important task. This paper suggests the use of rooting algorithm with Nai??ve Bayes Classifier to the problem of document...
This paper proposed a new algorithm of multi-category SVM incremental learning by analyzing the distribution characteristics of the intrusion detection data. Samples used in learning were selected by measuring the distance between samples and their class-centers, and they are just those samples which will most possibly be the SVs in incremental learning. By several binary-class hyper-planes, the zones...
Machine learning has become the predominant problem-solving strategy for computational linguistics problems in the last decade. In this paper, we present an implemented machine learning system for the automatic identification of non-referential pronouns in Arabic texts. Our system is based on a Bayesian network which has shown its efficiency for modeling NLP problems. We have evaluated our approach...
This paper, presents an Intelligent diagnosis system using Hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. Feature extraction using Independent Component Analysis (ICA) and Power spectrum, together with the RR interval then serve as input feature vector, this feature were used as input of ANFIS classifiers. six types of...
In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier...
Price index forecasting is one of the most important problems in financial markets. In the past decades the prediction of stock index has played a vital role in the financial situation of several companies which have stocks in the market. In this paper we use Multi Layer Perceptron (MLP) neural network in stock index prediction. Three searching algorithms were used to get the best network architecture...
We present a hybrid based Noun Phrase (NP) translator that combines rule-based transfer technique with a statistical n-gram language model for selecting the best translation. Noun Phrase is the dominating construct in natural language text and targeting it for focused processing increases effectiveness of language processing systems. Manipulation of Noun Phrases is an effective subtask in Statistical...
The objective of study is to develop intelligent decision support system to aid radiologist in diagnosis using pattern recognition techniques to estimate diagnostic function. In this study 3 approaches investigated namely statistical, neural networks and optimization techniques which were applied on the Wisconsin dataset. Trained neural networks, with the data set used as input, improve on the independent...
Knowledge Discovery in Databases (KDD) is a complex interactive and iterative process which involves many steps that must be done sequentially. Supporting the whole KDD process has enjoyed a great popularity in recent years, with advances in research. We however still lack of a generally accepted underlying framework and this hinders the further development of the field. We believe that the quest...
This paper deals with the comparison of the two neural network methods of learning: supervised (classical feedforward neural networks: multi-layer neural networks (MLP), radial basis function (RBF) and probabilistic neural networks (PNN)) and unsupervised (self organizing feature maps (SOFM), or Kohonen map), in order to assess their performances on a labeled breast cancer database. By revealing their...
This paper presents a comparison between two stochastic, population based and real-valued algorithms. These algorithms are namely Differential Evolution (DE) and Particle Swarm Optimization (PSO). These algorithms are used in the training of feed-forward neural network to be used in the prediction of the daily stock market prices. Stock market prediction is the act of trying to determine the future...
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image...
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