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A modified learning algorithm of Artificial Neural Networks (ANN) is introduced in this paper to solve imbalanced data set problems. In solving imbalanced data set, it is critical to predict the minority class due to their imbalanced nature. In order to improve the standard ANN classifier prediction performance, this paper focuses on optimizing the decision boundary of the step function at the output...
In this paper we proposed a classification system for cardiac arrhythmia from standard 12 lead ECG recordings data, using a Generalized Feedforward Neural Network (GFNN) classifier. The GFNN classifier is trained using static backpropagation algorithm to classify arrhythmia cases into normal and abnormal classes. In this study, we are mainly interested in producing high confident arrhythmia classification...
Intrusion detection (ID) is an interesting approach that could be used to improve the security of network systems. IDS detects suspected patterns of network traffic on the remaining open parts through monitoring user activities (runtime gathering of data from system operations), and the subsequent analysis of these activities. The purpose of this work is to contribute ideas of finding a solution to...
Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs supervised classifier system (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a...
An intelligent, automated visual inspection system is investigated in this paper. It is used for pattern recognition and classification of four different types of cork tiles. The process includes image acquisition with a CCD camera, texture feature extraction, statistical processing of the feature vectors, and cork tiles classification with feed-forward Neural Networks (NN) employing a hybrid global...
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