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In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained...
Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving classification rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. The rough-set theory has served as a good mathematical tool for dealing with data classification...
Ensemble methods have proved to be an effective tool to increase the performance of pattern recognition applications. An ensemble method behaves like an expert committee in predicting the class to which a sample belongs. In this paper, we present a novel ensemble method with high classification accuracy and resistance to noisy data. In our proposed method, we exploit a type of bagging in which the...
Water-jet cutting machine is one of the high-tech products setting of ultra-high pressure technology, numerical control technology, computer application technology as a whole, make use of the kinetic energy of abrasive water-jet cutting of various materials to achieve the purpose of cutting. It has advantages such as no chemical changes, no heat distortion, thin cutting gap, high accuracy, aspect...
Biometric authentication techniques such as lips, face, and eyes are more reliable and efficient than conventional authentication techniques such as password authentication, token, cards, personal identification number, etc. In this research paper, the emphasis has been laid on the speaker identification based on lip features. In this study, we have presented a detailed comparative analysis for speaker...
High computational cost has always been a constraint in processing huge network intrusion data. This problem can be mitigated through feature selection to reduce the size of the network data involved. In this research work, we first consider existing feature selection methods that are computationally feasible for processing huge network intrusion datasets. Each of the feature selection methods was...
This paper deals with the problem of classifying patterns encountered in complex systems. It describes an approach to pattern recognition that results in a complete and reliable classification technique. It is noted that the majority of existing pattern recognition methods initiate their classification acts on identification of similarities between the members of various classes. On contrast, the...
On the basis of analyzing and studying the correlation technique of Pathology Expert System on Artificial Neural Network, the paper elaborates the Inference Mechanism based on artificial neural networks and expert system in detail. With the knowledge and experience of pathology experts, we design an expert system of recognizing automatically abnormal cell by using technology of pattern recognition...
With the rapid growth in the credit industry, credit scoring classifiers are being widely used for credit admission evaluation. Effective classifiers have been regarded as a critical topic, with the related departments striving to collect huge amounts of data to avoid making the wrong decision. Finding effective classifier is important because it will help people make an objective decision instead...
The development of credit scoring model has been regarded as a critical topic. This study proposed four approaches combining with the KNN (K-nearest neighbor) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different models combined with KNN classifier were constructed by selecting features. KNN classifier combines with conventional...
Learning under imbalanced dataset can be difficult since traditional algorithms are biased towards the majority class, providing low predictive accuracy over the minority one. Among the several methods proposed in the literature to overcome such a limitation, the most recent uses multi-experts system (MES) composed of balanced classifiers, whose decisions are aggregated according to a combination...
The aim of this paper is to detect incoherences in concepts, ideas, values, and others contained in technical document corpora. The way in which document collections are generated, modified or updated generates problems and mistakes in the information coherency, leading to legal, economic and social problems. A solution based on summarization, matching and neuro-fuzzy systems is proposed to dealt...
The credit scoring has been regarded as a critical topic. Creating an effective classificatory model will objectively help managers instead of intuitive experience. This study proposed four strategies combining with the SVM (support vector machine) classifier for features selection that retains sufficient information for classification purpose. Different features preprocessing steps were constructed...
Information entropy is an effective description for the uncertainty of a system, and could be used for the symptom to detect the vibration changes of steam turbine. Based on the faulty signals collected from rotor test rig, three information entropy: singular spectrum entropy, power spectrum entropy, wavelet energy spectrum entropy were calculated as information entropy data. Probability neural networks(PNNs)...
A hybrid neural network-fuzzy expert system is developed to forecast one hour to forty-eight hour ahead electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership value of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and...
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