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Most architecture of mobile ad hoc network is in the form of decentralized, self-configuring and dynamic topologies. Nodes are mobile in network. The mobility of node in network is common problem in peer-to-peer technology. Object replication is one of the techniques applied in order to share objects in the mobile peer-to-peer environment. Predicting the estimated time for the node to exit is a great...
The algorithm of GASEN (Genetic Algorithm based Selective Ensemble Network) has been proven to be a very effective way to select a subset of neural networks to form an ensemble classifier or a regressor of enhanced generation ability. And yet performance of GASEN on class-imbalance data sets hasn't been discussed widely, while class-imbalance learning itself is an increasingly important issue. In...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
In this paper we introduce a set of adaptive signal procedure techniques which could be used. Firstly, we introduce discrete wavelet transform and extract the characteristics of Electrocardiogram (ECG) optimization. Then, we make use of Radial Basis Function (RBF) neural network to achieve the classification of ECG and to compare the performance of their respectively. Among which two types of ECG...
Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal components...
This paper describes the possibilities of using artificial neural networks in the following fields of machine learning: data mining and semantic integration in large databases. Possibility of using analog components for developing neural networks is investigated.
Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of disease. The proposed system concentrates on the two different cognitive tests for the diagnosis of different stages of dementia. Dementia is considered as the fourth most common disorder among the elderly. Early detection of dementia and correct staging of the...
This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a memetic Pareto evolutionary approach based on the NSGA2 algorithm (MPENSGA2), where we...
Particle swarm optimization (PSO), a new promising evolutionary optimization technique, has a wide range of application in optimization problems including training of artificial neural networks. In this paper, an attempt is made to completely train a RBF neural network architecture including the centers, optimum spreads, and the number of hidden units. The proposed method has been evaluated on some...
Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The neural networks (NN) and genetic programming (GP) learning techniques are used in the models...
Neural network techniques have been widely applied to areas of such as data mining, information integration and grid computing. This paper proposes a new learning algorithm based on trust region optimization theory. In the paper, the Dogleg-algorithm to obtain the valid trust region steps is presented, and a self-adjustable method with variable coefficients is given to resolve the problem of oscillatory...
Together with fast development of different areas of pattern analysis, an increasing demand on new models and techniques is observed. Especially new information retrieval tasks, oriented on data meaning rather than layout, prove to be demanding for most known techniques. neuronal group learning concept presented in this article, together with prototype implementation gives flexibility of utilization...
In this paper we compared the performance of the automatic data reduction system (ADRS) and principal component analysis (PCA) as a preprocessor to artificial neural networks (ANN). ADRS is based on a Bayesian probabilistic classifier that is used with a quantization process that results in a simplification of the feature space, including elimination of irrelevant features. ADRS has the advantage...
Recent development of various domains of artificial intelligence including information retrieval and text/image understanding created demand on new, sophisticated, contextual methods for data analysis. This article formulates neuronal group and extended neuron somatic concepts that can be vastly used in creating such methods. Neural interrelations are described using graphs, construction of which...
Frying of potatoes causes some changes in their microstructures. By studying these changes we have presented quite suitable features for automatic analysis of microscopic images taken from fried potatoes, and have also introduced a new mechanism based on machine learning for automatic quality control of fried potatoes. Experimental results show that the presented structure may well be used for controlling...
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically...
In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have applied this method to two different types of datasets,...
Shape recognition is an important part of machine intelligence in both decision making and data processing. A good shape representation in shape recognition should describe the shape in the way that makes it distinguishable from other shapes and be invariant to transform of position, size, angle and skew. More importantly, developing and finding appropriate shape representation are still a challenging...
The applicability of learning methods to raw data coming from different areas of human activity is one of the main concerns in data mining research today. This paper emphasizes the need for a sound preprocessing method to improve the quality of the learning process through data imputation. Three classification methods we have previously developed are presented, with a focus on their evaluations. The...
In this paper, the classification results obtained from several kinds of support vector machines (SVM) and neural networks (NN) are compared with our proposed classifier. Our approach is based on neural networks and interval neutrosophic sets which are used to classify the input patterns into one of the two binary class outputs. The comparison is based on several classical benchmark problems from...
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