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Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve...
Convolutional Neural Networks (CNNs) are multi-layer deep structures that have been very successful in visual recognition tasks. These networks basically consist of the convolution, pooling, and the nonlinearity layers, each of which operates on the representation produced by the preceding layer and generates a new representation. Convolution layers naturally compute some inner product between a plane...
Nowadays, humans can play an important role in control of robots. Some researches have used signals that coming directly from humans for control interfaces. In this paper, electromyogram (EMG) signals from the muscles of the human's upper limb are used as the control interface between the user and a robot arm. A Multi-Layer Perceptron (MLP) is trained by additional unsupervised pre-training to decode...
Multi-class classification is a challenging problem in pattern recognition. Clustering-based Classification (CC) is one of the most effective classification methods that first divides data into several clusters, each cluster then being described by a One-Class Classifier (OCC). Scalability and accuracy are two key advantages of this clustering-enhanced approach. In continuation of this strategy, in...
The rapidly growing amount of data being produced in the world has become a challenging problem for decision support systems. These data are located in disparate sites while time, cost and privacy concerns makes it impossible to aggregate them into one location. Information fusion systems aim to make decisions by getting the outputs of the distributed sources. Since each source is making its local...
Nowadays, we are facing the rapidly growing amount of data being produced in many organizations, social networks and internet. These data are generated in disparate locations and their aggregation into one location is exceedingly time and space consuming. Traditional statistical methods are not sufficient for processing of this massive multi-source data. In this paper, we propose a new fuzzy-based...
In this paper a new optimization algorithm based on Chaos Optimization algorithm(COA) combined with traditional Baum Welch (BW) method is presented for training Hidden Markov Model (HMM) for Continues speech recognition. The BW algorithm easily trapped in local optimum, which might deteriorate the speech recognition rate, while an important character of COA is global search. so we can get a globally...
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