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The paper deals with the problem of the detection of rare patterns in an unbalanced dataset related to an industrial problem concerning the identification of manufactured defective metal products on the basis of product and process parameters. Within this work several approaches have been attempted for the development of a classifier whose performance are able to meet the industrial requirements,...
Machine learning methodologies such as artificial neural networks (ANN), fuzzy logic or genetic programming, as well as principal component analysis (PCA) and intelligent control have been recently introduced in medicine. ANNs imitate the structure and workings of the human brain by means of mathematical models able to adapt several parameters. ANNs learn the input/output behavior of a system through...
Financial marketing is very common in the world to make money or to control the company strategy. Nearly all events trigger to each other and moreover countries. Some predicting methods, on guessing the marketing depends on natural behavior of the events. When, have a scrutinize to backwards, it can be evaluated that some upfront events occur periodically and trigger to each others and may lead to...
We present a so-called neural map, a novel memory framework for visual object recognition and categorization systems. The properties of its computational theory include self-organization and intelligent matching of the image features that are used to build their object models. Its performance for representing the visual object knowledge comprised by these models and for recognizing unknown objects...
As is known to all, the neutral network has made a great progress in many fields. But due to some strict theoretical system, there are still many defaults in practical application. In this paper, we present an active learning artificial neural network (ALANN). The key issue of this kind of approach is what information can be analysis and forecast about time series(TS). However, the parameters of ALANN...
In this paper, we propose a memetic algorithm (MA) for classifier optimization based on a clustering method that applies the k-means algorithm over a specific derived space. In this space, each classifier or individual is represented by the set of the accuracies of the classifier for each class of the problem. The proposed sensitivity clustering is able to obtain groups of individuals that perform...
This work presents a new artificial neural network (ANN) Controller for implementing the Direct control method (DCM) for Matrix converters (MC) to decrease the time of calculation of the conventional DSP control system. To avoid the difficult calculation of ANN-DCM, the design uses the individual training strategy with the fixed weight and the supervised models. A computer simulation program is developed...
In latest decades credit risk assessment has been a heavy problem in the society especially in the financial system. Credit risk assessment is a decision level decision problem. Information fusion in multi-sensor system is a very complex process, especially in the decision level fusion process. Presently some useful and representative methods, such as neural networks and Dempster-Shafer evidence theory,...
Several structures of artificial neural networks (ANNs) with different training patterns were investigated so as to compare their performances on detecting the cluster of microcalcifications (CM) on mammography. 150 region-of-interests (ROIs) around mass containing both positive and negative microcalcifications were selected for training the network by a standard or modified error-back-propagation...
How to create highly-reputable designs and hot-selling products is an essential issue on product design. This paper presents an experimental study to explore the relationship between the consumers' perceptions and product form elements, using one linear quantitative technique (i.e. the grey model) and one nonlinear quantitative technique (i.e. the neural network model). Thirty representative personal...
Data mining techniques have been successfully applied in intrusion detection because they can detect both misuse and anomaly. One of the unsupervised ways to define anomalies is by saying that anomalies are not concentrated, which depend on the density of data set. In this paper, the anomalies can be specified by choosing a reference measure ?? which determines a density and a level value r. In order...
Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to face recognition tasks, there is always the need to construct large image repositories from people. Images...
A collaborative Emergency call taking information system in the Czech Republic processes calls on the European 112 emergency number. Amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a human,...
Edition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside...
Neural network tree (NNTree) is a hybrid model for machine learning. Compared with single model fully connected neural networks, NNTrees are more suitable for structural learning, and faster for decision making. To increase the realizability of the NNTrees, we have tried to induce more compact NNTrees through dimensionality reduction. So far, we have used principal component analysis (PCA) and linear...
A common approach to pattern classification problems is to train a bank of layered perceptrons or other classifiers by clustering the input training data and training each classifier with just the data from a specific cluster. There is no provision in such an approach, however, to assure the component layered perceptron is well suited to learn the training data cluster it is assigned. An alternate...
The increasing availability of digital educational resources in the Internet, called learning objects, has been followed by the definition of indexing standards. However, the lack of consensus about the definition of learning objects, as well the diversity of metadata approaches for its classification hinders the selection process of these elements. This scenario requires new investigations that allow...
To track the state of charge (SOC) of Ni-MH battery pack at the hybrid electric vehicle, an artificial neural network (ANN) is designed. Current, voltage and the previous SOC are used to inputs of ANN, and output is SOC. The result show that, this artificial neural network can track the state of charge (SOC) of the batteries accurately, in the average tracking error less than 5%; the ANN is in low...
This paper presents MP-Draughts (MultiPhase-Draughts): a multiagent environment for Draughts, where one agent - named IIGA- is built and trained such as to be specialized for the initial and the intermediate phases of the games and the remaining ones for the final phases of them. Each agent of MP-Draughts is a neural network which learns almost without human supervision (distinctly from the world...
The R4-rule is a heuristic algorithm for distance-based neural network (DBNN) learning. Experimental results show that the R4-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R4-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R4-rule, we investigate three approaches...
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