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Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This...
Data mining technique is an effective tool used to obtain desired knowledge from massive data. Neural network is a new method in the application of data mining. Although it may have shortcomings of complex structure, long training time and uneasily understandable representation of results, neural network has high accuracy which is superior to other methods and this makes it more available in data...
In this paper the frequency domain vibration signals of the gearbox of MF285 tractor is used for fault classification in three class: Healthy gear, Worn tooth face and broken gear. The effect of applying statistical parameters to signals on accuracy is studied. In addition, Influence of feature selection using Improved Distance Evaluation on classification performance and training speed is another...
Finding the optimal parameters in anionic co-doped titanium dioxide (TiO2) is an important task in the compound preparation on either photocatalytic-oriented or mechanical-preferred properties. This work proposes a neural network-based system to optimize the process parameters of the deposition of TiCxOyNz films. The proposed system comprises three stages, which are data processing, parameter training...
With the rapid development of the Internet services and the fast increasing of intrusion problems, the traditional intrusion detection methods cannot work well with the more and more complicated intrusions. So introducing machine learning into intrusion detection systems to improve the performance has become one of the major concerns in the research of intrusion detection. Intrusion detection systems...
In this paper we discuss various machine learning approaches used in mining of data. Further we distinguish between symbolic and sub-symbolic data mining methods. We also attempt to propose a hybrid method with the combination of Artificial Neural Network (ANN) and Cased Based Reasoning (CBR) in mining of data.
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.
Information management and extraction in the field of biomedical research has become a requirement with the rapid increase in the amount of data being published in this area. In this paper, a graphical model, Conditional Random Fields has been used to extract a particular gene-gene relationship called ??coexpression?? from the existing literature. First, a Conditional Random Fields based model has...
This paper presents a new method to recognize machine-printed traditional Mongolian characters by using back-propagation (BP) neural networks. First, the set of traditional Mongolian characters is divided into five subsets according to each character's position (initial, medial or final) within a word and some steady structural features. Then, each subset is trained and recognized by using a BP neural...
Adaptive resonance theory (ART) architectures are important neural networks for unsupervised clustering. ART 2-A is one version of the ART family capable of clustering both binary and numeric data. However, real-world problems usually contain categorical data that cannot be processed by ART 2-A. A simple solution is using binary encoding to preprocess categorical data. Binary encoding is a simple...
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...
Based on discussing in the alternative covering neural networks (ACNN), the integrated algorithm are proposed based on rough set (RS) theory and ACNN. RS is applied to reduce and process the original data. While ensuring the integrity of information, the data dimension is reduced. ACNN is used to design multi-layer forward network. Through using RS to reduce data dimension, the calculation of ACNN...
A new inductive transfer-learning algorithm called NEDRT is presented in this paper in order to improve the classification accuracy of a domain task by using the knowledge learned from labeled data generated from a different domain. NEDRT introduces a novel error function for a constructed neural network by summing a weighted squared difference between the real output and the neural network output...
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,...
To acquire knowledge by learning automatically from the data, through a process of inference, model fitting, or learning from example is one of the rare field of email management. And when an artificial system can perform "intelligent", tasks similar to those performed by the human brain and such is implemented in email classification, such a system will be is extremely intelligent. Using...
Trained speed of model based on traditional BP neural network was slowly and produced emanative result. A novel land evaluation model based on neural network with genetic optimization algorithm was presented in this paper. The neural network of model is front-network which comprised with five layers architecture which composed of dynamic inference with fuzzy rules where the consequent sub-models are...
In order to eliminate the ambiguity and uncertainty exist in the conventional classification for remote sensing images, the BP neural network was presented. However, the BP network itself also exist some limitations and shortages which are primarily represented in the aspects of network training speed low, optimization for convergence to integer not easy and so on. This paper improves the BP neural...
Newly, the effectiveness of Gradient features has been verified for writer identification of Latin texts. However, no researches on the performance of these features on Persian handwritten have been reported. Special styles of Persian handwritten assert different approaches to identify the writer in compare with other alphabets. This paper introduces a text-independent Persian writer identification...
Differentiating between Web services that share similar functionalities is becoming a major challenge into the discovery of Web services. In this paper we propose a framework for enabling the efficient discovery of Web services using artificial neural networks (ANN) best known for their generalization capabilities. The core of this framework is applying a novel neural network model to Web services...
Artificial neural networks have been widely used for knowledge extraction from biomedical datasets and constitute an important role in bio-data exploration and analysis. In this work, we proposed a new curvilinear algorithm for training large neural networks which is based on the analysis of the eigenstructure of the memoryless BFGS matrices. The proposed method preserves the strong convergence properties...
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