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Nonlinear multilayer principal component analysis (NMPCA) is well-known as an improved version of principal component analysis (PCA) using a five layer bottleneck neural network. NMPCA enables us to extract nonlinear hidden structure from high dimensional data, however, it has been difficult for users to understand obtained results, because trained results of NMPCA have many different locally optimal...
Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy...
Auto anti-lock braking system (ABS) bench test is a safe, high-efficiency and low-cost method for ABS performance detection. The key parameters such as slip ratio, adhesion coefficient utilization rate and deceleration can be obtained quickly. In this paper, a classification model based on neural network for ABS bench test results was established. And the detailed BP network structure design process...
This paper suggests a methodology for segmentation of masses in digital mammograms. The masses are distinguished from other breast tissue by its homogeneous and differentiated density in relation to other breast tissues. The segmentation strategy is based on the assessment of density using multiscale wavelet transform. The density data obtained by processing with wavelet are used to train multilayer...
Predicting revenue from tenants for an enterprise having several malls cannot be easily done using conventional approach, such as spreadsheet or manual calculations. Such an enterprise has abundant data yet inadequate resources to analyze such data. This paper presents the data mining method, namely the Artificial Neural Network (ANN), to predict the revenue based on the previous data. ANN can help...
Data in the real world is seldom complete. Occlusions or temporally unavailable sensors often lead to situations where incomplete data is presented for analysis. Approaches to handle incomplete data have been proposed using neural networks such as fuzzy ARTMAP and back propagation. In this paper we propose a novel approach extending the unsupervised neural network based clustering technique called...
The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential...
Although traditional techniques of machine learning have, in many cases, presented good results, they have been inefficient for data which are constantly expanding and changing over time. To address these problems, new learning techniques have been proposed in the literature. In this paper we propose a technique called ePNN presenting aspects of this recent paradigm of learning. We carried out a series...
Effective and meaningful visualization techniques are quite important for multidimensional DNA microarray gene expression data analysis. Elucidating the cluster properties of these multidimensional data are often complex. Patterns, hypotheses on the relationships, and ultimately of the function of the gene can be analyzed and visualized by non-linear reduction of the multidimensional data to a lower...
Clustering is a major tool in data analysis, dividing objects into different groups, based on unsupervised training procedures. Clustering algorithms attempt to group a set of objects into well-defined subgroups, based on some similarity between them. The results of the clustering process may not be confirmed by our knowledge of the data. The self-organizing map (SOM) neural network is an excellent...
The Artificial neural networks are relatively crude electronic networks of "neurons" based on the neural structure of the brain. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The errors from the initial prediction of the first record is fed back into the network, and used to modify the networks algorithm...
Real-time monitoring of vital physiological signals is of significant clinical relevance. Disruptions in the signals are frequently encountered and make it difficult for precise diagnosis. Thus, the ability to accurately predict/recover the lost signals could greatly impact medical research and application. In response to the PhysioNet/CinC Challenge 2010: Mind the Gap, we develop an algorithm based...
Objective: Discussion based on neural networks in the 31P MR spectroscopy to distinguish hepatocellular carcinoma, normal liver and cirrhosis in value. Methods: Using self-organizing map neural network (SOM) analyse 66 data of 31P MRS, including hepatocellular carcinoma (13 samples), normal liver (16 samples) and liver cirrhosis (37 samples). Results: 31P MRS can be used for the diagnosis and differential...
Through the evaluation of the 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS), we can distinguish three types of diagnosis: hepatocellular carcinoma, normal and cirrhosis. 71 samples of 31P-MRS data are selected including hepatocellular carcinoma, normal and cirrhosis tissue. Back-propagation neural network (BP) and Radial Basis Function Neural Network (RBF) are applied to analyze 31P-MRS data,...
To discriminate the quality on traditional Chinese medicines Eucommia Bark real-time, according to the characters of Eucommia Bark finger printer, the basic concepts of rough set are introduced briefly. For rough sets can only deal with discrete data, the discretization of data is the key factor in the rough sets applied in quality assessment, we present a method of discretization based on cluster...
SOMs have been successfully applied in various fields. In this paper, we proposed an expanded SOM model for word learning which is a classic problem in cognitive science. In spite of simple computation of this model, the simulation results are consistent with the conclusion of the newest Bayesian model in the same learning cases. It implies that this model has the ability like human to properly response...
New techniques to enable the prediction of a reliable brain death index (BDI) measures are needed to improve patient care in the intensive care unit (ICU). The utilization of robust indicators combined with improved methods of data analysis and modeling is likely to deliver this facility. Like many forms of indicators, a combination of different measurement types can always improve the assessment...
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...
Distributed neural network based on RBF network is used to establish the model of fire detection in view of complexity of fire process and multiplicity of fire environment. For guaranteeing network adaptability, the number of subnets neural network and degree of sample belong to subnet are determined by fuzzy nearest clustering, RBF network is optimized though a improved genetic algorithm by adaptive...
The back propagation training algorithm, used to train non-linear feed forward multi-layer artificial neural networks, is capable of estimating the error present in the data presented to a network. While of no use during the training of a network, such information can be useful after training to permit the input data to be itself adjusted to better fit the internal model of a trained neural network...
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