The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In this paper, the SOM algorithm is applied to the analysis of the technology of world paper and pulp industry. It is seen that the method can be used on environmental, technological and nancial data to produce a comprehensive view of the industry as a whole.

The main purpose of this paper is to propose a novel soft computing inference engine model for intrusion detection. Our approach is anomaly based and utilizes causal knowledge inference based fuzzy cognitive maps (FCM) and multiple self organizing maps (SOM). A set of parallel neural network classifiers (SOM) are used to do an initial recognition of the network traffic flow to detect abnormal b...

The objective of this work is to develop a digitized mammograms’ feature extraction approach using Kohonen’s Self-Organizing Maps (SOM). Once developed, the SOM network will be used as the first processing stage in a breast cancer computer aided diagnosis (CAD) system. Its role will be to offer segmented data as input to a second stage dedicated to the diagnosis task, which will be implemented ...

We construct a benchmarking model in the form of a twodimensional self-organising map (SOM) to compare the performance of nonbanking financial institutions (NFIs) in Romania. The NFIs are characterized by a number of performance dimensions such as capital adequacy, assets’ quality and profitability. First, we apply Kohonen’ SOM algorithm (an unsupervised neural network algorithm) to group the N...

The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial 5 knowledge into the algorithm. The performance of the ...

Segmentation refers to the process of partitioning a digital image into multiple segments known as super-pixels. Image segmentation is typically used to locate objects and boundaries in images. SOM-K, a new unsupervised natural image segmentation method based on SOM and k-means. Intensity and L*, U*, V* values of a color image are taken as features to be trained by a SOM network. The output pro...

With the increase of computer usage, the number of digital documents is reaching values that make unviable conducting the tasks of text organization by humans. There is a demand for text organization tools that can operate with little human intervention and that can display the results of the organization in the most commonly used visual interface: the two-dimensional (2D) plane. One of the tec...

We address a contrastive study between the well known Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks and a SOM based supervised architecture in a number of data classification tasks. Well known databases like Breast Cancer, Parkinson and Iris were used to evaluate the three architectures by constructing confusion matrices. The results are encouraging and indicate t...

An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ) scheme is utilized in this proposed approach as the framework of the proposed algorithm. The edg...

In this paper we are studying the tolerance of Hopfield neural network for storage and recalling of fingerprint images. The feature extraction of these images is performed with FFT, DWT and SOM. These feature vectors are stored as associative memory in Hopfield Neural Network with Hebbian learning and Pseudoinverse learning rules. The objective of this study is to determine the optimal weight m...