Our research created a network Intrusion Detection Math (ID Math) consisting of two components: (1) a way of specifying intrusion detection types in a manner which is more suitable for an analytical environment; and (2) a computational model which describes methodology for preparing intrusion detection data stepwise from network packets to data structures in a way which is appropriate for sophi...

We propose a two-stage hierarchical arti®cial neural network for the segmentation of color images based on the Kohonen self-organizing map (SOM). The ®rst stage of the network employs a ®xed-size two-dimensional feature map that captures the dominant colors of an image in an unsupervised mode. The second stage combines a variable-sized one-dimensional feature map and color merging to control th...

Abstract The self organizingmap SOM of Kohonen is one of the most successful models of unsupervised learning Its popularity is partially due to the visualization topography preservation of relations among clusters in high dimensional input space SOM learns slowly especially in the initial phase and the preservation of topography by SOM maps is not based on any quantitative criteria We have obta...

The Self-Organizing Map (SOM) is an artificial neural network that is very effective for clustering via visualization. Ideally, so as produce a good model, the output space dimension of the SOM should match the intrinsic dimension of the data. However, because it is very difficult or even impossible to visualize SOM’s with more than two dimensions, the vast majority of applications use SOM with...

In this paper we investigate the performance of the Kohonen’s self organizing map (SOM) as a strategy for the analysis of multispectral and multi-resolution remote sensed images. The paper faces the problem of data fusion, by extracting and combining multi-spectral and textural features. Moreover we address the problem of low-quantity and low-quality of labelled pixels in the training set, inve...

Artificial Intelligence (AI) has recently been recognized as a worthwhile tool for supporting manufacturing operations. This paper reviews AI-related approaches to Group Technology (GT) and presents the Self-Organizing Map (SOM) network, a special type of neural networks, as an intelligent tool for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform the...

The artificial neural network class of self-organizing maps (SOMs) is a powerful and promising cognitive modeling tool in the study of the brain and its disorders. Under this premise, this paper proposes a novel modification of the standard SOM algorithm in the form of an oscillating Topological Neighborhood (TN) width function. Existing research in neuroscience indicates that SOMs with oscilla...

-Self-Organizing Maps (SOM) are popular unsupervised artificial neural network used to reduce dimensions and visualize data. Visual interpretation from Self-Organizing Maps (SOM) has been limited due to grid approach of data representation, which makes inter-scenario analysis impossible. The paper proposes a new way to structure SOM. This model reconstructs SOM to show strength between variable...

Satellite remote sensing has revolutionized modern oceanography, providing frequent synoptic-scale information that can be used to deduce ocean processes. However, it is often difficult to extract interpretable patterns from satellite images, as data sets are large and often non-linear. In this methodological paper, we describe the self-organizing map (SOM), a type of artificial neural network ...

Dr. D. Hari Prasad Professor, Department of Computer Applications, SNR Sons College, Coimbatore Abstract A primary drawback of the traditional SOM is that the size of the mapis fixed and the number of neurons in the map should be determined a priori. This might not be feasible for some applications and result in a significant limitation on the final mapping. Several dynamic SOM models have been...