This paper concerns with free form surface reorganization and assessment of free formmodel complexity, grouping particular surface geometrical properties within patch boundaries, using self organized Kohonen neural network (SOKN). Neural network proved itself as an adequate tool for considering all topological non-linearities appearing in free form surfaces. Coordinate values of point cloud dis...

The paper presents a design of parameters for air quality modelling and the classification of districts into classes according to their pollution. Further, it presents a model design, data pre-processing, the designs of various structures of Kohonen’s Self-organizing Feature Maps (unsupervised methods), the clustering by K-means algorithm and the classification by Learning Vector Quantization n...

Spatial hypertext systems use physical properties as color, dimensions, and position to represent relationships between documents. These systems allows the user to express a lot of different relationships between information but the structure should be build by hand by the user. This can be complex if a large number of information is involved. Self-organizing neural networks map can automatical...

Due to large datavolumes when remote sensing or other kind of images are used, there is need for methods to decrease the volume of data. Methods for decreasing the feature dimension, in other words number of channels, are called feature selection and feature extraction. In the feature selection, important channels are selected using some search technique and these channels are used for current ...

Handwritten signatures are the most natural way of authenticating a person’s identity. An offline signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction, recognition and verification. This paper presents a method for verifying handwritten signature by using NN architecture. In proposed methods the multi-layer perceptron (MLP), mo...

This paper presents a method for color image segmentation which uses classification to group pixels into regions. The chromaticity is used as data source for the method because it is normalized and considers only hue and saturation, excluding the luminance component. The classification is carried out by means of a self-organizing map (SOM), which is employed to obtain the main chromaticities pr...

Crime intelligence analysis and criminal investigations are increasingly making use of geospatial methodologies to improve tactical and strategic decision-making. However, the full potential of geospatial technologies is yet to be exploited. In particular, geospatial technology currently applied by law enforcement is somewhat limited in handling the increasing volume of police recorded and rela...

This work investigates the self-organizing representation of temporal data in prototypebased neural networks. Extensions of the supervised learning vector quantization (LVQ) and the unsupervised self-organizing map (SOM) are considered in detail. The principle of Hebbian learning through prototypes yields compact data models that can be easily interpreted by similarity reasoning. In order to ob...

Maximizing the profit and minimizing the loss notwithstanding the trend of the market is always desirable in any investment strategy. The present research develops an investment strategy, which has been verified effective in the real world, by employing self-organizing map neural network for mutual funds and tracking the trends of stock market indices according to macro-economy indicators, weig...

Fatigued bills have harmful influence on the daily operation of Automated Teller Machine(ATM). To make the classification of fatigued bills more efficient, the development of an automatic fatigued bill classification method is desirable. We propose a new method to estimate the fatigue level of bill from the feature-selected frequency band acoustic energy pattern of banking machines. By using a ...