during an 11 days mission in february 2000 the shuttle radar topography mission (srtm) collected data over 80% of the earth's land surface, for all areas between 60 degrees n and 56 degrees s latitude. since srtm data became available, many studies utilized them for application in topography and morphometric landscape analysis. exploiting srtm data for recognition and extraction of topographic ...

Connectionist systems (often termed "neural networks") are an alternative way to solve data processing tasks. They differ radically from conventional "von-Neumann" computing devices. Recent work on neural networks in clinical chemistry was done using supervised learning schemes, resulting in models which resemble classical discriminant analysis. The aim of the present study is to make clinical ...

Collaborative ventures—both equity-based partnerships as well as project-based alliances—have dominated the international business scene over the past two decades. By means of this study we investigate the patterns of related and unrelated collaborative venture formation. Using a large database of over 90,000 collaborative ventures formed during the 1985–2001 period, this study clusters collabo...

 Automatic pattern classification is a very important field of artificial intelligence. For these kind of tasks different techniques have been used. In this work a combination of decision trees and self-organizing neural networks is presented as an alternative to attack the problem. For the construction of these trees growth processes are applied. In these processes, the evaluation of classifi...

The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...

The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...

We introduce a concept of self-organizing Hybrid Neurofuzzy Networks (HNFN), a hybrid modeling architecture combining neurofuzzy (NF) and polynomial neural networks(PNN). The development of the Self-organizing HNFN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the Self-organizing HNFN results from a...

The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...