Web 2.0 services have enabled people to express their opinions, experience and feelings in the form of user-generated content. Sentiment analysis or opinion mining involves identifying, classifying and aggregating opinions as per their positive or negative polarity. This paper investigates the efficacy of different implementations of Self-Organizing Maps (SOM) for sentiment based visualization ...

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent ext...

In recent years, a variety of visualization techniques for visual data exploration based on self-organizing maps (SOMs) have been developed. To support users in data exploration tasks, a series of software tools emerged which integrate various visualizations. However, the focus of most research was the development of visualizations which improve the support in cluster identification. In order t...

In this paper, we propose a novel approach to generate the topology-preserving mapping of structural shapes using self-organizing maps (SOMs). The structural information of the geometrical shapes is captured by relational attribute vectors. These vectors are quantised using an SOM. Using this SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another ...

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples, and many existing face recognition techniques rely heavily on the size and representative of training set. Those algorithms may suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. In this paper, we presen...

This paper presents the application of shallow neural networks (SNNs): multi-layer perceptron (MLP) and self-organizing Kohonen maps (SOMs) to early detection classification stator rotor faults in permanent magnet synchronous motors (PMSMs). The were trained based on vector coming from measurements a real object. elements input SNNs constituted selected amplitudes diagnostic signal spectrum. cu...

Adaptation neighborhoods of self-organizing maps for image restoration are presented in this study. Generally, self-organizing maps have been studied for the ordering process and the convergence phase of weight vectors. As a new approach of self-organizing maps, some methods of adaptation neighborhoods for image restoration are proposed. The present algorithm creates a map containing one unit f...

XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands sources. Manually searching interesting patterns in light curves is impractical, requiring an automated data-mining approach characterization Straightforward fitting temporal models to not a sure way identify them, especially with noisy data. We used unsupervised machin...

Among the large number of research publications discussing the SOM (Self-Organizing Map) [1, 2, 18, 19] different variants and extensions have been introduced. One of the SOM based models is the Growing Hierarchical Self-Organizing Map (GHSOM) [3-6]. The GHSOM is a neural architecture combining the advantages of two principal extensions of the self-organizing map, dynamic growth and hierarchica...

Perceiving similarity relationships in melodies is a fundamental musical process effortlessly performed by the cognitive system of listeners. For this reason, computationally heavy methods such as string-matching algorithms may not be plausible as perceptually oriented computational models of this process. Instead of direct comparison of musical events, similarity can be thought of as a higher ...