This article introduces a version of the Self-Organizing Migrating Algorithm with narrowing search space strategy named iSOMA. Compared to previous two versions, SOMA T3A and Pareto that ranked 3rd 5th respectively in IEEE CEC (Congress on Evolutionary Computation) 2019 competition, iSOMA is equipped more advanced features notable improvements including applying jumps order, immediate update, i...

Urban space is a dynamic system influenced by many factors. The formation of the transport structure in city determines type planning that forms spatial framework, and then functional organization territories determined. This allows us to create an effective urban space. But cities are changing, their needs, definitions, especially functions so modifying existing approaches pressing issue for u...

We introduce a method for deriving a metric, locally based on the Fisher information matrix, into the data space. A self-organizing map (SOM) is computed in the new metric to explore financial statements of enterprises. The metric measures local distances in terms of changes in the distribution of an auxiliary random variable that reflects what is important in the data. In this paper the variab...

AbsfructWe propose a self-organizing neural structure with dynamic and spatial changing weights for a feature space representation of concept formation. An essential core of this self-organization is based on an Unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for the spatial changing. A concept formation problem requires the neural network...

The architecture of houses, design and management are a collection of behaviors that are realized in space. The recognition of traditional houses requires having space in possession; that is to say, seeing the building and seeking the key to appreciating it. On the one hand, it refers to the different dimensions and features of the building and on the other, it represents the main objectives of...

Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural n...

In this paper, we focus on applying sentiment analysis to resources from online collections, by exploiting, as information source, tags intended as textual traces that visitors leave for commenting artworks on social platforms. Our aim is to create a semantic social space where artworks can be dynamically organized according to an ontology of emotions. We propose to tackle this issue in a seman...

Abstract — This study presents a new concept that generalizes the self-organizing map (SOM) by adopting the idea of modular network, which we call “modular network SOM (mnSOM)”. In the mnSOM, each codebook vector in the conventional SOM is replaced by a functional module which is a neural network. With mnSOM, the application targets can be widely expanded from fields involving vectorized data t...

Bodies of information available through the Internet, such as digital libraries and distributed file-sharing systems, often form a self-organizing networked information space, i.e. a collection of interconnected information entities generated incrementally over time by a large number of agents. The collection of electronically available research papers in Computer Science, linked by their citat...