This paper challenges the idea of creating an architectural approach for designing a complex hybrid cognitive agent, i.e. an agent which possesses all reactive, deliberative and affective capabilities, outside the hierarchical paradigm. Thus, it presents an agent architecture based on Marvin Minsky’s “Society of Mind” (SoM) metaphor on human mind and cognition and shows how SoM agents, through ...

The large volume of nowadays document collections has increased the need of fast trainable document organization systems. This paper presents and evaluates a hybrid system to self-organization of massive document collections based on self-organizing map (SOM). The hybrid system uses prototypes generated by a clustering algorithm to train the document maps, thus reducing the training time of lar...

This paper presents a method using hybridization of self organizing map (SOM ), particle swarm optimization(PSO) and k-means clustering algorithm for document clustering. Document representation is an important step for clustering purposes. The common way of represent a text is bag of words approach. This approach is simple but has two drawbacks viz. synonymy and polysemy which arise because of...

In this paper, we proposed a hybrid system to combine the self-organizing map (SOM) of neural network with case-based reasoning (CBR) method, for sales forecast of new released books. CBR systems have been successfully used in several domains of artificial intelligence. In order to enhance efficiency and capability of CBR systems, we connected the SOM method to deal with cluster problems of CBR...

Successful river flow time series forecasting is a primary goal and an essential procedure required in the planning and water resources management. River flow data are important for engineers to design, build and operate various water projects and development. The monthly river flow data taken from Department of Irrigation and Drainage, Malaysia are used in this study. This study aims to develo...

This paper presents the results of modelling to predict the effluent biological oxygen demand (BOD5) concentration for primary clarifiers using a hybridisation of unsupervised and supervised artificial neural networks. The hybrid model is based on the unsupervised self-organising map (SOM) whose features were then used to train a multi-layered perceptron, feedforward back propagation artificial...

it seems that the presence of soil organic matter (som) increases p availability to plants. this effect has commonly been ascribed as competition between the decomposition products of som and p for soil sorption sites resulting in p concentrations increase of soil solution. this study was carried out to obtain information about soil organic matter (som) influence on kinetics and thermodynamics ...

In this paper we present a technique for the summarization and spatiotemporal scaling of video content. A Self Organizing Map (SOM) neural network can be used o acquire a rough generalization of the spatiotemporal trajectories of moving objects, in the form of few selected nodes along these trajectories. We introduce a hybrid technique, combining SOM with geometric analysis to properly densify ...

The self-organizing map’s unsupervised clustering method can be used as a data visualisation technique. Within this paper different techniques to visualise selforganizing maps (SOM) and their effectiveness are investigated in relation to the organisation of a large software collection and its visualisation. GENISOM, an offspring component of the GENESIS software engineering platform, incorporat...

In this research, a hybrid approach by combining Self-Organizing Map (SOM) and Evolving Fuzzy Case-Based Reasoning (EFCBR) for flow time prediction is proposed. Genetic Algorithms (GAs) is applied to fine-tune the fuzzy term numbers and weights of fuzzy features in the CBR model. The flow time and related shop floor status are collected and fed into the SOM for classification. Then, correspondi...