The Artificial Neural Networks method is applied on visual working efficiency of cockpit. A Self-Organizing Map (SOM) network is demonstrated selecting material with near properties. Then a Back-Propagation (BP) network automatically learns the relationship between input and output. After a set of training, the BP network is able to estimate material characteristics using knowledge and criteria...

Cortical GABAergic interneurons represent a highly diverse neuronal type that regulates neural network activity. In particular, interneurons in the hippocampal CA1 oriens/alveus (O/A-INs) area provide feedback dendritic inhibition to local pyramidal cells and express somatostatin (SOM). Under relevant afferent stimulation patterns, they undergo long-term potentiation (LTP) of their excitatory s...

This paper intends to introduce an implementation of a novel Self-Organization Map (SOM) in Host-based Stepping Stone Detection (SSD). Previous works have introduced Artificial Intelligence (AI) approaches such as Artificial Neural Network (ANN), however we found that the approaches are complex due to the requirement of variable to be known and tested to detect a stepping stone. SOM provides un...

The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy function. The Self-Organising Mixture Network ...

In this paper we describe an implementation of a network based Intrusion Detection System (IDS) using Self-Organizing Maps (SOM). The system uses a structured SOM to classify real-time Ethernet network data. A graphical tool continuously displays the clustered data to reflect network activities. Different system parameters such as data collection, data preprocessing and classifier structure are...

Detecting network intrusion has been not only important but also difficult in the network security research area. In Medical Sensor Network(MSN), network intrusion is critical because the data delivered through network is directly related to patients’ lives. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes i...

In this paper, two generalizations of the SOM are introduced. The first of these extends the SOM to deal with more generalized classes of objects besides the vector dataset. This generalization is realized by employing modular networks instead of reference vector units and is thus called a modular network SOM (mnSOM). The second generalization involves the extension of the SOM from ‘map’ to ‘ho...

A self-organizing map (SOM) is an artificial neural network algorithm that can learn from the training data consisting of objects expressed as vectors and perform non-hierarchical clustering to represent input vectors into discretized clusters, with vectors assigned to the same cluster sharing similar numeric or alphanumeric features. SOM has been used widely in transcriptomics to identify co-e...

Today, information networks play an important role in supply chain management. Therefore, in this article, clustering-based routing protocols, which are one of the most important ways to reduce energy consumption in wireless sensor networks, are used to optimize the supply chain informational cloud network. Accordingly, first, a clustering protocol is presented using self-organizing map neu...

The self-organizing map (SOM) network was originally designed for solving problems that involve tasks such as clustering, visualization, and abstraction. While Kohonen’s SOM networks have been successfully applied as a classi6cation tool to various problem domains, their potential as a robust substitute for clustering and visualization analysis remains relatively unresearched. We believe the in...