MOTIVATION Anchoring of proteins to the extracytosolic leaflet of membranes via C-terminal attachment of glycosylphosphatidylinositol (GPI) is ubiquitous and essential in eukaryotes. The signal for GPI-anchoring is confined to the C-terminus of the target protein. In order to identify anchoring signals in silico, we have trained neural networks on known GPI-anchored proteins, systematically opt...

Coupled ocean-atmosphere science steadily advances with increasing information obtained from long-records of in situ observations, multiple-year archives of remotely sensed satellite images, and long time series of numerical model outputs. However, the percentage of data actually used tends to be low, in part because of a lack of efficient and effective analysis tools. For instance, it is estim...

The main purpose of this paper is to detect and follow the pipeline in sonar image. This work is performed by two steps. The first one is to split an transformed line image of pipeline signal into regions of uniform texture using the Gray Level Co-occurrence Matrix Method (GLCM) which is widely used in texture segmentation application. The last one addresses the unsupervised learning method bas...

The majority of today's Neural Networks are either Multi-Layer-Perceptron networks (MLP) or Feature-Maps like Kohonen's Self-Organizing Map (SOM). Usually they are simulated on ordinary single-processor von-Neumann hardware to be used for some kind of vector quantization, classiication or coding. However, these simulated Neural Networks are expensive in respect to the computational costs they d...

The Self Organizing Map (SOM) proposed by Kohonen [7] is a well known neural model which provides both quantization and clustering of the observation space. In this paper, we adapt the Bernoulli mixture approach, proposed by [6], to the model of binary topological map [2] and show that using a probabilistic formalism gives rise to better quantization process and classi cation performances.

Identification of the short DNA sequence motifs that serve as binding targets for transcription factors is an important challenge in bioinformatics. Unsupervised techniques from the statistical learning theory literature have often been applied to motif discovery, but effective solutions for large genomic datasets have yet to be found. We present here three self-organizing neural networks that ...

The goal of the article is to presents intrusion detections systems and design architecture of intrusion detection based on neural network self organizing map. In the report is described base problematic of neural network and intrusion detection system. The article further deals with specific design of intrusion detection architecture based on user anomaly behavior. A core of the designed archi...

Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART) was subsequently applied to the SOM t...

As a result from modeling cortical processes of self-organization occuring during speech acquisition, a comprehensive neural model of speech production has been developed by using self-organizing neural networks and feedforward neural networks. This model is capable of generating acoustic speech signals and sensory feedback signals by using a high quality 3-dimensional articulatory-acoustic spe...