Engineering applications of the self-organizing map

Kohonen, Teuvo; Oja, Erkki; Simula, Olli; Visa, Ari; Kangas, Jari

doi:10.1109/5.537105

Cited by 746 publications

(407 citation statements)

References 119 publications

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“…After this step, the experimental data to give mapped To determine whether the input vector The normal vector or a vector of attack. If that BMU selected is a normal neuron with labels In this case the normal vector of the detected Otherwise traffic in general, the attack is detected [6]. Figure 2. shows the architecture.…”

Section: The Architecture Self-organizing Map Methodsmentioning

confidence: 99%

Intrusion Detection System Using Self Organizing Map Algorithms

khodaie¹,

Jamali²,

Farzan³

2014

IJCATR

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Abstract:With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based on neural networks that are suitable for Intrusion Detection Systems (IDS). The name of this algorithm is "Self Organizing Maps" (SOM). So far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the methods used to detect attacks in intrusion detection is done, In this paper we investigate the Self-Organizing Map method.

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Section: The Architecture Self-organizing Map Methodsmentioning

confidence: 99%

Intrusion Detection System Using Self Organizing Map Algorithms

khodaie¹,

Jamali²,

Farzan³

2014

IJCATR

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“…With this conversion, the algorithm compresses the data while preserving the most important topological and metric relationships of the initial data elements [80]. This creates a special kind of abstraction.…”

Section: Self Organizing Map (Som)mentioning

confidence: 99%

Application of infrared thermography in computer aided diagnosis

Faust

Acharya

et al. 2014

Infrared Physics & Technology

100

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The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even nonlinear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based ComputerAided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

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“…The SOM is an unsupervised neural network algorithm that implements a non-linear mapping of high-dimensional input data onto a two-dimensional array of weight vectors (Kohonen, 1982(Kohonen, , 1990Kohonen et al, 1996). The process of reducing the data's dimensionality can be thought of as a compression of the input information, whereby the most important topological and metric relationships are preserved.…”

Section: Algorithmmentioning

confidence: 99%

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

et al. 2010

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Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order to link biodiversity with functions, but have not been applied to broad-scale NGS-based metagenomics yet. Here, we provide a novel implementation, demonstrate its potential and practicability, and provide a web-based service for public usage. Evaluation with published data sets mimicking varyingly complex habitats resulted into classification specificities and sensitivities of close to 100% to above 90% from phylum to genus level for assemblies exceeding 8 kb for low and medium complexity data. When applied to five real-world metagenomes of medium complexity from direct pyrosequencing of marine subsurface waters, classifications of assemblies above 2.5 kb were in good agreement with fluorescence in situ hybridizations, indicating that biodiversity was mostly retained within the metagenomes, and confirming high classification specificities. This was validated by two protein-based classifications (PBCs) methods. SOMs were able to retrieve the relevant taxa down to the genus level, while surpassing PBCs in resolution. In order to make the approach accessible to a broad audience, we implemented a feature-rich web-based SOM application named TaxSOM, which is freely available at http://www.megx.net/toolbox/taxsom. TaxSOM can classify reads or assemblies exceeding 2.5 kb with high accuracy and thus assists in linking biodiversity and functions in metagenome studies, which is a precondition to study microbial ecology in a holistic fashion.

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Engineering applications of the self-organizing map

Cited by 746 publications

References 119 publications

Intrusion Detection System Using Self Organizing Map Algorithms

Intrusion Detection System Using Self Organizing Map Algorithms

Application of infrared thermography in computer aided diagnosis

Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

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