In this paper, we introduce an algorithm of Self-Organizing Maps(SOM) which can map the genome sequence continuously on the map. The DNA sequences are considered to have the special features depending on the regions where the sequences are taken from or the gene functions of the proteins which are translated from the sequences. If the hidden features of the DNA sequences are extracted from the ...

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM w...

Recently, all genome of some species were determined and the functions of many genes were also determined. But, it will need much efforts to determine the functions of all genes. Furthermore, not all regions (regulatory, promoters, exons, introns and etc.) on the genome sequences are distinguished yet. To determine the functions of the genes or the regions on genome, the comparison between the ...

Recently, security of the computer systems becomes an important problem. Almost all computers use the password mechanism for the user authentication. But password mechanism has many issues. In this paper, we propose a kind of biometrics authentication method using the combinations of key stroke timings and pen calligraphy. For this method, selection of the phrase is important. We analyzed the k...

An art installation was on display in the Centre Pompidou National Museum of Modern Art in Paris, were visitors could contribute with their own personal objects, adding keyword descriptions and quantified semantic features such as age or hardness. The data was projected in real-time onto a Self-Organizing Map (SOM) and shown in the gallery. In this paper we analyze the same data by extracting v...

Artificially recognizing the human face is a challenging problem and is one of those challenging problems having no technique that provides a robust solution to all situations. This paper provides a new technique for human face recognition. Principal Component Analysis (PCA) is used for dimensionality reduction and for feature extraction. A Self Organize Map (SOM) is used as classifier to ident...

The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable ...

We propose a new method called C-SOM for function approximation. C-SOM extends the standard Self-Organizing Map (SOM) with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve standard SOMs' generalization capabilities. CSOM uses the gradient information provided by the LLM technique to compute a cubic spline interpolation in the input space bet...

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this i...

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to ana...