The Coronaviruses are worldwide in distribution and caused an epidemic in China in 2003. The rapidly mutating Coronaviruses spreads fast by taking different forms and infects not only human beings but also cattle, pigs, rodents, cats, dogs and birds. In fighting against these viruses, it is important to elucidate the evolutionary links among the different types of Coronaviruses. This article ex...

In this investigation, an approach using Coactive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the relationships between the large measured factors, which can be possibly resolve...

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve comple...

[1] Organizing or clustering data into natural groups is one of the most fundamental aspects of understanding and mining information. The recent explosion in sensor networks and data storage associated with hydrological monitoring has created a huge potential for automating data analysis and classification of large, high-dimensional data sets. In this work, we develop a new classification tool ...

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...

We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the ...

-Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network class...

Outline: This paper describes application of rough set theory, on the analysis of hydrocyclone operation. In this manner, using Self Organizing Map (SOM) as preprocessing step, best crisp granules of data are obtained. Then, using a combining of SOM and rough set theory (RST)-called SORST-, the dominant rules on the information table, obtained from laboratory tests, are extracted. Based on thes...

Recently, the spread of the Internet makes familiar to the incident concerning the Internet, such as a DoS attack and a DDoS attack. Some methods which detect the abnormal traffics in the network using the information from headers and payloads of IP-packets transmitted in the networks are proposed. In this research, the method for the analysis of the IP packet based on SOM (Self-Organizing Map)...

In recent times Artificial Neural Networks (ANNs) are more and more widely applied. The ANN is an information processing system consisting of numerous simple processing units (neurons) that are arranged in layers and have weighted connections to each other. In the present study the possible application of an unsupervised neural network model, the self-organizing map (SOM), for the delineation o...