Lately, cross-modal retrieval has attained plenty of attention due to enormous multi-modal data generation every day in the form audio, video, image, and text. One vital requirement is reduce heterogeneity gap among various modalities so that one modality’s results can be efficiently retrieved from other. So, a novel unsupervised framework based on associative learning been proposed this paper ...

Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightinga...

► SiM would in future publish three issues per year; ► SiM would only accept papers that deal with fungal cultures, and/or fungal DNA, which should be deposited at CBS to be optimally accessible to the scientific community; ► SiM would publish papers by invitation or if a volume or special topic was supported by one of its associate editors; ► SiM would move to a print-on-demand system that all...

In this paper we show a new on-line parametric model for time series forecasting based on VapnikChervonenkis (VC) theory. Using the strong connection between support vector machines (SVM) and Regularization theory (RT), we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) with the corresponding expressions for updating neural parameters. ...

Polycyclic aromatic hydrocarbons (PAHs), abundant in mixed contaminant sites, often coexist with heavy metals. The fate and remediation of PAHs depend heavily on the sorption and desorption behavior of these contaminants. The sorption behavior can in turn be highly affected by certain soil components and properties, such as soil organic matter (SOM) and the presence of heavy metals. Through rev...

Data mining or knowledge discovery in database (KDD) refers to the non-trivial process of discovering interesting, implicit, and previously unknown knowledge from databases. Such a task implies to be able to perform analyses both on high-dimensional input data and large dataset. The most popular models used in KDD are the symbolic models. Unfortunately, these models suffer of very serious limit...

Near-Infrared reflectance spectra of planets can be used to infer surface parameters, sometimes with relevance to recent geologic history. Accurate prediction of parameters (such as composition, temperature, grain size, crystalline state, and dilution of one species within another) is often difficult because parameters manifest subtle but significant details in noisy spectral observations, beca...

In this paper, the methods of correcting skew vehicle license plate and segmenting characters in plate are discussed first. An approach making use of self-organizing map (SOM) is introduced to find the tilt angle of plate which simultaneously educes seven important points with coordinates being elements of weight matrix. After necessary processing to corrected plate, a character segmentation al...

Network Intrusion Detection (NID) is the process of identifying network activity that can lead to the compromise of a security policy. In this paper, we will look at four intrusion detection approaches, which include ANN or Artificial Neural Network, SOM, Fuzzy Logic and SVM. ANN is one of the oldest systems that have been used for Intrusion Detection System (IDS), which presents supervised lea...

This paper presents a hybrid framework combining self-organising map (SOM) and fuzzy theory for textual classification. Clustering using selforganizing maps is applied to produce multiple targets. In this paper, we propose that an amalgamation of SOM and association rule theory may hold the key to a more generic solution, less reliant on initial supervision and redundant user interaction. The r...