Search results for "Mining"

A neural multi-agent based system for smart html pages retrieval

2003

A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically …

Ad-Hoc Segmentation Pipeline for Microarray Image Analysis

2006

Microarray is a new class of biotechnologies able to help biologist researches to extrapolate new knowledge from biological experiments. Image Analysis is devoted to extrapolate, process and visualize image information. For this reason it has found application also in Microarray, where it is a crucial step of this technology (e.g. segmentation). In this paper we describe MISP (Microarray Image Segmentation Pipeline), a new segmentation pipeline for Microarray Image Analysis. The pipeline uses a recent segmentation algorithm based on statistical analysis coupled with K-Means algorithm. The Spot masks produced by MISP are used to determinate spots information and quality measures. A software …

Mortality associated with the use of inappropiate drugs according Beers Criteria: a systematic review

2013

The aims of this systematic review are to identify and analyse the scientist literature available evidence about the use of potentially inappropriate medications, according to the Beers Criteria, that is associated with mortality in the elderly people.It have been made a search of publications in most traditional electronic databases among the scientific community (Pubmed / Medline, EMBASE and Web of Science) and it have been selected publications that obey the criteria of 'observational study', 'elderly' and 'Beers Criteria' and that they had as a result the mortality of the study population. After publications selection it proceeded to dump data by two researchers independently to avoid s…

Tranexamic acid for trauma

2010

Neural networks for animal science applications: Two case studies

2006

Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…

Using SOM and PCA for analysing and interpreting data from a P-removal SBR

2008

This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the…

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

2014

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …

Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators

2016

Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…

Complexity Selection of the Self-Organizing Map

2002

This paper describes how the complexity of the Self-Organizing Map can be selected using the Minimum Message Length principle. The use of the method in textual data analysis is also demonstrated.

Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

2006

This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …