Search results for "adaptive resonance theory"
showing 6 items of 6 documents
Visualizing Time Series State Changes with Prototype Based Clustering
2009
Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information and concentrate only for the indentification of normal and abnormal operational states. We present a new method for visualizing operational states and overall order of the transitions between them. This method is implemented to a visualization tool which helps the user to see the overall development of operational states allowing to find causes for abnormal behaviour. In the end visualization tool is tested in practice with real time series data collected from gear unit.
Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques
2013
HighlightsDifferent prediction algorithms were used to predict Hb levels in CRF patients.Prediction errors in the validation cohorts of patients were around 0.6g/dl.Difficulty to obtain lower errors due to the measuring machine precision (0.2g/dl).Relevance analysis of features have been applied for each predictor. Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of …
An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal
2007
This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www…
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…
An AI Walk from Pharmacokinetics to Marketing
2009
This work is intended for providing a review of reallife practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections: • Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF). • Optimization of a recommender system for citizen web portal users. • Optimization of a marketing campaign. The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility …
Koneoppiminen
2015
Koneoppiminen on monipuolinen ja tehokas työkalu erilaisiin tehtäviin. Tässä tutkielmassa on tarkoituksena tutustua sekä ohjatun että ohjaamattoman oppimisen yleisimpiin menetelmiin. Tarkoituksena on käsitellä nämä menetelmät yleisellä tasolla niin, että asiasta tietämätön ymmärtää perusperiaatteet miten eri menetelmät toimivat. Tarkempia yksityiskohtia ja matemaattisia algoritmejä ei tulla käsittelemään. Machine learning is powerfull and versatile tool for a multitude of tasks. The point of this study is to familiarize oneself with a couple of different methods of both supervised and unsupervised machine learning, so that anyone can understand the basic principles behind the different meth…