Articolele autorului Liviu Vladutu
Link la profilul stiintific al lui Liviu Vladutu

„Neural Network Approaches for Children’s Emotion Recognition in Intelligent Learning Applications”, by F. Albu, D. Hagiescu, L. Vladutu and M. Puica presented at the „EDULEARN15: 7th annual International Conference on Education and New Learning Technologies”, Barcelona, Spain, 6th-8th of July, 2015 (paper ID 887).
„Shape Recognition for Irish Sign Language Understanding”, by L. Vladutu, presented at the 9th WSEAS International Conference on Simulation, Modeling and Optimization” (SMO ’09)- BEST PAPER award at „Simulation, Modeling and Optimization”.
„Intelligent Tutor for First Grade Children’s Handwriting Application”, by F. Albu, D. Hagiescu, M. Puica and L. Vladutu, at INTED2015, the 9th International Technology, Education and Development Conference which took place in Madrid (Spain), on the 2nd, 3rd and 4th of March, 2015.
An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms, by L. Vladutu, AG Tselentis
Shape Recognition for Irish Sign Language Understanding
Computational Intelligence Methods on Biomedical Signal Analysis”
Online Neural Network Training for Automatic Ischemia Episode Detection
Computational Intelligence Methods on Biomedical Signal Analysis and Data Mining in Medical Records

This thesis is centered around the development and application of computationally effective solutions based on artificial neural networks (ANN) for biomedical signal analysis and data mining in medical records. It is important to recall that the ultimate goal of this work in the field of Biomedical Engineering is to provide the clinician with the best possible information needed to make an accurate diagnosis (in our case of myocardial ischemia) and

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The Supervised Network Self-Organizing Map for Classification of Large Data Sets

Complex application domains involve difficult pattern classification problems. The state space of these problems consists of regions that lie near class separation boundaries and require the construction of complex discriminants while for the rest regions the classification task is significantly simpler. The motivation for developing the Supervised Network Self-Organizing Map (SNet-SOM) model is to exploit this fact for designing computationally

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Generalized Radial Basis Function Networks Trained with Instance Based Learning for Data Mining of Symbolic Data

The application of neural networks to domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Traditional distances are inadequate to access the differences between the symbolic patterns. This work proposes the utilization of a statistically extracted distance measure in the context of Generalized Radial Basis Function (GRBF) networks. The main

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