Liviu Vladutu received the Diploma of Engineer (with Honors degree) in Automation & Computer Science Engineering Dept. from the University of Craiova, Romania, in 1988, a Master in Science and the Ph.D. degree from the School of Medicine, University of Patras, Greece, in December 1999, and, respectively November 2004. From November 2004 until September 2005 he has been a Postdoctoral Fellow with the Department of Neuroscience, Medical University of South Carolina. From November 2005 until 2009 Dr. Vladutu has worked as a researcher in the field of Irish Sign-Language recognition at the School of Computing, Dublin City University- Ireland. Liviu Vladutu authored/co-authored approx. 30 scientific publications in the areas of Computational Intelligence, image/signal processing, pattern recognition and bioinformatics applications. Liviu Vladutu is a Member of EuCogII (2nd European Network for the Advancement of Artificial Cognitive Systems, Interaction and Robotics), and a Member of the IEEE.
Abstract: The recognition of human activities from video sequences is currently one of the most active areas of research because of its many applications in video surveillance, multimedia communications, medical diagnosis, forensic research and sign language recognition. The work described in this paper describes a new method designed to precisely identify human gestures for Sign Language recognition. The system is to be developed and implemented on a standard personal computer (PC) connected to a colour video camera. The present paper tackles the problem of shape recognition for deformable objects like human hands using modern classification techniques derived from artificial intelligence.
Abstract: Myocardial ischemia is caused by a lack of oxygen and nutrients to the contractile cells and may lead to myocardial infarction with its severe consequence of heart failure and arrhythmia. An electrocardiogram (ECG) represents a recording of changes occurring in the electrical potentials between different sites on the skin as a result of the cardiac activity. Since the ECG is recorded easily and non–invasively, it becomes very important to provide means of reliable ischemia detection. Ischemic changes of the ECG frequently affect the entire repolarization wave shape. In this paper we propose a new classification methodology that draws from the disciplines of clustering and artificial neural networks, and apply it to the problem of myocardial ischemia detection. The results obtained are promising.
Abstract: The application of the Radial Basis Function neural networks in domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. This distance in addition to providing information about the proximity of patterns should also obey some mathematical criteria in order to be applicable. Traditional distances are inadequate to access the differences between symbolic patterns. This work proposes the utilization of a statistically extracted distance measure for Generalized Radial Basis Function (GRBF) networks. The main properties of these networks are retained in the new metric space. Especially, their regularization potential can be realized with this type of distance. However, the examples of the training set for applications involving symbolic patterns are not all of the same importance and reliability. Therefore, the construction of effective decision boundaries should consider the numerous exceptions to the general motifs of classification that are frequently encountered in data mining applications. The paper supports that heuristic Instance Based Learning (IBL) training approaches can uncover information within the uneven structure of the training set. This information is exploited for the estimation of an adequate subset of the training patterns serving as RBF centers and for the estimation of effective parameter settings for those centers. The IBL learning steps are applicable to both the traditional and the statistical distance metric spaces and improve significantly the performance in both cases. The obtained results with this two-level learning method are significantly better than the traditional nearest neighbour schemes in many data mining problems.
Abstract: The problem of maximizing the performance of the detection of ischemia episodes is a difficult pattern classification problem. The motivation for developing the supervising network self-organizing map (sNet-SOM) model is to exploit this fact for designing computationally effective solutions both for the particular ischemic detection problem and for other applications that share similar characteristics. Specifically, the sNet-SOM utilizes unsupervised learning for the “simple” regions and supervised for the “difficult” ones in a two stage learning process. The unsupervised learning approach extends and adapts the self-organizing map (SOM) algorithm of Kohonen. The basic SOM is modified with a dynamic expansion process controlled with an entropy based criterion that allows the adaptive formation of the proper SOM structure. This extension proceeds until the total number of training patterns that are mapped to neurons with high entropy reduces to a size manageable numerically with a capable supervised model. The second learning phase has the objective of constructing better decision boundaries at the ambiguous regions. At this phase, a special supervised network is trained for the computationally reduced task of performing the classification at the ambiguous regions only. The utilization of sNet-SOM with supervised learning based on the radial basis functions and support vector machines has resulted in an improved accuracy of ischemia detection especially in the last case. The highly disciplined design of the generalization performance of the support vector machine allows designing the proper model for the number of patterns transferred to the supervised expert
Abstract: The problem of maximising the performance of ST-T segment automatic recognition for ischaemia detection is a difficult pattern classification problem. The paper proposes the network self-organising map (NetSOM) model as an enhancement to the Kohonen self-organised map (SOM) model. This model is capable of effectively decomposing complex large-scale pattern classification problems into a number of partitions, each of which is more manageable with a local classification device. The NetSOM attempts to generalise the regularisation and ordering potential of the basic SOM from the space of vectors to the space of approximating functions. It becomes a device for the ordering of local experts (i.e. independent neural networks) over its lattice of neurons and for their selection and co-ordination. Each local expert is an independent neural network that is trained and activated under the control of the NetSOM. This method is evaluated with examples from the European ST-T database. The first results obtained after the application of NetSOM to ST-T segment change recognition show a significant improvement in the performance compared with that obtained with monolithic approaches, i.e. with single network types. The basic SOM model has attained an average ischaemic beat sensitivity of 73.6% and an average ischaemic beat predictivity of 68.3%. The work reports and discusses the improvements that have been obtained from the implementation of a NetSOM classification system with both multilayer perceptrons and radial basis function (RBF) networks as local experts for the ST-T segment change problem. Specifically, the NetSOM with multilayer perceptrons (radial basis functions) as local experts has improved the results over the basic SOM to an average ischaemic beat sensitivity of 75.9% (77.7%) and an average ischaemic beat predictivity of 72.5% (74.1%).
