elektronická verzia publikácie - FIIT STU

20 STU Faculty of Informatics and Information TechnologiesDNA sequence data is one among many approaches to pursue theseobjectives and we have chosen it. Goal of the whole diploma project isan analysis of decomposition techniques for binary tensors with considerationof DNA region classification domain. We proposed an innovativedecomposition technique using an idea of approachconsidering both content and discrimination characteristics of binarytensors. We offer detailed design, derivation and implementation ofsuch decomposition technique with algorithms supporting its experimentalevaluation. We have experimentally verified data reductionproperties of proposed decomposition technique on the generated dateand real DNA data as well. Our knowledge discovery model thusshowed its capabilities of supervised dimensionality reduction. Wedemonstrated an improvement towards the model we had started from.We think that in age of demand for more and more multilinear dataprocessing this model has much to offer.Student name: Marián HönschThesis title: Virtual Community Detection in Vast Information SpacesSupervisor: Michal Barla, PhD.Defended on: May 2011Degree program: Software EngineeringAnnotation: Thesis describes our work on identifying communities of individualsbased on their interests while browsing the web. A user can belong toseveral communities at a~time, where each community representsparts of his interests. We assume that recommendations coming fromsuch communities are more accurate than from communities based ona~whole user profile. We describe how to record and identify particularinterests for each user. Interests evolve from analysis of the resourcesthat the user has viewed in the past and are defined as clusterof keywords. To evaluate our approach we built articles recommenderfor a news portal. As recommender systems are tailored to the specificdomain, we also adapted our approach slightly to better fit the newsportal domain, which is highly dynamic and with frequent changes.Student name: Matej KrchniakThesis title: Genetic Programming on Graphics Processing UnitSupervisor: Peter Trebatický, PhD.Defended on: May 2011Degree program: Software EngineeringAnnotation: Performance of central processing units is constantly increasing, but inthe field of artificial intelligence it is not enough. In the effort of increasingeffectiveness of calculation we may consider moving part ofthe computation on the graphics processing units, which now mayhave more than hundreds of processors. This work describes the basicsof parallel computation on graphics processing units. Terminologydescribed in this work form the basis of parallel computation ongraphics processing units in CUDA and OpenCL. In the followingpart I am using genetic algorithm to solve specific problem on centralprocessing unit and graphics processing units. The results of this work