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22 results on '"N. Kussul"'

4. Information techniques of deep machine learning for the analysis of land cover changes

Author

Mykola Lavreniuk, A.Yu. Shelestov, I.N. Butko, and Nataliya N. Kussul

Subjects
021103 operations research, 010504 meteorology & atmospheric sciences, business.industry, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Land cover, Machine learning, computer.software_genre, 01 natural sciences, Artificial intelligence, business, computer, 0105 earth and related environmental sciences
Published
2016
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5. Land Cover Changes Analysis Based on Deep Machine Learning Technique

Author

Igor N. Butko, Nataliya N. Kussul, Nikolay S. Lavreniuk, A. Shelestov, and Bogdan Ya. Yailymov

Subjects
010504 meteorology & atmospheric sciences, Computer science, business.industry, 0211 other engineering and technologies, 02 engineering and technology, Land cover, Machine learning, computer.software_genre, 01 natural sciences, Control and Systems Engineering, Artificial intelligence, business, computer, Software, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Information Systems
Published
2016
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6. COMPARATIVE ANALYSIS OF NEURAL NETWORKS AND STATISTICAL APPROACHES TO REMOTE SENSING IMAGE CLASSIFICATION

Author

Serhiy Skakun, Olga Kussul, and Nataliya N. Kussul

Subjects
Gaussian maximum likelihood, Artificial neural network, Contextual image classification, Computer Networks and Communications, Computer science, business.industry, Computer Science::Neural and Evolutionary Computation, Modular design, ComputingMethodologies_PATTERNRECOGNITION, Hardware and Architecture, Remote sensing (archaeology), Multilayer perceptron, Classifier (linguistics), Computer Science (miscellaneous), Satellite, business, Software, Information Systems, Remote sensing
Abstract

This paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, in particular Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer perceptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied.

Published
2014
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9. Disaster Risk Assessment Based on Heterogeneous Geospatial Information

Author

Yarema I. Zyelyk, Sergey V. Skakun, Nataliya N. Kussul, A. Shelestov, Vyacheslav Zelentsov, and Boris Sokolov

Subjects
Geospatial analysis, Control and Systems Engineering, Environmental science, Integrated approach, computer.software_genre, Risk assessment, computer, Data science, Software, Information Systems
Published
2010
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10. Using the fuzzy-ellipsoid method for robust estimation of the state of a grid system node

Author

Nataliya N. Kussul and A. Yu. Shelestov

Subjects
General Computer Science, Control theory, Computer science, Robustness (computer science), Node (networking), Fuzzy set, Ellipsoid method, A priori and a posteriori, Object (computer science), Fuzzy logic
Abstract

Constructing a workload model for a node of a Grid system is considered in terms of control theory. A node is represented as a linear dynamic object acted upon by perturbations with unknown statistical properties. Fuzzy set estimates are used to evaluate the state of the object. They ensure the robustness of the algorithm proposed and its usability under the lack of a priori information on the unknown vector and the inaccuracy of current measurements.

Published
2008
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11. Information Service for Estimate of the Pre-Black Sea Area Biodiversity Concerning Development of the Ukrainian Segment of GEOSS

Author

A. Yu. Shelestov, Mikhail A. Popov, A. N. Kravchenko, A.O. Kozlova, Mykhailo Korbakov, Nataliya N. Kussul, and Sergey A. Stankevich

Subjects
business.industry, media_common.quotation_subject, Ukrainian, Environmental resource management, Biodiversity, language.human_language, Geography, Global Earth Observation System of Systems, Environmental protection, Service (economics), language, Black sea, business, media_common
Published
2007
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13. Adaptive Anomaly Detection in the Behavior of Computer Systems Users on the Basis of Markov Chains of Variable Order. Part II. Anomaly Detection Methods and Experimental Results

Author

Nataliya N. Kussul and Artem Sokolov

Subjects
Markov chain, Basis (linear algebra), Computer science, business.industry, Order (ring theory), Pattern recognition, Markov model, Variable (computer science), Control and Systems Engineering, Anomaly detection, Artificial intelligence, business, Algorithm, Software, Information Systems
Published
2003
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17. Recognition of Reflected Ultrasonic Signals Using Neural Networks

Author

Nataliya N. Kussul, Roman Kuc, Victor V. Volosov, A. Shelestov, and Gennadiy M. Bakan

Subjects
Neutral network, Artificial neural network, Control and Systems Engineering, Computer science, business.industry, Pattern recognition (psychology), Pattern recognition, Ultrasonic sensor, Artificial intelligence, business, Software, Information Systems
Published
1999
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21. Grid computing technology for hydrological applications

Author

G. Lecca, M. Petitdidier, L. Hluchy, M. Ivanovic, N. Kussul, N. Ray, V. Thieron, Center for Advanced Studies, Research and Development in Sardinia (CRS4), SPACE - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institute of Informatics of the Slovak Academy of Sciences, Slovak Academy of Science [Bratislava] (SAS), Faculty of Science of the University of Kragujevac, University of Kragujevac, Space Research Institute of the NASU and NSAU, National Academy of Sciences of Ukraine (NASU)-National Space Agency of Ukraine (NSAU), EnviroSPACE Laboratory - Forel Institute, Institute for Environmental Sciences [Geneva] (ISE), Université de Genève = University of Geneva (UNIGE)-Université de Genève = University of Geneva (UNIGE), Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), and University of Geneva [Switzerland]-University of Geneva [Switzerland]

Subjects
E-Infrastructure, 010504 meteorology & atmospheric sciences, Exploit, Standardization, Operations research, Computer science, Environmental data, Computational hydrology, Data management, [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, 02 engineering and technology, computer.software_genre, 01 natural sciences, 11. Sustainability, ddc:550, Data Protection Act 1998, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, 0105 earth and related environmental sciences, Water Science and Technology, ddc:333.7-333.9, Data grid, business.industry, e-Infrastructure, Grid, Data science, 6. Clean water, Grid computing, 13. Climate action, Water resources management, business, computer
Abstract

International audience; Advances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure 2010 work-programme, data and compute infrastructures and their underlying technologies, either oriented to tackle scientific challenges or complex problem solving in engineering, are expected to converge together into the so-called knowledge infrastructures, leading to a more effective research, education and innovation in the next decade and beyond. Grid technology is recognized as a fundamental component of e-Infrastructures. Nevertheless, this emerging paradigm highlights several topics, including data management, algorithm optimization, security, performance (speed, throughput, bandwidth, etc.), and scientific cooperation and collaboration issues that require further examination to fully exploit it and to better inform future research policies. The paper illustrates the results of six different surface and subsurface hydrology applications that have been deployed on the Grid. All the applications aim to answer to strong requirements from the Civil Society at large, relatively to natural and anthropogenic risks. Grid technology has been successfully tested to improve flood prediction, groundwater resources management and Black Sea hydrological survey, by providing large computing resources. It is also shown that Grid technology facilitates e-cooperation among partners by means of services for authentication and authorization, seamless access to distributed data sources, data protection and access right, and standardization.

Published
2011
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22. Neural Networks Learning Using Method of Fuzzy Ellipsoidal Estimates

Author

Nataliya N. Kussul

Subjects
Neuro-fuzzy, Artificial neural network, Computer science, business.industry, Machine learning, computer.software_genre, Ellipsoid, Fuzzy logic, Control and Systems Engineering, Artificial intelligence, Types of artificial neural networks, business, computer, Software, Information Systems
Published
2001
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