Work place: Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations, University of Ostrava, Ostrava, Czech Republic
Research Interests: Artificial intelligent in learning, Swarm Intelligence, Machine Learning, Computational Learning Theory, Artificial Intelligence, Online learning
Oleksii K. Tyshchenko got his MSc from Kharkiv National University of Radio Electronics in 2008. He got his PhD in Computer Science in 2013. He is currently working as Senior Researcher at Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine and holds a postdoc scholar position at Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations, University of Ostrava, Ostrava, Czech Republic. He has currently published more than 70 publications. He is a reviewer of such journals as Neural Computing and Applications (NCAA); Soft Computing (SoCo); Evolving Systems (EvoS); Neurocomputing (Neurocomp); Expert Systems With Applications (ESWA); IEEE Transactions on Cybernetics; IEEE Transactions on Fuzzy Sets and Systems; Fuzzy Sets and Systems; Pattern Recognition Letters.
His current research interests are Evolving Cascade Neuro-Fuzzy Systems; Computational Intelligence; Machine Learning; Deep Learning; High-Dimensional Fuzzy Clustering.
DOI: https://doi.org/10.5815/ijisa.2018.07.07, Pub. Date: 8 Jul. 2018
The techniques of Dynamic Time Warping (DTW) have shown a great efficiency for clustering time series. On the other hand, it may lead to sufficiently high computational loads when it comes to processing long data sequences. For this reason, it may be appropriate to develop an iterative DTW procedure to be capable of shrinking time sequences. And later on, a clustering approach is proposed for the previously reduced data (by means of the iterative DTW). Experimental modeling tests were performed for proving its efficiency.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.11.02, Pub. Date: 8 Nov. 2017
Temporal clustering (segmentation) for video streams has revolutionized the world of multimedia. Detected shots are principle units of consecutive sets of images for semantic structuring. Evaluation of time series similarity is based on Dynamic Time Warping and provides various solutions for Content Based Video Information Retrieval. Time series clustering in terms of the iterative Dynamic Time Warping and time series reduction are discussed in the paper.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.09.04, Pub. Date: 8 Sep. 2017
An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN’s adaptive learning algorithm allows solving classification problems in a real-time fashion.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.06.03, Pub. Date: 8 Jun. 2017
An adaptive neural system which solves a problem of clustering data with missing values in an online mode with a permanent correction of restorable table elements and clusters’ centroids is proposed in this article. The introduced neural system is characterized by both a high speed and a simple numerical implementation. It can process information in a real-time mode.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.05.07, Pub. Date: 8 May 2017
Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.02.01, Pub. Date: 8 Feb. 2017
A task of clustering data given on the ordinal scale under conditions of overlapping clusters has been considered. It’s proposed to use an approach based on membership and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2017.01.07, Pub. Date: 8 Jan. 2017
A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2016.10.01, Pub. Date: 8 Oct. 2016
An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2016.09.01, Pub. Date: 8 Sep. 2016
Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2016.05.02, Pub. Date: 8 May 2016
A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders’ blurriness. Clusters’ quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.[...] Read more.
DOI: https://doi.org/10.5815/ijmecs.2015.02.01, Pub. Date: 8 Feb. 2015
A new neuro-fuzzy system’s architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers’ location of membership functions and the receptive field’s parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results proves the effectiveness of the developed architecture and the learning procedure.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2015.02.03, Pub. Date: 8 Jan. 2015
A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2014.08.02, Pub. Date: 8 Jul. 2014
A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.[...] Read more.
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