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  • 1.
    Alzghoul, Ahmad
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Hållander, Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania.
    Screening paper runnability in a web-offset pressroom by data mining2009In: Proceedings of the 9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, Berlin: Springer Berlin/Heidelberg, 2009, p. 161-175Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.

  • 2.
    Bacauskiene, Marija
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania.
    Cibulskis, Vladas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Selecting variables for neural network committees2006In: Advances in neural networks - ISNN 2006: third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006 ; proceedings. I / [ed] Jun Wang, Berlin: Springer Berlin/Heidelberg, 2006, p. 837-842Conference paper (Refereed)
    Abstract [en]

    The aim of the variable selection is threefold: to reduce model complexity, to promote diversity of committee networks, and to find a trade-off between the accuracy and diversity of the networks. To achieve the goal, the steps of neural network training, aggregation, and elimination of irrelevant input variables are integrated based on the negative correlation learning [1] error function. Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed.

  • 3.
    Bacauskiene, Marija
    et al.
    Kaunas University of Technology.
    Gelzinis, Adas
    Kaunas University of Technology.
    Kaseta, Marius
    Kaunas University of Medicine.
    Kovalenko, Marina
    Kaunas University of Technology.
    Pribuisiene, Ruta
    Kaunas University of Medicine.
    Uloza, Virgilijus
    Kaunas University of Medicine.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Multiple feature sets and genetic search based discrimination of pathological voices2007In: Proceedings of the International ConferenceModels and Analysis of Vocal Emissions for Biomedical Applications”, MAVEBA, 2007, p. 195-198Conference paper (Refereed)
  • 4.
    Bacauskiene, Marija
    et al.
    Department of Applied Electronics, Kaunas University of Technology LT-3031, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Selecting salient features for classification based on neural network committees2004In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 25, no 16, p. 1879-1891Article in journal (Refereed)
    Abstract [en]

    Aggregating outputs of multiple classifiers into a committee decision is one of the most important techniques for improving classification accuracy. The issue of selecting an optimal subset of relevant features plays also an important role in successful design of a pattern recognition system. In this paper, we present a neural network based approach for identifying salient features for classification in neural network committees. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. The accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features.

  • 5.
    Bacauskiene, Marija
    et al.
    Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    The Evidence Theory Based Post-Processing of Colour Images2004In: Informatica (Vilnius), ISSN 0868-4952, E-ISSN 1822-8844, Vol. 15, no 3, p. 315-328Article in journal (Refereed)
    Abstract [en]

    The problem of post-processing of a classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. A post-processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.

  • 6.
    Bacauskiene, Marija
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Lithuania .
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania .
    Valincius, Donatas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania .
    A feature selection technique for generation of classification committees and its application to categorization of laryngeal images2009In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 42, no 5, p. 645-654Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in Classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.

  • 7.
    Bacauskiene, Marija
    et al.
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Vegiene, Aurelija
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas, Lithuania.
    Random forests based monitoring of human larynx using questionnaire data2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 5, p. 5506-5512Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.

  • 8.
    Bergman, Lars
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Intelligent Monitoring of the Offset Printing Process2004In: Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence, ACTA Press, 2004, p. 173-178Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a neural networks and image analysis based approach to assessing colour deviations in an offset printing process from direct measurements on halftone multicoloured pictures--there are no measuring areas printed solely to assess the deviations. A committee of neural networks is trained to assess the ink proportions in a small image area. From only one measurement the trained committee is capable of estimating the actual amount of printing inks dispersed on paper in the measuring area. To match the measured image area of the printed picture with the corresponding area of the original image, when comparing the actual ink proportions with the targeted ones, properties of the 2-D Fourier transform are exploited.

  • 9.
    Bergman, Lars
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology.
    Unsupervised colour image segmentation applied to printing quality assessment2005In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 23, no 4, p. 417-425Article in journal (Refereed)
    Abstract [en]

    We present an option for colour image segmentation applied to printing quality assessment in offset lithographic printing by measuring an average ink dot size in halftone pictures. The segmentation is accomplished in two stages through classification of image pixels. In the first stage, rough image segmentation is performed. The results of the first segmentation stage are then utilized to collect a balanced training data set for learning refined parameters of the decision rules. The developed software is successfully used in a printing shop to assess the ink dot size on paper and printing plates.

  • 10.
    Bergman, Lars
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Englund, Cristofer
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Kindberg, J.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Olsson, J.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Sjögren, B.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Modelling and Control of the Web-Fed Offset Newspaper Printing Press2003In: Proceedings of the Technical Association of the Graphic Arts, TAGA, Technical Association of the Graphic (TAGA) , 2003, p. 27-29Conference paper (Refereed)
    Abstract [en]

    We present an approach to modelling and controlling the web-fed offset printing process. An image processing and artificial neural networks based device is used to measure the printing process output - the observable variables. The observable variables are measured on halftone areas and integrate information about both ink densities and dot sizes. From only one measurement the device is capable of estimating the actual relative amount of each cyan, magenta, yellow, and black ink dispersed on paper in the measuring area. We build and test linear and non-linear printing press models using the measured variables andother parameters characterising the press. The observable variables measured and the press model developed are then further used by a control unit for generating control signals - signals for controlling the ink keys - to compensate for colour deviation. The experimental investigations performed have shown that the non-linear model developed is accurate enough to be used in a control loop for controlling the printing process. The control accuracy - the tracking accuracy of the desired ink level - obtained from the controller was higher than that observed when controlling the press by the operator.

  • 11.
    Bigun, Josef
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Verikas, AntanasHalmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Proceedings SSBA '09: Symposium on Image Analysis, Halmstad University, Halmstad, March 18-20, 20092009Conference proceedings (editor) (Other academic)
  • 12.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Santosh, K. C.
    The University of South Dakota, Vermillion, South Dakota, USA.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Agreeing to disagree: active learning with noisy labels without crowdsourcing2018In: International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, E-ISSN 1868-808X, Vol. 9, no 8, p. 1307-1319Article in journal (Refereed)
    Abstract [en]

    We propose a new active learning method for classification, which handles label noise without relying on multiple oracles (i.e., crowdsourcing). We propose a strategy that selects (for labeling) instances with a high influence on the learned model. An instance x is said to have a high influence on the model h, if training h on x (with label y = h(x)) would result in a model that greatly disagrees with h on labeling other instances. Then, we propose another strategy that selects (for labeling) instances that are highly influenced by changes in the learned model. An instance x is said to be highly influenced, if training h with a set of instances would result in a committee of models that agree on a common label for x but disagree with h(x). We compare the two strategies and we show, on different publicly available datasets, that selecting instances according to the first strategy while eliminating noisy labels according to the second strategy, greatly improves the accuracy compared to several benchmarking methods, even when a significant amount of instances are mislabeled. © Springer-Verlag Berlin Heidelberg 2017

  • 13.
    Brorsson, Sofia
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Tonkonogi, Michail
    Dalarna University, Falun, Sweden.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Differences in the muscle activities in the forearm muscles in healthy men and women2012In: Proceedings of the XIXth Congress of the International Society of Electrophysiology & Kinesiology / [ed] Kylie Tucker et al., Brisbane, Australia, 2012, p. 437-437Conference paper (Refereed)
    Abstract [en]

    Balance between flexor and extensor muscle activity is essential for optimal function. This has been demonstrated previously for the lower extremity, trunk and shoulder function, but information on the relationship in hand function is lacking. AIM: Was to evaluate whether there are qualitative differences in finger extension force(fef), grip force, force duration, force balance and the muscle activities in the forearm flexor and extensor muscles in healthy men and women in different ages. 

  • 14.
    Ejnarsson, Marcus
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Nilsson, Carl Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A Kernel based multi-resolution time series analysis for screening deficiencies in paper production2006In: Advances in neural networks - ISNN 2006: third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006 ; proceedings. III / [ed] Jun Wang, Berlin: Springer Berlin/Heidelberg, 2006, p. 1111-1116Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions.

  • 15.
    Ejnarsson, Marcus
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Nilsson, Carl-Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Screening paper Formation variations on production line2007In: New Trends in Applied Artificial Intelligence, Proceedings / [ed] Okuno, HG and Ali, M, Berlin: Springer Berlin/Heidelberg, 2007, p. 511-520Conference paper (Other academic)
    Abstract [en]

    This paper is concerned with a multi–resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi–resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill.

  • 16.
    Ejnarsson, Marcus
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
    Nilsson, Carl Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Multi-resolution screening of paper formation variations on production line2009In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 36, no 2, part 2, p. 3144-3152Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a technique for detecting and monitoring abnormal paper formation variations in machine direction online in various frequency regions. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the time series block. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the kernel canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The experimental investigations performed have shown good repetitiveness and stability of the paper formation abnormalities detection results. The tools developed are used for online paper formation monitoring in a paper mill.

  • 17.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A hybrid approach to outlier detection in the offset lithographic printing process2005In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 18, no 6, p. 759-768Article in journal (Refereed)
    Abstract [en]

    Artificial neural networks are used to model the offset printing process aiming to develop tools for on-line ink feed control. Inherent in the modelling data are outliers owing to sensor faults, measurement errors and impurity of materials used. It is fundamental to identify outliers in process data in order to avoid using these data points for updating the model. We present a hybrid, the process-model-network-based technique for outlier detection. The outliers can then be removed to improve the process model. Several diagnostic measures are aggregated via a neural network to categorize data points into the outlier and inlier classes. We demonstrate experimentally that a soft fuzzy expert can be configured to label data for training the categorization of neural network.

  • 18.
    Englund, Cristofer
    et al.
    Viktoria Institute, Göteborg, Sweden.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    A novel approach to estimate proximity in a random forest: An exploratory study2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 17, p. 13046-13050Article in journal (Refereed)
    Abstract [en]

    A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel. © 2012 Elsevier Ltd. All rights reserved.

  • 19.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A SOM based model combination strategy2005In: Advances in Neural Networks – ISNN 2005 Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part I / [ed] Jun Wang, Xiaofeng Liao and Zhang Yi, Berlin: Springer Berlin/Heidelberg, 2005, p. 461-466Conference paper (Refereed)
    Abstract [en]

    A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and aggregation weights are specific for each input data point analyzed. The possibility to detect outliers is one more characteristic feature of the strategy.

  • 20.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A SOM-based data mining strategy for adaptive modelling of an offset lithographic printing process2007In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 20, no 3, p. 391-400Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a SOM-based data mining strategy for adaptive modelling of a slowly varying process. The aim is to follow the process in a way that makes a representative up-to-date data set of a reasonable size available at any time. The technique developed allows analysis and filtering of redundant data, detection of the need to update the process models and the core-module of the system itself and creation of process models of adaptive, data-dependent complexity. Experimental investigations performed using data from a slowly varying offset lithographic printing process have shown that the tools developed can follow the process and make the necessary adaptations of the data set and the process models. A low-process modelling error has been obtained by employing data-dependent committees for modelling the process.

  • 21.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Combining traditional and neural-based techniques for ink feed control in a newspaper printing press2007In: Advances in Data Mining: Theoretical Aspects and Applications, Proceedings / [ed] Perner, P., Berlin / Heidelberg: Springer Berlin/Heidelberg, 2007, p. 214-227Conference paper (Refereed)
    Abstract [en]

    A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and aggregation weights are specific for each input data point analyzed. The possibility to detect outliers is one more characteristic feature of the strategy.

  • 22.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Ink feed control in a web-fed offset printing press2008In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 39, no 9-10, p. 919-930Article in journal (Refereed)
    Abstract [en]

    Automatic and robust ink feed control in a web- fed offset printing press is the objective of this work. To achieve this goal an integrating controller and a multiple neural models-based controller are combined. The neural networks-based printing process models are built and updated automatically without any interaction from the user. The multiple models-based controller is superior to the integrating controller as the process is running in the training region of the models. However, the multiple models-based controller may run into generalisation prob- lems if the process starts operating in a new part of the input space. Such situations are automatically detected and the integrating controller temporary takes over the process control. The developed control configuration has success- fully been used to automatically control the ink feed in the web-fed offset printing press according to the target amount of ink. Use of the developed tools led to higher print quality and lower ink and paper waste.

  • 23.
    Englund, Cristofer
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Ink flow control by multiple models in an offset lithographic printing process2008In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 55, no 3, p. 592-605Article in journal (Refereed)
    Abstract [en]

    A multiple model-based controller has been developed aiming at controlling the ink flow in the offset lithographic printing process. The control system consists of a model pool of four couples of inverse and direct models. Each couple evaluates a number of probable control signals and the couple, generating the most suitable control signal is used to control the printing press, at that moment. The developed system has been tested at a newspaper printing shop during normal production. The results show that the developed modelling and control system is able to drive the output of the printing press to the desired target levels.

  • 24.
    Gelzinis, A.
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Automated speech analysis applied to laryngeal disease categorization2008In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 91, no 1, p. 36-47Article in journal (Refereed)
    Abstract [en]

    The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases. The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated. A k-NN classifier, SVM, and a committee build using various aggregation options are used for the classification. The study was made using the mixed gender database containing 312 voice recordings. The correct classification rate of 84.6% was achieved when using an SVM committee consisting of four members. The pitch and amplitude perturbation measures, cepstral energy features, autocorrelation features as well as linear prediction cosine transform coefficients were amongst the feature sets providing the best performance. In the case of two class classification, using recordings from 79 subjects representing the pathological and 69 the healthy class, the correct classification rate of 95.5% was obtained from a five member committee. Again the pitch and amplitude perturbation measures provided the best performance.

  • 25.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Sulcius, Sigitas
    Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania.
    Paskauskas, Ricardas
    Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania.
    Oleninaz, Irina
    Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania.
    Boosting performance of the edge-based active contour model applied to phytoplankton images2012In: Proceedings of the 13th IEEE International Symposium on Computational Intelligence and Informatics, Piscataway, NJ: IEEE Press, 2012, p. 273-277Conference paper (Refereed)
    Abstract [en]

    Automated contour detection for objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the core goal of this study. The speciesis known to cause harmful blooms in many estuarine and coastal environments. Active contour model (ACM)-based image segmentation is the approach adopted here as a potential solution. Currently, the main research in ACM area is highly focused ondevelopment of various energy functions having some physical intuition. This work, by contrast, advocates the idea of rich and diverse image preprocessing before segmentation. Advantage of the proposed preprocessing is demonstrated experimentally by comparing it to the six well known active contour techniques applied to the cell segmentation in microscopy imagery task. © 2012 IEEE.

  • 26.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Kelertas, Edgaras
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uloza, Virgilijus
    Kaunas University of Technology, Kaunas, Lithuania.
    Vegiene, Aurelija
    Kaunas University of Technology, Kaunas, Lithuania.
    Categorizing sequences of laryngeal data for decision support2009In: ECT 2009: Electrical and Control Technologies / [ed] Navickas, A, Kaunas: Kaunas University Technology Press , 2009, p. 99-Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with kernel-based techniques for categorizing laryngeal disorders based on information extracted from sequences of laryngeal colour images. The features used to characterize a laryngeal image are given by the kernel principal components computed using the N-vector of the 3-D colour histogram. The least squares support vector machine (LS-SVM) is designed for categorizing an image sequence into the healthy, nodular and diffuse classes. The kernel function employed by the SVM classifier is defined over a pair of matrices, rather than over a pair of vectors. An encouraging classification performance was obtained when testing the developed tools on data recorded during routine laryngeal videostroboscopy.

  • 27.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Lithuania.
    Kelertas, Edgaras
    Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Virgilijus, Uloza
    Kaunas University of Medicine, Lithuania.
    Vegiene, Aurelija
    Kaunas University of Medicine, Lithuania.
    Categorizing sequences of laryngeal data for decision support2009In: Electrical and Control Technologies: Proceedings of the 4th international conference, ECT 2009 / [ed] A. Navickas (general editor), A. Sauhats, A. Virbalis, M. Ažubalis, V. Galvanauskas, A. Jonaitis, Kaunas: IFAC Committee of National Lithuanian Organisation , 2009, p. 99-102Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with kernel-based techniques forcategorizing laryngeal disorders based on information extracted fromsequences of laryngeal colour images. The features used tocharacterize a laryngeal image are given by the kernel principalcomponents computed using the $N$-vector of the 3-D colourhistogram. The least squares support vector machine (LS-SVM) isdesigned for categorizing an image sequence into the healthy, nodular and diffuse classes. The kernel functionemployed by the SVM classifier is defined over a pair of matrices, rather than over a pair of vectors. An encouraging classificationperformance was obtained when testing the developed tools on datarecorded during routine laryngeal videostroboscopy.

  • 28.
    Gelzinis, Adas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Categorizing laryngeal images for decision support2007In: Advanced Concepts for Intelligent Vision Systems: 9th International Conference, ACIVS 2007, Delft, The Netherlands, August 28-31, 2007 ; proceedings / [ed] Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Berlin: Springer Berlin/Heidelberg, 2007, p. 521-530Chapter in book (Refereed)
    Abstract [en]

    This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.

  • 29.
    Gelzinis, Adas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Increasing the discrimination power of the co-occurrence matrix-based features2007In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 40, no 9, p. 2367-2372Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with an approach to exploiting information available from the co-occurrence matrices computed for different distance parameter values. A polynomial of degree n is fitted to each of 14 Haralick's coefficients computed from the average co-occurrence matrices evaluated for several distance parameter values. Parameters of the polynomials constitute a set of new features. The experimental investigations performed substantiated the usefulness of the approach.

  • 30.
    Gelzinis, Adas
    et al.
    Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Olenina, Irina
    Department of Marine Research, Environmental Protection Agency, Klaipeda, Lithuania.
    Olenin, Sergej
    Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania.
    Categorizing cells in phytoplankton images2011In: Recent Advances in Signal Processing, Computational Geometry and Systems Theory / [ed] Myriam Lazard ... [et al.], Athens: World Scientific and Engineering Academy and Society, 2011, p. 82-87Conference paper (Refereed)
    Abstract [en]

    This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088  P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species.

  • 31.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Olenina, Irina
    Coastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania.
    Olenin, Sergej
    dCoastal Research and Planning Institute, Klaipeda University, Klaipeda, Lithuania.
    Detecting P. minimum cells in phytoplankton images2011In: Electrical and Control Technologies : proceedings of the 6th international conference on Electrical and Control Technologies ECT 2011 / Kaunas University of Technology, IFAC Committee of National Lithuanian Organisation, Kaunas, Lithuania: Kaunas University of Technology, Lithuania , 2011, p. 61-66Conference paper (Refereed)
    Abstract [en]

    This article is concerned with detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), - which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280x960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93,25% of the objects. The results are rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species.

  • 32.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Learning Accurate Active Contours2013In: Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I / [ed] Lazaros Iliadis, Harris Papadopoulos & Chrisina Jayne, Berlin Heidelberg: Springer Berlin/Heidelberg, 2013, Vol. 383, p. 396-405Conference paper (Refereed)
    Abstract [en]

    Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments. We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images. © Springer-Verlag Berlin Heidelberg 2013.

  • 33.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Lithuania.
    Kelertas, Edgaras
    Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Lithuania.
    Vegiene, Aurelija
    Kaunas University of Medicine, Lithuania.
    Towards video laryngostroboscopy-based automated screening for laryngeal disorders2009In: Proceedings of the 6th International Conference “Models and Analysis of Vocal Emissions for Biomedical Applications”, MAVEBA 2009 / [ed] C. Manfredi, Florence, Italy: Firenze University Press , 2009, p. 125-128Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with kernel-based techniques for automatedcategorization of laryngeal colour image sequences obtained by videolaryngostroboscopy. Features used to characterize a laryngeal imageare given by the kernel principal components computed using the$N$-vector of the 3-D colour histogram. The least squares supportvector machine (LS-SVM) is designed for categorizing an imagesequence (video) into the healthy, cancerous and noncancerous classes. The kernel function employed by theLS-SVM is defined over a pair of matrices, rather than over a pairof vectors. The classification accuracy of over 85% was obtainedwhen testing the developed tools on data recorded during routinelaryngeal videostroboscopy.

  • 34.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Kaunas University of Technology, Studentu 50 3031, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Quality function for unsupervised classification and its use in graphic arts1999In: Journal of Advanced Computational Intelligence, Vol. 3, no 6, p. 532-540Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose quality function for an unsupervised neural classification. The function is based on the third order polynomials. The objective of the quality function is to find a place of the input space sparse in data points. By maximising the quality function, we find decision boundary between data clusters instead of centres of the clusters. The shape and place of the decision boundary are rather insensitive to the magnitude of the weight vector established during the maximisation process. A superiority of the proposed quality function over other similar functions as well as conventional clustering algorithms tested has been observed in the experiments. The proposed quality function has been successfully used for colour image segmentation.

  • 35.
    Gelzinis, Adas
    et al.
    Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory. Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electrical Power Systems & Department of Information Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electrical Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    A novel technique to extract accurate cell contours applied to analysis of phytoplankton images2015In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 26, no 2-3, p. 305-315Article in journal (Refereed)
    Abstract [en]

    Active contour model (ACM) is an image segmentation technique widely applied for object detection. Most of the research in ACM area is dedicated to the development of various energy functions based on physical intuition. Here, instead of constructing a new energy function, we manipulate values of ACM parameters to generate a multitude of potential contours, score them using a machine-learned ranking technique, and select the best contour for each object in question. Several learning-to-rank (L2R) methods are evaluated with a goal to choose the most accurate in assessing the quality of generated contours. Superiority of the proposed segmentation approach over the original boosted edge-based ACM and three ACM implementations using the level-set framework is demonstrated for the task of Prorocentrum minimum cells’ detection in phytoplankton images. Experiments show that diverse set of contour features with grading learned by a variant of multiple additive regression trees (λ-MART) helped to extract precise contour for 87.6 % of cells tested.

  • 36.
    Gelzinis, Adas
    et al.
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uloza, Virgilijus
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Department of Otolaryngology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders2014In: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Piscataway, NJ: IEEE Press, 2014, p. 125-132Conference paper (Refereed)
    Abstract [en]

    Exploration of various features and different structures of data dependent random forests in screening for laryngeal disorders through analysis of sustained phonation recorded by acoustic and contact microphones is the main objective of this study. To obtain a versatile characterization of voice samples, 14 different sets of features were extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We proposed a new, data dependent random forest-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest was also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the Perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the LP-coefficients and LPCT-coefficients feature sets exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for classification. The proposed data dependent random forest significantly outperformed traditional designs. © 2014 IEEE.

  • 37.
    Gelzinis, Adas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Šulčius, Sigitas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Open Access Centre for Nature Research, Nature Research Centre, Vilnius, Lithuania.
    Staniulis, Juozas
    Laboratory of Plant Viruses, Nature Research Centre, Institute of Botany, Vilnius, Lithuania.
    Paškauskas, Ričardas
    Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania & Laboratory of Algology and Microbial Ecology, Nature Research Centre, Vilnius, Lithuania.
    Automatic detection and morphological delineation of bacteriophages in electron microscopy images2015In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 64, p. 101-116Article in journal (Refereed)
    Abstract [en]

    Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.

  • 38.
    Guzaitis, Jonas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    An Efficient Technique to Detect Visual Defects in Particleboards2008In: Informatica (Vilnius), ISSN 0868-4952, E-ISSN 1822-8844, Vol. 19, no 3, p. 363-376Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with the problem of image analysis based detection of local defects embedded in particleboard surfaces. Though simple, but efficient technique developed is based on the analysis of the discrete probability distribution of the image intensity values and the 2D discrete Walsh transform. Robust global features characterizing a surface texture are extracted and then analyzed by a pattern classifier. The classifier not only assigns the pattern into the quality or detective class, but also provides the certainty value attributed to the decision. A 100% correct classification accuracy was obtained when testing the technique proposed on a set of 200 images.

  • 39.
    Guzaitis, Jonas
    et al.
    Kaunas Univ Technol, Dept Appl Elect, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    ANN and ICA sparse code shrinkage de-noising based defect detection in pavement tiles2008In: Information Technologies' 2008, Proceedings, Kaunas, Lithuania: Kaunas University of Technology Press , 2008, p. 62-71Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with the problem of image analysis based detection of local defects embedded in pavement tiles surfaces. The technique developed is based on the ICA sparse code shrinkage denoising, the local 2D discrete Walsh transform and ANN. To reduce random noise, the ICA sparse code shrinkage de-noising is applied. Next, robust local features characterizing the surface texture are extracted based on the 2D Walsh transform and then analyzed by an artificial Neural Network. A 100% correct classification rate was obtained when testing the technique proposed on a set of surface images recorded from 400 tiles.

  • 40.
    Guzaitis, Jonas
    et al.
    Kaunas University of Technology.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    A framework for designing a fuzzy rule-based classifier2009In: Algorithmic Decision Theory: Proceedings of the 1st International Conference, ADT 2009, Venice, Italy, October 2009 / [ed] Francesca Rossi, Alexis Tsoukias, Berlin: Springer Berlin/Heidelberg, 2009, p. 434-445Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with a general framework for designing afuzzy rule-based classifier. Structure and parameters of theclassifier are evolved through a two-stage genetic search. Theclassifier structure is constrained by a tree created using theevolving SOM tree algorithm. Salient input variables are specificfor each fuzzy rule and are found during the genetic search process.It is shown through computer simulations of four real world problemsthat a large number of rules and input variables can be eliminatedfrom the model without deteriorating the classification accuracy.

  • 41.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A novel approach to exploring company’s financial soundness: Investor’s perspective2013In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 13, p. 5085-5092Article in journal (Refereed)
    Abstract [en]

    Prediction of company's life cycle stage change; creation of an ordered 2D map allowing to explore company's financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique ''t-distributed stochastic neighbor embedding''. Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company's financial soundness from a rating agency perspective. The proposed use of information not only from companies' financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody's rating agency. Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company's management team. It was also found that company's life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging. © 2013 Elsevier Ltd. All rights reserved.

  • 42.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory. Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    A novel approach to designing an adaptive committee applied to predicting company’s future performance2013In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 6, p. 2051-2057Article in journal (Refereed)
    Abstract [en]

    This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company's future performance. Current liabilities/Current assets, Total liabilities/Total assets, Net income/Total assets, and Operating Income/Total liabilities are the attributes used in this paper. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. A random forest is used a basic model in this study. The developed technique was tested on data concerning companies from ten sectors of the healthcare industry of the United States and compared with results obtained from averaging and weighted averaging committees. The proposed adaptivity of a committee size and aggregation weights led to a statistically significant increase in prediction accuracy if compared to other types of committees. © 2012 Elsevier Ltd. All rights reserved.

  • 43.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    A Novel Technique to Design an Adaptive Committee of Models Applied to Predicting Company’s Future Performance2013In: International Conference on Computer Research and Development: ICCRD 2013 / [ed] Fan Yama, New York, NY: ASME Press, 2013, p. 65-70Conference paper (Refereed)
    Abstract [en]

    This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company’s future performance. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. The proposed technique led to a statistically significant increase in prediction accuracy if compared to other types of committees.

  • 44.
    Kalsyte, Zivile
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Vasiliauskaite, Asta
    Kaunas University of Technology, Kaunas, Lithuania.
    Predicting trends of financial attributes by an adaptive committee of models2012In: Proceedings of the 7th International Conference on Electrical and Control Technologies ECT 2012 / [ed] A. Navickas (Editor-in-Chief), A. Sauhats, A. Virbalis, M. Ažubalis, V. Galvanauskas, K. Brazauskas & A. Jonaitis, Kaunas: Kaunas University of Technology , 2012, p. 48-53Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to designing an adaptive, data dependent, committee of multilayer perceptrons (MLP) for predicting trends (positive or negative change) of five financial attributes used for assessing future performance of a company. Total Asset Turnover [TAT], Current Ratio [CR], Gross Margin [GM], Operating Margin [OM], and Return on Equity [ROE] are the attributes used in this paper. A two- and three-years ahead prediction of change is considered. A Self-Organizing Map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation parameters) for each data point analyzed. When tested on data concerning 59 companies of the United States biotechnology sector, committees built according to the proposed technique outperformed both averaging and weighted averaging committees.

  • 45.
    Kontrimas, Vilius
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, 51368, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Neural networks based screening of real estate transactions2007In: Neural Network World, ISSN 1210-0552, Vol. 17, no 1, p. 17-30Article in journal (Refereed)
    Abstract [en]

    Aiming to hide the real money gains and to avoid taxes, fictive prices are sometimes recorded in the real estate transactions. This paper is concerned with artificial neural networks based screening of real estate transactions aiming to categorize them into "clear" and "fictitious" classes. The problem is treated as an outlier detection task. Both unsupervised and supervised approaches to outlier detection are studied here. The soft minimal hyper-sphere support vector machine (SVM) based novelty detector is employed to solve the task without the supervision. In the supervised case, the effectiveness of SVM, multilayer perceptron (MLP), and a committee based classification of the real estate transactions are studied. To give the user a deeper insight into the decisions provided by the models, the real estate transactions are not only categorized into "clear" and "fictitious" classes, but also mapped onto the self organizing map (SOM), where the regions of "clear", "doubtful" and "fictitious" transactions are identified. We demonstrate that the stability of the regions evolved in the SOM during training is rather high. The experimental investigations performed on two real data sets have shown that the categorization accuracy obtained from the supervised approaches is considerably higher than that obtained from the unsupervised one. The obtained accuracy is high enough for the technique to be used in practice. © ICS AS CR 2007.

  • 46.
    Kontrimas, Vilius
    et al.
    Department of Information Systems, Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    The mass appraisal of the real estate by computational intelligence2011In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, no 1, p. 443-448Article in journal (Refereed)
    Abstract [en]

    Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches - support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. (C) 2009 Elsevier B.V. All rights reserved

  • 47.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of people in their own homesManuscript (preprint) (Other academic)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

  • 48.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of smart home residents2016In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

    Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

    The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

  • 49.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Assessing print quality by machine in offset colour printing2013In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 37, p. 70-79Article in journal (Refereed)
    Abstract [en]

    Information processing steps in printing industry are highly automated, except the last one print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several printquality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer. (C) 2012 Elsevier B.V. All rights reserved.

  • 50.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing2010In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), Piscataway, NJ: IEEE Press, 2010, p. 1145-1151Conference paper (Refereed)
    Abstract [en]

    Nowadays in printing industry most of information processing steps are highly automated, except the last one–print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.

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