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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.
    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.

  • 3.
    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.

  • 4.
    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.

  • 5.
    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.

  • 6.
    Kaestner, Anders P.
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Nilsson, Carl Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Estimating the relative shrinkage profile of newsprint2003In: Optical Engineering: The Journal of SPIE, ISSN 0091-3286, E-ISSN 1560-2303, Vol. 42, no 5, p. 1467-1475Article in journal (Refereed)
    Abstract [en]

    When water is removed from the paper during paper making, a dimensional change occurs in which the paper shrinks in the direction perpendicular to the direction of processing. The dimensional changes vary across the web and influence, e.g., the surface and compression properties of the paper; they also complicate the control of the paper machine. In this article, a robust method for estimating the relative shrinkage profile is presented. The method is based on a one-dimensional recording of the imprints from the forming fabric, using a fluorescence technique. The recording is transformed into a time-frequency spectrum, on which three different frequency estimators have been evaluated. In simulations on synthetic data and measurements on paper profiles the estimator that maximizes the correlation energy showed the most robust and accurate performance of the methods evaluated, even at a low signal-to-noise ratio.

  • 7.
    Nilsson, Carl Magnus
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Carlsson, Jörgen
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Malmqvist, Lennart
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Persson, Willy
    Semtech Metallurgy AB, Lund, Sweden.
    Application of optical spectroscopy to paper production1999In: Optical measurement systems for industrial inspection / [ed] Malgorzata Kujawinska, Bellingham, Washington: SPIE - International Society for Optical Engineering, 1999, p. 318-325Conference paper (Refereed)
    Abstract [en]

    Fluorescence from paper following excitation by either ultraviolet or visible light gives information on the chemical composition of the paper. This can be used for on-line monitoring of the paper during production. Such measurements can be performed non-intrusively at sampling rates high enough to give a sub-millimetre resolution at paper webs moving at velocities higher than 20 metres per second. Two types of fluorescence meters, operating at different wavelengths, have been constructed. Together with an optical speedometer they have been tested at newsprint producing paper mills. A fluorescence based method for scanning cross-directional newsprint profiles in the laboratory has been developed. From these measurements the relative shrinkage of the paper during drying can be calculated using time-frequency analysis.

  • 8.
    Rögnvaldsson, Thorsteinn
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Brink, Joachim
    Halmstad University.
    Florén, Henrik
    Halmstad University, School of Business, Engineering and Science, Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Gaspes, Veronica
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Holmgren, Noél
    University of Skövde, Skövde, Sweden.
    Lutz, Mareike
    Halmstad University.
    Nilsson, Pernilla
    Halmstad University, School of Education, Humanities and Social Science, Research on Education and Learning within the Department of Teacher Education (FULL).
    Olsfelt, Jonas
    Halmstad University.
    Svensson, Bertil
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ericsson, Claes
    Halmstad University, School of Education, Humanities and Social Science, Research on Education and Learning within the Department of Teacher Education (FULL).
    Gustafsson, Linnea
    Halmstad University, School of Education, Humanities and Social Science, Contexts and Cultural Boundaries (KK).
    Hoveskog, Maya
    Halmstad University, School of Business, Engineering and Science, Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Hylander, Jonny
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Jonsson, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nygren, Jens
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Rosén, Bengt-Göran
    Halmstad University, School of Business, Engineering and Science, Mechanical Engineering and Industrial Design (MTEK).
    Sandberg, Mikael
    Halmstad University, School of Education, Humanities and Social Science, Center for Social Analysis (CESAM).
    Benner, Mats
    Lund University, Lund, Sweden.
    Berg, Martin
    Halmstad University, School of Education, Humanities and Social Science, Center for Social Analysis (CESAM).
    Bergvall, Patrik
    Halmstad University.
    Carlborg, Anna
    Halmstad University.
    Fleischer, Siegfried
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Hållander, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Mattsson, Marie
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Olsson, Charlotte
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Pettersson, Håkan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Rundquist, Jonas
    Halmstad University, School of Business, Engineering and Science, Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Sahlén, Göran
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Waara, Sylvia
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Weisner, Stefan
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    ARC13 – Assessment of Research and Coproduction: Reports from the assessment of all research at Halmstad University 20132014Report (Other (popular science, discussion, etc.))
    Abstract [en]

    During 2013, an evaluation of all the research conducted at Halmstad University was carried out. The purpose was to assess the quality of the research, coproduction, and collaboration in research, as well as the impact of the research. The evaluation was dubbed the Assessment of Research and Coproduction 2013, or ARC13. (Extract from Executive Summary)

  • 9.
    Ungh, Jörgen
    et al.
    StoraEnso, Falun Research Centre, Sweden.
    Nilsson, Carl Magnus
    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), Intelligent systems (IS-lab).
    Analysis of paper, print and press interaction from online measurements in a press room2007In: Nordic Pulp & Paper Research Journal, ISSN 0283-2631, E-ISSN 2000-0669, Vol. 22, no 3, p. 383-387Article in journal (Refereed)
    Abstract [en]

    A measurement platform for online studies of print runnability in a full-scale four-high web offset printing press is described. Results from two trial runs showed no effect of reel tightness on print runnability. Differences were, however, found for so called edge reels.

  • 10.
    Vaiciukynas, Evaldas
    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.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    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.
    Padervinskis, Evaldas
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Uloza, Virgilijus
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Fusing voice and query data for non-invasive detection of laryngeal disorders2015In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 42, no 22, p. 8445-8453Article in journal (Refereed)
    Abstract [en]

    Topic of this study is exploration and fusion o fnon-invasive measurements for an accurate detection of pathological larynx. Measurements for human subject encompass answers to items of a specific survey and information extracted by the openSMILE toolkit from several audio recordings of sustained phonation (vowel/a/).

  • 11.
    Vaiciukynas, Evaldas
    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.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    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.
    Padervinskis, Evaldas
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Uloza, Virgilijus
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Towards Voice and Query Data-based Non-invasive Screening for Laryngeal Disorders2015In: Advances in Electrical and Computer Engineering: Proceedings of the 17th International Conference on Automatic Control, Modelling & Simulation (ACMOS '15): Proceedings of the 14th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '15): Proceedings of the 6th International Conference on Circuits, Systems, Control, Signals (CSCS '15): Tenerife, Canary Islands, Spain, January 10-12, 2015 / [ed] Nikos E. Mastorakis & Imre J. Rudas, Athens: WSEAS Press , 2015, p. 32-39Conference paper (Refereed)
    Abstract [en]

    Topic of the research is exploration and fusion of non-invasive measurements for an accurate detection of pathological larynx. Measurements for human subject encompass results of a specific survey and information extracted by openSMILE toolkit from several audio recordings of sustained phonation (vowel/a/). Clinical diagnosis, assigned by medical specialist, is a target attribute for binary classification into healthy and pathological cases. Random forest (RF) is used here as a base-learner and also as a meta-learner for decision-level fusion. Fusion combines decisions from ensemble of 5 RF classifiers built on 3 variants of audio recording data (raw and after two types of voice activity detection) and 2 variants of questionnaire (with 9 and 26 questions) data. Out-of-bag equal error rate (EER) was found to be higher for audio data and lower for querry, but each variant was outperformed by the fusion where the lowest EER of 4.8% was achieved. Finally, due to noteworthy performance of the querry data, 22 association rules (11 healthy + 11 pathological) using 17 questions were obtained for comprehensible insights.

  • 12.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, 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).
    Soft Computing for Assessing the Quality of Colour Prints2006In: Advances in applied artificial intelligence: proceedings / [ed] Ali, M and Dapoigny, R., Berlin: Springer, 2006, p. 701-710Conference paper (Refereed)
    Abstract [en]

    We present a soft computing techniques based option for assessing the quality of colour prints. The values of several print distortion attributes are evaluated by employing data clustering, support vector regression, and image analysis procedures and then aggregated into an overall print quality measure using fuzzy integration. The experimental investigations performed have shown that the print quality evaluations provided by the measure correlate well with the print quality rankings obtained from the experts. The developed tools are successfully used in a printing shop for routine print quality control.

  • 13.
    Verikas, Antanas
    et al.
    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, Studentu 50, 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).
    Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures2007In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 16, no 2, p. 187-195Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.

  • 14.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Kaseta, Marius
    Kaunas University of Medicine, Kaunas, Lithuania.
    Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders2010In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 49, no 1, p. 43-50Article in journal (Refereed)
    Abstract [en]

    Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data.

    Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search.

    Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging.

    Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.

  • 15.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    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, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    Alzghoul, Ahmad
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Screening web breaks in a pressroom by soft computing2011In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, no 3, p. 3114-3124Article in journal (Refereed)
    Abstract [en]

    The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of 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 second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.

  • 16.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    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
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation: Acoustic versus contact microphone2015In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 2, p. 210-218Article in journal (Refereed)
    Abstract [en]

    Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is 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 linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) 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 the classification. The proposed data dependent random forest significantly outperformed the traditional random forest. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

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