hh.sePublications
Change search
Refine search result
1 - 12 of 12
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    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.

  • 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).
    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.

  • 3.
    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.

  • 4.
    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.

  • 5.
    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.

  • 6.
    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.

  • 7.
    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/).

  • 8.
    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 Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas, Lithuania.
    Questionnaire- versus voice-based screening for laryngeal disorders2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 6, p. 6254-6262Article in journal (Refereed)
    Abstract [en]

    The usefulness of questionnaire and voice data to screen for laryngeal disorders is explored. Answers to 14 questions form a questionnaire data vector. Twenty-three variables computed by the commercial "Dr.Speech" software from a digital voice recording of a sustained phonation of the vowel sound/a/constitute a voice data vector. Categorization of the data into a healthy class and two classes of disorders, namely diffuse and nodular mass lesions of vocal folds is the task pursued in this work. Visualization of data and automated decisions is also an important aspect of this work. To make the categorization, a support vector machine (SVM) is designed based on genetic search. Linear as well as nonlinear canonical correlation analysis (CCA) is employed, to study relations between the questionnaire and voice data sets. The curvilinear component analysis, performing nonlinear mapping into a two-dimensional space, is used for visualizing data and decisions. Data from 240 patients were used in the experimental studies. It was found that the questionnaire data provide more information for the categorization than the voice data. There are 3-4 common directions along which the statistically significant variations of the questionnaire and voice data occur. However, the linear relations between the variations occurring in the two data sets are not strong. On the other hand, very strong linear relations were observed between the nonlinear variates obtained from the questionnaire data and linear ones computed from the voice data. Questionnaire data carry great potential for preventive health care in laryngology. © 2011 Elsevier Ltd. All rights reserved.

  • 9.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania .
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania .
    Olenina, Irina
    Klaipeda University, Kaunas, Lithuania.
    Olenin, Sergej
    Klaipeda University, Klaipeda, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania .
    Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 5, p. 6069-6077Article in journal (Refereed)
    Abstract [en]

    A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to 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 1280 × 960 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. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species. © 2011 Elsevier Ltd. All rights reserved.

  • 10.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology.
    Kovalenko, Marina
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Selecting features from multiple feature sets for SVM committee-based screening of human larynx2010In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 37, no 10, p. 6957-6962Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a two stage procedure for designing a sequential SVM committee and selecting features for the committee from multiple feature sets. It is assumed that features of one type comprise one feature set. Selection of both features and hyper-parameters of SVM classifiers comprising the committee is integrated into one learning process based on genetic search. The designing process focuses on feature selection for pair-wise classification implemented by the SVM. In the first stage, a series of pair-wise SVM are designed starting from the original feature sets as well as from sets created by simple random selection from the original ones. Outputs of the SVM are then converted into probabilities and used as inputs to the second stage SVM. When testing the technique in a three-class classification problem of voice data, a statistically significant improvement in classification accuracy was obtained if compared to parallel committees. The number of feature types and features selected for the pair-wise classification are class specific.

  • 11.
    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).
    Lundström, Jens
    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 Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Advances in computational intelligence-based print quality assessment and control in offset colour printing2011In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, no 10, p. 13441-13447Article in journal (Refereed)
    Abstract [en]

    Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

  • 12.
    Verikas, Antanas
    et al.
    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.
    Parker, James
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Olsson, M. Charlotte
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Exploring relations between EMG and biomechanical data recorded during a golf swing2017In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 88, p. 109-117Article in journal (Refereed)
    Abstract [en]

    Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used. The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between lin- ear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2 = 0 . 958 and R2 = 0 . 943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis , indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canon- ical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices. ©2017 Elsevier Ltd. All rights reserved.

1 - 12 of 12
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf