hh.sePublications
Change search
Refine search result
1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • 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.
    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.

  • 2.
    Guo, Xiaoyi
    et al.
    University of Electronic Science and Technology of China, Chengdu, PR China; University of Electronic Science and Technology of China, Quzhou, PR China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Zhang, Ying
    Beidahuang Industry Group General Hospital, Harbin, PR China.
    Han, Shuguang
    University of Electronic Science and Technology of China, Quzhou, PR China.
    Wang, Yansu
    University of Electronic Science and Technology of China, Chengdu, PR Chin.
    Ding, Yijie
    University of Electronic Science and Technology of China, Quzhou, PR China.
    Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins2022In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 151, article id 106268Article in journal (Refereed)
    Abstract [en]

    DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase,prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of theconventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years,a variety of machine learning-based methods that have been used on a large scale for large-scale screeningof DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparserepresentation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solvenonlinear classification problems. And 𝐿2,1-matrix norm is introduced to get sparse solution of model. Toevaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRCachieves better performance in experimental results. © 2022 Elsevier Ltd. 

  • 3.
    Qu, Zhiguo
    et al.
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Shi, Wenke
    Nanjing University Of Information Science And Technology, Nanjing, China.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation2023In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 166, p. 1-13, article id 107549Article in journal (Refereed)
    Abstract [en]

    To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG. © 2023 The Author(s)

  • 4.
    Singh, Ram
    et al.
    Baba Ghulam Shah Badshah University Rajouri, Rajouri, India.
    Tiwari, Prayag
    Halmstad University, School of Information Technology.
    Band, Shahab S.
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan.
    Rehman, Attiq U.
    Baba Ghulam Shah Badshah University Rajouri, Rajouri, India.
    Mahajan, Shubham
    Ajeenka D Y University, Pune, Maharashtra, India; iNurture Education Solutions Pvt. Ltd., Bangalore, India; School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, India.
    Ding, Yijie
    Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
    Liu, Xiaobin
    Department of Nephrology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China.
    Pandit, Amit Kant
    School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, India.
    Impact of quarantine on fractional order dynamical model of Covid-192022In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 151, Part A, article id 106266Article in journal (Refereed)
    Abstract [en]

    In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease. © 2022 The Author(s). Published by Elsevier Ltd. All rights reserved.

  • 5.
    Stasiunas, Antanas
    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), Intelligent systems (IS-lab). Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Kemesis, Povilas
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Miliauskas, Rimvydas
    Department of Physiology, Kaunas University of Medicine, Kaunas, Lithuania.
    Stasiuniene, Natalija
    Department of Biochemistry, Kaunas University of Medicine, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system2005In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 35, no 6, p. 495-510Article in journal (Refereed)
    Abstract [en]

    An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.

  • 6.
    Stasiunas, Antanas
    et al.
    a 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).
    Miliauskas, Rimvydas
    Department of Physiology, Kaunas University of Medicine, Lithuania.
    Stasiuniene, Natalija
    Department of Biochemistry, Kaunas University of Medicine, Lithuania.
    An adaptive model simulating the somatic motility and the active hair bundle motion of the OHC2009In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 9, p. 800-809Article in journal (Refereed)
    Abstract [en]

    The outer hair cells (OHC) of the mammalian inner ear change the sensitivity and frequency selectivity of the filtering system of the cochlea using two kinds of mechanical activity: the somatic motility and the active hair bundle motion. We designed a non-linear adaptive model of the OHC employing both mechanisms of the mechanical activity. The modeling results show that the high sensitivity and frequency selectivity of the filtering system of the cochlea depend on the somatic motility of the OHC. However, both mechanisms of mechanical activity are involved in the adaptation to sound intensity and efferent-synaptic influence. The fast (alternating) component (AC) of the mechanical–electrical transduction signal controls the motor protein prestin and fast changes in axial length of the cell. The slow (direct) component (DC) appearing at high signal intensity affects the axial stiffness, the cell length and the position of the hair bundle. The efferent influence is realized by the same mechanism.

  • 7.
    Verikas, Antanas
    et al.
    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, Studentu 50, LT-51368 Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Kaseta, Marius
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Using the patient's questionnaire data to screen laryngeal disorders2009In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 2, p. 148-155Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied. When testing the developed tools on the set of data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.

1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • 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