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Semantic web techniques for clinical topic detection in health care

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The scope of this paper is that it investigates and proposes a new clustering method thattakes into account the timing characteristics of frequently used feature words and thesemantic similarity of microblog short texts as well as designing and implementing mi-croblog topic detection and detection based on clustering results. The aim of the proposedresearch is to provide a new cluster overlap reduction method based on the divisions ofsemantic memberships to solve limited semantic expression and diversify short microblogcontents. First, by defining the time-series frequent word set of the microblog text, a fea-ture word selection method for hot topics is given; then, for the existence of initial clusters,according to the time-series recurring feature word set, to obtain the initial clustering ofthe microblog.
Rocznik
Strony
139--155
Opis fizyczny
Bibliogr. 23 poz., tab., wykr.
Twórcy
autor
  • Department of Electronics and Communication Engineering, Aditya Collegeof Engineering, Surampalem, Andhra Pradesh, India
  • Department of Computing Technologies, School of Computing, SRM Institute of Scienceand Technology, Tamil Nadu, India
autor
  • Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology,Tathawade, Pune, India;
  • Department of Computer Science & Engineering and Information Science, PresidencyUniversity, Bangaluru, India
  • School of Information Technology and Engineering, Vellore Institute of Technology,India
  • Department of Computer Science Engineering, Maulana Azad National Institute ofTechnology, Bhopal, India
Bibliografia
  • 1. A. Ejnioui, C.E. Otero, A.A. Qureshi, Formal semantics of interactions in sequence diagrams for embedded software, [in:] 2013 IEEE Conference on Open Systems (ICOS), 2–4 December, Kuching, Malaysia, 2013, pp. 106–111, doi: 10.1109/ICOS.2013.6735057.
  • 2. J. Mansouri, B. Seddik, S. Gazzah, T. Chateau, Coarse localization using space-time and semantic-context representations of geo-referenced video sequences, [in:] 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 10–13 November, Orleans, France, 2015, pp. 355–359, doi: 10.1109/IPTA.2015.7367165.
  • 3. L. Yao et al., HSD: Hybrid MARTE sequence diagram, [in:] 2015 IEEE International Conference on Software Quality, Reliability and Security, 3–5 August, Vancouver, BC, Canada, 2015, pp. 189–194, doi: 10.1109/QRS.2015.35.
  • 4. Y. Fang et al., Salient object detection by spatiotemporal and semantic features in realtime video processing systems, IEEE Transactions on Industrial Electronics, 67(11): 9893–9903, 2020, doi: 10.1109/TIE.2019.2956418.
  • 5. A. Shobanadevi et al., Internet of things-based data hiding scheme for wireless communication, Wireless Communications and Mobile Computing, 2022: Article ID 6997190, 8 pages, 2022, doi: 10.1155/2022/6997190.
  • 6. Z. Xiang, S. Zhi-qing, ASM semantic modeling and checking for sequence diagram, [in:] 2009 Fifth International Conference on Natural Computation, 14–16 August, Tianjian, China, pp. 527–530, 2009, doi: 10.1109/ICNC.2009.218.
  • 7. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80: 23983–24004, 2021, doi: 10.1007/s11042-021-10840-0.
  • 8. M. Soni, G. Dhiman, B.S. Rajput, R. Patel, N.K. Tejra, Energy-effective and secure data transfer scheme for mobile nodes in smart city applications, Wireless Personal Communications, 127: 2041–2061, 2021, doi: 10.1007/s11277-021-08767-8.
  • 9. C. Jia, M.B. Carson, X. Wang, J. Yu, Concept decompositions for short text clustering by identifying word communities, Pattern Recognition, 76: 691–703, 2018, doi: 10.1016/j.patcog.2017.09.045.
  • 10. A. Cioppa, M.V. Droogenbroeck, M. Braham, Real-time semantic background subtraction, [in:] 2020 IEEE International Conference on Image Processing (ICIP), 25–28 October, Abu Dhabi, UAE, pp. 3214–3218, 2020, doi: 10.1109/ICIP40778.2020.9190838.
  • 11. T. Wang, K. Jia, M. Yao, Sequence matching with discriminative binary features for robust and fast light-rail localization at high frame rate, [in:] 2020 IEEE International Conference on Big Data (Big Data), 10–13 December, Atlanta, GA, USA, pp. 1266–1272, 2020, doi: 10.1109/BigData50022.2020.9378494.
  • 12. N.H. Kirk, K. Ramírez-Amaro, E. Dean-León, M. Saveriano, G. Cheng, Online prediction of activities with structure: Exploiting contextual associations and sequences, [in:] 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 3–5 November, Seoul, South Korea, pp. 744–749, 2015, doi: 10.1109/HUMANOIDS.2015.7363453.
  • 13. Q. Li, B. Tian, M. Zhang, An event sequence based method for audio scene analysis, [in:] 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology, 28–30 October, Shenzen, China, pp. 255–259, 2011, doi: 10.1109/ICBNMT.2011.6155936.
  • 14. S. Han, Z. Xi, Dynamic scene semantics SLAM based on semantic segmentation, IEEE Access, 8: 43563–43570, 2020, doi: 10.1109/ACCESS.2020.2977684.
  • 15. C. Li, H. Xiao, K. Tateno, F. Tombari, N. Navab, G.D. Hager, Incremental scene understanding on dense SLAM, [in:] 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9–14 October, Daejeon, South Korea, pp. 574–581, 2016, doi: 10.1109/IROS.2016.7759111.
  • 16. M. Siam, A. Kendall, M. Jagersand, Video class agnostic segmentation benchmark for autonomous driving, [in:] 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 20–25 June, on-line, pp. 2819–2828, 2021, doi: 10.1109/CVPRW53098.2021.00317.
  • 17. J. Yang, F. Wang, J. Yang, Dynamic gesture recognition using LBRCN combined with attention mechanism, [in:] 2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT), 11–13 June, Changsha, China, pp. 466–469, 2021, doi: 10.1109/ISCIPT53667.2021.00100.
  • 18. X. Li, Y. Tian, F. Zhang, S. Quan, Y. Xu, Object detection in the context of mobile augmented reality, [in:] 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 9–13 November, Porto de Galinhas, Brazil, pp. 156–163, 2020, doi: 10.1109/ISMAR50242.2020.00037.
  • 19. C. Chakraborty, Performance analysis of compression techniques for chronic wound image transmission under smartphone-enabled tele-wound network, International Journal of E-Health and Medical Communications, 10(2): 1–20, 2019, doi: 10.4018/ijehmc.2019040101.
  • 20. K.N. Mishra, C. Chakraborty, A novel approach toward enhancing the quality of life in smart cities using clouds and IoT-based technologies, [in:] Digital Twin Technologies and Smart Cities, Springer, pp. 19–35, 2019, doi: 10.1007/978-3-030-18732-3_2.
  • 21. S.K. Narayanasamy, K. Srinivasan, Y.-C. Hu, S.K. Masilamani, K.-Y. Huang, A contemporary review on utilizing semantic web technologies in healthcare, virtual communities, and ontology-based information processing systems, Electronics, 11(3): 453, 2022, doi: 10.3390/electronics11030453.
  • 22. A. Kishor, W. Jeberson, Diagnosis of heart disease using internet of things and machine learning algorithms, [in:] Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, Springer Singapore, vol. 203, pp. 691–702, 2021, doi: 10.1007/978-981-16-0733-2_49.
  • 23. C. Friedrich, A. Lechler, A. Verl, A planning system for generating manipulation sequences for the automation of maintenance tasks, [in:] 2016 IEEE International Conference on Automation Science and Engineering (CASE), 21–25 August, Fort Worth, Texas, pp. 843–848, 2016, doi: 10.1109/COASE.2016.7743489.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-42d8d038-9d35-4b7a-b464-3097824dbb80
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