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Journal of Medical Systems

, 43:13 | Cite as

A new approach to classify and rank events based videos based on Event of Detection

  • R. G. Sakthivelan
  • P. Rajendran
  • M. Thangavel
Image & Signal Processing
  • 10 Downloads
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

In the ongoing days, the development of sight and sound substance and information stockpiling produces colossally. Clients can extricate any kind of data they require from recordings. This outcomes in quick development of video information, client’s discover complexity while procurement their important data. To address this, EBR (Event Based Ranking) propose another way to deal with group and rank mixed media occasions based recordings as per client’s advantage. Clients are generally keen on the best positioned and occasion pertinent recordings of returned query output. An occasion based positioning methodology which empowers clients to iteratively peruse the video as per their inclination. The proposed conspire has new way to deal with order and rank occasions based recordings. It improves the exactness of video recovery which incorporates certain functionalities for customized look. The data of clients’ input is used in re-positioning technique to additionally enhance the recovering exactness. It gives the customized lastly re-positioned pertinent outcomes to shape a brought together precise query output. EBR is more precise in foreseeing and positioning client particular inclinations and diminishes the time many-sided quality. This Paper proposed a calculation comprises of: video rank calculation, occasion term suggestion, and client criticism and client session.

Keywords

Preprocessing Event recommendation User session EBR 

Notes

Compliance with Ethical Standards

Conflict of Interest

The author’s has no conflict of interest in submitting the manuscript to this journal.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Lokesh, S., Kumar, P. M., Devi, M. R., Parthasarathy, P., and Gokulnath, C., An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Computing and Applications, 1–11, 2018.Google Scholar
  2. 2.
    Kanisha, B., Lokesh, S., Kumar, P. M., Parthasarathy, P., and Chandra Babu, G., Speech recognition with improved support vector machine using dual classifiers and cross fitness validation. Personal and Ubiquitous Computing, 1–9, 2018.Google Scholar
  3. 3.
    Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., and Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems 86:527–534, 2018.CrossRefGoogle Scholar
  4. 4.
    Chandra, I., Sivakumar, N., Gokulnath, C. B., and Parthasarathy, P., IoT based fall detection and ambient assisted system for the elderly. Cluster Computing, 1–9, 2018.Google Scholar
  5. 5.
    Mathan, K., Kumar, P. M., Panchatcharam, P., Manogaran, G., & Varadharajan, R.,A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Automation for Embedded Systems, 1–18, 2018.Google Scholar
  6. 6.
    Parthasarathy, P., and Vivekanandan, S., Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Healthinformation science and systems 6:1–6, 2018.Google Scholar
  7. 7.
    Parthasarathy, P., and Vivekanandan, S., A comprehensive review on thin film based nano-biosensor for uric acid determination: arthritis diagnosis. World Review ofScience, Technology and Sustainable Development 14(1):52–71, 2018.CrossRefGoogle Scholar
  8. 8.
    Parthasarathy, P., and Vivekanandan, S., A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Informatics in Medicine Unlocked 2018.Google Scholar
  9. 9.
    Varadharajan, R., Priyan, M. K., Panchatcharam, P., Vivekanandan, S., and Gunasekaran, M., A new approach for prediction of lung carcinoma using back propogation neural network with decision tree classifiers. Journal of Ambient Intelligence andHumanized Computing, 1–12, 2018.Google Scholar
  10. 10.
    Parthasarathy, P., and Vivekanandan, S., Urate crystal deposition, prevention and various diagnosis techniques of GOUT arthritis disease: a comprehensive review. Health information science and systems 6(1):19, 2018.CrossRefGoogle Scholar
  11. 11.
    Manohar, S., Yadav, N., Priyadarshi, S., and Mittal, V., “Content-Based Video Retrieval Using Vector Quantization, International Journal Of Research In Science & Engineering E-ISSN: 2394–8299 Volume: 3 Issue: 2 March–April P-ISSN: 2394–8280, 2017.Google Scholar
  12. 12.
    Parveen M., “Content Based Video Mining: A Survey” International Journal of Advanced Research in Computer Science “Volume 7, No. 5, September–October 2016.Google Scholar
  13. 13.
    Kavitha, D., and Kalpana, B., “A Personalized Web Search System using Frequency Based Ranking Method (FBRM) for Web Log” International Journal of Control Theory and Applications, Volume 10, Number 12, 2017.Google Scholar
  14. 14.
    Kurimo, J., “Unsupervised feature extraction for multimedia event detection and Ranking using audio content” 2014 IEEE International Conference on Acoustic, 978–1–4799-2893-4.Google Scholar
  15. 15.
    Batra, N., and Singh, D., “Content Based Hidden web Ranking Algorithm (CHWRA)” IEEE, P (586–589), 2014.Google Scholar
  16. 16.
    Chivadshetti, P., and Sadafale, K., “Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results”, 2015 International Conference on Information Processing (ICIP) Vishwakarma Institute of Technology. Dec16–19, 2015Google Scholar
  17. 17.
    Dhage, S., and Meshram, B. B., “Disk Load Balancing and Video Ranking Algorithm for Efficient Access in Video server” 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Oct. 19–20.Google Scholar
  18. 18.
    Geetha, P., “An Effective Video Search Re-Ranking for Content Based Video Retrieval”978–1–4673-0131, IEEE, 2011.Google Scholar
  19. 19.
    Hajeer, S. I., Ismail, R. M., Badr, N. L., and Taiba, M. F., “An Efficient Hybrid Usage-Based Ranking Model for Information Retrieval Systems & Web Search Engine” 6th International Conference on Information and Communication Systems (ICICS), 2015.Google Scholar
  20. 20.
    Hoi, S. C. H., and Lyu, M. R., “A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval” IEEE Transactions on Multimedia, Vol. 10, No. 4, 2008.Google Scholar
  21. 21.
    Hu, J., Zhu, S., Liang, H., and Wang, B., “Image Retrieval Based on Relevance-Quality Rank”, (September–October) 36(5):507–519, 2013.Google Scholar
  22. 22.
    Chivadshetti, P., Sadafale, K., and Thakare, K., “Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results” International Journal of Engineering Research and Development, Volume 11, Issue 08 (August), PP. 72–80, 2015.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • R. G. Sakthivelan
    • 1
  • P. Rajendran
    • 2
  • M. Thangavel
    • 3
  1. 1.Department of CSEAVS Engineering CollegeSalemIndia
  2. 2.Department of CSEKnowledge Institute of TechnologyKakapalayamIndia
  3. 3.Department of ECEKnowledge Institute of TechnologyKakapalayamIndia

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