Open Access
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 03001
Number of page(s) 11
Section Ethics, Privacy and Trust, Computer Network, Big Data Systems
Published online 01 June 2023
  1. Yaya, X., Bi-Geng, Z. (2020, May). Research on medical image storage and retrieval system based on Hadoop. In Journal of Physics: Conference Series (Vol. 1544, No. 1, p. 012119). IOP Publishing. [CrossRef] [Google Scholar]
  2. Dhulavvagol P.M., Totad S.G., Meti A.S., Shashidhara V. (2019) An Adaptiveand Dynamic Dimensionality Reduction Method for Efficient Retrieval of Videos. In: Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science [Google Scholar]
  3. Bao, Shunxing, Bennett Landman, and Aniruddha Gokhale. “Algorithmic enhancements to big data computing frameworks for medical image processing.” In 2017 IEEE International Conference on Cloud Engineering (IC2E), pp. 13-16. IEEE, 2017. [Google Scholar]
  4. Xun, Y., Zhang, J., Qin, X., Zhao, X. (2016). FiDoop-DP: Data partitioning in frequent itemset mining on hadoop clusters. IEEE Transactions on parallel and distributed systems, 28(1), 101-114. [Google Scholar]
  5. ÖZTÜRK, Ş., Alhudhaif, A., Polat, K. (2021). Attention-based end-to-end CNN framework for content-based X-ray imageretrieval. Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), 2680-2693. [CrossRef] [Google Scholar]
  6. Pébaÿ, Philippe P., et al. “A Novel Shard-Based Approach for Asynchronous ManyTask Models for In Situ Analysis.” Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization. 2017. 27-31. [Google Scholar]
  7. Annamalai, M., Ravichandran, K., Srinivas, H., Zinkovsky, I., Pan, L., Savor, T., ... Stumm, M. (2018). Sharding the shards: managing datastore locality at scale with Akkio. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (pp. 445-460). [Google Scholar]
  8. S. Jai-Andaloussi, A. Elabdouli, A. Chaffai, N. Madrane and A. Sekkaki, “Medical content based image retrieval by using the Hadoop framework, ” ICT 2013, 2013, pp. 1-5, doi:10.1109/ICTEL.2013.6632112. [Google Scholar]
  9. Khan, S. M. H., Hussain, A., Alshaikhli, I. F. T. (2012, November). Comparative study on content-based image retrieval (CBIR). In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (pp. 61-66). IEEE. [Google Scholar]
  10. Dhulavvagol P.M., Totad S.G., Sourabh S. (2019) Performance Analysis of Job Scheduling Algorithms on Hadoop Multi-cluster Environment. In: Sridhar V., Padma M., Rao K. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 545. Springer, Singapore. [Google Scholar]
  11. Wan, Ji, Dayong Wang, Steven Chu Hong Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, and Jintao Li. “Deep learning for content-based image retrieval: A comprehensive study.” In Proceedings of the 22nd ACM international conference on Multimedia, pp. 157-166. 2014. [Google Scholar]
  12. Sakhare, Swati V., and Vrushali G. Nasre. “Design of feature extraction in content based image retrieval (CBIR) using color and texture.” International Journal of Computer Science Informatics 1, no. 2 (2011): 57-61. [Google Scholar]
  13. Sakhare, Swati V., and Vrushali G. Nasre. “Design of feature extraction in content based image retrieval (CBIR) using color and texture.” International Journal of Computer Science Informatics 1, no. 2 (2011): 57-61. [Google Scholar]
  14. Müller, Henning, Wolfgang Müller, David McG Squire, Stéphane MarchandMaillet, and Thierry Pun. “Performance evaluation in content-based image retrieval [Google Scholar]
  15. P. M. Dhulavvagol, S. G. Totad and N. Bhandage, “Topic Based Partitioning for Selective Search Using Sharding Technique, ” 2022 International Conference for Advancement in Technology (ICONAT). [Google Scholar]
  16. Hong, Zi-Quan. “Algebraic feature extraction of image for recognition.” Pattern recognition 24.3 (1991): 211-219. [CrossRef] [MathSciNet] [Google Scholar]
  17. Zhao, Qibin, and Liqing Zhang. “ECG feature extraction and classification using wavelet transform and support vector machines.” In 2005 International Conference on Neural Networks and Brain, vol. 2, pp. 1089-1092. IEEE, 2005. [Google Scholar]
  18. Lee, Chulhee, and David A. Landgrebe. “Feature extraction based on decision boundaries.” IEEE Transactions on Pattern Analysis and Machine Intelligence 15.4 (1993): 388-400. [CrossRef] [Google Scholar]
  19. Miura, Takeshi, Takaaki Kaiga, Takeshi Shibata, Katsubumi Tajima, and Hideo Tamamoto. “Low-dimensional feature vector extraction from motion capture data by phase plane analysis.” Journal of Information Processing 25 (2017): 884-887. [CrossRef] [Google Scholar]
  20. Liu, Ke, Yong-Qing Cheng, and Jing-Yu Yang. “Algebraic feature extraction for image recognition based on an optimal discriminant criterion.” Pattern recognition 26, no. 6 (1993): 903-911. [CrossRef] [Google Scholar]
  21. Praveen M Dhulavvagol, Vijayakumar H Bhajantri, S G Totad, Performance Analysis of Distributed Processing System using Shard Selection Techniques on Elasticsearch, Procedia Computer Science, Volume 167, 2020, Pages 1626-1635, ISSN 1877-0509, [CrossRef] [Google Scholar]
  22. Akgül, Ceyhun Burak, Daniel L. Rubin, Sandy Napel, Christopher F. Beaulieu, Hayit Greenspan, and Burak Acar. “Content-based image retrieval in radiology:current status and future directions.” Journal of digital imaging 24, no. 2 (2011):208-222. [CrossRef] [Google Scholar]
  23. Grace, R. K., Manimegalai, R., Kumar, S. S. (2014, March). Medical image retrieval system in grid using hadoop framework. In 2014 international conference on computational science and computational intelligence (Vol. 1, pp. 144-148). IEEE. [Google Scholar]
  24. Li, X., Yang, J., Ma, J. (2021). Recent developments of content-based image retrieval (CBIR). Neurocomputing, 452, 675-689. [CrossRef] [Google Scholar]
  25. P. M. Dhulavvagol, A. Desai and R. Ganiger, “Vehical Tracking and Speed Estimation of Moving Vehicles for Traffic Surveillance Application. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.