Abstract
Instagram is a great place to look for descriptive tags for photographs and other types of information. Inaccordance with the learning by example paradigm, the tags–image pairs can be utilised to train automated image annotation (AIA) systems. In earlier research, we found that, on average. Approximately 22% of Instagram hashtags are related to the image's visual content,accompany, in the sense that they are descriptive hashtags, whereas there are many irrelevant hashtags, in the sense that they are not descriptive hashtags.Stop using hashtags on completely different photographs merely to get more clicks and likes.Enhancement of searchability We provide a revolutionary methodology in this study that is based on the collective intelligence principles that aid in the discovery of those hashtags. We demonstrate this in particular that the use of a modified version of the widely used hyper link induced topic search. In the context of crowd tagging, the (HITS) algorithm provides an effective and consistent method for locating pairs of Instagram photographs and hashtags, resulting in representative and noise-free results. Content-based image retrieval training sets We used crowdsourcing as a proof of concept platform Figure-eight to enable for the collection of collective intelligence in the form of tag selection.For Instagram hashtags, this is known as (crowdtagging). Figure-crowdtagging eight's data is utilised to create bipartite networks in which the first kind of node relates to the annotators and the second type of node corresponds to the annotations input the hashtags they've chosen. The HITS algorithm is used to rank the annotators in the first place,in terms of their efficiency in the crowdtagging activity, and then to find the appropriate hashtags for each situation image.
Keywords: Bipartite graphs, collective intelligence, crowdtagging, FolkRank, hyperlinkinduced topic search (HITS) algorithm, image retrieval, image tagging, Instagram hashtags.
REFERENCES
1. A. Argyrou, S. Giannoulakis, and N. Tsapatsoulis, "Topic modelling on Instagram hashtags: An alternative to automatic image annotation?" in Proceedings of the 13th International Workshop on Semantic Social Media Adaptation Personalization, pp. 61-67, 2018.
2. B. I. Aydin, Y. S. Yilmaz, Y. Li, Q. Li, J. Gao, and M. Demirbas, "Crowdsourcing for multiplechoice question answering," in Proceedings of the 28th AAAI Conf. Artif. Intell., pp. 2946-2953, 2014.
3. Cabrall, C. D. D. et al., C. D. D. Cabrall et al., C. D. D. Ca "Validity and trustworthiness of naturalistic driving scene classificationconclusions drawn from crowdsourcing ", Accident Analysis and Prevention, vol. 114, no. 5, May 2018, pp. 25-33.
4. Q. Cheng, Q. Zhang, P. Fu, C. Tu, and S. Li, Q. Cheng, Q. Zhang, P. Fu, C. Tu, and S. Li, Q. Cheng, Q "An investigation and study of automatic image processinga comment "Pattern Recognition, vol. 79, no. 2, pp. 242-259, July 2018.
5. N. Craswell, "Mean reciprocal rank," Encyclopedia of Database Systems, London, 1997.
6. H. Cui, Q. Li, H. Li, and Z. Yan, H. Cui, Q. Li, H. Li, and Z. Yan, H. Cui, Q "The identification of healthcare fraud is predicated on the trustworthiness of the people involved,medical professionals "74-81 in Proc. Trustcom/BigDataSE/I SPA, 2016.
7. R. Hoffman, A. R. Daer, and S. Goodman, "The rhetorical roles of hashtags on social mediaapplications in the media ", CD-ROM, Proc. 32nd ACM International Conference on Design Communication, 2014.
8. Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara, Ferrara "Popularity and thematic interests on the internet Instagram's camera ", in Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 24-34, 2014.
9. T. Wennekers and J. M. Fletcher, "Using centrality measurements to move from structure to activity neural activity prediction ", International Journal of Neural Systems, vol. 28, no. 2, 2018.
10. M. Gao, L. Chen, B. Li, Y. Li, W. Liu, and Y.-C. Xu, 10.M. Gao, L. Chen, B. Li, Y. Li, W. Liu, and Y.-C. Xu, "Link prediction using projections in a two-way network ".
11. "From structure to activity: Using centrality measures to predict neural activity," International Journal of Neural Systems, vol. 28, no. 2, 2018.
12. M. Gao, L. Chen, B. Li, Y. Li, W. Liu, and Y.-C. Xu,M. Gao, L. Chen, B. Li, Y. Li, W. Liu, and Y.-C. Xu, "Link prediction using projections in a two-way network ", Inf. Sci., vol. 376, Jan. 2017, pp. 158-171.
13. "Bipartite graph" by S. I. Gass and C. M. Harris in Encyclopedia of Operations Research and Management Science.Management Science, Springer, Boston, MA, USA, pp. 126, 2013.
14. S. Giannoulakis and N. Tsapatsoulis, "Evaluating the descriptive power of instagram hashtags", J. Innov. Digit.
Ecosyst., vol. 3, no. 2, pp. 114-129, 2016.
15. S. Giannoulakis and N. Tsapatsoulis, "Defining and identifying stophashtags in instagram", Proc. INNS Conf. Big Data, pp. 304-313, 2016.
16. S. Giannoulakis, N. Tsapatsoulis and K. Ntalianis, "Identifying image tags from Instagram hashtags using the HITS algorithm", Proc. 3rd Int. Conf. Big Data Intell. Comput. Cyber Sci. Technol. Congr. (DASC/PiCom/DataCom/CyberSciTech), pp. 89-94, 2017.
17. M. V. Giuffrida, F. Chen, H. Scharr and S. A. Tsaftaris, "Citizen crowds and experts: Observer variability in image-based plant phenotyping", Plant Methods, vol. 14, no. 1, pp. 12, 2018.
18. M. Gupta, R. Li, Z. Yin and J. Han, "Survey on social tagging techniques", SIGKDD Explor. Newslett., vol. 12, no. 1, pp. 58-72, Jun. 2010.
19. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
20. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
21. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019).
22. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
23. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
24. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
25. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
26. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
27. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
28. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
29. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
30. V.Senthilkumar , K. Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
31. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021 32. S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar “Semantic Risk Analysis Model Cancer Data Prediction”, Journal on Science Engineering and Technology, Vol 5 Issue No 2 2018