ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
- Title
- ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence
- Creator
- Deepak G.; Iyer K.; Shukla S.
- Description
- There is a requirement for an automatic semantic-oriented framework for Web video tagging in the epoch of Web 3.0, as Web 3.0 is much denser, intelligent, but more cohesive compared to Web 2.0. This paper proposes the ATRSI framework which is the Automatic Tag Recommender framework which encompasses the semantic-oriented Artificial Intelligence that outgrows the dataset by making the use of informative terms using TF-IDF and bag of words model to build the intermediate semantic network which is further organized using an Lin similarity measure and is optimized using red deer optimization by encompassing the entities from the World Wide Web to focused crawling. RNN is a classifier that is used for the classification of the dataset, it is a strong deep-learning classifier. Semantic-oriented Intelligence is achieved using the CoSim rank and Morisita's overlap index. The bag of lightweight graphs is obtained from the semantic network which is an intermediate knowledge representation mechanism that is further embedded in the intrinsic model. A semantically consistent system for video recommendation, ATRSI outperforms the other baseline models in terms of average accuracy, average precision and F-measure for a variety of recommendations. 2024 IEEE.
- Source
- Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 594-599.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bag of Words model; CoSim Rank; Lin similarity; RNN
- Coverage
- Deepak G., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Iyer K., Manipal Institute of Technology Bengaluru, Manipal Academy of Higer Education, Department of Cse, Manipal, India; Shukla S., School of Engineering, Christ (Deemed to Be University), Department of Cse, Kengeri Campus, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038328-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Deepak G.; Iyer K.; Shukla S., “ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19455.