A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
- Title
- A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
- Creator
- Andi H.K.; Senbagam S.; Sharma D.K.; Shelke M.; Kumar N.K.S.; Poonia R.C.
- Description
- In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE.
- Source
- 2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 438-443.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- captioning; Digital data; disabled individuals; machine learning; media augmentation; similarity score; tags; vector; words
- Coverage
- Andi H.K., Asia Metropolitan University, Centre for Postgraduate Studies, Malaysia; Senbagam S., Dhanalakshmi Srinivasan College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nandu, Mamallapuram, India; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Shelke M., Aissms Ioit, Department of Artificial Intelligence and Data Science, Pune, India; Kumar N.K.S., School of Computing, Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology (Deemed to Be University), Department of Computer Science and Engineering, Tamil Nadu, Chennai, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036996-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Andi H.K.; Senbagam S.; Sharma D.K.; Shelke M.; Kumar N.K.S.; Poonia R.C., “A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19663.