Type-2 Neutrosophic Markov Chain Model for Subject-Independent Sign Language Recognition: A New Uncertainty–Aware Soft Sensor Paradigm
Muslem Al-Saidi, Áron Ballagi, Oday Ali Hassen, Saad M. Saad · 2024
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Description
Muslem Al-Saidi, Áron Ballagi, Oday Ali Hassen, and Saad Saad published a 2024 paper on a sign language recognition system built to handle uncertainty. Signers vary in style, and lighting or blocked hands add more noise. A system that knows how unsure it is can cope better with that variation.
Their model joins hidden Markov models, a classic sequence tool, with type-2 neutrosophic sets, a math frame that states truth, doubt, and falsity as ranges. That pairing lets the system carry its uncertainty as intervals through each step. The authors frame the result as a soft sensor that should work for new signers without retraining on them. They discuss the cost in computing power and the limits that remain for real time use.
Catalog record
| CREATOR | Muslem Al-Saidi, Áron Ballagi, Oday Ali Hassen, Saad M. Saad |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 83 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |