Leveraging sequential deep learning models for detecting multitude of human action categories

Leveraging sequential deep learning models for detecting multitude of human action categories

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Opis bibliograficzny
Główni autorzy: Pranta, Kazi Al Refat, Islam, Fahad Mohammad Rejwanul, Ahmed, Khandakar Fahim, Saha, Prince, Rahman, Naimur
Kolejni autorzy: Reza, Tanzim
Format: Praca dyplomowa
Język:English
Wydane: Brac University 2024
Hasła przedmiotowe:
Human action recognition
Intelligent decision-making
Recurrent neural network (RNN)
Machine learning
Convolutional long short-term memory
Deep learning (Machine learning)
Neural networks (Computer science)
Cognitive learning theory (Deep learning)
Dostęp online:http://hdl.handle.net/10361/22890

Internet

http://hdl.handle.net/10361/22890

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