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.

Detalhes bibliográficos
Principais autores:	Pranta, Kazi Al Refat, Islam, Fahad Mohammad Rejwanul, Ahmed, Khandakar Fahim, Saha, Prince, Rahman, Naimur
Outros Autores:	Reza, Tanzim
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2024
Assuntos:	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)
Acesso em linha:	http://hdl.handle.net/10361/22890

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

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