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.

Manylion Llyfryddiaeth
Prif Awduron:	Pranta, Kazi Al Refat, Islam, Fahad Mohammad Rejwanul, Ahmed, Khandakar Fahim, Saha, Prince, Rahman, Naimur
Awduron Eraill:	Reza, Tanzim
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	Brac University 2024
Pynciau:	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)
Mynediad Ar-lein:	http://hdl.handle.net/10361/22890

Eitemau Tebyg

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The influence of neuromarketing: machine learning based empirical analysis
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Intracranial hemorrhage detection using CNN-LSTM fusion model
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Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
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Empowering mobile network planning through deep learning: a path to democratization
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A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring
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An efficient deep learning approach to detect skin Cancer
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Corn leaf disease detection using deep convolution neural network
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An efficient deep learning approach to classify white blood cells
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Bang-lish sentiment classification using deep learning
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Visual object classification from fMRI data
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Skin cancer detection and classification using multiple optimized deep convolutional neural network
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An efficient ML approach to detect brain tumor using MRI images
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Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
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Prostate cancer detection using deep learning neural network with transfer learning approach
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An efficient deep learning approach to detect skin cancer using image data
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Leveraging deep learning algorithms for the timely detection of diseases in bean leaves
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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
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Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning
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Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
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Classification of peripheral blood cell images using deep learning
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Damaged road detection using Image Processing and Deep Learning
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Document template identification and data extraction using machine learning and deep learning approach
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Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
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Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
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Cyhoeddwyd: (2020)

ProteoKnight: phage virion protein classification with CNN and uncertainty quantification
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A modern technique to detect potholes by Computer Vision and Deep Learning
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Application of CNN based architectures in detection of distracted drivers
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Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
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Forecasting market movement in Dhaka Stock Exchange: LSTM vs. ARIMA
gan: Chakraborty, Pranjal
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A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI
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Classification of retinal diseases from OCT images using deep learning models
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Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models
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