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.

Bibliografske podrobnosti
Main Authors:	Pranta, Kazi Al Refat, Islam, Fahad Mohammad Rejwanul, Ahmed, Khandakar Fahim, Saha, Prince, Rahman, Naimur
Drugi avtorji:	Reza, Tanzim
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2024
Teme:	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)
Online dostop:	http://hdl.handle.net/10361/22890

Podobne knjige/članki

An android application to predict human activity using a deep learning LSTM model
od: Sikder, Debabrata
Izdano: (2024)

Deep learning based predictive analytics for decentralized content caching in hierarchical edge networks
od: Chakraborty, Dhruba, et al.
Izdano: (2022)

A conventional & deep learning strategy for analyzing & detecting Bengali fake news in online medium
od: Ahmed, Istiak, et al.
Izdano: (2023)

Sentiment analysis to determine employee job satisfaction using machine learning techniques
od: Mouli, Nazifa, et al.
Izdano: (2023)

Predicting temperature of major cities using machine learning and deep learning
od: Jaharabi, Wasiou, et al.
Izdano: (2024)

Silent voice: harnessing deep learning for lip-reading in Bangla
od: Shaheen, Munia, et al.
Izdano: (2024)

The influence of neuromarketing: machine learning based empirical analysis
od: Muktadir, MD. Abdullah Al, et al.
Izdano: (2024)

Intracranial hemorrhage detection using CNN-LSTM fusion model
od: Ahmed, Kazi sabab, et al.
Izdano: (2022)

Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
od: Biswas, Sunanda, et al.
Izdano: (2022)

Empowering mobile network planning through deep learning: a path to democratization
od: Nabi, Syed Tauhidun
Izdano: (2024)

A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring
od: Islam, Md Rashidul
Izdano: (2024)

An efficient deep learning approach to detect skin Cancer
od: Islam, Ashfaqul, et al.
Izdano: (2022)

Corn leaf disease detection using deep convolution neural network
od: Rabbi, Rawhatur, et al.
Izdano: (2023)

An efficient deep learning approach to classify white blood cells
od: Turja, Afif Ibna Kadir Khan, et al.
Izdano: (2024)

Bang-lish sentiment classification using deep learning
od: Saleheen, Abrar, et al.
Izdano: (2023)

Visual object classification from fMRI data
od: Newaz, Syed Mishar, et al.
Izdano: (2022)

Skin cancer detection and classification using multiple optimized deep convolutional neural network
od: Sakir, Adnan, et al.
Izdano: (2023)

An efficient ML approach to detect brain tumor using MRI images
od: Muktadir, MD. Arafat, et al.
Izdano: (2024)

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
od: Ifty, Md. Hasin Sarwar, et al.
Izdano: (2024)

Demystifying second hand car market in Bangladesh using multimodal machine learning techniques
od: Islam, Roman, et al.
Izdano: (2024)

Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
od: Datta, Nirjhor, et al.
Izdano: (2024)

Prostate cancer detection using deep learning neural network with transfer learning approach
od: Badhon, Ariful Islam Mahmud, et al.
Izdano: (2022)

An efficient deep learning approach to detect skin cancer using image data
od: Chowdhury, Yaser Al Rahman, et al.
Izdano: (2023)

Leveraging deep learning algorithms for the timely detection of diseases in bean leaves
od: Fiona, Driciru, et al.
Izdano: (2024)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
od: Turjo, Aquib Abtahi, et al.
Izdano: (2021)

Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning
od: Arafat, Sheikh Mohammad, et al.
Izdano: (2021)

Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
od: Ghosh, Kawshik Kumar, et al.
Izdano: (2023)

Classification of peripheral blood cell images using deep learning
od: Aadi, Oyshik Ahmed, et al.
Izdano: (2024)

Damaged road detection using Image Processing and Deep Learning
od: Swadesh, Shimran Mahbub, et al.
Izdano: (2022)

Document template identification and data extraction using machine learning and deep learning approach
od: Roy, Kaushik, et al.
Izdano: (2024)

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
od: Toufique, S.M, et al.
Izdano: (2024)

Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
od: Rayhan, Mohammad, et al.
Izdano: (2020)

ProteoKnight: phage virion protein classification with CNN and uncertainty quantification
od: Bhuiyan, Abir Ahammed, et al.
Izdano: (2024)

A modern technique to detect potholes by Computer Vision and Deep Learning
od: Saif, Muntasir Mahmud, et al.
Izdano: (2023)

Application of CNN based architectures in detection of distracted drivers
od: Arafin, Irfana, et al.
Izdano: (2023)

Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
od: Hasan, M. M. Kamrul, et al.
Izdano: (2022)

Forecasting market movement in Dhaka Stock Exchange: LSTM vs. ARIMA
od: Chakraborty, Pranjal
Izdano: (2019)

A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI
od: Mubin, Kazi Ehsanul, et al.
Izdano: (2022)

Classification of retinal diseases from OCT images using deep learning models
od: Mokarrom, Md Saif, et al.
Izdano: (2024)

Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models
od: Fahim, Kaji Mehedi Hasan, et al.
Izdano: (2024)