Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models

Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models

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

Bibliografski detalji
Glavni autori:	Fahim, Kaji Mehedi Hasan, Nyla, Nasita, Saha, Priti, Akter, Mst. Shamima, Shourav, Musfiqur Rahman
Daljnji autori:	Sadeque, Farig Yousuf
Format:	Disertacija
Jezik:	English
Izdano:	Brac University 2024
Teme:	Natural language processing Machine learning Fast text embedding Deep learning (Machine learning)
Online pristup:	http://hdl.handle.net/10361/23576

Similar Items

An integrated approach: fake review detection using convBERT-BiLSTM classification
od: Mahmud, Md. Anas, i dr.
Izdano: (2024)

Heart disease prediction using machine learning algorithms
od: Ali, Musfiq, i dr.
Izdano: (2019)

Analysis of the impact of online education using EEG signals and machine learning algorithms
od: Hoque, Ehsanul, i dr.
Izdano: (2021)

Optical flow based violence detection from video footage using hybrid MobileNet and Bi-LSTM
od: Haque, Tashfia, i dr.
Izdano: (2024)

Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN
od: Dewanjee, Tanmoy, i dr.
Izdano: (2021)

Public vehicle ETA system using machine learning
od: Kabir, Md. Miraz, i dr.
Izdano: (2018)

Predicting depression in Bangladeshi undergraduates using machine learning
od: Choudhury, Ahnaf Atef, i dr.
Izdano: (2019)

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

Alumni opportunities using machine learning
od: Siaum, Arshif Sharlimin, i dr.
Izdano: (2019)

Machine learning as an indicator for breast cancer prediction
od: Shadman, Tahsin Mohammed, i dr.
Izdano: (2019)

Chronic kidney disease detection using ensemble classi ers and feature set reduction
od: Shawan, Naveed Rahman, i dr.
Izdano: (2019)

A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning
od: Raizan, Syed Ahsan, i dr.
Izdano: (2021)

Observing mega cities to grow from space and predicting its growth using machine learning techniques
od: Chakma, Mitesh, i dr.
Izdano: (2018)

Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
od: Alhossen, Md., i dr.
Izdano: (2021)

Pattern recognition with Quantum Support Vector Machine(QSVM) on near term quantum processors.
od: Ahmed, Sajjad
Izdano: (2019)

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

An implementation and analysis of deep learning models for the detection of wheat diseases
od: Zubair, Ahmad, i dr.
Izdano: (2024)

Real-time foul detection in football matches using machine learning techniques
od: Siddiqui, Bishal Sadi, i dr.
Izdano: (2024)

Detection of acute Lymphocytic Leukemia (ALL) and its type by image processing and machine learning
od: Chowdhury, Himadri, i dr.
Izdano: (2019)

Predicting climate induced floods using machine learning
od: Saleh, Chowdhury Nafis, i dr.
Izdano: (2022)

Crop prediction based on geographical and climatic data using machine learning and deep learning
od: Alif, Al Amin, i dr.
Izdano: (2019)

Application of machine learning in credit risk assessment: a prelude to smart banking
od: Dhruba, Mir Ishrak Maheer, i dr.
Izdano: (2019)

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

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

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

A machine learning approach to detect depression and anxiety using supervised learning
od: Ullas, Md Tahmidur Rahman, i dr.
Izdano: (2019)

Detecting self-esteem level and depressive indication due to different parenting style using supervised learning techniques
od: Al Taawab, Abdullah, i dr.
Izdano: (2022)

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

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

Fault detection in waste water treatment plant using statistical analysis & machine learning
od: Haque, Md. Ezazul, i dr.
Izdano: (2019)

Genetic algorithms in search, optimization, and machine learning /
od: Goldberg, David E. (David Edward), 1953-
Izdano: (1989)

Machine Learning /
od: Mitchell, Tom M. (Tom Michael), 1951-
Izdano: (1997)

Pattern recognition and machine learning /
od: Bishop, Christopher M.
Izdano: (2006)

Health monitoring IoT device with risk prediction using cloud computing and machine learning
od: Das, Anindya, i dr.
Izdano: (2021)

Using deep learning algorithms to detect violent activities
od: Ammar, S.M. Rojin, i dr.
Izdano: (2019)

Weather Pattern Extraction using Statistical Methods & Machine Learning Techniques
od: Habib, Adria Binte
Izdano: (2021)

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

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

RED-LSTM: Real time emotion detection using LSTM
od: Labiba, Mansura Rahman, i dr.
Izdano: (2023)

Multimodal human detection in disaster situations using deep learning & artificial intelligence
od: Rafat, Md. Tasbi, i dr.
Izdano: (2023)