Full stack development of UBERA system

Full stack development of UBERA system

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

Xehetasun bibliografikoak
Egile nagusia:	An Noor, Akib
Beste egile batzuk:	Alam, Md. Ashraful
Formatua:	Project
Hizkuntza:	English
Argitaratua:	BRAC University 2024
Gaiak:	Earthquake Deep learning Image processing Convolutional neural network Structural health monitoring Machine learning Application software > Development Computer programming Management information systems
Sarrera elektronikoa:	http://hdl.handle.net/10361/23588

Antzeko izenburuak

Image processing and deep learning approach to evaluate earthquake resistance of urban buildings
nork: Ahmed, Yasin, et al.
Argitaratua: (2018)

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

A novel lightweight CNN approach for Bangladeshi sign language gesture recognition
nork: Rahman, Aryan, et al.
Argitaratua: (2023)

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

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

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

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

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

Efficient image processing and machine learning approach for predicting retinal diseases
nork: Hasib, Mehadi Hasan, et al.
Argitaratua: (2021)

Deep learning based early Glaucoma detection
nork: Islam, Aabrar, et al.
Argitaratua: (2024)

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

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

Enhancing multiclass brain tumor classification using deep learning: leveraging superior imaging representations to improve inferior modality performance
nork: Hossain, Shah Md. Shakhawath, et al.
Argitaratua: (2024)

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

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

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

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

Classification of different magnetic structures from image data using deep neural networks
nork: Ibn Hasib, Fahad, et al.
Argitaratua: (2023)

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

An eﬃcient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling
nork: Sagor, Mostofa Kamal, et al.
Argitaratua: (2021)

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

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
nork: Ferdous, Md.Rubaiat, et al.
Argitaratua: (2024)

Analysis of transformer and CNN based approaches for classifying renal abnormality from image data
nork: Reza, S. M. Mushfiq, et al.
Argitaratua: (2024)

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

Detection of handwritten text using convolutional neural network
nork: Jasim, Rabib Bin, et al.
Argitaratua: (2024)

Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
nork: Tasawar, Ihtyaz Kader, et al.
Argitaratua: (2021)

Visual object recognition using deep convolutional neural network
nork: Ahmed, Sabbir
Argitaratua: (2017)

Pneumonia Disease detection using the convolutional neural network
nork: Ashrafee, Md. Iftid, et al.
Argitaratua: (2024)

Plant leaf disease identification
nork: Hasan Mahin, Mohammad Rakibul, et al.
Argitaratua: (2024)

Deep learning based automated diagnosis of Ocular Toxoplasmosis in fundus images using convolutional neural network
nork: Choudhury, Prionto Kumar, et al.
Argitaratua: (2024)

Anti-aliasing for real-time applications in 3D using deep convolutional neural network
nork: Siam, F. M. Jamius, et al.
Argitaratua: (2021)

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

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

An efficient deep learning approach to detect neurodegenerative diseases using retinal images
nork: Irfanuddin, Chowdhury Mohammad, et al.
Argitaratua: (2023)

Leveraging sequential deep learning models for detecting multitude of human action categories
nork: Pranta, Kazi Al Refat, et al.
Argitaratua: (2024)

Comparison of deep transfer learning models for cancer diagnosis
nork: Joya, Nadia Islam, et al.
Argitaratua: (2023)

Cancer classification using deep learning from medical image data
nork: Monir, Raiyan Janik, et al.
Argitaratua: (2022)

Dynamic image analysis for abnormal behavior detection
nork: Ahmed, Md. Ashik, et al.
Argitaratua: (2017)

Early Schizophrenia Diagnosis with 3D Convolutional Neural Network
nork: Ashraf, S.M. Nabil, et al.
Argitaratua: (2021)

Emotion recognition from facial expression of autism spectrum disordered children using image processing and machine learning algorithms
nork: Tasnim, Nishat, et al.
Argitaratua: (2019)