Image processing and deep learning approach to evaluate earthquake resistance of urban buildings

Image processing and deep learning approach to evaluate earthquake resistance of urban buildings

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Bibliografske podrobnosti
Main Authors:	Ahmed, Yasin, Alam, Tawsiful, Shakil, Md. Hasibur Rahman, Hossain, Md. Tanjidul
Drugi avtorji:	Alam, Dr. Md. Ashraful
Format:	Thesis
Jezik:	English
Izdano:	BRAC University 2018
Teme:	Earthquake Deep learning Image processing Machine learning Image processing > Digital techniques. Natural disasters.
Online dostop:	http://hdl.handle.net/10361/11025

Podobne knjige/članki

Transfer learning based industrial steel plates fault diagnosis using industrial fault signals
od: Mizan, Mubasshira, et al.
Izdano: (2024)

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

Deep learning-based real-time pothole detection for avoiding road accident
od: Basher, Rafsan, et al.
Izdano: (2022)

Detecting Deepfake images using deep convolutional neural network
od: Dhar, Arpita, et al.
Izdano: (2022)

Structurally and semantically coherent deep image inpainting
od: Sami, Mirza Tanzim, et al.
Izdano: (2019)

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

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

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

A study on earthquake preparedness in schools in Dhaka North city corporation
od: Ziauddin, Nafisa
Izdano: (2017)

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

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
od: Sharma, Tanmoyee, et al.
Izdano: (2021)

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

An efficient deep learning approach to detect citrus leaves disease
od: Emon, Shaharear Hossain, et al.
Izdano: (2024)

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
od: Hassan, Mehadi, et al.
Izdano: (2021)

Early stage detection and classification of colon cancer using deep learning and explainable AI on histopathological images
od: Hossain, Mainul, et al.
Izdano: (2022)

Earthquake resistant design manual /
Izdano: (2006)

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

Protovision: utilizing prototypical networks for retinal diseases classification based on few-shot learning
od: Nabil, Sheikh MD. Nafis Noor, et al.
Izdano: (2024)

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

Traumatic meningeal enhancement detection by deep learning-based biomedical image analysis and handcrafted features extraction
od: Uddin, Mohammad Sakib, et al.
Izdano: (2024)

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

Image processing based human detection and social distancing measurements with monitoring via fine tuned deep learning and computer vision
od: Datta, Anurag, et al.
Izdano: (2022)

Role of the corporate sector in earthquake disaster: a case study of Dhaka city
od: Ferdous, Mushfequa
Izdano: (2010)

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

Image recognition by deep learning
od: Mohiuddin, Karishma, et al.
Izdano: (2018)

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

Skin disease detection and classification using deep learning
od: Shuvon, Mehedi Hasan, et al.
Izdano: (2022)

Analysis of deep learning models on low-light pest detection
od: Irtiza, Md. Samin, et al.
Izdano: (2023)

An efficient deep learning approach for detecting Alzheimer’s disease using brain images
od: Sani, Mehedi Hasan, et al.
Izdano: (2022)

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

Full stack development of UBERA system
od: An Noor, Akib
Izdano: (2024)

Monitoring of endangered fish using image processing and AI tools
od: Hossain, Naheen, et al.
Izdano: (2019)

Density based traffic control system for a four way intersection
od: Chowdhury, Faizul Bari, et al.
Izdano: (2024)

Detection of common thorax diseases from X-Ray images using a fusion of transfer and statistical learning method
od: Rafi, Ahmad Abdur, et al.
Izdano: (2023)

Detection of multiple sclerosis using deep learning
od: Jannat, Sabila Al, et al.
Izdano: (2021)

Generation of realistic images from human drawn sketches using deep learning
od: Mahbub, Mohammed Julfikar Ali, et al.
Izdano: (2022)

Utilizing deep learning architectures for early detection of lung diseases in chest X-ray images
od: Ahmed, Sarder Tanvir, et al.
Izdano: (2024)

Real time dynamic facial recognition of subject at motion using angular image
od: Tasneem, Sharah, et al.
Izdano: (2024)

RetinalNet-500: a newly developed CNN model for eye disease detection
od: Toki, Sadikul Alim, et al.
Izdano: (2023)

In-depth analysis of deep learning architectures for brain tumor classification in MRI scans
od: Haque, Hossain MD. Hasibul, et al.
Izdano: (2024)