A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks

A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.

Bibliografske podrobnosti
Main Authors:	Mahmud, Shakib Izaz, Shovon, Sartaz Islam, Hasnat, Md. Abrar, Na s, Md. Fahim
Drugi avtorji:	Alam, Md. Ashraful
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2022
Teme:	Auto Encoded Techniques Reflection Co efficient Neural networks Deep learning KNN ANN Random forest Logistic regression Naive bayes Decision tree Digital Illuminance meter Cognitive learning theory (Deep learning) Neural networks (Computer science)
Online dostop:	http://hdl.handle.net/10361/17125

Podobne knjige/članki

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

Fighting depression: psychological approaches among Bangladeshi university students
od: Sen, Sanjib Kumar, et al.
Izdano: (2022)

An autoencoder-based decentralized clustering leveraging model aggregation fusion strategy
od: Hasan, Nahid
Izdano: (2024)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
od: Alam, Auninda, et al.
Izdano: (2021)

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

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

Analysis of real-time hostile activitiy detection from spatiotemporal features using time distributed deep convolutional neural networks, recurrent neural networks and attention-based mechanisms
od: Siddique, Labib Ahmed, et al.
Izdano: (2022)

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

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

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

Weather forecasting using Deep Learning
od: Fahad, Sheikh Abdul, et al.
Izdano: (2023)

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

Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification
od: Datta, Nirjhor, 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)

Analysis of uncertainty in different neural network structures using monte carlo dropout
od: Islam, Md. Farhadul, et al.
Izdano: (2024)

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

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

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

Early Schizophrenia Diagnosis with 3D Convolutional Neural Network
od: Ashraf, S.M. Nabil, 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)

Analysis of uncertainty in different neural network structures using Monte Carlo Dropout
od: Islam, Md. Farhadul, et al.
Izdano: (2024)

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

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

Visual object recognition using deep convolutional neural network
od: Ahmed, Sabbir
Izdano: (2017)

Diabetic retinopathy detection and classification by using deep learning
od: Hossain, Shahriar, et al.
Izdano: (2022)

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

Reflective teaching, reflective learning : how to develop critically engaged readers, writers, and speakers /
Izdano: (2005)

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

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

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

BanglaBait: using transformers, neural networks & statistical classifiers to detect clickbaits in New Bangla Clickbait Dataset
od: Mahtab, Motahar, et al.
Izdano: (2022)

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

A color vision approach based on the autoencoder technique and deep neural networks for reconstructing color images under various lighting conditions
od: Faisal, Asm, et al.
Izdano: (2024)

Bangali Handwritten characters classification using Deep Convolutional Neural Network
od: Sikder, Shihab Uddin, et al.
Izdano: (2022)

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

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

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

Identification of childhood leukemia using deep learning
od: Tultul, Farana Naz
Izdano: (2018)

Pothole detection using lightweight network models
od: Abdullah, S. M., et al.
Izdano: (2024)