Classification of arsenic contamination in water using Machine learning

Classification of arsenic contamination in water using Machine learning

Cataloged from PDF version of thesis report.

Bibliografski detalji
Glavni autori:	Leon, Yeasir Hossain, Mosharrof, Adib
Daljnji autori:	Rahman, Mohammad Zahidur
Format:	Disertacija
Jezik:	English
Izdano:	BRAC University 2014
Teme:	Computer science and engineering
Online pristup:	http://hdl.handle.net/10361/2940

Similar Items

Arsenic contamination : Bangladesh perspective /
Izdano: (2003)

Arsenic contamination of groundwater in Bangladesh /
Izdano: (2001)

Arsenic contamination of groundwater in Bangladesh /
Izdano: (2001)

Arsenic contamination of groundwater in Bangladesh /
Izdano: (2001)

Arsenic contamination of groundwater in Bangladesh /
Izdano: (2001)

Text classification using machine learning algorithms
od: Hasnat, Fahim, i dr.
Izdano: (2018)

Status of arsenic test and exposure to arsenic-contaminated tubewell water: a population-based study in Southern Bangladesh
od: Dey, Nepal C, i dr.
Izdano: (2019)

A study on development of an arsenic test kit and some arsenic contamination issues /
od: Khair, Abul
Izdano: (2003)

Lung cancer detection and classification using machine learning
od: Arefin, Mahbubul, i dr.
Izdano: (2023)

Geogenic arsenic and Microbial contamination in drinking water sources: Exposure risks to the coastal population in Bangladesh
od: Saha, Ratnajit, i dr.
Izdano: (2022)

Bangla text classification using machine learning and deep learning techniques
od: Salehin, Kamrus, i dr.
Izdano: (2021)

Using sentiment analysis & machine learning for security price forecasting
od: Roy, Jyotirmoy, i dr.
Izdano: (2016)

Heart disease prediction using techniques of classification in machine learning
od: Afrose, Sadia, i dr.
Izdano: (2021)

Interpretable Bangla fake news classification using BERT and traditional machine learning approaches
od: Anan, Ramisa, i dr.
Izdano: (2023)

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

Daily arsenic contamination associated with Gestational diabetes mellitus and neonatal outcome
od: Sinthi, Nudrat Ansari
Izdano: (2016)

Machine learning engineering /
od: Burkov, Andriy
Izdano: (2020)

Comparative study of toxic comments classification using machine learning algorithms
od: Razzak, Razia, i dr.
Izdano: (2021)

Learning computer programming using e-learning as a tool
od: Alam, Asharf, i dr.
Izdano: (2010)

Gene classification and pattern analysis using data mining and machine learning techniques
od: Ramisa, Aiman Jabeen, i dr.
Izdano: (2021)

Sound classification using deep learning for hard of hearing and deaf people
od: Habib, Md.Adnan, i dr.
Izdano: (2022)

Performance analysis of machine learning algorithms for Malware classification
od: Bushra, Raisa Hasan, i dr.
Izdano: (2023)

The use of alternative safe water option to mitigate the arsenic problem in Bangladesh : community perspective /
od: Jakariya, Md
Izdano: (2003)

The use of alternative safe water option to mitigate the arsenic problem in Bangladesh : community perspective /
od: Jakariya, Md
Izdano: (2003)

Using machine learning on a diverse class of problems : from rainfall to criminal actions
od: Munira, Sirajum, i dr.
Izdano: (2016)

A Review of groundwater arsenic contamination in Bangladesh: the millennium development goal era and beyond
od: Md. Yunus, Fakir, i dr.
Izdano: (2016)

A study of malware classification using deep learning
od: Rahman, Mohammad Muhibur, i dr.
Izdano: (2024)

Analysis and manipulation of the data set obtained from the study of pre-primary education development using machine learning
od: Khan, Sushmita Laila, i dr.
Izdano: (2014)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
od: Tasnim, Anika, i dr.
Izdano: (2022)

Depression classification with MDD (Major Depressive Disorder) using signal processing and machine learning
od: Saleque, Shahriar, i dr.
Izdano: (2021)

Arsenic in tubewell water and health consequences: mitigation component
od: Jakariya, Md.
Izdano: (2019)

Awareness and choice of options of Arsenic-free drinking water
od: Hadi, Abdullahel
Izdano: (2019)

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

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

Foundations of machine learning /
od: Mohri, Mehryar, i dr.
Izdano: (2018)

Remote monitoring and detection of water contamination in effluent treatment plants
od: Hossain, Akib, i dr.
Izdano: (2024)

An analytical assessment of GPL kit for field level detection of arsenic in water
od: Hossain, Md. Zabed
Izdano: (2020)

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

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

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