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.

Xehetasun bibliografikoak
Egile Nagusiak:	Leon, Yeasir Hossain, Mosharrof, Adib
Beste egile batzuk:	Rahman, Mohammad Zahidur
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	BRAC University 2014
Gaiak:	Computer science and engineering
Sarrera elektronikoa:	http://hdl.handle.net/10361/2940

Antzeko izenburuak

Arsenic contamination : Bangladesh perspective /
Argitaratua: (2003)

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

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

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

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

Text classification using machine learning algorithms
nork: Hasnat, Fahim, et al.
Argitaratua: (2018)

Status of arsenic test and exposure to arsenic-contaminated tubewell water: a population-based study in Southern Bangladesh
nork: Dey, Nepal C, et al.
Argitaratua: (2019)

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

Lung cancer detection and classification using machine learning
nork: Arefin, Mahbubul, et al.
Argitaratua: (2023)

Geogenic arsenic and Microbial contamination in drinking water sources: Exposure risks to the coastal population in Bangladesh
nork: Saha, Ratnajit, et al.
Argitaratua: (2022)

Bangla text classification using machine learning and deep learning techniques
nork: Salehin, Kamrus, et al.
Argitaratua: (2021)

Using sentiment analysis & machine learning for security price forecasting
nork: Roy, Jyotirmoy, et al.
Argitaratua: (2016)

Heart disease prediction using techniques of classification in machine learning
nork: Afrose, Sadia, et al.
Argitaratua: (2021)

Interpretable Bangla fake news classification using BERT and traditional machine learning approaches
nork: Anan, Ramisa, et al.
Argitaratua: (2023)

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

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

Machine learning engineering /
nork: Burkov, Andriy
Argitaratua: (2020)

Comparative study of toxic comments classification using machine learning algorithms
nork: Razzak, Razia, et al.
Argitaratua: (2021)

Learning computer programming using e-learning as a tool
nork: Alam, Asharf, et al.
Argitaratua: (2010)

Gene classification and pattern analysis using data mining and machine learning techniques
nork: Ramisa, Aiman Jabeen, et al.
Argitaratua: (2021)

Sound classification using deep learning for hard of hearing and deaf people
nork: Habib, Md.Adnan, et al.
Argitaratua: (2022)

Performance analysis of machine learning algorithms for Malware classification
nork: Bushra, Raisa Hasan, et al.
Argitaratua: (2023)

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

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

Using machine learning on a diverse class of problems : from rainfall to criminal actions
nork: Munira, Sirajum, et al.
Argitaratua: (2016)

A Review of groundwater arsenic contamination in Bangladesh: the millennium development goal era and beyond
nork: Md. Yunus, Fakir, et al.
Argitaratua: (2016)

A study of malware classification using deep learning
nork: Rahman, Mohammad Muhibur, et al.
Argitaratua: (2024)

Analysis and manipulation of the data set obtained from the study of pre-primary education development using machine learning
nork: Khan, Sushmita Laila, et al.
Argitaratua: (2014)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
nork: Tasnim, Anika, et al.
Argitaratua: (2022)

Depression classification with MDD (Major Depressive Disorder) using signal processing and machine learning
nork: Saleque, Shahriar, et al.
Argitaratua: (2021)

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

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

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

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

Foundations of machine learning /
nork: Mohri, Mehryar, et al.
Argitaratua: (2018)

Remote monitoring and detection of water contamination in effluent treatment plants
nork: Hossain, Akib, et al.
Argitaratua: (2024)

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

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

Bang-lish sentiment classification using deep learning
nork: Saleheen, Abrar, et al.
Argitaratua: (2023)

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