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dc.contributor.advisorRahman, Mohammad Zahidur
dc.contributor.advisorMostakim, Moin
dc.contributor.authorLeon, Yeasir Hossain
dc.contributor.authorMosharrof, Adib
dc.date.accessioned2014-02-17T06:19:05Z
dc.date.available2014-02-17T06:19:05Z
dc.date.copyright2014
dc.date.issued1/14/2014
dc.identifier.otherID 13301095
dc.identifier.otherID 13341001
dc.identifier.urihttp://hdl.handle.net/10361/2940
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 41).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.en_US
dc.description.abstractArsenic is a semi-metal element in the periodic table that is odorless and tasteless. It enters drinking water supplies from natural deposits in the earth or from agriculture and industrial practices. In South Asian countries, especially in Bangladesh, arsenic contamination is a big concern for a mass population because the main sources of drinking water are shallow and deep tube wells. This causes deadly effects to humans as it causes different types of diseases and can also lead to cancer. An NGO, Asia Arsenic Network, has performed laboratory tests on samples of arsenic contaminated water from some areas of Bangladesh, and the resulting data has been provided to us. There are 11 features in the data, and one output feature, arsenic level, which has 5 classes. Introducing Machine Learning, a branch of Artificial Intelligence, into the arsenic contamination data will help to produce a better diagnosis of this threat. Algorithms like Neural Networks and Support Vector Machines have been applied on this dataset and the performances of each algorithm has been analyzed to find out which algorithm performs best in the classification of arsenic contamination in the data set provided. Error analysis has been done using precision, recall and F1 score.en_US
dc.description.statementofresponsibilityYeasir Hossain Leon
dc.description.statementofresponsibilityAdib Mosharrof
dc.format.extent43 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectComputer science and engineering
dc.titleClassification of arsenic contamination in water using Machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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