Classification of arsenic contamination in water using Machine learning
Cataloged from PDF version of thesis report.
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BRAC University
2014
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| On-line přístup: | http://hdl.handle.net/10361/2940 |
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10361-29402022-01-26T10:05:00Z Classification of arsenic contamination in water using Machine learning Leon, Yeasir Hossain Mosharrof, Adib Rahman, Mohammad Zahidur Mostakim, Moin Department of Computer Science and Engineering, BRAC University Computer science and engineering Cataloged from PDF version of thesis report. Includes bibliographical references (page 41). This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. Arsenic 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. Yeasir Hossain Leon Adib Mosharrof B. Computer Science and Engineering 2014-02-17T06:19:05Z 2014-02-17T06:19:05Z 2014 1/14/2014 Thesis ID 13301095 ID 13341001 http://hdl.handle.net/10361/2940 en BRAC 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. 43 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Computer science and engineering |
| spellingShingle |
Computer science and engineering Leon, Yeasir Hossain Mosharrof, Adib Classification of arsenic contamination in water using Machine learning |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Rahman, Mohammad Zahidur |
| author_facet |
Rahman, Mohammad Zahidur Leon, Yeasir Hossain Mosharrof, Adib |
| format |
Thesis |
| author |
Leon, Yeasir Hossain Mosharrof, Adib |
| author_sort |
Leon, Yeasir Hossain |
| title |
Classification of arsenic contamination in water using Machine learning |
| title_short |
Classification of arsenic contamination in water using Machine learning |
| title_full |
Classification of arsenic contamination in water using Machine learning |
| title_fullStr |
Classification of arsenic contamination in water using Machine learning |
| title_full_unstemmed |
Classification of arsenic contamination in water using Machine learning |
| title_sort |
classification of arsenic contamination in water using machine learning |
| publisher |
BRAC University |
| publishDate |
2014 |
| url |
http://hdl.handle.net/10361/2940 |
| work_keys_str_mv |
AT leonyeasirhossain classificationofarseniccontaminationinwaterusingmachinelearning AT mosharrofadib classificationofarseniccontaminationinwaterusingmachinelearning |
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1814307162292224000 |