Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Ngôn ngữ: | English |
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Brac University
2022
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/16364 |
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10361-163642022-02-28T21:01:30Z Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh Biswas, Sunanda Sarkar, Spandan Islam, MD. Manazir Md. Saiful Islam Islam, Md. Saiful Rahman, Rafeed Department of Computer Science and Engineering, Brac University Deep learning Linear regression Long short term memory Multi-layer perceptron Emission factors Machine learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 44-47). Nature and technology are two di erent subject matter with having much dissension between each. Only a few years back, technological growth looked like a threat to nature. However, the bene t of having huge computational power and Machine Learning applications, computers now have the capability of visualizing the vital component of nature. By using the concept of machine learning, researchers have exhibited the limitless use of arti cial intelligence. As a part of that process, we have identi ed a speci c problem on air pollution to tackle by using machine learning that just the human brain is unable to determine. We have taken Bangladesh's harmful emission factors into account, then trained them by using several machine learning techniques like regression and deep learning to predict the emission level. In consequence, we have applied models such as Linear Regression, Long Short Term Memory and Multi- layer Perceptron and found highest 99.05% of accuracy rate also described how this research can be extended in the context of other countries in future years. Sunanda Biswas Spandan Sarkar MD. Manazir Islam B. Computer Science 2022-02-28T05:50:04Z 2022-02-28T05:50:04Z 2021 2021-10 Thesis ID 17101449 ID 17101381 ID 17101524 http://hdl.handle.net/10361/16364 en Brac University theses 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. 47 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep learning Linear regression Long short term memory Multi-layer perceptron Emission factors Machine learning Cognitive learning theory (Deep learning) |
| spellingShingle |
Deep learning Linear regression Long short term memory Multi-layer perceptron Emission factors Machine learning Cognitive learning theory (Deep learning) Biswas, Sunanda Sarkar, Spandan Islam, MD. Manazir Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Md. Saiful Islam |
| author_facet |
Md. Saiful Islam Biswas, Sunanda Sarkar, Spandan Islam, MD. Manazir |
| format |
Thesis |
| author |
Biswas, Sunanda Sarkar, Spandan Islam, MD. Manazir |
| author_sort |
Biswas, Sunanda |
| title |
Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| title_short |
Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| title_full |
Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| title_fullStr |
Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| title_full_unstemmed |
Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh |
| title_sort |
implementation of machine learning to estimate the air pollutants such as carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in bangladesh |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/16364 |
| work_keys_str_mv |
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| _version_ |
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