IoT based air components collection for machine learning reinforcement
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Ngôn ngữ: | English |
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Brac University
2024
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/23654 |
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10361-236542024-07-03T21:05:44Z IoT based air components collection for machine learning reinforcement Islam, Tanjima Rabbi, Fahad Ahmed, Rushana Rahman, Md Muhtashemur Ahmed, Mashrur Mukta, Jannatun Noor Department of Computer Science and Engineering, Brac University Internet-of-Things(IoT) AQI Air Quality Index Time series analysis PM2.5 Regression analysis LSTM Deep learning Prediction VNC viewer MQ sensor RTC DHT11 Arduino Internet of things Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 66-71). Air pollution has been a noteworthy threat for a long time now in the 21st century. Human lives have never faced such an obscene amount of threat from the very air it needs to breathe to stay alive. As technology evolves more and more with every passing month, year, and decade, the emissions caused by the modern utilities are increasing as well. The measurement of air quality is done through an index called “AQI” which elaborates as the Air Quality index. The proposed work revolves around the collection of air component data through an IoT device and determining the AQI periodically and creating a proper dataset for the air quality index of the city of Dhaka. The IoT device is configurable to receive sensor data periodically. MQ- 7, MQ-131, MQ-135 for air component detection, PMS5003 for particulate matter detection, DHT11 for humidity and temperature measurement and RTC DS3231 real-time clock module for timestamp has been used to make the device a complete frontrunner for a cheap data collection source. The data collection has been curated in such a way that pre-processing of datasets for certain machine learning and deep learning algorithm get much easier. All the sensors and modules are connected and worked in harmony by connecting them to a microcontroller (Arduino) and is stored and accessed remotely via an MPU (Raspberry Pi). The remote access is granted via cloud service (VNC Viewer). The acquired datasets are then ran through machine learning and deep learning layers (such as Random forest, Lasso Regression, Linear Regression, KNN, LSTM etc.) for the further prediction of the AQI. Tanjima Islam Fahad Rabbi Rushana Ahmed Md Muhtashemur Rahman Mashrur Ahmed B.Sc. in Computer Science 2024-07-03T05:39:32Z 2024-07-03T05:39:32Z 2022 2022-05 Thesis ID 18101545 ID 18101031 ID 18101507 ID 18101078 ID 18101409 http://hdl.handle.net/10361/23654 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. 71 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Internet-of-Things(IoT) AQI Air Quality Index Time series analysis PM2.5 Regression analysis LSTM Deep learning Prediction VNC viewer MQ sensor RTC DHT11 Arduino Internet of things Machine learning Cognitive learning theory |
| spellingShingle |
Internet-of-Things(IoT) AQI Air Quality Index Time series analysis PM2.5 Regression analysis LSTM Deep learning Prediction VNC viewer MQ sensor RTC DHT11 Arduino Internet of things Machine learning Cognitive learning theory Islam, Tanjima Rabbi, Fahad Ahmed, Rushana Rahman, Md Muhtashemur Ahmed, Mashrur IoT based air components collection for machine learning reinforcement |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Mukta, Jannatun Noor |
| author_facet |
Mukta, Jannatun Noor Islam, Tanjima Rabbi, Fahad Ahmed, Rushana Rahman, Md Muhtashemur Ahmed, Mashrur |
| format |
Thesis |
| author |
Islam, Tanjima Rabbi, Fahad Ahmed, Rushana Rahman, Md Muhtashemur Ahmed, Mashrur |
| author_sort |
Islam, Tanjima |
| title |
IoT based air components collection for machine learning reinforcement |
| title_short |
IoT based air components collection for machine learning reinforcement |
| title_full |
IoT based air components collection for machine learning reinforcement |
| title_fullStr |
IoT based air components collection for machine learning reinforcement |
| title_full_unstemmed |
IoT based air components collection for machine learning reinforcement |
| title_sort |
iot based air components collection for machine learning reinforcement |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23654 |
| work_keys_str_mv |
AT islamtanjima iotbasedaircomponentscollectionformachinelearningreinforcement AT rabbifahad iotbasedaircomponentscollectionformachinelearningreinforcement AT ahmedrushana iotbasedaircomponentscollectionformachinelearningreinforcement AT rahmanmdmuhtashemur iotbasedaircomponentscollectionformachinelearningreinforcement AT ahmedmashrur iotbasedaircomponentscollectionformachinelearningreinforcement |
| _version_ |
1814309818932920320 |