| dc.contributor.author | Ekaba, Brian | |
| dc.date.accessioned | 2023-02-06T12:47:54Z | |
| dc.date.available | 2023-02-06T12:47:54Z | |
| dc.date.issued | 2023-02-06 | |
| dc.identifier.citation | Ekaba, Brian. (2023). Anomaly Detection in low cost environmental sensor data using Machine learning. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/15471 | |
| dc.description | A research report submitted to the College of Engineering Design and Art in partial fulfillment of the requirement for the award of the degree Bachelor of Science Electrical Engineering of Makerere University. | en_US |
| dc.description.abstract | In 2021, a system was deployed at the makerere university weather station. This system consisted of four sensors that record temperature and humidity. These sensors are the SHT, HTC,BME and HDU. In environmental monitoring, a reliable sensor is is very crucial. Therefore, we developed an LSTM model based on the LSTM encoder-decoder architecture to detect anomalous points in the temperature and humidity data recorded by the four sensors to determine the most reliable for deployment in Uganda. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makaerere university | en_US |
| dc.subject | Anomaly Detection | en_US |
| dc.subject | Low cost environmental sensor | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Anomaly Detection in low cost environmental sensor data using Machine learning | en_US |
| dc.type | Thesis | en_US |
