Carmel Abigail Clement Loo (2022) Garbage detection system with garbage level prediction using machine learning in Internet of Things (IoT). Universiti Malaysia Sabah. (Unpublished)
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GARBAGE DETECTION SYSTEM WITH GARBAGE LEVEL PREDICTION USING MACHINE LEARNING IN INTERNET OF THINGS (IOT).24pages.pdf Download (608kB) |
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GARBAGE DETECTION SYSTEM WITH GARBAGE LEVEL PREDICTION USING MACHINE LEARNING IN INTERNET OF THINGS (IOT).pdf Restricted to Registered users only Download (3MB) |
Abstract
There has been a rise in the development of waste in recent years, particularly in university hostels, where there are limited number of bins and shared among all the students. Due to the spill over of waste in the hostel area, the contaminated condition may trigger various serious diseases in the surroundings. This project proposes a Garbage Detection System with Garbage Level Prediction using Machine Learning in Internet of Things (IoT) where the system would measure the current level of waste in all garbage bins available around the area and notify the hostel management to collect the waste whenever the bin is loaded. The machine learning model will be required to learn and predict the waste that will be produced in the future. The methodology that will be used in this project is iterative and incremental model. Through this project, manual monitoring will not be needed anymore since this project will be able to send push notifications indicating that the garbage is almost full and predict the current waste level based on current day and time.
Item Type: | Academic Exercise |
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Keyword: | Waste , Garbage detection system , Garbage level prediction , Machine learning , Internet of Things , IoT |
Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software |
Department: | FACULTY > Faculty of Computing and Informatics |
Depositing User: | DG MASNIAH AHMAD - |
Date Deposited: | 18 Jul 2022 12:21 |
Last Modified: | 18 Jul 2022 12:21 |
URI: | https://eprints.ums.edu.my/id/eprint/33264 |
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