Manuscript Title:

AN INTELLIGENT ENVIRNMENT MONITORING SYSTEM USING DECISION TREE MODEL

Author:

SOFIA TAHIR, RAFAQAT KAZMI, AQSA, MUHAMMAD MURAD KHAN, ALI SAMAD TAUNI, SUNNIA IKRAM, AMNA IKRAM

DOI Number:

DOI:10.17605/OSF.IO/HP4KY

Published : 2023-03-23

About the author(s)

1. SOFIA TAHIR - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
2. RAFAQAT KAZMI - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
3. AQSA - Department of Software Engineering, The Islamia University of Bahawalpur, Pakistan.
4. MUHAMMAD MURAD KHAN - Government College University Faisalabad.
5. ALI SAMAD TAUNI - Department of Data Science. The Islamia University of Bahawalpur.
6. SUNNIA IKRAM - Department of Software Engineering, The Islamia University of Bahawalpur.
7. AMNA IKRAM - Department of Computer Science & IT, Government Sadiq College Women University, Bahawalpur, Pakistan.

Full Text : PDF

Abstract

We are living in 21st century and the world is progressing day by day. For progress the environment should be healthy to seek healthy life. With this progress, we are facing some issues. One of the major issue which is ubiquitous problem, it’s about hygiene. In olden times we had to regularly check bins ourselves and empty filled, spill over and stinking bins. These conditions mess up the environment and spread different kind of diseases. Which infect different age groups of people. Garbage monitoring system is required to keep the environment clean and mitigate waste. The paper intends to build an intelligent smart dustbin through which we can do monitoring of trash by using sensors and provide the details about different factor present in trash such as garbage level in trash, germs productions (which are harmful for health), gasses (which causes stinking and germs production), humidity level in trash. In this research we proposed an effective smart bin solution integrated with decision tree model using Rapid miner. This system ensures the proper garbage monitoring and keeps the environment clean. The system shows the accuracy level of 89.47%.


Keywords

garbage pile, IOT, machine learning, decision tree, sensor and smart.