Research Output
Smart Waste Management System
Development of In-situ Sensing System and Classification of Water Quality using Machine Learning Approach
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Publication
The increasing amount of waste in landfill has created a serious environmental problem which demands a more reliable solution in handling the collection of wastes. To this date, recycling is one of the solutions to manage the waste as it collects and processes recyclable materials into new products instead of throwing the trash to the landfill. However, the consciousness of recycling in our society is still devastatingly lower than expected as people are faced with many challenges that impede them to recycle. One of the challenges is to segregate the waste according to its group. People are still having difficulty to clearly distinguish recyclable materials due to the lack of recycling knowledge. Thus, this paper aims to develop a system that can separate the waste automatically and channel them to the proper bins. To do that, a camera is used to capture the image of the waste. Then, image classification using deep learning model is used to classify different types of wastes. The developed model is then embedded in Raspberry Pi and a servo motor is used to direct the waste to the respective bins for real-world implementation. Experimental results show that the proposed system can identify the categories of waste within the accuracy of 77–85%. This system is expected to deliver the importance of recycling and cultivate recycling practices to the public and finally reduced waste generation on land.
Publication
Quality of water applied to the agriculture sector is one of the factors for agriculture farming to be successful. The use of bad quality irrigation water can cause soil problems. In general, determining water quality model is one of the many interests as it can be used to classify the conditions of water. This project focuses on developing the in-situ sensing system of water quality sensors that can detect parameters of water quality such as pH level, electric conductivity, temperature and total dissolved solid. To validate the approach, there are three types of water samples in a dataset that was collected which include water pipes, soap water and drain water. The types of machine learning models used for classification process are Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree. The performance showed that SVM model was the highest, ANN was intermediate, and Decision Tree was the lowest. This shows that the SVM model of machine learning approach is the most suitable to be used as the classification model to classify the status of water quality.