Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/1630 2019 2019 Ministry of communication and information technology, Egypt Ahmed Sleem Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt Ibrahim Elhenawy This paper proposes a deep learning-based intelligent waste management system that can accurately classify waste types and optimize waste disposal processes. The proposed system utilizes a convolutional model to concisely identify the waste type from images captured by a camera system. Our system uses intelligent data augmentation to perform large datasets of waste item images and achieves a high classification accuracy rate. The waste types are classified into several categories, including glass, cardboard, metal, plastic, paper, and trash. Experimental results show that our system achieves high accuracy rates in waste classification and improves waste disposal efficiency compared to traditional waste management systems. Our system has the potential to significantly reduce the negative impact of waste on the environment and to promote sustainable waste management practices. 2020 2020 102 108 10.54216/JISIoT.010205 https://www.americaspg.com/articleinfo/18/show/1630