Smart City Gnosys
Smart city article details
| Title | Deep Learning - Based Hybrid Image Classification Model For Solid Waste Management |
|---|---|
| ID_Doc | 17800 |
| Authors | Murugan P.R.; Muneeswaran V.; Sri Vardhan N.V.; Venkatesh B.; Kumar P.P.; Amrutha Varshini G. |
| Year | 2024 |
| Published | 2024 International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024 |
| DOI | http://dx.doi.org/10.1109/ICC-ROBINS60238.2024.10534019 |
| Abstract | Waste generation, categorization, and administration have become essential because of the growing population and the rise of smart city projects. In recent years, Solid Waste Management (SWM) has drawn greater interest in intelligent and environmentally friendly growth, particularly in the developing nations. The SWM method consists of multiple interrelated procedures that perform various intricate tasks. Deep Learning (DL) has gained traction recently as a means of offering substitute computational methods for figuring out how to solve different SWM difficulties. Plenty of research has already been reported on this subject due to research attention, particularly in the previous ten years. According to the literature, every investigation assesses DL's capability to address the different SWM issues. To help administrators organize waste management more effectively and its various processes - including gathering, separating, recycling, and disposal, DL techniques are being used extensively. Finding the most effective deep learning method for waste forecasting is difficult, though. To break the big problem of dealing with waste, the proposed method in this research brings together a new idea using Convolutional Neural Networks (CNNs) along with an easy-to-use HTML and Django setup. With this smart idea, Individuals can sort their waste into different categories, and give important information about whether it breaks down naturally or needs other ways to get rid of it, like recovering or throwing it away duly. By mixing clever computer programs with a simple design that anyone can use, the proposed system helps people figure out exactly what kind of waste they have. This helps them make better choices about how to get rid of their waste in a way that is good for the terrain. © 2024 IEEE. |
| Author Keywords | Biodegradability; Classification; Convolutional Neural Network (CNN); Deep Learning (DL); Recycling; Solid Waste Management (SWM) |
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