Abstract
Efficient waste sorting plays a pivotal role in recycling processes, yet it often involves laborious and error-prone procedures. To expedite this critical stage, one can suggest a unique waste management paradigm that actively employs convolutional neural networks (CNNs). The approach we employ makes use of a CNN that has already been taught to reliably classify waste materials despite contamination or mixing. By training CNN on a comprehensive dataset of labeled waste images, we empower it to discern between diverse materials with heightened precision. The system improves the quality of recycled materials, reduces the need for manual intervention, and increases sorting accuracy.
Keywords
Waste Management, Waste Sorting, Convolutional Neural Networks (CNNs), Image Classification, Resource Recovery
Citations
IRE Journals:
Bhurneni Sai Purushotham
"Detecting Waste Through Multi-Layered Networking: A Novel Approach" Iconic Research And Engineering Journals Volume 7 Issue 10 2024 Page 97-100
IEEE:
Bhurneni Sai Purushotham
"Detecting Waste Through Multi-Layered Networking: A Novel Approach" Iconic Research And Engineering Journals, 7(10)
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