Current Volume 7
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)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12