TY  - JOUR
KW  - Internet of Things; deep learning; smart city; LoRaWAN; sanitation; healthcare
A1  - Gude, Dhanvanth Kumar
A1  - Bandari, Harshavardan
A1  - Challa, Anjani Kumar Reddy
A1  - Tasneem, Sabiha
A1  - Tasneem, Zarin
A1  - Bhattacharjee, Shyama Barna
A1  - Lalit, Mohit
A1  - López Flores, Miguel Ángel
A1  - Goyal, Nitin
IS  - 17
UR  - http://doi.org/10.3390/su16177626
AV  - public
N2  - The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model?s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system.
JF  - Sustainability
TI  - Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing
ID  - uneatlantico17896
VL  - 16
SN  - 2071-1050
Y1  - 2024/09//
ER  -