eprintid: 17896 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/96 datestamp: 2025-12-15 23:30:12 lastmod: 2025-12-15 23:30:13 status_changed: 2025-12-15 23:30:12 type: article metadata_visibility: show creators_name: Gude, Dhanvanth Kumar creators_name: Bandari, Harshavardan creators_name: Challa, Anjani Kumar Reddy creators_name: Tasneem, Sabiha creators_name: Tasneem, Zarin creators_name: Bhattacharjee, Shyama Barna creators_name: Lalit, Mohit creators_name: López Flores, Miguel Ángel creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: miguelangel.lopez@uneatlantico.es creators_id: title: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: Internet of Things; deep learning; smart city; LoRaWAN; sanitation; healthcare abstract: 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. date: 2024-09 publication: Sustainability volume: 16 number: 17 pagerange: 7626 id_number: doi:10.3390/su16177626 refereed: TRUE issn: 2071-1050 official_url: http://doi.org/10.3390/su16177626 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés 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. metadata Gude, Dhanvanth Kumar; Bandari, Harshavardan; Challa, Anjani Kumar Reddy; Tasneem, Sabiha; Tasneem, Zarin; Bhattacharjee, Shyama Barna; Lalit, Mohit; López Flores, Miguel Ángel y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, miguelangel.lopez@uneatlantico.es, SIN ESPECIFICAR (2024) Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing. Sustainability, 16 (17). p. 7626. ISSN 2071-1050 document_url: http://repositorio.uneatlantico.es/id/eprint/17896/1/sustainability-16-07626-v2.pdf