…
comment: The repository administrator has not yet configured an RDF license.
primary topic: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing
type: AcademicArticle
type: Article
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
volume: 16
issue: 17
is part of: Sustainability
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Texto)
has document: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Otro)
subject: Engineering

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Texto)
type: Document
has file: sustainability-16-07626-v2.pdf
has part: sustainability-16-07626-v2.pdf

Sustainability
type: Collection
issn: 20711050

Mohit Lalit
type: Person
given name: Mohit
family name: Lalit

Zarin Tasneem
type: Person
given name: Zarin
family name: Tasneem

Dhanvanth Kumar Gude
type: Person
given name: Dhanvanth Kumar
family name: Gude

Nitin Goyal
type: Person
given name: Nitin
family name: Goyal

Anjani Kumar Reddy Challa
type: Person
given name: Anjani Kumar Reddy
family name: Challa

Sabiha Tasneem
type: Person
given name: Sabiha
family name: Tasneem

Shyama Barna Bhattacharjee
type: Person
given name: Shyama Barna
family name: Bhattacharjee

Miguel Ángel López Flores
type: Person
given name: Miguel Ángel
family name: López Flores

Harshavardan Bandari
type: Person
given name: Harshavardan
family name: Bandari

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Otro)
type: Document
is version of: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Texto)
is volatile version of: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Texto)
is index codes version of: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17896 Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing
format: text/html
primary topic: Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing

Engineering
type: Concept
preferred label: Ingeniería @es