An internet of things based for smart recycle waste classification / Akmal Md Nasir

Innovative solutions utilising modern technologies have been created in response to the growing global concern for effective waste management and recycling practises. This thesis introduces a smart recycle waste categorization system built on the Internet of Things (IoT) with the goal of automating...

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Main Author: Md Nasir, Akmal
Format: Thesis
Language:English
Published: 2023
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/88978/1/88978.pdf
https://ir.uitm.edu.my/id/eprint/88978/
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spelling my.uitm.ir.889782024-03-19T07:07:21Z https://ir.uitm.edu.my/id/eprint/88978/ An internet of things based for smart recycle waste classification / Akmal Md Nasir Md Nasir, Akmal TD Environmental technology. Sanitary engineering Innovative solutions utilising modern technologies have been created in response to the growing global concern for effective waste management and recycling practises. This thesis introduces a smart recycle waste categorization system built on the Internet of Things (IoT) with the goal of automating and improving waste sorting procedures. To accurately categorise waste into the categories of metal, paper, plastic, and maybe other waste types, the system uses an image classification model based on the ResNet algorithm. A large dataset that included a range of waste images was aquired in order to train the classifiction model. With a high accuracy rate for waste classification, the ResNet algorithm proved to be effective. Real-time monitoring of the smart bin's capacity is made possible by the integration of IoT capabilities, and users of the Blynk app receive notifications through email as a result. The suggested system provides a dependable, automated, and flexible alternative to the drawbacks of traditional waste sorting techniques. The approach improves waste sorting accuracy by extending the classification model to incorporate additional waste types and adding a "reject" class for unclassifiable or irrelevant images. As a result, recycling procedures are enhanced, and sustainable waste management is promoted. The results of this study show how the Internet of Things and image classification algorithms have the potential to rerevolutionizearbage classification and management. An effective way to streamline waste sorting procedures and enable effective recycling techniques is to deploy the smart recycle waste classification system. This technology is expected to significantly influence waste management initiatives, fostering sustainability and environmental protection. 2023 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/88978/1/88978.pdf An internet of things based for smart recycle waste classification / Akmal Md Nasir. (2023) Degree thesis, thesis, Universiti Teknologi MARA, Melaka. <http://terminalib.uitm.edu.my/88978.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic TD Environmental technology. Sanitary engineering
spellingShingle TD Environmental technology. Sanitary engineering
Md Nasir, Akmal
An internet of things based for smart recycle waste classification / Akmal Md Nasir
description Innovative solutions utilising modern technologies have been created in response to the growing global concern for effective waste management and recycling practises. This thesis introduces a smart recycle waste categorization system built on the Internet of Things (IoT) with the goal of automating and improving waste sorting procedures. To accurately categorise waste into the categories of metal, paper, plastic, and maybe other waste types, the system uses an image classification model based on the ResNet algorithm. A large dataset that included a range of waste images was aquired in order to train the classifiction model. With a high accuracy rate for waste classification, the ResNet algorithm proved to be effective. Real-time monitoring of the smart bin's capacity is made possible by the integration of IoT capabilities, and users of the Blynk app receive notifications through email as a result. The suggested system provides a dependable, automated, and flexible alternative to the drawbacks of traditional waste sorting techniques. The approach improves waste sorting accuracy by extending the classification model to incorporate additional waste types and adding a "reject" class for unclassifiable or irrelevant images. As a result, recycling procedures are enhanced, and sustainable waste management is promoted. The results of this study show how the Internet of Things and image classification algorithms have the potential to rerevolutionizearbage classification and management. An effective way to streamline waste sorting procedures and enable effective recycling techniques is to deploy the smart recycle waste classification system. This technology is expected to significantly influence waste management initiatives, fostering sustainability and environmental protection.
format Thesis
author Md Nasir, Akmal
author_facet Md Nasir, Akmal
author_sort Md Nasir, Akmal
title An internet of things based for smart recycle waste classification / Akmal Md Nasir
title_short An internet of things based for smart recycle waste classification / Akmal Md Nasir
title_full An internet of things based for smart recycle waste classification / Akmal Md Nasir
title_fullStr An internet of things based for smart recycle waste classification / Akmal Md Nasir
title_full_unstemmed An internet of things based for smart recycle waste classification / Akmal Md Nasir
title_sort internet of things based for smart recycle waste classification / akmal md nasir
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/88978/1/88978.pdf
https://ir.uitm.edu.my/id/eprint/88978/
_version_ 1794641268229275648
score 13.201949