Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities Article Swipe
Meena Malik
,
Chander Prabha
,
Punit Soni
,
Varsha Arya
,
Wadee Alhalabi
,
Brij B. Gupta
,
Aiiad Albeshri
,
Ammar Almomani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4018/ijswis.324105
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4018/ijswis.324105
Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.
Related Topics
Concepts
Computer science
Smart city
Transfer of learning
Artificial intelligence
Convolutional neural network
Machine learning
Artificial neural network
Task (project management)
Plan (archaeology)
Deep learning
Data science
World Wide Web
Systems engineering
Engineering
Internet of Things
History
Archaeology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijswis.324105
- https://www.igi-global.com/ViewTitle.aspx?TitleId=324105&isxn=9781668479094
- OA Status
- diamond
- Cited By
- 31
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380082979
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380082979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4018/ijswis.324105Digital Object Identifier
- Title
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Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart CitiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-09Full publication date if available
- Authors
-
Meena Malik, Chander Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi, Brij B. Gupta, Aiiad Albeshri, Ammar AlmomaniList of authors in order
- Landing page
-
https://doi.org/10.4018/ijswis.324105Publisher landing page
- PDF URL
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https://www.igi-global.com/ViewTitle.aspx?TitleId=324105&isxn=9781668479094Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://www.igi-global.com/ViewTitle.aspx?TitleId=324105&isxn=9781668479094Direct OA link when available
- Concepts
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Computer science, Smart city, Transfer of learning, Artificial intelligence, Convolutional neural network, Machine learning, Artificial neural network, Task (project management), Plan (archaeology), Deep learning, Data science, World Wide Web, Systems engineering, Engineering, Internet of Things, History, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 17, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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