Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Meena Malik, Chander Prabha, Punit Soni, Varsha Arya, Wadee Alhalabi Alhalabi, Brij B. Gupta, Aiiad A. Albeshri, Ammar Almomani

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)19(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781668479094|DOI: 10.4018/IJSWIS.324105

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MLA

Malik, Meena, et al. "Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities." IJSWIS vol.19, no.1 2023: pp.1-20. http://doi.org/10.4018/IJSWIS.324105

APA

Malik, M., Prabha, C., Soni, P., Arya, V., Alhalabi, W. A., Gupta, B. B., Albeshri, A. A., & Almomani, A. (2023). Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities. International Journal on Semantic Web and Information Systems (IJSWIS), 19(1), 1-20. http://doi.org/10.4018/IJSWIS.324105

Chicago

Malik, Meena, et al. "Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities," International Journal on Semantic Web and Information Systems (IJSWIS) 19, no.1: 1-20. http://doi.org/10.4018/IJSWIS.324105

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Abstract

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.