151. Friendly Waste Seggregator Using Deep Transfer Learning
- Author
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V. Umme Kulsum Faiza, G. Shajedha Parveen, H. Shakira Banu, M. Samreen Taj, K. Samira Thahsin, and K. O. Mohammed Aarif
- Subjects
business.industry ,Waste production ,Computer science ,Deep learning ,Isolation (database systems) ,Artificial intelligence ,Smart bin ,Process engineering ,business ,Transfer of learning ,Convolutional neural network - Abstract
Taking a glance at the population increase in India, waste production and isolation are significant issues in the present situation. Huge amounts of blended waste are dumped without isolating it appropriately which prompts issues in decay. Because of this blended waste, a few different issues emerge over some stretch of time. To maintain a strategic distance from this, squander isolation at any rate at the fundamental level is especially required. In this paper, we have presented a friendly waste segregator using deep transfer learning. The essential thought is that when the waste is to be dumped in the trash receptacle, the system will capture the image and classify the kind of waste using a pre-trained convolutional neural network. We have different classes like plastic, paper, metal, and so on which will be further divided into bio-degradable and non-biodegradable waste.
- Published
- 2021
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