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Syed Matheen Pasha, Shanta Kumar Patil, N. Ashok Kumar, G. Likeeth, Hemanth Kumar S, Karthik Reddy
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- Professor, Professor, Student, Student, Student, Student Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore, Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore, Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore, Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore, Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore, Department of Computer Science and Engineering, Sai Vidya Institute of Technology, Bangalore Karnataka, Karnataka, Karnataka, Karnataka, Karnataka, Karnataka India, India, India, India, India, India
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Abstract
nAny physical object that can be identified by an IP address to permit data transmission over a network can become a part of the Internet of Things by installing electronic gear like sensors, networking hardware, and software. Improved connectivity for a variety of devices, services, protocols, and applications is provided via the Internet of Things (IoT). Its heterogeneous character further defines it. IoT has proven effective not just in homes and smart cities. In Urban regions produce a lot of solid rubbish, which is composed of many materials like paper, plastic, metal, glass, and organic waste. For waste management to be effective, these products must be treated separately. To solve this problem, governments have passed laws mandating the classification of waste into dry and wet categories. Following these recommendations not only helps with efficient waste management but also drastically lowers the cost of trash segregation, freeing up money for further waste treatment expenditures. This study suggests a way to classify waste as dry or moist only by looking at pictures of it. The proposed application allows local government bodies to upload images of trash cans for inspection, which expedites the process. Machine learning techniques are employed to detect waste, potentially facilitating future examination of waste disposal methods in different locations. This study can help guide targeted awareness initiatives to improve waste disposal methods.
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Keywords: Waste analysis, machine learning, artificial intelligence, internet of things (IoT), IP addresses.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Alternate Energy Sources & Technologies(joaest)]
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References
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Journal of Alternate Energy Sources & Technologies
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| Volume | 15 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 9, 2024 | |
| Accepted | May 18, 2024 | |
| Published | May 28, 2024 |
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