Paper Title :IOT Based Intelligent Garbage Segregation and Recycling System Using Deep Learning Algorithm

Author :Menaga B, V. Vijayalakshmi

Article Citation :Menaga B ,V. Vijayalakshmi , (2022 ) " IOT Based Intelligent Garbage Segregation and Recycling System Using Deep Learning Algorithm " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 62-70, Volume-10,Issue-8

Abstract : Abstract - Explosive growth in the population has imposed a great hurdle in the Management, Recycling and Disposal of Garbage. People are more prone to infectious diseases. Also, the entire world is facing newer types of health hazards. Existing techniques still have a lot of limitations in management and segregation of waste in an effective manner. For alleviating the process of garbage disposal and to keep up the cleanliness it is mandatory to have a Smart Garbage Managing System. Also, newer techniques of segregation and recycling of waste in precise manner is the need of the hour. This project proposes a deep learning algorithm which is implemented with back propagation algorithm for segregation of waste and its management. Waste in the dustbin is detected and segregated by CNN Classification. IoT enabled environment along with Deep Learning Algorithm is used to detect and segregate the Garbage in the dustbins with the Sensor devices. IR sensor is used to move the conveyor belt, by the nearness of the waste. The moisture sensor is used to segregate the organic waste segregatedby the moisture content present in the waste separately in the bin. Metal sensor is used to segregate the metallicwaste separately. Data collected by each smart truck is shared with the nearby Industries for the disposal of the Garbage. Deep Learning Algorithm is also used to recognize the image to separate recyclable waste. This project involves simulation of proposed Deep learning algorithm with backpropagation technique for segregation of waste such as paper box, glass and plastic. Hence the advantages of the proposed algorithm include improved computational efficiency, stable error gradient with accurate and fast learning. It isimplemented and analyzed with efficient performance metrics such as accuracy, sensitivity and specificity. When simulated it was inferred that the proposed algorithm outperformed the drawbacks of the existingalgorithm. Keywords - Deep Learning, Internet of Things (IoT), Convolutional Neural Networks (CNN).

Type : Research paper

Published : Volume-10,Issue-8

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