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Smart city article details

Title	Machine Learning And Deep Learning-Based Smart City Infrastructure To Connect Intelligent Domain Using Internet Of Things
ID_Doc	35887
Authors	Das S.; Sarkar S.; Dutta S.; Ghosh S.; Dhar S.; Pradhan B.; Sahana S.
Year	2023
Published	Lecture Notes in Electrical Engineering, 1046 LNEE
DOI	http://dx.doi.org/10.1007/978-981-99-2710-4_23
Abstract	Recycling and landfilling are steps in waste management that destroy trash. The Internet of Things (IoT) and the different machine learning and deep learning approaches presented in the article work together to provide an agile solution for real-time data monitoring and categorization. This study seeks to close the gap between ML and DL algorithms in innovative waste management in smart cities. The traditional trash management system may be replaced with intelligent sensors incorporated into the system to perform real-time monitoring and improve waste management thanks to the advent of the Internet of Things (IoT) and artificial intelligence (AI). ML algorithms, including logistic regression, support vector machine, random forest, and DL algorithms like Yolov3 and CNN, were mentioned in this work. Six object classes have been trained into the Yolov3 network (namely: cardboard, glass, metal, paper, plastic, and organic waste). The convolutional neural network (CNN) model, a popular deep learning approach, provides a better technique to separate biological from non-biological waste. Real-time trash monitoring will enable this intelligent system to be adjusted to domestic activities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Convolutional neural network; Internet of things (IoT); Smart waste management system; Yolov3 algorithm

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