Abstract:Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two de… Show more
“…Machine learning algorithms are efficient at illustrating complex nonlinear processes and have been gradually adopted to improve waste management and facilitate sustainable environmental development over the past few years. A review presented in [10] summarizes the application of machine learning algorithms in the whole process, from waste generation to collection, transportation, and final disposal.…”
Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.
“…Machine learning algorithms are efficient at illustrating complex nonlinear processes and have been gradually adopted to improve waste management and facilitate sustainable environmental development over the past few years. A review presented in [10] summarizes the application of machine learning algorithms in the whole process, from waste generation to collection, transportation, and final disposal.…”
Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.
“…ML is a data-mining technique that aims to make computers update or adjust their behavior (Xia et al 2022 ; Rolnick et al 2023 ). This action might be anything from predicting an occurrence to directing a machine, such as an intelligent robot.…”
Section: Significant Applications Of ML For the Covid-19 Pandemicmentioning
confidence: 99%
“…The term “supervised” learning refers to the concept that training this sort of algorithm is similar to having a teacher oversee the entire process. (Xia et al 2022 ; Algorithms 2020 ). Fig.…”
Section: Significant Applications Of ML For the Covid-19 Pandemicmentioning
Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-022-23392-z.
“…Based on the review of literature, the recycling of waste is influenced by some significant obstacles. The key obstacles to waste recycling include the following [ 5 , 8 , 11 , 16 , 21 , 22 , 26 , 27 ] Government policies and regulations, which is inclusive of inadequate planning, regulations, and budgeting for the management of solid waste; Household level of education: the ignorance of households regarding the relevance of self-waste recycling also affects the proper disposal of waste; Technology: the absence of effective technology for recycling; and Management expense: the management of waste, mostly manual waste classification, is accompanied by the high cost. …”
Waste generation in smart cities is a critical issue, and the interim steps towards its management were not that effective. But at present, the challenge of meeting recycling requirements due to the practical difficulty involved in waste sorting decelerates smart city CE vision. In this paper, a digital model that automatically sorts the generated waste and classifies the type of waste as per the recycling requirements based on an artificial neural network (ANN) and features fusion techniques is proposed. In the proposed model, various features extracted using image processing are combined to develop a sophisticated classifier. Based on the different features, different models are built, and each model produces a single decision. Besides, the kind of class is determined using machine learning. The model is validated by extracting relevant information from the dataset containing 2400 images of possible waste types recycled across three categories. Based on the analysis, it is observed that the proposed model achieved an accuracy of 91.7%, proving its ability to sort and classify the waste as per the recycling requirements automatically. Overall, this analysis suggests that a digital-enabled CE vision could improve the waste sorting services and recycling decisions across the value chain in smart cities.
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