AIMC Topic: Waste Management

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Investigating the factors affecting the intention to separate e-waste among mobile phone repairers in an emerging economy: A hybrid structural equation modelling and artificial neural network approach.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The growing number of mobile phone users on a global scale has led to enormous amounts of electronic waste (e-waste) being generated annually. Insufficient knowledge of e-waste separation causes individuals to dispose of e-waste along with other wast...

Predicting and investigating water quality index by robust machine learning methods.

Journal of environmental management
This study addresses the critical challenges of waste management and water quality in urban environments, where accelerated urbanization has exacerbated environmental degradation and public health risks. Employing advanced machine learning algorithms...

Smart waste management and air pollution forecasting: Harnessing Internet of things and fully Elman neural network.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
As the Internet of things (IoT) continues to transform modern technologies, innovative applications in waste management and air pollution monitoring are becoming critical for sustainable development. In this manuscript, a novel smart waste management...

Reducing food waste in the HORECA sector using AI-based waste-tracking devices.

Waste management (New York, N.Y.)
This study assesses the effectiveness of an intervention employing an AI-based, fully automatic waste-tracking system for food waste reduction in HORECA establishments. Waste-tracking devices were installed in a restaurant within a holiday resort and...

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

Environmental monitoring and assessment
The global consequences of electronic waste significantly affect the environment and human health. Accurate classification is essential for effective recycling and management to mitigate serious environmental harm caused by improper disposal. However...

Classification and predictive leaching risk assessment of construction and demolition waste using multivariate statistical and machine learning analyses.

Waste management (New York, N.Y.)
Managing construction and demolition waste (CDW) poses serious concerns regarding landfilling and recycling because of the potential release of hazardous elements after leaching. Ceramic materials such as bricks, tiles, and porcelain account for more...

Trustworthy and Human Centric neural network approaches for prediction of landfill methane emission and sustainable waste management practices.

Waste management (New York, N.Y.)
Landfills rank third among the anthropogenic sources of methane gas in the atmosphere, hence there is a need for greater emphasis on the quantification of landfill methane emission for mitigating environmental degradation. However, the estimation and...

Prototype of AI-powered assistance system for digitalisation of manual waste sorting.

Waste management (New York, N.Y.)
Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through inte...

Enhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.

Environmental monitoring and assessment
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet,...

A systematic review of plastic recycling: technology, environmental impact and economic evaluation.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
In this systematic review, advancements in plastic recycling technologies, including mechanical, thermolysis, chemical and biological methods, are examined. Comparisons among recycling technologies have identified current research trends, including a...