AIMC Topic: Waste Management

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Lightweight deep learning model for underwater waste segmentation based on sonar images.

Waste management (New York, N.Y.) Sep 14, 2024
In recent years, the rapid accumulation of marine waste not only endangers the ecological environment but also causes seawater pollution. Traditional manual salvage methods often have low efficiency and pose safety risks to human operators, making au...
Deep Learning Recycling Waste Management Neural Networks, Computer
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Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line.

Waste management (New York, N.Y.) Aug 30, 2024
Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB...
Image Processing, Computer-Assisted Copper Machine Learning Neural Networks, Computer Waste Management Algorithms Metallurgy
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Exploring artificial intelligence role in improving service building engagement in sorting.

Waste management (New York, N.Y.) Aug 22, 2024
Waste management researchers have identified that the correct disposal of solid waste is better addressed upstream, where people properly sort their solid waste. Sorting solid waste is a practice that requires a behaviour friendly to sorting and will...
Solid Waste Refuse Disposal Humans Waste Management Artificial Intelligence
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MSW-Net: A hierarchical stacking model for automated municipal solid waste classification.

Journal of the Air & Waste Management Association (1995) Jul 15, 2024
Efficient solid waste management is crucial for urban health and welfare in the midst of fast industrialization and urbanization. In this changing environment, government authorities have a significant role in addressing and reducing the effects of s...
Neural Networks, Computer Cities Solid Waste Waste Management Models, Theoretical Bayes Theorem Refuse Disposal
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Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Waste management (New York, N.Y.) May 30, 2024
Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes...
Wood Heating Waste Products Sewage Refuse Disposal Neural Networks, Computer Manure Animals Solid Waste Waste Management
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Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods.

Journal of environmental management May 25, 2024
In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensiv...
Machine Learning China Refuse Disposal Solid Waste Waste Management
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Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification.

Waste management (New York, N.Y.) May 11, 2024
This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environme...
Waste Management Refuse Disposal Cities Solid Waste Neural Networks, Computer Artificial Intelligence Algorithms
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Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Environmental monitoring and assessment May 10, 2024
Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have bec...
Geographic Information Systems Environmental Monitoring Waste Disposal Facilities Morocco Waste Management Fuzzy Logic Refuse Disposal Solid Waste
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Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA May 9, 2024
Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapi...
Electronic Waste Waste Management Alloys Spectrum Analysis Lasers Aluminum Principal Component Analysis Machine Learning
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Agro-industrial waste management employing benefits of artificial intelligence.

Environmental science and pollution research international May 7, 2024
By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-indus...
Recycling Waste Management Artificial Intelligence Agriculture Industrial Waste
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