AI Medical Compendium Journal:
Waste management (New York, N.Y.)

Showing 41 to 50 of 68 articles

An intelligent identification and classification system of decoration waste based on deep learning model.

Waste management (New York, N.Y.)
Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional me...

Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.

Waste management (New York, N.Y.)
Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of...

Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling.

Waste management (New York, N.Y.)
Waste recycling is a critical issue for environment pollution management while garbage classification determines the recycling efficiency. In order to reduce labor costs and increase garbage classification capacity, a machine vision system is establi...

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

Waste management (New York, N.Y.)
The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to ...

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR).

Waste management (New York, N.Y.)
Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation and energy demand duality. How...

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review.

Waste management (New York, N.Y.)
Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could...

Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels.

Waste management (New York, N.Y.)
Chlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use of RDF is that chlorine in the waste weakens the cement, increases the risk of corrosion in the kiln...

Spatial distribution and influencing factors of litter in urban areas based on machine learning - A case study of Beijing.

Waste management (New York, N.Y.)
Littering in urban areas negatively affects their appearance, is harmful to the environment and increases pollution. It is a typical urban problem looming large upon Beijing and other megacities striving for liveability and harmony in economy, societ...

Deep learning-based waste detection in natural and urban environments.

Waste management (New York, N.Y.)
Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic ...

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Waste management (New York, N.Y.)
Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of m...