Applications of convolutional neural networks for intelligent waste identification and recycling: A review
With the implementations of “Zero Waste” and Industry 4.0, the rapidly increasing
applications of artificial intelligence in waste management have generated a large amount of …
applications of artificial intelligence in waste management have generated a large amount of …
[HTML][HTML] Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature …
Digital technologies hold enormous potential for improving the performance of future-
generation sorting and processing plants; however, this potential remains largely untapped …
generation sorting and processing plants; however, this potential remains largely untapped …
A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment
Waste collection, classification, and planning have become crucial as industrialization and
smart city advancement activities have increased. A recycling process of waste relies on the …
smart city advancement activities have increased. A recycling process of waste relies on the …
A transfer learning framework with a one-dimensional deep subdomain adaptation network for bearing fault diagnosis under different working conditions
R Zhang, Y Gu - Sensors, 2022 - mdpi.com
Accurate and fast rolling bearing fault diagnosis is required for the normal operation of
rotating machinery and equipment. Although deep learning methods have achieved …
rotating machinery and equipment. Although deep learning methods have achieved …
Convolutional neural network with near-infrared spectroscopy for plastic discrimination
J Xia, Y Huang, Q Li, Y Xiong, S Min - Environmental Chemistry Letters, 2021 - Springer
Plastic pollution is a global issue of increasing health concern, thus requiring innovative
waste management. In particular, there is a need for advanced methods to identify and …
waste management. In particular, there is a need for advanced methods to identify and …
Dual image-based cnn ensemble model for waste classification in reverse vending machine
A reverse vending machine motivates citizens to bring recyclable waste by rewarding them,
which is a viable solution to increase the recycling rate. Reverse vending machines …
which is a viable solution to increase the recycling rate. Reverse vending machines …
State-of-the-art applications of machine learning in the life cycle of solid waste management
Due to the superiority of machine learning (ML) data processing, it is widely used in
research of solid waste (SW). This study analyzed the research and developmental progress …
research of solid waste (SW). This study analyzed the research and developmental progress …
A Systematic Literature Review of Waste Identification in Automatic Separation Systems
Proper waste separation is essential for recycling. However, it can be challenging to identify
waste materials accurately, especially in real-world settings. In this study, a systematic …
waste materials accurately, especially in real-world settings. In this study, a systematic …
Enhancing Ensemble Learning Using Explainable CNN for Spoof Fingerprints
N Reza, HY Jung - Sensors, 2023 - mdpi.com
Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great
accuracy in classification problems. However, the lack of interpretability of the predictions …
accuracy in classification problems. However, the lack of interpretability of the predictions …
Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform
S Deng, W Wu, K Zou, H Qin, L Cheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recognition and early warning of plant diseases is one of the keys to agricultural disaster
prevention and mitigation. Deep learning-based image recognition methods give us a new …
prevention and mitigation. Deep learning-based image recognition methods give us a new …