A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Laser powder bed fusion (LPBF) as one of the most promising additive manufacturing (AM)
technologies, has been widely used to produce metal parts and applied in fields such as …
technologies, has been widely used to produce metal parts and applied in fields such as …
[HTML][HTML] Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
[HTML][HTML] Air pollution prediction using machine learning techniques–an approach to replace existing monitoring stations with virtual monitoring stations
A Samad, S Garuda, U Vogt, B Yang - Atmospheric Environment, 2023 - Elsevier
Air pollution in the modern world is a matter of grave concern. Due to rapid expansion in
commercial social, and economic aspects, the pollutant concentrations in different parts of …
commercial social, and economic aspects, the pollutant concentrations in different parts of …
S3Net: Spectral–spatial Siamese network for few-shot hyperspectral image classification
Z Xue, Y Zhou, P Du - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has shown great potential for hyperspectral image (HSI) classification
due to its powerful ability of nonlinear modeling and end-to-end optimization. However, DL …
due to its powerful ability of nonlinear modeling and end-to-end optimization. However, DL …
[HTML][HTML] Understanding the linkage between urban growth and land surface temperature—a case study of Bangalore City, India
Planning for a sustainable future involves understanding the past and present problems
associated with urban centers. Rapid urbanization has caused significant adverse impacts …
associated with urban centers. Rapid urbanization has caused significant adverse impacts …
[HTML][HTML] Above-ground biomass estimation in oats using UAV remote sensing and machine learning
Current strategies for phenotyping above-ground biomass in field breeding nurseries
demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can …
demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can …
[HTML][HTML] Dealing with clustered samples for assessing map accuracy by cross-validation
Mapping of environmental variables often relies on map accuracy assessment through cross-
validation with the data used for calibrating the underlying mapping model. When the data …
validation with the data used for calibrating the underlying mapping model. When the data …
Remote sensing image classification using an ensemble framework without multiple classifiers
Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an
effective method for improving remote sensing classification accuracy. Although these …
effective method for improving remote sensing classification accuracy. Although these …
[HTML][HTML] Crop type mapping by using transfer learning
A Nowakowski, J Mrziglod, D Spiller, R Bonifacio… - International Journal of …, 2021 - Elsevier
Crop type mapping currently represents an important problem in remote sensing. Accurate
information on the extent and types of crops derived from remote sensing can help …
information on the extent and types of crops derived from remote sensing can help …
Comparing pan-sharpened Landsat-9 and Sentinel-2 for land-use classification using machine learning classifiers
This paper evaluates the ability of two machine learning algorithms, Random Forest (RF)
and Support Vector Machine (SVM), to generate land-use maps using the recently launched …
and Support Vector Machine (SVM), to generate land-use maps using the recently launched …