[HTML][HTML] Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review
I Malashin, V Tynchenko, A Gantimurov, V Nelyub… - …, 2024 - pmc.ncbi.nlm.nih.gov
This review explores the application of Long Short-Term Memory (LSTM) networks, a
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost …
Abstract Machine learning algorithms are now being extensively used in our daily lives,
spanning across diverse industries as well as academia. In the field of high energy physics …
spanning across diverse industries as well as academia. In the field of high energy physics …
Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models
X Lu, L Sun, Y Zhang, J Du, G Wang, X Huang… - Science of The Total …, 2024 - Elsevier
The traditional prediction of the Cd content in grains (Cd g) of crops primarily relies on the
multiple linear regression models based on soil Cd content (Cd s) and pH, neglecting inter …
multiple linear regression models based on soil Cd content (Cd s) and pH, neglecting inter …
Enhancing structural analysis and electromagnetic shielding in carbon foam composites with applications in concrete integrating XGBoost machine learning, carbon …
Electromagnetic shielding in carbon foam composites involves using the natural conductivity
of carbon foam to block or absorb electromagnetic fields. These composites protect sensitive …
of carbon foam to block or absorb electromagnetic fields. These composites protect sensitive …
A novel multi-model ensemble framework for fluvial flood inundation map**
Floods pose a significant threat to communities and infrastructure, necessitating timely
predictions for effective disaster management. Conventional hydrodynamic models often …
predictions for effective disaster management. Conventional hydrodynamic models often …
Porosity prediction and uncertainty estimation in tight sandstone reservoir using non-deterministic XGBoost
Understanding porosity is crucial in various industries, especially those involved in resource
exploration and production, such as oil and gas, mining, and geology. Since porosity …
exploration and production, such as oil and gas, mining, and geology. Since porosity …
[HTML][HTML] Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data
H Lei, H Li, W Hu - Ecological Informatics, 2024 - Elsevier
Streamflow simulation is crucial for flood mitigation, ecological protection, and water
resource planning. Process-based hydrological models and machine learning algorithms …
resource planning. Process-based hydrological models and machine learning algorithms …
Effects of various information scenarios on layer-wise relevance propagation-based interpretable convolutional neural networks for air handling unit fault diagnosis
Deep learning (DL), especially convolutional neural networks (CNNs), has been widely
applied in air handling unit (AHU) fault diagnosis (FD). However, its application faces two …
applied in air handling unit (AHU) fault diagnosis (FD). However, its application faces two …
Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques
Soil moisture (SM) is a critical variable influencing various environmental processes, but
traditional microwave sensors often lack the spatial resolution needed for local-scale …
traditional microwave sensors often lack the spatial resolution needed for local-scale …
Representative sample size for estimating saturated hydraulic conductivity via machine learning: A proof‐of‐concept study
Abstract Machine learning (ML) has been extensively applied in various disciplines.
However, not much attention has been paid to data heterogeneity in databases and number …
However, not much attention has been paid to data heterogeneity in databases and number …