Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China
Q Guo, Z He, Z Wang - Toxics, 2023 - mdpi.com
Anthropogenic sources of fine particulate matter (PM2. 5) threaten ecosystem security,
human health and sustainable development. The accuracy prediction of daily PM2. 5 …
human health and sustainable development. The accuracy prediction of daily PM2. 5 …
[HTML][HTML] Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review
I Malashin, D Martysyuk, V Tynchenko… - …, 2024 - pmc.ncbi.nlm.nih.gov
The integration of machine learning (ML) into material manufacturing has driven
advancements in optimizing biopolymer production processes. ML techniques, applied …
advancements in optimizing biopolymer production processes. ML techniques, applied …
Predicting the governing factors for the release of colloidal phosphorus using machine learning
Predicting the parameters that influence colloidal phosphorus (CP) release from soils under
different land uses is critical for managing the impact on water quality. Traditional modeling …
different land uses is critical for managing the impact on water quality. Traditional modeling …
Inverse dynamics modelling and tracking control of conical dielectric elastomer actuator based on GRU neural network
This paper presents intelligent modelling and tracking control methods for a conical
dielectric elastomer actuator (CDEA) utilized in soft robots. Firstly, an inverse dynamics …
dielectric elastomer actuator (CDEA) utilized in soft robots. Firstly, an inverse dynamics …
Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation
Recent research indicates that early detection of breast cancer (BC) is critical in achieving
favorable treatment outcomes and reducing the mortality rate associated with it. With the …
favorable treatment outcomes and reducing the mortality rate associated with it. With the …
Elucidating nitrogen removal performance and response mechanisms of anammox under heavy metal stress using big data analysis and machine learning
J Yang, Z Chen, X Wang, Y Zhang, J Li, S Zhou - Bioresource Technology, 2023 - Elsevier
In this study, machine learning algorithms and big data analysis were used to decipher the
nitrogen removal rate (NRR) and response mechanisms of anammox process under heavy …
nitrogen removal rate (NRR) and response mechanisms of anammox process under heavy …
Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach
Modern financial markets produce massive datasets that need to be analysed using new
modelling techniques like those from (deep) Machine Learning and Artificial Intelligence …
modelling techniques like those from (deep) Machine Learning and Artificial Intelligence …
[HTML][HTML] Augmented machine learning for sewage quality assessment with limited data
JQ Lv, WX Yin, JM Xu, HY Cheng, ZL Li… - Environmental Science …, 2025 - Elsevier
Physical, chemical, and biological processes within sewers significantly alter sewage
composition during conveyance. This leads to the formation of sulfide and methane …
composition during conveyance. This leads to the formation of sulfide and methane …
Use of artificial neural networks to predict the progression of glaucoma in patients with sleep apnea
Aim: To construct neural models to predict the progression of glaucoma in patients with
sleep apnea. Materials and Methods: Modeling the use of neural networks was performed …
sleep apnea. Materials and Methods: Modeling the use of neural networks was performed …