Integrated structural health monitoring in bridge engineering

Z He, W Li, H Salehi, H Zhang, H Zhou, P Jiao - Automation in construction, 2022‏ - Elsevier
Integrated structural health monitoring (SHM) uses the mechanism analysis, monitoring
technology and data analytics to diagnose the classification, location and significance of …

SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

A Fernández, S Garcia, F Herrera, NV Chawla - Journal of artificial …, 2018‏ - jair.org
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Machine learning assisted prediction and optimization of mechanical properties for laser powder bed fusion of Ti6Al4V alloy

Y Cao, C Chen, S Xu, R Zhao, K Guo, T Hu, H Liao… - Additive …, 2024‏ - Elsevier
Due to the complex physical metallurgy phenomena and enormous parameter combination,
the traditional trial-and-error method makes the microstructure tailoring of laser additive …

[HTML][HTML] A predictive analytics approach for stroke prediction using machine learning and neural networks

S Dev, H Wang, CS Nwosu, N Jain, B Veeravalli… - Healthcare …, 2022‏ - Elsevier
The negative impact of stroke in society has led to concerted efforts to improve the
management and diagnosis of stroke. With an increased synergy between technology and …

Machine learning for perovskite solar cells and component materials: key technologies and prospects

Y Liu, X Tan, J Liang, H Han, P **ang… - Advanced Functional …, 2023‏ - Wiley Online Library
Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …

[HTML][HTML] Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

J Li, MS Herdem, J Nathwani, JZ Wen - Energy and AI, 2023‏ - Elsevier
Abstract Information technologies involving artificial Intelligence, big data, Internet of Things
devices and blockchain have been developed and implemented in many engineering fields …

Big data preprocessing: methods and prospects

S García, S Ramírez-Gallego, J Luengo, JM Benítez… - Big data analytics, 2016‏ - Springer
The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization

M Pan, C Li, R Gao, Y Huang, H You, T Gu… - Journal of Cleaner …, 2020‏ - Elsevier
Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage
of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term …

Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost

C Wang, C Deng, S Wang - Pattern recognition letters, 2020‏ - Elsevier
Abstract The paper presents Imbalance-XGBoost, a Python package that combines the
powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced …

A survey on data preprocessing for data stream mining: Current status and future directions

S Ramírez-Gallego, B Krawczyk, S García, M Woźniak… - Neurocomputing, 2017‏ - Elsevier
Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …