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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2, Pages 490-505 doi: 10.1007/s11709-020-0669-5
Keywords: seismic soil liquefaction Bayesian belief network cone penetration test parameter learning structurallearning
Estimation of optimum design of structural systems via machine learning
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6, Pages 1441-1452 doi: 10.1007/s11709-021-0774-0
Keywords: optimization metaheuristic algorithms harmony search structural designs machine learning artificial
Spatial prediction of soil contamination based on machine learning: a review
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1
● A review of machine learning (ML) for spatial prediction of soil
Keywords: Soil contamination Machine learning Prediction Spatial distribution
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5
● A novel integrated machine learning method to analyze O3
Keywords: Ozone Integrated method Machine learning
State-of-the-art applications of machine learning in the life cycle of solid waste management
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x
● State-of-the-art applications of machine learning (ML) in solid waste
Keywords: Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle
Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2, Pages 183-197 doi: 10.1007/s11705-021-2073-7
Keywords: machine learning flowsheet simulations constraints exploration
Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang
Engineering 2023, Volume 28, Issue 9, Pages 93-104 doi: 10.1016/j.eng.2021.12.014
Keywords: Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and
Evaluation and prediction of slope stability using machine learning approaches
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4, Pages 821-833 doi: 10.1007/s11709-021-0742-8
Keywords: slope stability factor of safety regression machine learning repeated cross-validation
Machine learning in building energy management: A critical review and future directions
Frontiers of Engineering Management 2022, Volume 9, Issue 2, Pages 239-256 doi: 10.1007/s42524-021-0181-1
Keywords: building energy management machine learning integrated framework knowledge evolution
Big data and machine learning: A roadmap towards smart plants
Frontiers of Engineering Management Pages 623-639 doi: 10.1007/s42524-022-0218-0
Keywords: big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1
● MSWNet was proposed to classify municipal solid waste.
Keywords: Municipal solid waste sorting Deep residual network Transfer learning Cyclic learning rate Visualization
Development of machine learning multi-city model for municipal solid waste generation prediction
Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6
● A database of municipal solid waste (MSW) generation in China was established.
Keywords: Municipal solid waste Machine learning Multi-cities Gradient boost regression tree
Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1
● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.
Keywords: Elemental composition Infrared spectroscopy Machine learning Moisture interference Solid waste Spectral
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature
Frontiers of Medicine 2023, Volume 17, Issue 4, Pages 768-780 doi: 10.1007/s11684-023-0982-1
Keywords: machine learning methods hypertrophic cardiomyopathy genetic risk
Machine learning for fault diagnosis of high-speed train traction systems: A review
Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2
Keywords: high-speed train traction systems machine learning fault diagnosis
Title Author Date Type Operation
Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential
Mahmood AHMAD, Xiao-Wei TANG, Jiang-Nan QIU, Feezan AHMAD, Wen-Jing GU
Journal Article
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
Journal Article
State-of-the-art applications of machine learning in the life cycle of solid waste management
Journal Article
Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet
Journal Article
Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization
Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang
Journal Article
Machine learning in building energy management: A critical review and future directions
Journal Article
MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal
Journal Article
Development of machine learning multi-city model for municipal solid waste generation prediction
Journal Article
Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
Journal Article
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature
Journal Article