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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
Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES
Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10, Pages 1249-1266 doi: 10.1007/s11709-022-0858-5
Keywords: machine learning gridshell structure regression sensitivity analysis interpretability methods
Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI
Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5, Pages 657-666 doi: 10.1007/s11709-022-0827-z
Keywords: safety rural accidents multiple logistic regression artificial neural networks
Prediction of hydro-suction dredging depth using data-driven methods
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3, Pages 652-664 doi: 10.1007/s11709-021-0719-7
Keywords: sedimentation water resources dam engineering machine learning heuristic
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
Teng Zhou, Zhen Song, Kai Sundmacher
Engineering 2019, Volume 5, Issue 6, Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011
Keywords: Big data Data-driven Machine learning Materials screening Materials design
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
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
Title Author Date Type Operation
Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature
Journal Article
Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods
Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES
Journal Article
Presentation of machine learning methods to determine the most important factors affecting road traffic
Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI
Journal Article
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
Journal Article
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of MachineLearning for Materials Design
Teng Zhou, Zhen Song, Kai Sundmacher
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
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