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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

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Advancing agriculture with machine learning: a new frontier in weed management

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2024564

Abstract:

● Machine learning offers innovative and sustainable weed management

Keywords： Weed management     herbicides     machine learning     agricultural practices     environmental impact    

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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

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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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

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machinelearning models has recently been proposed.

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 444-460 doi: 10.1007/s11709-024-1050-x

Abstract: In the present paper, several machine learning models were applied to predict the torsional capacityAlgorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificialneural network and support vector machine, were trained and tested by 10-fold cross-validation methodPredictive performances of proposed machine learning models were evaluated and compared, both with eachThe results demonstrated that better predictive performance was achieved by machine learning models,

Keywords： RC members     torsional capacity     machine learning models     design codes    

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Improving lipid production by for renewable fuel production based on machine learning

Frontiers of Chemical Science and Engineering 2024, Volume 18, Issue 5, doi: 10.1007/s11705-024-2410-8

Abstract: And then, leveraging the collated data, a variety of machine learning algorithms were used to model and

Keywords： microbial lipid     machine learning     artificial neural network     support vector machine     genetic algorithm    

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Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management 2022, Volume 9, Issue 4,   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: advanced data processing, storage and analysis, advanced process control, artificial intelligence and machinelearning, cloud computing, and virtual and augmented reality.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

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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

Abstract:

● 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    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 2,   Pages 294-308 doi: 10.1007/s11709-024-1045-7

Abstract: This paper proposes a machine-learning-based methodology to automatically classify different types ofThe convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms

Keywords： steel weld     machine learning     convolutional neural network     weld defect detection     classification task    

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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

Frontiers of Environmental Science & Engineering 2024, Volume 18, Issue 2, doi: 10.1007/s11783-024-1777-6

Abstract:

● A machine learning approach was applied to predict free chlorine

Keywords： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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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

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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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

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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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

Abstract: method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machinelearning modeling and interactome network detection techniques based on whole-exome sequencing data.Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 12, doi: 10.1007/s11783-023-1752-7

Abstract:

● Online learning models accurately predict influent flow rate at

Keywords： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

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Title Author Date Type Operation

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Advancing agriculture with machine learning: a new frontier in weed management

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

Journal Article

Improving lipid production by for renewable fuel production based on machine learning

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Automated identification of steel weld defects, a convolutional neural network improved machine learning

Journal Article

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Journal Article