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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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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

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

Abstract: Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementAdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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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

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 657-666 doi: 10.1007/s11709-022-0827-z

Abstract: The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R2 value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.

Keywords： safety     rural accidents     multiple logistic regression     artificial neural networks    

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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

Abstract: In this study, data-driven methods (DDMs) including different kinds of group method of data handling) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better

Keywords： sedimentation     water resources     dam engineering     machine learning     heuristic    

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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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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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Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of MachineLearning for Materials Design Review

Teng Zhou, Zhen Song, Kai Sundmacher

Engineering 2019, Volume 5, Issue 6,   Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011

Abstract: modern experimental and computational techniques is becoming more readily available, data-driven or machinelearning (ML) methods have opened new paradigms for the discovery and rational design of materials.In this review article, we provide a brief introduction on various ML methods and related software or

Keywords： Big data     Data-driven     Machine learning     Materials screening     Materials design    

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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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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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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 theDifferent ML methods for the factor of safety (FOS) prediction are studied and compared hoping to makethe best use of the large variety of existing statistical and ML regression methods collected.By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods.

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

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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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Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   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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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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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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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

Abstract:

● 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    

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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

Prediction of hydro-suction dredging depth using data-driven methods

Journal Article

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

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

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

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

Journal Article

Big data and machine learning: A roadmap towards smart plants

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