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机器学习 27

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

《医学前沿（英文）》页码 768-780 doi: 10.1007/s11684-023-0982-1

摘要： Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

关键词： 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

《结构与土木工程前沿（英文）》 2022年第16卷第10期页码 1249-1266 doi: 10.1007/s11709-022-0858-5

摘要： The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f₁(x) and the maximum displacement to self-weight ratio as f₂(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f₁(x) and f₂(x).

关键词： 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

《结构与土木工程前沿（英文）》 2022年第16卷第5期页码 657-666 doi: 10.1007/s11709-022-0827-z

摘要： 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.

关键词： safety rural accidents multiple logistic regression artificial neural networks

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

《结构与土木工程前沿（英文）》 2021年第15卷第3期页码 652-664 doi: 10.1007/s11709-021-0719-7

摘要： In this study, data-driven methods (DDMs) including different kinds of group method of data handling (GMDH) 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 (h_s). Sixty-seven experiments were conducted under different hydraulic conditions to measure the h_s. Also, 33 data samples from three previous studies were used. The model input variables consisted of pipeline diameter (d), the distance between the pipe inlet and sediment level (Z), the velocity of flow passing through the pipeline (u₀), the water head (H), and the medium size of particles (D₅₀). Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better than the PSO algorithm, whereas the PSO algorithm trained simple simulation equations more precisely. Among all used DDMs, the integrative GMDH-HGSO algorithm provided the highest accuracy (RMSE = 7.086 mm). The results also showed that the integrative GMDHs enhance the accuracy of polynomial GMDHs by ~14.65% (based on the RMSE).

关键词： sedimentation water resources dam engineering machine learning heuristic

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

《环境科学与工程前沿（英文）》 2023年第17卷第8期 doi: 10.1007/s11783-023-1693-1

摘要：

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

关键词： Soil contamination Machine learning Prediction Spatial distribution

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Application of machine learning models in groundwater quality assessment and prediction: progress and

《环境科学与工程前沿（英文）》 2022年第18卷第3期 doi: 10.1007/s11783-024-1789-2

摘要：

● The application of ML in groundwater quality assessment and prediction is reviewed.

关键词： Groundwater quality assessment Groundwater quality prediction Machine learning Groundwater modeling

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大数据为材料研究创造新机遇——材料设计的机器学习方法与应用综述 Review

周腾, Zhen Song, Kai Sundmacher

《工程（英文）》 2019年第5卷第6期页码 1017-1026 doi: 10.1016/j.eng.2019.02.011

摘要：

材料的发展在历史上是由人类的需求和欲望所驱动的，且在可预见的将来，这种情况应该会继续下去。到2050年，全球人口预计将达到100亿，人们对清洁高效能源、个性化消费产品、安全食品供应和专业医疗保健等方面的需求也将日益增加。新型功能材料是为目标属性或性能而定制的，这将是应对挑战的关键。从传统上讲，先进的材料都是通过经验或实验验证的方法发现的。因为现代实验和计算技术产生的大数据越来越容易获取，数据驱动或机器学习（ML）方法为发现和合理设计材料打开了新的蓝图。本文简要介绍了各种ML方法和相关的软件或工具。重点介绍了将ML方法应用于材料研究的主要思路和基本步骤。本文还总结了近期ML在多孔聚合材料、催化材料和含能材料的大规模筛选和优化设计中的重要应用。最后给出了结束语和展望。

关键词：大数据数据驱动机器学习材料筛选材料设计

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

《环境科学与工程前沿（英文）》 2023年第17卷第4期 doi: 10.1007/s11783-023-1644-x

摘要：

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.

关键词： 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

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 183-197 doi: 10.1007/s11705-021-2073-7

摘要： Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

关键词： machine learning flowsheet simulations constraints exploration

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Evaluation and prediction of slope stability using machine learning approaches

《结构与土木工程前沿（英文）》 2021年第15卷第4期页码 821-833 doi: 10.1007/s11709-021-0742-8

摘要： In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. 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. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.

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

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Big data and machine learning: A roadmap towards smart plants

《工程管理前沿（英文）》页码 623-639 doi: 10.1007/s42524-022-0218-0

摘要： Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

关键词： big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0 intelligent system digitalization

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

《工程管理前沿（英文）》 2022年第9卷第2期页码 239-256 doi: 10.1007/s42524-021-0181-1

摘要： Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.

关键词： building energy management machine learning integrated framework knowledge evolution

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

《环境科学与工程前沿（英文）》 2023年第18卷第2期 doi: 10.1007/s11783-024-1777-6

摘要：

● A machine learning approach was applied to predict free chlorine residuals.

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

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

《环境科学与工程前沿（英文）》 2023年第17卷第6期 doi: 10.1007/s11783-023-1677-1

摘要：

● MSWNet was proposed to classify municipal solid waste.

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

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Recent development on statistical methods for personalized medicine discovery

null

《医学前沿（英文）》 2013年第7卷第1期页码 102-110 doi: 10.1007/s11684-013-0245-7

摘要：

It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.

关键词： dynamic treatment regimes personalized medicine reinforcement learning Q-learning

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标题作者时间类型操作

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

期刊论文

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

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

期刊论文

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

期刊论文

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

期刊论文

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

期刊论文

Application of machine learning models in groundwater quality assessment and prediction: progress and

期刊论文

大数据为材料研究创造新机遇——材料设计的机器学习方法与应用综述

周腾, Zhen Song, Kai Sundmacher

期刊论文

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

期刊论文

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

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

Big data and machine learning: A roadmap towards smart plants

期刊论文

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

期刊论文

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

期刊论文

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

期刊论文

Recent development on statistical methods for personalized medicine discovery

null

期刊论文

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