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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3,   Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fractureA total of 147 raw input features are considered in our model.The presented model is compared with several benchmarks based on various metrics to prove its effectiveness

Keywords： XGBoost     deep neural network     healthcare     risk prediction    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionhorizontal acceleration ), for prediction of liquefaction.model predicts with accuracy of 89%.

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction of

Keywords： Water quality prediction     Grasshopper optimization algorithm     Variational mode decomposition     Long short-term    

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Improved analytical model for residual stress prediction in orthogonal cutting

Zhaoxu QI,Bin LI,Liangshan XIONG

Frontiers of Mechanical Engineering 2014, Volume 9, Issue 3,   Pages 249-256 doi: 10.1007/s11465-014-0310-1

Abstract: for residual stress prediction in orthogonal cutting.In application of the model, a problem of low precision of the surface residual stress prediction isThese shortages may directly lead to the low precision of the surface residual stress prediction.To eliminate these shortages and make the prediction more accurate, an improved model is proposed.Also, Jiann’s model and the improved model are simulated under the same conditions with cutting

Keywords： residual stress     analytical model     orthogonal cutting     cutting force     cutting temperature    

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Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 1,   Pages 125-136 doi: 10.1007/s11709-017-0396-8

Abstract: In this paper, we propose a 3D stochastic model to predict the percolation threshold and the effectiveWe consider the tunneling effect in our model so that the unrealistic interpenetration can be avoided

Keywords： electrical percolation     CNTs/Polymer composites     fracture model     electric conductivity     tunnelling effects    

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Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 56-68 doi: 10.1007/s11708-012-0216-8

Abstract: This paper presents the complete mathematical model and predicts the performance of switched reluctanceThe complete mathematical model is developed in three stages.First, a switching model is developed based on quasi-linear inductance profile.Finally, to track control voltage and current wave shapes, a small signal model is designed.The effectiveness of the complete multilevel model combining electrical machine, power converter, load

Keywords： generator     reluctance     switching model     small signal model     time average model    

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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 976-989 doi: 10.1007/s11709-022-0840-2

Abstract: This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN).

Keywords： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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Prediction method of foundation vibration responses induced by impact loading using modified andersonmodel

Fang Bo

Strategic Study of CAE 2014, Volume 16, Issue 11,   Pages 96-102

Abstract:

A synthetic method, which combines theoretical model and field measurementThe Anderson model was modified and verified by the data measured in field hammer impact tests.Then the impact induced vibration was predicted using the modified Anderson model.Finally, the prediction results were compared with the measured results.The results indicates that the prediction results approximately approach to the measured results.

Keywords： prediction method     impact loading     vibration effects     anderson model    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walledBased on the experimental data collected during the milling experiments, the proposed model proved toThe average classification accuracy obtained using the proposed deep learning model was 9.55% higherHence, the proposed hybrid model provides an efficient way of fusing different sources of process dataand can be adopted for prediction of the machining quality in noisy environments.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

Frontiers of Engineering Management 2021, Volume 8, Issue 1,   Pages 122-134 doi: 10.1007/s42524-019-0072-x

Abstract: Thus, the accurate and efficient prediction of aftershock-induced damage to buildings on a regional scale

Keywords： regional seismic damage prediction     city-scale nonlinear time-history analysis     mainshock–aftershock sequence     multiple degree-of-freedom (MDOF) model     2014 Ludian earthquake    

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Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

Frontiers in Energy doi: 10.1007/s11708-023-0906-4

Abstract: The prediction of the remaining useful life (RUL) of lithium batteries not only provides a referenceIn order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method, the features related to the capacity degradation of LIBs are utilized to train the neural network modelconsidered to have a similar degradation pattern, which is used to determine the initial Dual Exponential ModelExperiments show that the method does not need human intervention and has high prediction accuracy.

Keywords： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression    

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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 311-321 doi: 10.1007/s11709-019-0593-8

Abstract: neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength predictionIn addition for each model, two different sets of input variables are examined: a complete set and a

Keywords： data driven model     compressive strength     concrete     high temperature    

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Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1698-9

Abstract:

● Data acquisition and pre-processing for wastewater treatment were summarized.

Keywords： Chemical oxygen demand     Mining-beneficiation wastewater treatment     Particle swarm optimization     Support vector regression     Artificial neural network    

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A model for creep life prediction of thin tube using strain energy density as a function of stress triaxiality

Tahir MAHMOOD, Sangarapillai KANAPATHIPILLAI, Mahiuddin CHOWDHURY

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 2,   Pages 181-186 doi: 10.1007/s11465-013-0257-7

Abstract:

This paper demonstrates the application of a new multiaxial creep damage model developed by authorsThe model employs strain energy density and assumes that the uniaxial strain energy density of a componentThe verification and application of the model are demonstrated by applying it to thin tube for whichThe predicted failure times by the model are compared with the experimental results.The results show that the proposed model is capable of predicting failure times of the component made

Keywords： elastic-creep     elastic-plastic-creep     stress triaxiality     life prediction     pressure vessels     finite element    

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Prediction of the theoretical and semi-empirical model of ambient temperature

Foued CHABANE,Noureddine MOUMMI,Abdelhafid BRIMA,Abdelhafid MOUMMI

Frontiers in Energy 2016, Volume 10, Issue 3,   Pages 268-276 doi: 10.1007/s11708-016-0413-y

Abstract: A prediction is a statement about an uncertain event.Although guaranteed accurate information about the future is in many cases impossible, prediction canThe model proposed in this paper can provide an acceptable way to measure the change in ambient temperature

Keywords： ambient temperature     environment     correlation     theoretical model     semi-empirical    

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Title Author Date Type Operation

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

Improved analytical model for residual stress prediction in orthogonal cutting

Zhaoxu QI,Bin LI,Liangshan XIONG

Journal Article

Fracture model for the prediction of the electrical percolation threshold in CNTs/Polymer composites

Yang SHEN, Pengfei HE, Xiaoying ZHUANG

Journal Article

Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

Journal Article

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

Journal Article

Prediction method of foundation vibration responses induced by impact loading using modified andersonmodel

Fang Bo

Journal Article

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Journal Article

Regional seismic-damage prediction of buildings under mainshock–aftershock sequence

Xinzheng LU, Qingle CHENG, Zhen XU, Chen XIONG

Journal Article

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural

Journal Article

Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Journal Article

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Journal Article

A model for creep life prediction of thin tube using strain energy density as a function of stress triaxiality

Tahir MAHMOOD, Sangarapillai KANAPATHIPILLAI, Mahiuddin CHOWDHURY

Journal Article

Prediction of the theoretical and semi-empirical model of ambient temperature

Foued CHABANE,Noureddine MOUMMI,Abdelhafid BRIMA,Abdelhafid MOUMMI

Journal Article