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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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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: The classification accuracy of the popular machine learning methods has been evaluated in comparisonwith the proposed deep learning model.Based 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% higherthan the best machine learning algorithm considered in this paper.

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

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 340-352 doi: 10.1007/s11465-021-0629-3

Abstract: Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years.However, many deep learning methods cannot fully extract fault information to recognize mechanical healthTherefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNNLastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn featuresCompared with other classical deep learning methods, the proposed fault diagnosis method has considerable

Keywords： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

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Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1316-1330 doi: 10.1007/s11709-020-0646-z

Abstract: In this study, the deep learning models for estimating the mechanical properties of concrete containingTwo well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM)The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectivelyIn addition, this study found that deep learning, which has a very good prediction ability with little

Keywords： concrete     high temperature     strength properties     deep learning     stacked auto-encoders     LSTM network    

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Engineering 2023, Volume 21, Issue 2,   Pages 162-174 doi: 10.1016/j.eng.2021.11.021

Abstract: This paper proposes an image-based deep learning model to estimate urban rainfall intensity with highMore specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phonesThe trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based onThe results show that the irCNN model provides rainfall estimates with a mean absolute percentage error

Keywords： Urban flooding     Rainfall images     Deep learning model     Convolutional neural networks (CNNs)     Rainfall    

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Visual interpretability for deep learning: a survey Review

Quan-shi ZHANG, Song-chun ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 27-39 doi: 10.1631/FITEE.1700808

Abstract: Although deep neural networks have exhibited superior performance in various tasks, interpretabilityis always Achilles’ heel of deep neural networks.We believe that high model interpretability may help people break several bottlenecks of deep learning, e.g., learning from a few annotations, learning via human–computer communications at the semantic levelof CNNs with disentangled representations, and middle-to-end learning based on model interpretability

Keywords： Artificial intelligence     Deep learning     Interpretable model    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search.First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingSecond, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-termused HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-basedACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed DeepLearning Model Article

Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao

Engineering 2023, Volume 23, Issue 4,   Pages 181-195 doi: 10.1016/j.eng.2022.09.015

Abstract: , and deep learning to achieve both forward and inverse predictions of key parameters and molten poolThe experiment provides fundamental data, the mechanistic model significantly augments the dataset, andthe multilayer perceptron (MLP) deep learning model predicts the molten pool dimensions and processparameters based on the dataset built from the experiment and the mechanistic model.The prediction accuracy of the MLP model largely depends on the quality of the dataset as well.

Keywords： Additive manufacturing     Molten pool     Model     Deep learning     Learnability    

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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 450-469 doi: 10.1007/s11684-019-0726-4

Abstract: As a promising method in artificial intelligence, deep learning has been proven successful in severalWith medical imaging becoming an important part of disease screening and diagnosis, deep learning-basedDeep learning has been widely applied in medical imaging for improved image analysis.This paper reviews the major deep learning techniques in this time of rapid evolution and summarizesLastly, the application of deep learning techniques to the medical image and an analysis of their future

Keywords： deep learning     neural networks     pulmonary medical image     survey    

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Frontiers of Structural and Civil Engineering   Pages 564-575 doi: 10.1007/s11709-022-0829-x

Abstract: This paper introduces the idea of ensemble deep learning.At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learningFurthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited

Keywords： water conveyance tunnels     siltation images     remotely operated vehicles     deep learning     ensemble learning    

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering doi: 10.1007/s11705-022-2269-5

Abstract: This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), forThe model is comprised of self-organizing-map and the neural network parts.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledgeMoreover, the MISR model has smoother error convergence than the previous model.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which

Keywords： hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1,   Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: This paper presents a new approach for automatical classification of structural state through deep learningdesigned to fuse both the feature extraction and classification blocks into an intelligent and compact learningThe results show that CNN can achieve 99% classification accuracy for the research model.

Keywords： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Frontiers of Structural and Civil Engineering   Pages 1365-1377 doi: 10.1007/s11709-022-0882-5

Abstract: Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-timeOur proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypassThe trained DL model can predict the stress distributions with a mean absolute error of 0.9% and an absolute

Keywords： Deep Learning     finite element analysis     stress contours     structural components    

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Frontiers of Structural and Civil Engineering   Pages 1213-1232 doi: 10.1007/s11709-022-0880-7

Abstract: The present study describes a reliability analysis of the strength model for predicting concrete columnsconfinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and DeepNeural Network model (artificial neural network (ANN) with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters, i.e.The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive

Keywords： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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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

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

Journal Article

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed

Harun TANYILDIZI, Abdulkadir ŞENGÜR, Yaman AKBULUT, Murat ŞAHİN

Journal Article

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan

Journal Article

Visual interpretability for deep learning: a survey

Quan-shi ZHANG, Song-chun ZHU

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed DeepLearning Model

Mingzhi Zhao, Huiliang Wei, Yiming Mao, Changdong Zhang, Tingting Liu, Wenhe Liao

Journal Article

Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

Journal Article

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

Digital image correlation-based structural state detection through deep learning

Journal Article

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Journal Article

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Journal Article