Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting

The prediction of concrete strength is an interesting point of investigation and could be realized well, especially for the concrete with the complex system, with the development of machine learning and artificial intelligence. Therefore, an excellent algorithm should put emphasis to receiving increased attention from researchers. This study presents a novel predictive system as follows: extreme gradient boosting (XGBoost) based on grey relation analysis (GRA) for predicting the compressive strength of concrete containing slag and metakaolin. One of its highlights is a feature selection methodology, i.e., GRA, which was used to determine the main input variables. Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. For three same testing datasets, the average R2 values of ANN, GA-ANN, and XGBoost are 0.674, 0.829, and 0.880, respectively, indicating that XGBoost has the highest absolute fraction of variance (R2). XGBoost can provide best result by testing the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The average RMSE values of ANN, GA-ANN, and XGBoost are 15.569 MPa, 10.530 MPa, and 9.532 MPa, respectively, and those of MAPE of ANN, GA-ANN, and XGBoost are 11.224%, 9.140%, and 8.718%, respectively. Thus, the XGBoost definitely performed better than the ANN and GA-ANN. Finally, a type of application software based on XGBoost was developed for practical applications. This vivid software interfaces could help users in prediction and easy and efficient analysis.

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Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images

Geocarto International ◽

10.1080/10106049.2020.1737974 ◽

2020 ◽

pp. 1-19 ◽

Cited By ~ 4

Author(s):

Alireza Hamedianfar ◽

Mohamed Barakat A. Gibril ◽

Mohammadjavad Hosseinpoor ◽

Petri K. E. Pellikka

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Gradient Boosting ◽

Swarm Optimization ◽

Extreme Gradient Boosting ◽

Boosting Algorithms ◽

Artificial Neural

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Feature Selection for Health Care Costs Prediction Using Weighted Evidential Regression

Sensors ◽

10.3390/s20164392 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4392

Author(s):

Belisario Panay ◽

Nelson Baloian ◽

José A. Pino ◽

Sergio Peñafiel ◽

Horacio Sanson ◽

...

Keyword(s):

Neural Network ◽

Health Care ◽

Artificial Neural Network ◽

Health Care Costs ◽

Black Box ◽

Health Costs ◽

Gradient Boosting ◽

Healthcare Budget ◽

Artificial Neural ◽

Care Costs

Although many authors have highlighted the importance of predicting people’s health costs to improve healthcare budget management, most of them do not address the frequent need to know the reasons behind this prediction, i.e., knowing the factors that influence this prediction. This knowledge allows avoiding arbitrariness or people’s discrimination. However, many times the black box methods (that is, those that do not allow this analysis, e.g., methods based on deep learning techniques) are more accurate than those that allow an interpretation of the results. For this reason, in this work, we intend to develop a method that can achieve similar returns as those obtained with black box methods for the problem of predicting health costs, but at the same time it allows the interpretation of the results. This interpretable regression method is based on the Dempster-Shafer theory using Evidential Regression (EVREG) and a discount function based on the contribution of each dimension. The method “learns” the optimal weights for each feature using a gradient descent technique. The method also uses the nearest k-neighbor algorithm to accelerate calculations. It is possible to select the most relevant features for predicting a patient’s health care costs using this approach and the transparency of the Evidential Regression model. We can obtain a reason for a prediction with a k-NN approach. We used the Japanese health records at Tsuyama Chuo Hospital to test our method, which included medical examinations, test results, and billing information from 2013 to 2018. We compared our model to methods based on an Artificial Neural Network, Gradient Boosting, Regression Tree and Weighted k-Nearest Neighbors. Our results showed that our transparent model performed like the Artificial Neural Network and Gradient Boosting with an R2 of 0.44.

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Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

Vietnam Journal of Computer Science ◽

10.1142/s2196888819500246 ◽

2019 ◽

Vol 06 (04) ◽

pp. 439-455 ◽

Cited By ~ 1

Author(s):

Nahian Ahmed ◽

Nazmul Alam Diptu ◽

M. Sakil Khan Shadhin ◽

M. Abrar Fahim Jaki ◽

M. Ferdous Hasan ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Satellite Image ◽

Population Estimation ◽

Data Driven ◽

Gradient Boosting ◽

Ann Model ◽

Data Driven Approach ◽

Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

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Predicting 30-day Hospital Readmissions Using Artificial Neural Networks with Medical Code Embedding

10.1101/741504 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wenshuo Liu ◽

Karandeep Singh ◽

Andrew M. Ryan ◽

Devraj Sukul ◽

Elham Mahmoudi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Claims Data ◽

Prediction Models ◽

Hospital Performance ◽

Gradient Boosting ◽

Administrative Claims ◽

Hierarchical Logistic Regression ◽

Artificial Neural

ABSTRACTReducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural network. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models significantly improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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Predicting Health Care Costs Using Evidence Regression

Proceedings ◽

10.3390/proceedings2019031074 ◽

2019 ◽

Vol 31 (1) ◽

pp. 74 ◽

Cited By ~ 1

Author(s):

Belisario Panay ◽

Nelson Baloian ◽

José Pino ◽

Sergio Peñafiel ◽

Horacio Sanson ◽

...

Keyword(s):

Neural Network ◽

Health Care ◽

Artificial Neural Network ◽

Regression Model ◽

Health Care Costs ◽

Health Care Cost ◽

Gradient Boosting ◽

Boosting Method ◽

Artificial Neural ◽

Care Costs

People’s health care cost prediction is nowadays a valuable tool to improve accountability in health care. In this work, we study if an interpretable method can reach the performance of black-box methods for the problem of predicting health care costs. We present an interpretable regression method based on the Dempster-Shafer theory, using the Evidence Regression model and a discount function based on the contribution of each dimension. Optimal parameters are learned using gradient descent. The k-nearest neighbors’ algorithm was also used to speed up computations. With the transparency of the evidence regression model, it is possible to create a set of rules based on a patient’s vicinity. When making a prediction, the model gives a set of rules for such a result. We used Japanese health records from Tsuyama Chuo Hospital to test our method, which includes medical checkups, exam results, and billing information from 2016 to 2017. We compared our model to an Artificial Neural Network and Gradient Boosting method. Our results showed that our transparent model outperforms the Artificial Neural Network and Gradient Boosting with an R 2 of 0 . 44 .

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Statistical Applications to Downscale GRACE-Derived Terrestrial Water Storage Data and to Fill Temporal Gaps

Remote Sensing ◽

10.3390/rs12030533 ◽

2020 ◽

Vol 12 (3) ◽

pp. 533 ◽

Cited By ~ 8

Author(s):

Hossein Sahour ◽

Mohamed Sultan ◽

Mehdi Vazifedan ◽

Karem Abdelmohsen ◽

Sita Karki ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Land Surface ◽

Multivariate Regression ◽

Water Storage ◽

Test Site ◽

Gradient Boosting ◽

Terrestrial Water Storage ◽

Extreme Gradient Boosting ◽

Terrestrial Water

The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACETWS) and groundwater storage (GRACEGWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of similar geophysical signals (GRACETWS time series); three clusters were identified (cluster 1: 13,700 km2; cluster 2: 59,200 km2; cluster 3: 33,100 km2; Step I). Variables (total precipitation, normalized difference vegetation index (NDVI), snow cover, streamflow, Lake Michigan level, Lake Huron level, land surface temperature, soil moisture, air temperature, and evapotranspiration (ET)), which could potentially contribute to, or correlate with, GRACETWS over the test site were identified, and the dataset was randomly partitioned into training (80%) and testing (20%) datasets (Step II). Multivariate regression, artificial neural network, and extreme gradient boosting techniques were applied on the training dataset for each of the identified clusters to extract relationships between the identified hydro-climatic variables and GRACETWS solutions on a coarser scale (13,700–33,100 km2), and were used to estimate GRACETWS at a spatial resolution matching that of the fine-scale (0.125° × 0.125° or 120 km2) inputs. The statistical models were evaluated by comparing the observed and modeled GRACETWS values using the R-squared, the Nash–Sutcliffe model efficiency coefficient (NSE), and the normalized root-mean-square error (NRMSE; Step III). Lastly, temporal variations in GRACEGWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACETWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACETWS for local applications given their apparent enhanced performance (average value: R-squared: 0.86; NRMSE 0.37; NSE 0.86) over the multivariate regression (R-squared: 0.74; NRMSE 0.56; NSE 0.64) and artificial neural network (R-squared: 0.76; NRMSE 0.5; NSE 0.37) methods; and (2) identifying local hydrologic variables and the optimum downscaling approach for individual clusters is critical to implementing this method. The adopted method could potentially be used for groundwater management purposes on local scales in the study area and in similar settings elsewhere.

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Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

Applied Sciences ◽

10.3390/app10186432 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6432 ◽

Cited By ~ 3

Author(s):

Reza Daneshfar ◽

Amin Bemani ◽

Masoud Hadipoor ◽

Mohsen Sharifpur ◽

Hafiz Muhammad Ali ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Capacity ◽

Mean Squared Error ◽

Gradient Boosting ◽

Acentric Factor ◽

Inference System ◽

Stochastic Gradient Boosting ◽

Data Points ◽

Artificial Neural

This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.

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BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR–Drug Interaction Based on Wordbook Learning From Sequences

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.623858 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wangren Qiu ◽

Zhe Lv ◽

Yaoqiu Hong ◽

Jianhua Jia ◽

Xuan Xiao

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Validation Dataset ◽

Gradient Boosting ◽

Discrete Wavelet ◽

Learning Models ◽

Proposed Model ◽

Artificial Neural ◽

Machine Learning Models

Background: As a class of membrane protein receptors, G protein-coupled receptors (GPCRs) are very important for cells to complete normal life function and have been proven to be a major drug target for widespread clinical application. Hence, it is of great significance to find GPCR targets that interact with drugs in the process of drug development. However, identifying the interaction of the GPCR–drug pairs by experimental methods is very expensive and time-consuming on a large scale. As more and more database about GPCR–drug pairs are opened, it is viable to develop machine learning models to accurately predict whether there is an interaction existing in a GPCR–drug pair.Methods: In this paper, the proposed model aims to improve the accuracy of predicting the interactions of GPCR–drug pairs. For GPCRs, the work extracts protein sequence features based on a novel bag-of-words (BOW) model improved with weighted Silhouette Coefficient and has been confirmed that it can extract more pattern information and limit the dimension of feature. For drug molecules, discrete wavelet transform (DWT) is used to extract features from the original molecular fingerprints. Subsequently, the above-mentioned two types of features are contacted, and SMOTE algorithm is selected to balance the training dataset. Then, artificial neural network is used to extract features further. Finally, a gradient boosting decision tree (GBDT) model is trained with the selected features. In this paper, the proposed model is named as BOW-GBDT.Results: D92M and Check390 are selected for testing BOW-GBDT. D92M is used for a cross-validation dataset which contains 635 interactive GPCR–drug pairs and 1,225 non-interactive pairs. Check390 is used for an independent test dataset which consists of 130 interactive GPCR–drug pairs and 260 non-interactive GPCR–drug pairs, and each element in Check390 cannot be found in D92M. According to the results, the proposed model has a better performance in generation ability compared with the existing machine learning models.Conclusion: The proposed predictor improves the accuracy of the interactions of GPCR–drug pairs. In order to facilitate more researchers to use the BOW-GBDT, the predictor has been settled into a brand-new server, which is available at http://www.jci-bioinfo.cn/bowgbdt.

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