scholarly journals Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1643
Author(s):  
Deren Lu ◽  
Zhidong Chen ◽  
Faxing Ding ◽  
Zhenming Chen ◽  
Peng Sun
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Regression Tree ◽  
Predictive Ability ◽  
Data Set ◽  
Comprehensive Data ◽  
Cfst Columns ◽  
Robust Processing ◽  
Data Group ◽  
Stub Columns

In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the previous research (B, D, t, ρsa, fcu, fs). The advantage of GBRT is its strong predictive ability, which can naturally handle different types of data and very robust processing of outliers out of space. The comprehensive data set obtained from the FEM method which has been verified the accuracy and rationality by the existing literature. In order to make the data group closer to the engineering example, a large amount of experimental data collected in the literature was added to the data group to enhance the accuracy of the model. We compare a few regression models simply and the results show that the GBRT model has a good predictive effect on the mechanical properties of CFST columns. In summary, it can help pre-investigations for the CFST columns.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

Early prediction of chronic disease using an efficient machine learning algorithm through adaptive probabilistic divergence based feature selection approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeepkumar Hegde ◽  
Monica R. Mundada
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Chronic Disease ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Experimental Result ◽  
Data Set ◽  
Content Type ◽  
Learning Classifier ◽  
Feature Selection Approach

Purpose According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease. Design/methodology/approach A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases. Findings The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6. Originality/value The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(nk+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

scholarly journals Prediction of Liver Disease using Regression Tree

2021 ◽  
Vol 17 (02) ◽  
pp. 164
Author(s):  
Vinutha M.R. ◽  
Chandrika J.
Keyword(s):  
Machine Learning ◽  
Liver Cirrhosis ◽  
Liver Disease ◽  
Decision Trees ◽  
Regression Tree ◽  
Decisive Role ◽  
Integrated System ◽  
Common Disease ◽  
Data Set ◽  
Fully Integrated

<p class="0abstract"><strong>Abstract—</strong><strong> </strong>Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important.The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not.</p><p class="0abstract"> </p>

scholarly journals Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020 ◽  
Author(s):  
Romain Gaillac ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Dft Calculations ◽  
Force Field ◽  
Computational Cost ◽  
Training Data ◽  
Elastic Response ◽  
Data Set ◽  
Classical Level

<div> <div> <div> <p>The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. </p></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning

2020 ◽  
Author(s):  
Romain Gaillac ◽  
Siwar Chibani ◽  
François-Xavier Coudert
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Dft Calculations ◽  
Force Field ◽  
Computational Cost ◽  
Training Data ◽  
Elastic Response ◽  
Data Set ◽  
Classical Level

<div> <div> <div> <p>The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. </p></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Global Warming Prediction in India using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 4061-4065
Keyword(s):  
Machine Learning ◽  
Global Warming ◽  
Linear Regression ◽  
Greenhouse Gases ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Global Temperature ◽  
Support Vector ◽  
Data Set ◽  
Average Annual Temperature

Long term global warming prediction can be of major importance in various sectors like climate related studies, agricultural, energy, medical and many more. This paper evaluates the performance of several Machine Learning algorithm (Linear Regression, Multi-Regression tree, Support Vector Regression (SVR), lasso) in problem of annual global warming prediction, from previous measured values over India. The first challenge dwells on creating a reliable, efficient statistical reliable data model on large data set and accurately capture relationship between average annual temperature and potential factors such as concentration of carbon dioxide, methane, nitrous oxide. The data is predicted and forecasted by linear regression because it is obtaining the highest accuracy for greenhouse gases and temperature among all the technologies which can be used. It was also found that CO2 is the plays the role of major contributor temperature change, followed by CH4, then by N20. After seeing the analysed and predicted data of the greenhouse gases and temperature, the global warming can be reduced comparatively within few years. The reduction of global temperature can help the whole world because not only human but also different animals are suffering from the global temperature.

scholarly journals The importance of disease incidence rate on performance of GBLUP, threshold BayesA and machine learning methods in original and imputed data set

2020 ◽  
Vol 18 (3) ◽  
pp. e0405
Author(s):  
Yousef Naderi ◽  
Saadat Sadeghi
Keyword(s):  
Machine Learning ◽  
Disease Incidence ◽  
Predictive Ability ◽  
High Sensitivity ◽  
Machine Learning Algorithms ◽  
Genotype Imputation ◽  
Nucleotide Polymorphisms ◽  
Data Set ◽  
Single Nucleotide ◽  
Boosting Method

Aim of study: To predict genomic accuracy of binary traits considering different rates of disease incidence.Area of study: SimulationMaterial and methods: Two machine learning algorithms including Boosting and Random Forest (RF) as well as threshold BayesA (TBA) and genomic BLUP (GBLUP) were employed. The predictive ability methods were evaluated for different genomic architectures using imputed (i.e. 2.5K, 12.5K and 25K panels) and their original 50K genotypes. We evaluated the three strategies with different rates of disease incidence (including 16%, 50% and 84% threshold points) and their effects on genomic prediction accuracy.Main results: Genotype imputation performed poorly to estimate the predictive ability of GBLUP, RF, Boosting and TBA methods when using the low-density single nucleotide polymorphisms (SNPs) chip in low linkage disequilibrium (LD) scenarios. The highest predictive ability, when the rate of disease incidence into the training set was 16%, belonged to GBLUP, RF, Boosting and TBA methods. Across different genomic architectures, the Boosting method performed better than TBA, GBLUP and RF methods for all scenarios and proportions of the marker sets imputed. Regarding the changes, the RF resulted in a further reduction compared to Boosting, TBA and GBLUP, especially when the applied data set contained 2.5K panels of the imputed genotypes.Research highlights: Generally, considering high sensitivity of methods to imputation errors, the application of imputed genotypes using RF method should be carefully evaluated.

scholarly journals Property valuation by machine learning for the Norwegian real estate market

2021 ◽  
Author(s):  
Amandip Sangha
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Large Scale ◽  
State Of The Art ◽  
Regression Tree ◽  
Real Estate Market ◽  
Research Literature ◽  
Data Set ◽  
Boosted Regression Tree ◽  
Market Data

We train a machine learning model on large data set for predicting property values in the Norwegian real estate market. Our model is a gradient boosted regression tree. The data set is the largest market data set of properties in Norway considered in the research literature. We achieve state of the art accuracy. A large scale market data set of real estate properties is collected from sales and rental ads on publicly accessible internet sites. The property advertisements show property features and appraisal values made by real estate brokers. We train a gradient boosted regression tree model on selected features of the data set. This is a multivariate regression model built with supervised learning. We do 5-fold cross validation to assess the accuracy and robustness of the model. The gradient boosted regression tree models are already known to give the best prediction accuracy on real estate price valuations. We achieve state of the art pre- diction accuracy using a minimal feature set and only publicly and freely available sales advertisement data. The novelty of our work lies in the fact that we use a minimal feature set in our model, and we have the largest data set in the research literature, and moreover we have used only freely and publicly accessible data which are simple to obtain. This shows that useful estimation models with high accuracy can be built with quite simple resources.

scholarly journals Finding of the factors affecting the severity of COVID-19 based on mathematical models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiahao Qu ◽  
Brian Sumali ◽  
Ho Lee ◽  
Hideki Terai ◽  
Makoto Ishii ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Recognition Rate ◽  
Predictive Ability ◽  
Training Group ◽  
Acute Onset ◽  
Prognostic Indicators ◽  
Test Results ◽  
Data Set ◽  
Factors Affecting

AbstractSince 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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