Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718

This research work focuses on machine-learning-assisted prediction of the corrosion behavior of laser-powder-bed-fused (LPBF) and postprocessed Inconel 718. Corrosion testing data of these specimens were collected and fit into the following machine learning algorithms: polynomial regression, support vector regression, decision tree, and extreme gradient boosting. The model performance, after hyperparameter optimization, was evaluated using a set of established metrics: R2, mean absolute error, and root mean square error. Among the algorithms, the extreme gradient boosting algorithm performed best in predicting the corrosion behavior, closely followed by other algorithms. Feature importance analysis was executed in order to determine the postprocessing parameters that influenced the most the corrosion behavior in Inconel 718 manufactured by LPBF.

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An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

Journal of Marine Science and Engineering ◽

10.3390/jmse9020156 ◽

2021 ◽

Vol 9 (2) ◽

pp. 156

Author(s):

Jian He ◽

Yong Hao ◽

Xiaoqiong Wang

Keyword(s):

Machine Learning ◽

Decision Making ◽

Model Performance ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

State Control ◽

Extreme Gradient Boosting ◽

Decision Making Model ◽

Flag State

The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying ship detention. In this study, we propose a novel interpretable ship detention decision-making model based on machine learning, termed SMOTE-XGBoost-Ship detention model (SMO-XGB-SD), using the extreme gradient boosting (XGBoost) algorithm and the synthetic minority oversampling technique (SMOTE) algorithm to identify whether a ship should be detained. Our verification results show that the SMO-XGB-SD algorithm outperforms random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithm. In addition, the new algorithm also provides a reasonable interpretation of model performance and highlights the most important features for identifying ship detention using the Shapley additive explanations (SHAP) algorithm. The SMO-XGB-SD model provides an effective basis for aiding decisions on ship detention by inland flag state control officers (FSCOs) and the ship safety management of ship operating companies, as well as training services for new FSCOs in maritime organizations.

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Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39088 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1-10

Author(s):

Harsha A K

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Learning Algorithms ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Steady Increase ◽

Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

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Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090507 ◽

2020 ◽

Vol 9 (9) ◽

pp. 507

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Stuart Phinn

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Algorithms ◽

Principal Component ◽

Hyperspectral Data ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Forest Height ◽

Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

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Application of machine learning algorithms for flood susceptibility assessment and risk management

Journal of Water and Climate Change ◽

10.2166/wcc.2021.051 ◽

2021 ◽

Author(s):

R. Madhuri ◽

S. Sistla ◽

K. Srinivasa Raju

Keyword(s):

Climate Change ◽

Machine Learning ◽

Influencing Factors ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Climate Change Scenarios ◽

Adaptive Boosting ◽

Extreme Gradient Boosting ◽

Normalised Difference Vegetation Index

Abstract Assessing floods and their likely impact in climate change scenarios will enable the facilitation of sustainable management strategies. In this study, five machine learning (ML) algorithms, namely (i) Logistic Regression, (ii) Support Vector Machine, (iii) K-nearest neighbor, (iv) Adaptive Boosting (AdaBoost) and (v) Extreme Gradient Boosting (XGBoost), were tested for Greater Hyderabad Municipal Corporation (GHMC), India, to evaluate their clustering abilities to classify locations (flooded or non-flooded) for climate change scenarios. A geo-spatial database, with eight flood influencing factors, namely, rainfall, elevation, slope, distance from nearest stream, evapotranspiration, land surface temperature, normalised difference vegetation index and curve number, was developed for 2000, 2006 and 2016. XGBoost performed the best, with the highest mean area under curve score of 0.83. Hence, XGBoost was adopted to simulate the future flood locations corresponding to probable highest rainfall events under four Representative Concentration Pathways (RCPs), namely, 2.6, 4.5, 6.0 and 8.5 along with other flood influencing factors for 2040, 2056, 2050 and 2064, respectively. The resulting ranges of flood risk probabilities are predicted as 39–77%, 16–39%, 42–63% and 39–77% for the respective years.

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Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

10.1101/2021.11.24.469888 ◽

2021 ◽

Author(s):

Mandana Modabbernia ◽

Heather C Whalley ◽

David Glahn ◽

Paul M. Thompson ◽

Rene S. Kahn ◽

...

Keyword(s):

Machine Learning ◽

Computational Efficiency ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Sensitivity Analyses ◽

Gradient Boosting ◽

Support Vector ◽

Age Related ◽

Extreme Gradient Boosting ◽

Brain Age

Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in an independent holdout datasets, comprising 4,078 pre-adolescents (aged 9-10 years). The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.25 years for females and 1.57 years for males), Random Forest Regression (MAE of 1.23 years for females and 1.65 years for males) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.47 years for females and 1.72 years for males) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes during development.

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Research on dairy products detection based on machine learning algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/202235503008 ◽

2022 ◽

Vol 355 ◽

pp. 03008

Author(s):

Yang Zhang ◽

Lei Zhang ◽

Yabin Ma ◽

Jinsen Guan ◽

Zhaoxia Liu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Electronic Nose ◽

Milk Fat ◽

Dairy Products ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

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Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection

Sensors ◽

10.3390/s19010045 ◽

2018 ◽

Vol 19 (1) ◽

pp. 45 ◽

Cited By ~ 19

Author(s):

Huixiang Liu ◽

Qing Li ◽

Bin Yan ◽

Lei Zhang ◽

Yu Gu

Keyword(s):

Machine Learning ◽

Electronic Nose ◽

Optimal Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Oxide Semiconductor ◽

Gradient Boosting ◽

Support Vector ◽

Fermentation Processes ◽

Extreme Gradient Boosting

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

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Sign language dactyl recognition based on machine learning algorithms

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2021.239253 ◽

2021 ◽

Vol 4 (2(112)) ◽

pp. 58-72

Author(s):

Chingiz Kenshimov ◽

Zholdas Buribayev ◽

Yedilkhan Amirgaliyev ◽

Aisulyu Ataniyazova ◽

Askhat Aitimov

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Sign Language ◽

Gesture Recognition ◽

Research Work ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language

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Drilling Optimization Applying Machine Learning Regression Algorithms

10.4043/30934-ms ◽

2021 ◽

Author(s):

Freddy J. Marquez

Keyword(s):

Machine Learning ◽

Parameter Optimization ◽

Oil And Gas ◽

Model Performance ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Paper Machine ◽

Drilling Performance ◽

Sensitivity Parameter ◽

Extreme Gradient Boosting

Abstract Machine Learning is an artificial intelligence subprocess applied to automatically and quickly perform mathematical calculations to data in order to build models used to make predictions. Technical papers related to machine learning algorithms applications have being increasingly published in many oil and gas disciplines over the last five years, revolutionizing the way engineers approach to their works, and sharing innovating solutions that contributes to an increase in efficiency. In this paper, Machine Learning models are built to predict inverse rate of penetration (ROPI) and surface torque for a well located at Gulf of Mexico shallow waters. Three type of analysis were performed. Pre-drill analysis, predicting the parameters without any data of the target well in the database. Drilling analysis, running the model every sixty meters, updating the database with information of the target well and predicting the parameters ahead the bit. Sensitivity parameter optimization analysis was performed iterating weight on bit and rotary speed values as model inputs in order identify the optimum combination to deliver the best drilling performance under the given conditions. The Extreme Gradient Boosting (XGBoost) library in Python programming language environment, was used to build the models. Model performance was satisfactory, overcoming the challenge of using drilling parameters input manually by drilling bit engineers. The database was built with data from different fields and wells. Two databases were created to build the models, one of the models did not consider logging while drilling (LWD) data in order to determine its importance on the predictions. Pre-drill surface torque prediction showed better performance than ROPI. Predictions ahead the bit performance was good both for torque and ROPI. Sensitivity parameter optimization showed better resolution with the database that includes LWD data.

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Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Remote Sensing ◽

10.3390/rs12142234 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2234 ◽

Cited By ~ 6

Author(s):

Mostafa Emadi ◽

Ruhollah Taghizadeh-Mehrjardi ◽

Ali Cherati ◽

Majid Danesh ◽

Amir Mosavi ◽

...

Keyword(s):

Machine Learning ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Composite Surface ◽

Auxiliary Data ◽

Extreme Gradient Boosting

Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.

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