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4th ASME-ARPEL International Pipeline Geotechnical Conference (IPG)

作者： Aristizabal, Jaime Motta, Carlos Obregon, Nelson Capachero, Carlos Real, Leonardo Chaves, Julian Cenit Medellin Colombia Cenit Bogota Colombia Canal Clima Bogota Colombia

ISBN: (纸本)9780791885055

Cenit Transporte y Logistica de Hidrocarburos (CENIT), operator of about 7000 km of hydrocarbon transport systems, which constitutes it the largest operator in Colombia, has developed a strategic alliance to structure an adaptive geotechnical susceptibility zoning using supervised learning algorithms. Through this exercise, has been implemented operational decision inferences with simple linguistic values. The difficulties proposed by the method considers the hydroclimatology of Colombia, which is conditioned by several phenomena of Climate Variability that affect the atmosphere at different scales such as the Oscillation of the Intertropical Convergence Zone - ITCZ (seasonal scale) and the occurrence of macroclimatic phenomena such as El Nino-La Nina Southern Oscillation (ENSO) (interannual scale). Likewise, it considers the geotechnical complexity derived from the different geological formation environments, the extension and geographical dispersion of the infrastructure, and its interaction with the climatic regimes, to differentiate areas of interest based on the geohazards of hydrometeorological origin, when grouped into five clusters. The results of this exercise stand out the importance of keep a robust record of the events that affect the infrastructure of hydrocarbon transportation systems and using data-guided intelligence techniques to improve the tools that support decision-making in asset management.

关键词： Geohazards hydroclimatology Zoning supervised learning algorithms ITZC ENSO Susceptibility

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The Application of supervised learning algorithms in Predicting the Formation Energy of NLO Crystals

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ADVANCED THEORY AND SIMULATIONS 2024年第8期7卷

作者： Wang, Ai Yin, Yaohui Sun, Zhixin Jin, Guangyong Xin, Chao Changchun Univ Sci & Technol Sch Sci Jilin Key Lab Solid State Laser Technol & Applicat Changchun 130022 Peoples R China

Nonlinear optical crystals (NLO) are a key class of functional materials in the field of laser technology due to their excellent frequency conversion effects and physical-chemical stability. The research aims to find NLO crystals with superior stability by predicting their formation energy. In this study, only compositional information is utilized as input features and models are constructed using regression algorithms such as Random Forest Regression (RFR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Notably, the GBR model exhibited outstanding predictive performance, with an R-2 value of 0.935 and root mean square error (RMSE) of 0.248 eV per atom. Additionally, SHapley Additive exPlanations (SHAP) analysis is employed to elucidate the fundamental principles behind the predictions by assessing the contribution of each feature to the formation energy. To validate the reliability of the models, first-principles calculations are conducted to predict the formation energy of materials of GaP, ZnGeP2, and CdSiP2. The error range between the model predictions and the Generalized Gradient Approximation (GGA) calculated values is approximate to 0.1 eV per atom, confirming the accuracy of the models.

关键词： density functional theory formation energy machine learning nonlinear optical crystals supervised learning algorithms

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CREDIT RISK EVALUATION BASED ON supervised learning algorithms

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METALURGIA INTERNATIONAL 2012年第5期17卷 195-203页

作者： Novakovic, Jasmina Veljovic, Alempije Megatrend Univ Belgrade Belgrade Serbia Univ Kragujevac Tech Fac Cacak Kragujevac Serbia

Credit risk evaluation is a very important management science problem in the financial analysis area. The purpose with this paper is to present tools that may help to identify and foresee which clients will be good credit payers (or not) in relation to credit from banks. This paper investigates the impact of eight feature ranking and selection methods on seventeen classifiers on real life credit risk dataset. Consequences of choosing different supervised learning algorithms are monitored, together with the effects of different feature ranking and feature selection methods. Experimental results demonstrate the effectiveness of feature ranking and selection methods for different supervised learning algorithms. Although data in thing problems involve, in general, thousands or even millions of data, different from the problem presented here, the conclusion arrived at can be used as support for larger problems.

关键词： classification accuracy credit risk feature ranking and selection supervised learning algorithms

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Comparison of classification and supervised learning algorithms in assessing the hydraulic conditions of sewer collection systems: A case study of local sewer networks in Jinju City, Korea

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DESALINATION AND WATER TREATMENT 2018年 124卷 202-210页

作者： Ki, Seo Jin Lee, Chun-Sik Jung, Won-Hee Park, Hyun-Geoun Gyeongnam Natl Univ Sci & Technol Dept Environm Engn 33 Dongjin Ro Jinju Si 52725 Gyeongsangnam D South Korea

Accurate screening of sewer conditions from monitoring data contributes to maintaining their operations (in terms of water quality and quantity) safe as well as reducing their associated costs (for operation and maintenance). This study was designed to assess the performance deterioration in sewer systems using a series of data classification tools, namely classical classification and novel supervised learning algorithms. The hydraulic data available for four sewer systems at Jinju City in Korea in a daily format during the monitoring period of 2013-2017 were provided as example data sets to those algorithms, which were evaluated independently with 70% training and 30% test data sets randomly divided. A self-organizing map (SOM) with a specialty in extracting hidden patterns in data was used to classify the data sets into three warning levels in the absence of any definite warning criteria for individual parameters. Our findings showed that three supervised learning algorithms achieved comparable performance in predicting warning levels defined from SOM to exiting classification algorithm in terms of accuracy and error rate. The network architecture optimized for supervised learning algorithms, in fact, varied significantly depending on the data sets, including that with additional variables on top of the original data set. In contrast, exiting classification algorithm unexpectedly produced high error rates in case that the hydraulic parameters had low coefficient of variation values reaching as high as 16%. Overall, these results demonstrated that novel supervised learning algorithms were more universally applicable for the assessment of hydraulic and/or water quality conditions in sewer systems than classical classification algorithm, regardless of the amount of variability in the data sets.

关键词： supervised learning algorithms Classification algorithm Self-organizing map Hydraulic parameters Water quality Sewer systems

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Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners

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NEUROLOGICAL SCIENCES 2020年第12期41卷 3719-3727页

作者： Uncini, Antonino Aretusi, Graziano Manganelli, Fiore Sekiguchi, Yukari Magy, Laurent Tozza, Stefano Tsuneyama, Atsuko Lefour, Sophie Kuwabara, Satoshi Santoro, Lucio Ippoliti, Luigi Univ G DAnnunzio Dept Neurosci Imaging & Clin Sci Via Luigi Polacchi 11 I-66100 Chieti Italy Univ G DAnnunzio Dept Econ Stat Unit Chieti Italy Univ Naples Federico II Dept Neurosci Reprod Sci & Odontostomatol Naples Italy Chiba Univ Grad Sch Med Dept Neurol Chiba Japan Univ Limoges Natl Reference Ctr Rare Peripheral Neuropathies Limoges France Univ Limoges Dept Neurol Limoges France

Objective The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and three expert and three trainee neurophysiologists. Methods We enrolled 434 subjects with the following diagnoses: chronic inflammatory demyelinating polyneuropathy (99), Charcot-Marie-Tooth disease type 1A (124), hereditary neuropathy with liability to pressure palsy (46), diabetic polyneuropathy (67), and controls (98). In each diagnostic class, 90% of subjects were used as training set for SLAs to establish the best performing SLA by tenfold cross validation procedure and 10% of subjects were employed as test set. Performance indicators were accuracy, precision, sensitivity, and specificity. Results SVM showed the highest overall diagnostic accuracy both in training and test sets (90.5 and 93.2%) and ranked first in a multidimensional comparison analysis. Overall accuracy of neurophysiologists ranged from 54.5 to 81.8%. Conclusions This proof of principle study shows that SVM provides a high electrodiagnostic accuracy in polyneuropathies. We suggest that the use of SLAs in electrodiagnosis should be exploited to possibly provide a diagnostic support system especially helpful for the less experienced practitioners.

关键词： Polyneuropathies Electrodiagnosis Diagnostic accuracy supervised learning algorithms

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A Comparative Study of supervised learning algorithms for Re-opened Bug Prediction

A Comparative Study of Supervised Learning Algorithms for Re...

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17th European Conference on Software Maintenance and Reengineering (CSMR)

作者： Xia, Xin Lo, David Wang, Xinyu Yang, Xiaohu Li, Shanping Sun, Jianling Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9780769549484;9781467358330

Bug fixing is a time-consuming and costly job which is performed in the whole life cycle of software development and maintenance. For many systems, bugs are managed in bug management systems such as Bugzilla. Generally, the status of a typical bug report in Bugzilla changes from new to assigned, verified and closed. However, some bugs have to be reopened. Reopened bugs increase the software development and maintenance cost, increase the workload of bug fixers, and might even delay the future delivery of a software. Only a few studies investigate the phenomenon of reopened bug reports. In this paper, we evaluate the effectiveness of various supervised learning algorithms to predict if a bug report would be reopened. We choose 7 state-of-the-art classical supervised learning algorithm in machine learning literature, i.e., kNN, SVM, SimpleLogistic, Bayesian Network, Decision Table, CART and LWL, and 3 ensemble learning algorithms, i.e., AdaBoost, Bagging and Random Forest, and evaluate their performance in predicting reopened bug reports. The experiment results show that among the 10 algorithms, Bagging and Decision Table (IDTM) achieve the best performance. They achieve accuracy scores of 92.91% and 92.80%, respectively, and reopened bug reports F-Measure scores of 0.735 and 0.732, respectively. These results improve the reopened bug reports F-Measure of the stateof-the-art approaches proposed by Shihab et al. by up to 23.53%.

关键词： bug reports reopened reports classification supervised learning algorithms comparative study

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Emotion Recognition on Selected Facial Landmarks Using supervised learning algorithms

Emotion Recognition on Selected Facial Landmarks Using Super...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Baculo, Maria Jeseca C. Azcarraga, Judith J. Don Mariano Marcos Mem State Univ Coll Comp Sci La Union Philippines De La Salle Univ Coll Comp Studies Manila Philippines

ISBN: (纸本)9781538666500

Facial landmarks may be used to localize the movement of facial muscles that help identify an emotion. It is important that these points are appropriately represented to achieve a successful emotion recognition rate. In this paper, the extraction of 68 facial landmarks, normalization methods and classification of 7 basic emotions are presented. The Cohn-Kanade Database is used as a test bed for the different emotion recognition tasks. The images are normalized by transforming the inputs based on similarity (CKCT) and the mean shape (CKMS). Forward Search and Principal Component Analysis are used to identify the most important features among the 68 facial points. Decision Tree, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron algorithms are used in building classifiers on reduced and complete feature set. It is interesting to note that facial points in the mouth area are found to be significant in the classification of emotions.

关键词： emotion recognition facial landmarks feature selection supervised learning algorithms

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Classification of Hadith According to Its Content Based on supervised learning algorithms

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IEEE ACCESS 2019年 7卷 152379-152387页

作者： Abdelaal, Hammam M. Elemary, Berihan R. Youness, Hassan A. Menia Univ Fac Engn Dept Comp & Syst Engn Al Minya 61519 Egypt Damietta Univ Dept Stat Fac Commerce Dumyat 34511 Egypt

Given the importance of the Prophets Hadith for Muslims all over the world, where it is the second source of Islam after the Quran and the fundamental resource of legislation in the Islam community. This study is focused on the Classification of hadith automatically into different categories according to its content, based on Hadith text. The objective of this study is to build a classifier model can classify and differentiate hadith categories, to predict its topic like prayer, fasting, and zakat;using data mining and machine learning techniques. In this study, many supervised learning algorithms plus combination methods such as the stacking algorithm was used to improve classification accuracy. The best three classifiers were evaluated mainly: the Decision Tree (DT), Random Forest (RF), and Nave Bayes (NB), which achieved higher accuracy reached up to 0.965, 0.956, and 0.951 respectively. Also, Binary (Boolean algebra) and TF-IDF methods as term weighting was applied to determine the frequency of each word in the hadith text, and identify the most significant features in training dataset using Information Gain (IG), and Chi-square (CHI). The experimental results showed that re-train these classifiers after applying IG and CHI as features selection;gave better accuracy compared to the previous results. Additional to, the best classifier gave high accuracy was DT, it has achieved higher accuracy in most test cases whether in the Boolean algebra or TF-IDF because it can deal with missing values and identifying the most essential features from the training dataset, known as features engineering.

关键词： Licenses Data mining supervised learning algorithms text classification term weighting features selection prophetic hadith

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Investigating the performance of the supervised learning algorithms for estimating NPPs parameters in combination with the different feature selection techniques

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ANNALS OF NUCLEAR ENERGY 2021年 158卷 108299-108299页

作者： Moshkbar-Bakhshayesh, Khalil Sharif Univ Technol Dept Energy Engn Azadi Ave Tehran Iran

Several reasons such as no free lunch theorem indicates that any learning algorithm in combination with a specific feature selection (FS) technique may give more accurate estimation than other learning algorithms. Therefore, there is not a universal approach that outperforms other algorithms. Moreover, due to the large number of FS techniques, some recommended solutions such as using synthetic dataset or combining different FS techniques are very tedious and time consuming. In this study to tackle the issue of more accurate estimation of NPPs parameters, the performance of the major supervised learning algorithms in combination with the different FS techniques which are appropriate for parameters estimation is considered. The target parameters/transients of the Bushehr nuclear power plant (BNPP) are examined as the case study. By comparing three major supervised learning algorithms (i.e. the MLP-BR, the MLP-LM, and the SVM) in combination with six principal FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) for estimation of three important parameters of NPP (i.e. FMT, CMT, and the DNBR), the BR learning algorithm gives the more accurate results. Therefore, the results show that if the number of FS techniques is m and the number of learning algorithms is n, the search space for more accurate estimation of the NPPs important parameters can be reduced from n x m to 1 x m. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Bayesian regularization Feature selection techniques No free lunch theorem NPPs parameters estimation supervised learning algorithms

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Hybrid Approach for supervised Machine learning algorithms to Identify Damage in Bridges

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JOURNAL OF BRIDGE ENGINEERING 2024年第8期29卷

作者： Bud, Mihai Adrian Nedelcu, Mihai Moldovan, Ionut Figueiredo, Eloi Tech Univ Cluj Napoca Fac Civil Engn Memorandumului 28 Cluj Napoca 400114 Romania Lusofona Univ Fac Engn Campo Grande 376 P-1749024 Lisbon Portugal

In bridges, monitoring data usually correspond to normal operational and environmental conditions, resulting in a lack of damage-related data. For this reason, machine learning algorithms for damage detection are typically unsupervised. Conversely, numerical models are employable as surrogates for extreme events rarely encountered during the existence of a bridge and for common damage scenarios, enabling the training of supervised machine learning algorithms. In this paper, hybrid data obtained by integrating monitoring and numerical observations from the Z-24 Bridge benchmark are used for training supervised machine learning algorithms to classify damage. Special attention is dedicated to the numerical model that, while being simple enough to be used on thousands of runs, must capture the complex nonlinear behavior that typifies damaged conditions. A model updating technique is used for the preliminary calibration of the finite-element model. Numerical data are generated in a probabilistic manner starting from the initial finite-element model, assuming the Gaussian distribution of the uncertain parameters. Three undamaged scenarios and three damaged scenarios are modeled. Subsequently, several supervised learning classifiers are trained with the same hybrid database and a comparative summary of their effectiveness is presented. The paper introduces a novel soft classification method that accounts for the overlapping of observations belonging to different structural conditions in the feature space. This study proves that data generated from probabilistic-based finite-element models can be used for structural health monitoring and damage identification in the context of bridges, therefore providing a hybrid supervised approach that can be easily applied in practice. Because bridges are one-of-a-kind, expensive structures, considerable efforts are made to ensure they remain in service for as long as possible. Consequently, monitoring systems are being installed on mor

关键词： Structural health monitoring supervised learning algorithms Damage identification Environmental variability Finite-element modeling Hybrid training database

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