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A new data augmentation method to use in machine learning algorithms using statistical measurements

MEASUREMENT 2021年 180卷 109577-109577页

作者： Avuclu, Emre Aksaray Univ Dept Comp Technol Aksaray Turkey

Cancer disease is among the leading causes of death in the world today. It has been scientifically proven that if the disease is detected in the first stages, the success rate in treatment can be close to 100%. Accordingly, it can be said that the problems caused by breast cancer can be solved to a great extent thanks to early diagnosis. Data sets that can be processed in early diagnosis are required. Researchers use artificial intelligence techniques to develop systems to assist specialist doctors. It is of great importance to have a data set on which researchers can work. The more parameters of these data sets and the number of these parameters, the more artificial learning process takes place. In this study, both a new data augmentation method for two different classes in the database of breast cancer was presented and breast cancer was diagnosed with this method. First, the database was analyzed statistically with the proposed data augmentation method. Then, the new values obtained from the data augmentation process were added to the database with a 5-times and 10-times augmentation. Experimental studies were conducted between raw database and databases with augmentatied data. The proposed model with 5 different machine learning algorithms (MLA) (k Nearest Neighbors (k-NN), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF)) and Support Vector machine (SVM)) were tested. The accuracy rates in train and test operations were increased in the following order;for k-NN, same - 15%, for DT, 0.1-13.01%, for RF, same 14.03%, for NB, 14.7%-19.7%, for SVM, 15.9%-14.06%. The rates obtained from the experimental results proved to be very promising. The proposed method can be used as an alternative data augmentation method for researchers to get more accurate results in their studies.

关键词： Biomedical diagnostics Breast cancer Data augmentation Statistical measurements machine learning algorithms

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Development of tool condition monitoring system in end milling process using wavelet features and Hoelder's exponent with machine learning algorithms

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MEASUREMENT 2021年 173卷 108671-108671页

作者： Mohanraj, T. Yerchuru, Jayanthi Krishnan, H. Aravind, R. S. Nithin Yameni, R. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore Tamil Nadu India

An effort was made to monitor the flank wear using wavelet analysis by extracting the Hoelder's exponent as a feature and using various machine learning algorithms to forecast the tool condition. The test was conducted on a Tungsten carbide insert with selected cutting parameters and the acquired vibration signals were used to develop the prediction model. The wavelet coefficients, Hoelder's exponent, and statistical features were extracted from the vibration signals. These features were used in machine learning algorithms like SVM, KNN, Kernel Bayes, Multilayer perceptron, and Decision trees to forecast the flank wear. The accuracy of the machining algorithm was analyzed through the confusion matrix and accuracy. The results revealed that HE along with wavelet coefficients performed better than statistical features. From the analysis, it was found that DT and SVM had the highest accuracy of 100% and 99.86% respectively. The performance of the selected ML was verified with benchmarking datasets and proves its accuracy.

关键词： Inconel 625 End milling Flank wear Vibration signals Hoelder's exponent machine learning algorithms Tool condition monitoring

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UHPLC-UV-Q-Orbitrap HRMS combined with machine learning algorithms reveals the chemical markers of Euodiae Fructus among closely related cultivars

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INDUSTRIAL CROPS AND PRODUCTS 2021年 162卷 113279-113279页

作者： Li, Caihong Zhao, Yan Li, Wenjing Xing, Rongrong Lu, Jing Liu, Rongxia Yantai Univ Key Lab Mol Pharmacol & Drug Evaluat Collaborat Innovat Ctr Adv Drug Delivery Syst & B Sch PharmMinist Educ Yantai 264005 Peoples R China

Euodiae Fructus (EF, the fruit of Tetradium ruticarpum) belongs to the family Rutaceae which has a complex genetic background, making classification a challenge. In order to classify the three closely related cultivars (small flower EF, medium flower EF, and big flower EF) and simplify the identification procedure in the future, an approach integrating ultra-high performance liquid chromatography-ultraviolet spectrum-hybrid quadrupoleOrbitrap high resolution mass spectrometry (UHPLC-UV-Q-Orbitrap HRMS) with machine learning algorithms was developed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) were used to assort the processed EF products and select the chemical markers to differentiate among the three EF cultivars, respectively. Then the thresholds of classification were determined using the eXtreme Gradient Boosting (XGboost) model based on the quantitative data of chemical markers by the UHPLC-Q-Orbitrap HRMS. The results demonstrated that the approach successfully discriminated among three closely related cultivars of EF, and five chemical markers (neochlorogenic acid, (+)-catechin, chlorogenic acid, dehydroevodiamine, and limonin) were selected. Thresholds were obtained to classify the three cultivars. If the content of neochlorogenic acid was greater than 7.52 mg/g, the sample belonged to small flower EF, otherwise, it belonged to medium flower EF or big flower EF. Furthermore, if the content of dehydroevodiamine was greater than 3.84 mg/g, the sample was medium flower EF, otherwise, it was big flower EF. In general, this research provides a novel strategy for the discrimination of Euodiae Fructus cultivars to avoid misapplication and possible side effects.

关键词： Euodiae Fructus UHPLC-UV-Q-Orbitrap HRMS machine learning algorithms Chemical markers Classification thresholds

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Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using machine learning algorithms and Google Earth Engine

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REMOTE SENSING 2021年第15期13卷 2934-2934页

作者： Zhang, Meiwei Zhang, Meinan Yang, Haoxuan Jin, Yuanliang Zhang, Xinle Liu, Huanjun Northeast Agr Univ Sch Publ Adm & Law Harbin 150030 Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing 100089 Peoples R China Chinese Acad Forestry Key Lab Forest Ecol & Environm State Forestry Adm Inst Forest Ecol Environm & Protect Beijing 100091 Peoples R China Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tsinghua Univ Sch Environm Beijing 100089 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130012 Peoples R China

Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R-2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.

关键词： soil organic matter Sentinel-2A MODIS machine learning algorithms Google Earth Engine Songnen Plain China

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Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to machine learning algorithms

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SENSORS 2021年第6期21卷 1957-1957页

作者： Dubois, Amandine Bihl, Titus Bresciani, Jean-Pierre Lorraine Univ 2LPN CEMA Grp Cognit EMot Act EA 7489 F-57070 Metz France Univ Fribourg Dept Neurosci & Movement Sci CH-1700 Fribourg Switzerland Cantonal Hosp Frailty Care CH-1700 Fribourg Switzerland Grenoble Alpes Univ Lab Psychol & NeuroCognit CNRS LPNCUMR 5105 F-38000 Grenoble France

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients' movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians' assessment toolkit and improve fall prevention.

关键词： fall prevention monitoring at home elderly people machine learning algorithms depth camera

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New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2021年 144卷 106656-106656页

作者： Gravett, Dewald Z. Mourlas, Christos Taljaard, Vicky-Lee Bakas, Nikolaos Markou, George Papadrakakis, Manolis Univ Pretoria Dept Civil Engn Pretoria South Africa Natl Tech Univ Athens Inst Struct Anal & Seism Res 9 Iroon Polytech StrZografou Campus GR-15780 Athens Greece Cyprus Inst Computat Based Sci & Technol Res Ctr Nicosia Cyprus

The importance of designing safe and economic structures in seismically active areas is of great importance. Thus, developing tools that would help in accurately predicting the dynamic properties of buildings is undoubtable a crucial objective. One of the parameters that significantly affects the seismic design of any structure is the fundamental period that is used to compute the seismic forces. It is well documented that the current design formulae for the prediction of the fundamental period of reinforced concrete buildings are simplistic and often fail to capture accurately their expected natural frequency. In addition, the design formulae do not have the ability to account for the soil-structure interaction (SSI) effect that, in some cases, significantly affects the natural frequency of buildings due to the additional flexibility induced by the soft soil. In this research work, a computationally efficient and robust 3D modeling approach is used for the modal analysis in order to investigate the accuracy of different design formulae in predicting the fundamental period of reinforced concrete buildings with and without SSI effects. In this context, 3D detailed modeling is used to generate a dataset that consists of 475 modal analyses, which is subsequently used to train and produce three predictive formulae using a higher order, nonlinear regression modeling framework. The developed fundamental period formulae were validated through the use of 60 out-of-sample modal results and they are also compared to other existing formulae in the international literature and design codes. According to the numerical findings, the proposed fundamental period formulae are found to have superior predictive capabilities for the under-study types of buildings.

关键词： Fundamental mode formula machine learning algorithms Soil-structure interaction Reinforced concrete Finite element method 3D detailed modeling

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Application of basic machine learning algorithms in railway brake disc temperature prediction

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Transportation Research Procedia 2021年 55卷 715-722页

作者： Vladimír Pavelčík Erik Kuba University of Žilina Faculty of mechanical engineering Department of transport and handling machines

This article is focused on brake disc temperature prediction introducing basic machine learning algorithms to reach this goal. In the first part, the problematics of brake disc frictional heating is described. Next, the methods currently used for predicting/calculating are described as well as their pros and cons. In the following part, some basic machine learning algorithms are described only very briefly, since going into details of theories of those algorithms in this article would extend it significantly. In the last part, the experiment with small dataset is described and its results and future possible work are discussed.

关键词： brake disc temperature prediction machine learning algorithms

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Trust in the European Central Bank: Using Data Science and predictive machine learning algorithms 10

Trust in the European Central Bank: Using Data Science and p...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Skirka, Andrii Adamyk, Bogdan Adamyk, Oksana Valytska, Mariana Ternopil Natl Econ Univ Dept Banking Ternopol Ukraine Ternopil Natl Econ Univ Dept Econ & Audit Ternopol Ukraine Natl Pedag Univ Ternopil Volodymyr Hnatiuk Ternopol Ukraine

ISBN: (纸本)9781728167602

Purpose: This empirical scientific research project aims to apply data science and machine learning tools to determine the influence of different factors on the level of trust in the European Central Bank. This research based on the data from the European Commission's Eurobarometer Survey 89. The paper also aims to represent some predictive analytics techniques to anticipate the level of confidence towards cental bank. Besides that, we build a couple of data visualizing plots, in order to show the main significant impact on the dependent variable. We created the ECB TrustMap Plot, correlation heatmap matrix and Alluvial diagram. Using this plots, we represented changes in network structure over people responses and decision making. Methodology: to calculate the index of trust in the central bank we used Logistic Regressioin, Decision Tree, Random Forrest and Neural Network models. Verify the output and results by using the VIF of the Logistic Model, Cross-validation, Confusion matrix, ROC-curves and accuracy estimations. Main Findings: trust in one-single currency, inflation problems, expectations about the future of EU, indicator of happiness and other indicators has a significant impact on the the level of trust in the central bank.

关键词： Data Science machine learning algorithms data visualization predictive analytics trust central bank

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Using Attribute-based Feature Selection Approaches and machine learning algorithms for Detecting Fraudulent Website URLs 10

Using Attribute-based Feature Selection Approaches and Machi...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Aydin, Mustafa Butun, Ismail Bicakci, Kemal Baykal, Nazife Middle East Tech Univ METU Informat Inst Ankara Turkey Banking Regulat & Supervis Agcy Istanbul Turkey Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden TOBB Univ Econ & Technol Comp Engn Dept Ankara Turkey METU Comp Engn Dept Northern Cyprus Campus Guzelyurt Cyprus

ISBN: (纸本)9781728137834

Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naive Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm;Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly;SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naive Bayes performed poor and is the least recommended method among all.

关键词： Attribute-based feature selection Cyber theft Data analysis Fraudulent website detection machine learning algorithms

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A Predictive Tool For Grid Data Analysis Using machine learning algorithms 10

A Predictive Tool For Grid Data Analysis Using Machine Learn...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Penn, David Subburaj, Vinitha Hannah Subburaj, Anitha Sarah Harral, Mark West Texas A&M Univ Sch Engn Comp Sci & Math Canyon TX 79016 USA Grp NIRE Lubbock TX USA

ISBN: (纸本)9781728137834

Power and energy sectors are collecting vast amount of data from different sources and trying to use computational tools to analyze and identify useful patterns in the data collected. Some of challenges observed with such big data are high volume, heterogeneous, and rapidly growing data. To efficiently handle such big data, machine learning algorithms are used. In this paper, such machine learning algorithms are used to predict patterns in the grid data collected from the Distributed Energy Resources (DER) at a local electrical engineering company. Predictive framework developed to preprocess the big data, classify the test and training datasets, and the application of different machine learning algorithms is discussed in this paper. The results obtained after analyzing the big data with different machine learning algorithms are also discussed in this paper.

关键词： Bigdata Grid Data Distributed Energy Resources (DER) machine learning algorithms

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