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International Conference on Computer and Communications (ICCC)

作者： Zhang Tian Jing Xiang Sun Zhenxiao Zhang Yi Yan Yunqiang Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Software defect prediction is very important in software engineering. It uses the defects found in historical software modules to predict defects in new software modules, and provides decision support for planning and process management in software project. machine learning is one of the core research directions in the field of artificial intelligence and covers many disciplines. In the research of software defect prediction, various machine learning methods have been widely studied and applied, and have been verified to obtain good performance. In this paper, the basic framework of software defect prediction model based on machine learning is described, several important prediction models are studied and discussed. Finally, the main problems of software defect prediction are summarized, and corresponding solutions are proposed.

关键词： Software Predictive models machine learning machine learning algorithms Data models Classification algorithms Support vector machines

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machine learning algorithms for Classifying Abscessed and Impacted Tooth: Comparison Study

Machine Learning Algorithms for Classifying Abscessed and Im...

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International Conference on Biomedical Engineering (IBIOMED)

作者： Ni’matul ’Abdah Adhiya Fakhriy Igi Ardiyanto Hanung Adi Nugroho Gilang Nugraha Putu Pratama Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia Universitas Gadjah Mada Yogyakarta Indonesia

This paper presents a comparative study of machine learning algorithms for classifying normal, abscessed, and impacted tooth based on periapical radiograph images. Those methods are Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector machine (SVM). Haralick texture, Hu's moment invariants, and color histogram are utilized to obtain the feature vector of those images. The accuracy can be calculated with 10-fold cross-validation. We also verify the accuracy of the machine learning algorithms under the various number of training images. We take 30, 45, and 60 images from three classes. Regardless the number of training images, RF keeps outperforming the others in the term of accuracy.

关键词： Teeth Dentistry machine learning algorithms Radio frequency Support vector machines Image color analysis Forestry

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machine learning algorithms to Detect Penetrations of PVDF

Machine Learning Algorithms to Detect Penetrations of PVDF

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Yayu Zhai Ping Song Xiaoxiao Chen Lab. of Biomimetic Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781538665664;9781538665657

Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust. In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimental equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted. Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for our problem, and AD is not suitable for our problem.

关键词： machine learning algorithms Calibration Artificial neural networks Support vector machines Classification algorithms Detectors Training

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machine learning algorithms for Classification Geology Data from Well Logging

Machine Learning Algorithms for Classification Geology Data ...

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International Conference on Electronics, Computer and Computation (ICECCO)

作者： Timur Merembayev Rassul Yunussov Amirgaliyev Yedilkhan IICT Almaty Kazakhstan Satbayev University Almaty Kazakhstan Suleyman Demirel University Kaskelen Kazakhstan

machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

关键词： machine learning algorithms Rocks Geophysics Boosting Training Logistics

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Predicting countermovement jump heights by time domain, frequency domain, and machine learning algorithms

Predicting countermovement jump heights by time domain, freq...

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作者： Zhou, Zhaoxian Shakya, Sarbagya Sha, Zhanxin School of Computing University of Southern Mississippi HattiesburgMS United States School of Kinesiology University of Southern Mississippi HattiesburgMS United States

ISBN: (纸本)9781538636749

In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms. © 2017 IEEE.

关键词： Countermovement jump Frequency domain method machine learning algorithms Sports performance evaluation Time domain method Vertical ground reaction force

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An Evaluation of Eight machine learning Regression algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

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REMOTE SENSING 2020年第24期12卷 4015-4015页

作者： Zhang, Yuzhen Ma, Jun Liang, Shunlin Li, Xisheng Li, Manyao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China Univ Maryland Dept Geog Sci College Pk MD 20742 USA

This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optimal AGB model was developed using the training dataset (80%) and AGB was predicted on the test dataset (20%). Performance metrics, feature importance as well as overestimation and underestimation were considered as indicators for evaluating the performance of an algorithm. To reduce the impacts of the random training-test data split and sampling method on the performance, the above procedures were repeated 50 times for each algorithm under the random sampling, the stratified sampling, and separate modeling scenarios. The results showed that five tree-based ensemble algorithms performed better than the three nonensemble algorithms (multivariate adaptive regression splines, support vector regression, and multilayer perceptron), and the CatBoost algorithm outperformed the other algorithms for AGB estimation. Compared with the random sampling scenario, the stratified sampling scenario and separate modeling did not significantly improve the AGB estimates, but modeling AGB for each forest type separately provided stable results in terms of the contributions of the predictor variables to the AGB estimates. All the algorithms showed forest AGB were underestimated when the AGB values were larger than 210 Mg/ha and overestimated when the AGB values were less than 120 Mg/ha. This study highlighted the capability of ensemble algorithms to improve AGB estimates and the necessity of improving

关键词： forest biomass machine learning algorithms satellite data product model comparison ensemble algorithm

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A Data Analysis and Market Price Prediction of Ethiopian Commodity Market with machine learning algorithms

A Data Analysis and Market Price Prediction of Ethiopian Com...

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作者： Selam, Damtew Addis Ababa University

学位级别：硕士

The current Ethiopian market is conducted in a traditional manner and market drivers are still not used for prediction of future market price. Although, large amount of market data have been gathered throughout years by both governmental and non-governmental organizations, yet little have been done to analyze the data for future market price prediction. Moreover, the analysis methods were often manual creating inefficiency in time and quality of market prediction. Analyzing valuable data will show us what the future holds and accelerate the development goals of the country in the sector. The study examines features of current Ethiopian market attributes to find out most valuable features for predicting market price. Eighteen technical indicators are taken and tested for their individual ability of prediction and redundancy. From the feature selection of commodity marke, we have found that features like Stochastic %K, Stochastic %D, Close gain/loss, High, close price, Opening Price, Low, RSI, Ton and Moving Average Convergence/ divergence (MACD) founded to be in the top ten of individual performance evaluation. Moreover features namely Stochastic %K, Relative Strength Index (RSI), Bollinger Bands-Upper, Highest-High, close gain/loss, Simple Moving Average (SMA), Closing price, MACD-Fast, Exponential Moving Average (EMA), MACD-Slow and Low founded to be less redundant. The study also compares four machine learning models for their prediction ability of Ethiopian commodity market price. The outcomes of feature selection were used to compare the models. Two experiments were conducted; the first was comparison of the models with 10 fold cross validation using feature of high individual predictive ability and less redundancy. The second one was a comparison of models with separate train and test data using features of high individual predictive ability and less redundancy. From the models (Support Vector machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (

关键词： Feature selection Technical Indicators Price prediction machine learning algorithms

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Gut Microbiota in T1DM-Onset Pediatric Patients: machine-learning algorithms to Classify Microorganisms as Disease Linked

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JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM 2020年第9期105卷 E3114-E3126页

作者： Biassoni, Roberto Di Marco, Eddi Squillario, Margherita Barla, Annalisa Piccolo, Gianluca Ugolotti, Elisabetta Gatti, Cinzia Minuto, Nicola Patti, Giuseppa Maghnie, Mohamad d'Annunzio, Giuseppe IRCCS Ist Giannina Gaslini Anal Lab Mol Diagnost I-16145 Genoa Italy Univ Genoa DIBRIS I-16145 Genoa Italy IRCCS Ist Giannina Gaslini Pediat Clin Reg Ctr Pediat Diabet I-16147 Genoa Italy Univ Genoa Dept Pediat IRCCS Ist Giannina Gaslini I-16147 Genoa Italy Univ Genoa Dept Neurosci Rehabil Ophthalmol Genet Maternal & I-16147 Genoa Italy

Aims The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes. Methods The microbiome of 31 children with type 1 diabetes at onset and of 25 healthy children was determined using multiple polymorphic regions of the 16S ribosomal RNA. We performed machine-learning analyses and metagenome functional analysis to identify significant taxa and their metabolic pathways content. Results Compared with healthy controls, patients showed a significantly higher relative abundance of the following most important taxa: Bacteroides stercoris, Bacteroides fragilis, Bacteroides intestinalis, Bifidobacterium bifidum, Gammaproteobacteria and its descendants, Holdemania, and Synergistetes and its descendants. On the contrary, the relative abundance of Bacteroides vulgatus, Deltaproteobacteria and its descendants, Parasutterella and the Lactobacillus, Turicibacter genera were significantly lower in patients with respect to healthy controls. The predicted metabolic pathway more associated with type 1 diabetes patients concerns "carbon metabolism," sugar and iron metabolisms in particular. Among the clinical variables considered, standardized body mass index, anti-insulin autoantibodies, glycemia, hemoglobin A(1c), Tanner stage, and age at onset emerged as most significant positively or negatively correlated with specific clusters of taxa. Conclusions The relative abundance and supervised analyses confirmed the importance of B stercoris in type 1 diabetes patients at onset and showed a relevant role of Synergistetes and its descendants in patients with respect to healthy controls. In general the robustness and coherence of the showed results underline the relevance of studying the microbioma using multiple polymorphic regions, different types of analysis, and different approaches within each analysis.

关键词： adolescent autoimmunity children type 1 diabetes mellitus gut machine learning algorithms microbiota

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Tuning the Turkish Text Classification Process Using Supervised machine learning-based algorithms

Tuning the Turkish Text Classification Process Using Supervi...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Ömer Köksal ASELSAN Research Center ASELSAN Ankara Turkey

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

Text classification is the process of determining categories or tags of a document depending on its content. Although it is a well-known process, it has many steps that require tuning to have better mathematical models. In this context, as an agglutinative language, especially the Turkish text classification process requires some extra tuning and preprocessing steps. This paper proposes a methodology and expresses key-points for tuning the Turkish text classification process using supervised machine learning algorithms. For this purpose, we perform intensive experiments on an open Turkish news dataset. Our study shows that our methodology improves categorization results based on F1-score.

关键词： Text categorization Tuning Classification algorithms machine learning algorithms Support vector machines machine learning Mathematical model

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The Application Study of Credit Risk Model In Financial Institution via machine-learning algorithms

The Application Study of Credit Risk Model In Financial Inst...

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Yuanzhang Wang Jiongcheng Lu Jiehan Qin Chenyi Zhang Yiyang Chen Southwestern University Of Finance And Economics Chengdu Sichuan China Wenzhou-Kean University Wenzhou China University of California Irvine California United States Feng Chia University Taiwan China University of Warwick Coventry United Kingdom

As a kind of statistical model, credit scoring technology is widely used in the risk assessment of loan applicants. It can predict the credit risk of applicants based on the information provided by the borrowers, such as their historical data and the data from banking system. Based on the data provided by a financial institution, this paper focuses on the credit risk analysis and evaluation based on machine learning algorithms, including Logistic Regression algorithm, Decision Tree algorithm, and Random Forest algorithm. What’s more, this paper completes data preprocessing, variable selection, WOE coding discretization and credit scorecard creation. The basic score of this credit scorecard is 750 points. The probability of default will double when the score is reduced by every 60 points. The experimental results exhibit machine learning algorithms are feasible approaches to evaluate credit. Besides, within the study’s training and testing samples, such objective evaluation parameters as precision, recall, AUC, KS, and F1 score, which indicate Random Forest algorithm, Decision Tree algorithm, and Logistic Regression algorithm can all apply to financial risk analysis.

关键词： Training machine learning algorithms Input variables Predictive models Prediction algorithms Risk management Regression tree analysis

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