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Story Point-Based Effort Estimation Model with Machine Learning Techniques

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2020年第1期30卷 43-66页

作者： Gultekin, Muaz Kalipsiz, Oya Yildiz Tech Univ Dept Comp Engn TR-34349 Istanbul Turkey

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

关键词： Analogy-based effort estimation Scrum machine learning story point gradient boosting algorithm Support Vector Regression Random Forest Regression Multi-Layer Perceptron

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Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble gradient boosting in Estimating Respiratory Rate

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IEEE ACCESS 2020年 8卷 125648-125658页

作者： Lee, Soojeong Son, Chang-Hwan Albertini, Marcelo K. Fernandes, Henrique C. Sejong Univ Dept Comp Engn Seoul 05006 South Korea Kunsan Natl Univ Dept Software Convergence Engn Gunsan 54150 South Korea Univ Fed Uberlandia Sch Comp Sci BR-38408100 Uberlandia MG Brazil Fraunhofer JZFP Inst Nondestruct Testing Saarbruc D-66119 Saarbrucken Germany

Estimating the correct respiratory rate (RR) is an essential technique for intensive care units, hospitals, geriatric hospital facilities, and home care services. Capnography is a standard methodology used to monitor carbon dioxide concentrations or partial pressures of respiratory gases to provide the most accurate RR measurements. However, it is inconvenient to use and has been primarily used while administering anesthesia and during intensive care. Many researchers now use electrocardiogram signals to estimate RR. Despite the recent developments, the current hospital environments suffer from inaccurate respiratory monitoring. While various machine learning techniques, including deep learning, have recently been applied to the medical processing sector, only a few studies have been conducted in the field of RR estimation. Therefore, using photoplethysmography, machine-learning techniques such as the ensemble gradient boosting algorithm are being employed in RR estimation. Multi-phases are used based on various feature extraction and selection methodology to improve the performance for RR estimation. In this study, the number of ensembles is increased, and the proposed ensemble methodology is effectively learned to estimate the RR. The proposed ensemble-based gradient boosting algorithm are compared with those of ensemble-based long-short memory network, and ensemble-based supported vector regression techniques, 3.30 breaths per min (bpm), 4.82 bpm and 5.83 bpm based on mean absolute errors. The proposed method shows a more accurate estimate of the respiration rate.

关键词： Respiration rate estimation gradient boosting algorithm ensemble methodology photolethysmography signals

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An Improved LightGBM algorithm for Online Fault Detection of Wind Turbine Gearboxes

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ENERGIES 2020年第4期13卷 807-807页

作者： Tang, Mingzhu Zhao, Qi Ding, Steven X. Wu, Huawei Li, Linlin Long, Wen Huang, Bin Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst AKS D-47057 Duisburg Germany Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Guizhou Univ Finance & Econ Guizhou Key Lab Econ Syst Simulat Guiyang 550004 Peoples R China Univ South Australia Sch Engn Adelaide SA 5095 Australia

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

关键词： fault diagnosis maximum information coefficient Bayesian hyper-parameter optimization gradient boosting algorithm LightGBM

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In-process characterization of surface finish in cylindrical grinding process using vibration and power signals

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Procedia CIRP 2020年 88卷 335-340页

作者： Supriyo Mahata Piyush Shakya N. Ramesh Babu Pradeep K. Prakasam Department of Mechanical Engineering Indian Institute of Technology Madras Chennai 600036 India Advanced Manufacturing Technology Development Centre Indian Institute of Technology Madras Chennai 600036 India

Grinding being a finishing process, the quality of the ground surface is one of the most important performance evaluation parameters. Grinding process being highly stochastic in nature, surface finish is affected by many factors and experimental evaluation of each factor is a tedious task. In this study, the in-process signals collected using various sensors attached to a cylindrical grinding machine such as Accelerometer and Power are processed, and their features are correlated with a surface finish parameter. This correlation is modelled using gradient boosting algorithm and surface finish obtained is predicted and validated on an industrial application.

关键词： Empirical mode decomposition Feature extraction gradient boosting algorithm Surface finish prediction

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Early prediction of preeclampsia via machine learning

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AMERICAN JOURNAL OF OBSTETRICS & GYNECOLOGY MFM 2020年第2期2卷 100100页

作者： Maric, Ivana Tsur, Abraham Aghaeepour, Nima Montanari, Andrea Stevenson, David K. Shaw, Gary M. Winn, Virginia D. Stanford Univ Dept Pediat Sch Med Stanford CA 94305 USA Stanford Univ Dept Anesthesiol Perioperat & Pain Med Sch Med Stanford CA 94305 USA Stanford Univ Dept Obstet & Gynecol Sch Med Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Stat Stanford CA 94305 USA Sheba Med Ctr Dept Obstet & Gynecol Tel Hashomer Israel

BACKGROUND: Early prediction of preeclampsia is challenging because of poorly understood causes, various risk factors, and likely multiple pathogenic phenotypes of preeclampsia. Statistical learning methods are well-equipped to deal with a large number of variables, such as patients' clinical and laboratory data, and to select the most informative features automatically. OBJECTIVE: Our objective was to use statistical learning methods to analyze all available clinical and laboratory data that were obtained during routine prenatal visits in early pregnancy and to use them to develop a prediction model for preeclampsia. STUDY DESIGN: This was a retrospective cohort study that used data from 16,370 births at Lucile Packard Children Hospital at Stanford, CA, from April 2014 to January 2018. Two statistical learning algorithms were used to build a predictive model: (1) elastic net and (2) gradient boosting algorithm. Models for all preeclampsia and early-onset preeclampsia (<34 weeks gestation) were fitted with the use of patient data that were available at <16 weeks gestational age. The 67 variables that were considered in the models included maternal characteristics, medical history, routine prenatal laboratory results, and medication intake. The area under the receiver operator curve, true-positive rate, and false-positive rate were assessed via cross-validation. RESULTS: Using the elastic net algorithm, we developed a prediction model that contained a subset of the most informative features from all variables. The obtained prediction model for preeclampsia yielded an area under the curve of 0.79 (95% confidence interval, 0.75-0.83), sensitivity of 45.2%, and false-positive rate of 8.1%. The prediction model for early-onset preeclampsia achieved an area under the curve of 0.89 (95% confidence interval, 0.84-0.95), true-positive rate of 72.3%, and false-positive rate of 8.8%. CONCLUSION: Statistical learning methods in a retrospective cohort study automatically identif

关键词： early prediction of preeclampsia elastic net gradient boosting algorithm machine learning preeclampsia statistical learning

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Diagnosis of Alzheimer's Disease using Machine Learning 4

Diagnosis of Alzheimer's Disease using Machine Learning

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4th International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Lodha, Priyanka Talele, Ajay Degaonkar, Kishori Vishwakarma Inst Technol Dept EnTC Pune Maharashtra India

ISBN: (纸本)9781538652572

Machine learning is being widely used in various medical fields. Advances in medical technologies have given access to better data for identifying symptoms of various diseases in early stages. Alzheimer's disease is chronic condition that leads to degeneration of brain cells leading at memory enervation. Patients with cognitive mental problems such as confusion and forgetfulness, also other symptoms including behavioral and psychological problems are further suggested having CT, MRI, PET, EEG, and other neuroimaging techniques. The aim of this paper is making use of machine learning algorithms to process this data obtained by neuroimaging technologies for detection of Alzheimer's in its primitive stage.

关键词： Linear Regression Machine Learning algorithm Alzheimer's disease symptoms Random Forest gradient boosting algorithm Neural Network

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Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

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ENERGIES 2018年第1期11卷 70页

作者： Baek, Min-Kyu Lee, Duehee Konkuk Univ Elect Engn Seoul 05029 South Korea

Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios.

关键词： ensemble forecasting gradient boosting algorithm total daily solar irradiation input data classification kriging

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ANALYSIS OF ROAD ACCIDENTS IN INDIA USING DATA MINING CLASSIFICATION algorithmS

ANALYSIS OF ROAD ACCIDENTS IN INDIA USING DATA MINING CLASSI...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Suganya, E. Vijayarani, S. Bharathiar Univ Dept Comp Sci Coimbatore 641046 Tamil Nadu India

ISBN: (纸本)9781538640319

Classification is a model finding process which is used for segmenting the data into different classes based on some constraints. This work analyzes the road accidents in India data set using classification algorithms namely linear regression, logistic regression, decision tree, SVM, Wye Bayes, KNN, Random Forest and gradient boosting algorithm. Performance measures used are accuracy, error rate and execution time. This analysis is done in R data mining tool. The performance of KNN is better than other algorithms.

关键词： Classification SVM Regression Decision Tree Naive Bayes gradient boosting algorithm

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Diagnosis of Alzheimer's Disease using Machine Learning

Diagnosis of Alzheimer's Disease using Machine Learning

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International Conference on Computing Communication Control and Automation

作者： Priyanka Lodha Ajay Talele Kishori Degaonkar Department of EnTC Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781538652589;9781538652572

关键词： Linear regression Machine learning algorithm Alzheimer's disease symptoms Random forest gradient boosting algorithm Neural network

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