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A predictive model to estimate effort in a sprint using machine learning techniques

International Journal of Information Technology (Singapore) 2021年第3期13卷 1101-1110页

作者： Ramessur, Melvina Autar Nagowah, Soulakshmee Devi Department of Software and Information Systems Faculty of Information Communication and Digital Technologies University of Mauritius Reduit Mauritius

Effort estimation is an essential task in a software project as it helps to establish feasible plans for the implementation of a project. It largely influences success or failure of the project. Project planning becomes more efficient with accurate effort estimates, thus providing a number of benefits to the organization. Estimating effort in agile projects has been a challenging task for researchers. Several studies exist in that domain. While some have considered people-related factors, others have catered for project-related factors for estimating effort. Others have adopted machine learning (ML) techniques to produce an accurate estimation. This paper presents a model to estimate and predict effort in a sprint using ML techniques while considering various factors that affect a sprint. The model has been evaluated using various regression algorithms, namely linear regression, K-nearest neighbor, decision tree, polynomial kernel, radius basis function and multi-layer perception (MLP). The model has produced more reliable estimates, with low error values using MLP algorithm. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Agile project Effort estimation Prediction regression algorithm Scrum

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Data-driven Discovery of the Governing Equation for the Transmission Lines System

Data-driven Discovery of the Governing Equation for the Tran...

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IEEE International Joint Symposium on Electromagnetic Compatibility (EMC), Signal and Power Integrity (SIPI) and EMC Europe

作者： Zhang, Yanming Jiang, Lijun Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781665448888

Sophisticated structures in the transmission line system introduce the nonuniformity and nonlinearity, which brings the challenge for its characterization and modeling. In this work, a novel data-driven method is proposed to derive the governing partial differential equations of the transmission line. Based on the polynomial interpolation of the spatialtemporal samples of current and voltage, the time and spatial derivatives can be obtained. Then, the ridge regression algorithm is adopted to determine the active spatial differential terms from the candidate functions. Three benchmarks, the uniform and nonuniform transmission line, and a soliton generation system, are provided to demonstrate the validity of the newly proposed approach.

关键词： Transmission line regression algorithm partial differential equation (PDE) identification finite difference time domain (FDTD) method

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DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

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PEERJ COMPUTER SCIENCE 2021年 7卷 e411页

作者： Altay, Osman Ulas, Mustafa Alyamac, Kursat Esat Firat Univ Elazig Turkey

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.

关键词： Extreme learning machine Discrete-time chaotic systems Chaotic maps regression algorithm SFRSCC

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A New Fast Twin Support Vector regression 11

A New Fast Twin Support Vector Regression

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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Fan, Ying Shi, Yilin Kang, Kai Zheng, Fengde Su, Peng Yang, Jie Minist Publ Secur PRC Adm Residency Res Ctr Beijing Peoples R China Beijing Hisign Crop Ltd Beijing Peoples R China

ISBN: (纸本)9781728165790

Twin support vector regression (TSVR) was proposed recently as a novel regression algorithm that determines a pair of -insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR. However, it lack of complexity control and only implements empirical risk minimization principle. This paper proposed modified twin support vector regression that implements structural risk minimization principle by introducing the regularization term based on TSVR. The optimization problems are solved by successive overrelaxation technique. Experimental results on several datasets show good generalization performance and decreases computation time.

关键词： Twin support vector regression regression algorithm support vector regression

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Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 195卷 106837-106837页

作者： Ruchay, Alexey Kober, Vitaly Dorofeev, Konstantin Kolpakov, Vladimir Dzhulamanov, Kinispay Kalschikov, Vsevolod Guo, Hao Russian Acad Sci Fed Res Ctr Biol Syst & Agrotechnol 9 Yanvarya 29 Orenburg 460000 Russia Chelyabinsk State Univ Dept Math Bratiev Kashirinykh 129 Chelyabinsk 454001 Russia CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China

Body weight prediction of livestock helps us to control the health of animals, efficiently conduct genetic selection, and estimate the optimal slaughter time. Precise and expensive industrial scales are used to measure live weight on large farms. A more affordable alternative is weight estimation by indirect methods, based on morphometric measurements of livestock, followed by the use of regression equations relating such measurements to body weight. Manual measurements on animals with a tape measure require trained workers and are stressful for both the worker and animal. Nowadays, machine learning technologies are being used to accurately predict body weight. This paper provides a comparative analysis of various machine learning methods for estimating the live weight of Hereford cows in terms of the coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. We show that machine learning algorithms perform better than common linear regression algorithms. Specifically, the ExtraTreesRegressor algorithm yields the highest prediction quality of the live weight of Hereford cows in terms of R2 among the tested machine learning algorithms. Potential applicability of these methods in the livestock industry is also discussed.

关键词： Live weight estimation Machine learning Hereford cow Prediction regression algorithm

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A Model Average algorithm for Housing Price Forecast with Evaluation Interpretation

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Journal of Quantum Computing 2022年第3期4卷 147-163页

作者： Jintao Fu Yong Zhou Qian Qiu Guangwei Xu Neng Wan School of Computer Science Southwest Petroleum UniversityChengdu610500China Key Laboratory on Aero-Engine Altitude Simulation Technology Sichuan Gas Turbine EstablishmentAECCMianyang621000China Aero Engine Academy of China Aero Engine Corporation of ChinaBeijing101300China

In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in *** are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home *** regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing *** is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price *** today’s forecasting studies,ensemble learning is a very prevalent and well-liked study *** regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse *** Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate *** Average Model has a strong applicability in the field of regression prediction and significantly increases computational *** technique is also easier to replicate and very effective in regression *** using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel *** evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price *** experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accurac

关键词： Machine learning AM algorithm price forecast regression algorithm Model evaluation

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The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features

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NEUROIMAGE 2018年 178卷 622-637页

作者： Cui, Zaixu Gong, Gaolang Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Beijing Normal Univ IDG McGovern Inst Brain Res Beijing 100875 Peoples R China Univ Penn Perelman Sch Med Dept Psychiat Philadelphia PA 19104 USA Beijing Normal Univ Beijing Key Lab Brain Imaging & Connect Beijing 100875 Peoples R China

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behav

关键词： Individualized prediction Machine learning regression algorithm Sample size Functional magnetic resonance imaging (MRI) Resting-state functional connectivity

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A Novel Fitted Rolling Horizon Control Approach for Real-Time Policy Making in Microgrid

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IEEE TRANSACTIONS ON SMART GRID 2020年第4期11卷 3535-3544页

作者： Das, Avijit Ni, Zhen Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

In recent years, rolling horizon control (RHC) approaches have attracted growing attention due to its feature of reducing forecast errors for real-time/online operation and optimization. However, the performance of the existing RHC approach degrades if the intra-day forecast data is unavailable or missing due to Internet or cloud service provider outages, software/hardware failures, and many other factors. In this paper, we propose a new fitted-RHC approach to overcome this challenge. The proposed fitted-RHC framework is designed with a regression algorithm which utilizes the empirical knowledge to make the real-time decisions whenever the intra-day forecast data is unavailable. The regression algorithm utilizes a statistical relative probability method to calculate the relative probability for each decision vector, and output the proper optimization policy. In addition, we adopt a modified version of exogenous information transition function that is more suitable to conduct the simulations in a real-time environment. Simulation results in microgird show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case even with the missing data, and perform efficiently with the uncertain environment in stochastic case study. In comparison, the proposed fitted-RHC approach outperforms several other optimization techniques.

关键词： Optimization Microgrids Real-time systems Stochastic processes Predictive models Probability Reliability Rolling horizon control (RHC) regression algorithm mixed integer linear programming (MILP) microgrid energy optimization and renewable energy sources

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Automatic pavement damage predictions using various machine learning algorithms: Evaluation and comparison

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RESULTS IN ENGINEERING 2022年 16卷

作者： Nyirandayisabye, Ritha Li, Huixia Dong, Qiming Hakuzweyezu, Theogene Nkinahamira, Francois Fujian Univ Technol Sch Civil Engn Fuzhou 350108 Fujian Peoples R China Chinese Acad Sci Inst Rock & Soil Mech Wuhan 430071 Hubei Peoples R China Harbin Inst Technol Shenzhen Sch Civil & Environm Engn State Key Lab Urban Water Resource & Environm Shenzhen Key Lab Organ Pollut Prevent & Control Shenzhen 518055 Peoples R China

In recent years, different technologies based on Machine Learning (ML) algorithms have been developed to evaluate road distress quality and help with pavement maintenance, traffic safety, and road rehabilitation. However, the literature lacks a comparative study of various ML algorithms to detect pavement damage. This study aims to fill that gap by comparing various regression algorithms including Linear regression (LR), Support Vector Regressor (SVR), Random Forest Regressor (RFR), k-nearest neighbors, Gradient Boosting Regressor (GBR), Light Gradient Boosted Machine (LGBM), stacking regressor, and Decision Tree Regressor (DTR). To evaluate the accuracy of the models, different performance metrics, namely coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are used. Experiments are conducted to validate the models' performance using a dataset sponsored by the Michigan Department of Transportation and executed at Michigan State University for the Michigan road pavement damage. Obtained results indicate that the GBR modeling algorithm performs better with the most accurate asphalt pavement damage model of R2 = 99%, MAE = 0.02, and RMSE = 0.03. Thus, the use of GBR modeling might be an easy, robust, and efficient method for automated pavement damage prediction.

关键词： Pavement damage prediction regression algorithm Gradient boosting regressor (GBR) Traditional algorithm Assemble algorithm Pavement damage machine learning (ML)

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Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography:a multivariable predictive model study

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Chinese Medical Journal 2021年第14期134卷 1687-1694页

作者： Hai-Yang Liu Xing-Ru Zhao Meng Chi Xiang-Song Cheng Zi-Qi Wang Zhi-Wei Xu Yong-Li Li Rui Yang Yong-Jun Wu Xiao-Ju Zhang Department of Respiratory and Critical Care Medicine Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China Henan Joint International Research Laboratory of Diagnosis and Treatment of Pulmonary Nodules Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China Department of Medical Imaging Henan Provincial Chest HospitalZhengzhouHenan 450000China Department of Respiratory and Critical Care Medicine Fuwai Central China Cardiovascular HospitalZhengzhouHenan 450000China Department of Radiology Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhouHenan 450000China College of Public Health Zhengzhou UniversityZhengzhouHenan 450000China.

Background:Computed tomography images are easy to misjudge because of their complexity,especially images of solitary pulmonary nodules,of which diagnosis as benign or malignant is extremely important in lung cancer ***,there is an urgent need for a more effective strategy in lung cancer *** our study,we aimed to externally validate and revise the Mayo model,and a new model was ***:A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training,internal validation,and external validation sets(n=849,365,and 236,respectively).External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training *** performance of each model was evaluated using area under receiver operating characteristic curve(AUC).Finally,the model validation was completed on the validation data ***:The AUC of the Mayo model on the training set was 0.653(95%confidence interval[CI]:0.613–0.694).After re-estimation of the coefficients of all covariates included in the original Mayo model,the revised Mayo model achieved an AUC of 0.671(95%CI:0.635–0.706).We then developed a new model that achieved a higher AUC of 0.891(95%CI:0.865–0.917).It had an AUC of 0.888(95%CI:0.842–0.934)on the internal validation set,which was significantly higher than that of the revised Mayo model(AUC:0.577,95%CI:0.509–0.646)and the Mayo model(AUC:0.609,95%CI,0.544–0.675)(P<0.001).The AUC of the new model was 0.876(95%CI:0.831–0.920)on the external verification set,which was higher than the corresponding value of the Mayo model(AUC:0.705,95%CI:0.639–0.772)and revised Mayo model(AUC:0.706,95%CI:0.640–0.772)(P<0.001).Then the prediction model was presented as a nomogram,which is easier to ***:After external verification and recalibration of the Mayo model,the results show that they are not suitab

关键词： CT image Lung cancer Prediction model Pulmonary nodules regression algorithm

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