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Accuracy of the Enhanced Liver Fibrosis test, and combination of the Enhanced Liver Fibrosis and non-invasive tests for the diagnosis of advanced liver fibrosis in patients with non-alcoholic fatty liver disease

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HEPATOLOGY RESEARCH 2020年第6期50卷 682-692页

作者： Inadomi, Chika Takahashi, Hirokazu Ogawa, Yuji Oeda, Satoshi Imajo, Kento Kubotsu, Yoshihito Tanaka, Kenichi Kessoku, Takaomi Okada, Michiaki Isoda, Hiroshi Akiyama, Takumi Fukushima, Hideaki Yoneda, Masato Anzai, Keizo Aishima, Shinichi Nakajima, Atsushi Eguchi, Yuichiro Saga Univ Fac Med Div Metab & Endocrinol 5-1-1 Nabeshima Saga 8498501 Japan Saga Univ Fac Med Dept Pathol & Microbiol Saga Japan Saga Univ Hosp Liver Ctr Saga Japan Saga Univ Hosp Dept Clin Lab Med Saga Japan Yokohama City Univ Dept Gastroenterol & Hepatol Grad Sch Med Yokohama Kanagawa Japan Siemens Healthcare Diagnost KK Diagnost Business Area Tokyo Japan

Aim The Enhanced Liver Fibrosis (ELF) test comprises a logarithmic algorithm combining three serum markers of hepatic extracellular matrix metabolism. We aimed to evaluate the performance of ELF for the diagnosis of liver fibrosis and to compare it with that of liver stiffness measurement (LSM) by FibroScan in non-alcoholic fatty liver disease. Methods ELF cut-off values for the diagnosis of advanced fibrosis were obtained using receiver operating characteristic analysis in patients with biopsy-confirmed non-alcoholic fatty liver disease (training set;n = 200). Diagnostic performance was analyzed in the training set and in a validation set (n = 166), and compared with that of LSM in the FibroScan cohort (n = 224). Results The area under receiver operating characteristic curve was 0.81 for the diagnosis of advanced fibrosis, and the ELF cut-off values were 9.34 with 90.4% sensitivity and 10.83 with 90.6% specificity in the training set, and 89.8% sensitivity and 85.5% specificity in the validation set. There was no significant difference in the area under the receiver operating characteristic curve between ELF and LSM (0.812 and 0.839). A combination of ELF (cut-off 10.83) and LSM (cut-off 11.45) increased the specificity to 97.9% and the positive predictive value, versus ELF alone. Sequential use of the Fibrosis-4 index (cut-off 2.67) and ELF (cut-off 9.34) increased the sensitivity to 95.9%. Conclusions ELF can identify advanced liver fibrosis in non-alcoholic fatty liver disease, and its diagnostic accuracy is comparable to that of FibroScan. According to the clinical setting, combinations or sequential procedures using other non-invasive tests complement the diagnostic performance of ELF for the identification of advanced fibrosis.

关键词： combination algorithm non-invasive test sequential algorithm vibration-controlled transient elastography

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Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection

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JOURNAL OF GEOCHEMICAL EXPLORATION 2021年 231卷 106875-106875页

作者： Chen, Yongliang Zhao, Qingying Lu, Laijun Jilin Univ Coll Earth Sci Changchun 130061 Jilin Peoples R China

Machine learning techniques provide useful methods for high-dimensional geochemical anomaly detection for mineral exploration targeting. However, the instability of the machine learning models often leads to the uncertainty of high-dimensional geochemical anomaly detection result. Combining various individual models to form an adaptive ensemble anomaly detector is a feasible way to enhance the robustness of machine learning anomaly detectors. In this study, the average method, maximization method, average of maximum (AOM) method, and maximum of average (MOA) method were adopted to combine the outputs of various k-nearest neighbor (KNN) anomaly detectors to improve the robustness of the KNN models in the high-dimensional geochemical anomaly detection in the Baishan district (Jilin Province, China). The effectiveness of the four combination algorithms for high-dimensional geochemical anomaly detection was evaluated by comparing the ensemble models obtained by using the four combination algorithms with the single KNN model, Gaussian mixture model (GMM), one-class support vector machine (OCSVM), and isolation forest (IForest) in the case study. It is found that the four ensemble models (a) perform similarly well in high-dimensional geochemical anomaly detection, and (b) perform better than the single KNN model, GMM, OCSVM, and IForest in highdimensional geochemical anomaly detection. Therefore, the average method, maximization method, AOM method, and MOA method are potentially useful algorithms for combining the outputs of various KNN models to form robust ensemble models for high-dimensional geochemical anomaly detection.

关键词： K-nearest neighbor Gaussian mixture model One-class support vector machine Isolation forest combination algorithm Geochemical anomaly detection

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A Method of Automatic Test Case Generation Based on CT-LSSVM algorithm in FAO Systems 5

A Method of Automatic Test Case Generation Based on CT-LSSVM...

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IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Wang, Sha Shang, Qingyuan Ling, Zhujun Liu, Dandan Chen, Xiangxian UniTTEC Co Ltd Hangzhou Peoples R China Zhejiang Univ Dept Instrumentat Sci & Engn Hangzhou Peoples R China

ISBN: (纸本)9781728194097

The FAO system is a new-generation railway signaling system, the comprehensive and accurate testing is the main means to verify the safety and stability of the system, and the design and generation of test cases is an important link of the system testing. Traditionally, test cases are manually generated, which is inefficient, time-consuming and inaccurate. To solve this problem, we proposed an automatic test case generation method for the FAO system specified scenario using the CT-LSSVM algorithm. The CT algorithm was used to realize multi-factor combination to generate test cases, and the LSSVM method was used to predict and analyze the expected results of the test cases. The results showed that when the LSSVM method was used to model and analyze the test cases generated by the CT five-factor combination, the recognition rate of the calibration set was 96.58% and the recognition rate of the test set was 97.73%;At the same time, some test cases of the twelve-factor combination were predicted and analyzed, and the recognition rate reached 98.57% This proves that the CT-LSSVM method can be applied to the test case generation of the FAO system.

关键词： FAO system test cases combination algorithm least square support vector machine

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Base Model combination algorithm for Resolving Tied Predictions for K-Nearest Neighbor OVA Ensemble Models

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INFORMS JOURNAL ON COMPUTING 2013年第3期25卷 517-526页

作者： Lutu, Patricia E. N. Engelbrecht, Andries P. Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Model aggregation is the process of constructing several base models that are then combined into a single model for prediction. Ensemble classification has been studied by many researchers and found to provide significant performance improvements over single models. This paper presents a new base model combination algorithm for K-nearest neighbor (KNN) ensemble models based on One-Versus-All (OVA) classification. The proposed algorithm uses two decision functions to determine the best prediction among the many predictions provided by the base models. It is demonstrated in this paper that tied or conflicting predictions can be effectively resolved when a probabilistic function and a distance function are used by a combination algorithm for OVA KNN base model predictions. The resolution of tied predictions leads to improvements in predictive performance.

关键词： ensemble classification model aggregation combination algorithm OVA classification K-nearest neighbor classification predictive data mining data mining

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A multiscale direct solver for the approximation of flows in high contrast porous media

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2019年 359卷 88-101页

作者： Akbari, Hani Engsig-Karup, Allan P. Ginting, Victor Pereira, Felipe Univ Melbourne Dept Infrastruct Engn Parkville Vic Australia Tech Univ Denmark CERE Dept Appl Math & Comp Sci Lyngby Denmark Univ Wyoming Dept Math & Stat Laramie WY 82071 USA Univ Texas Dallas Dept Math Sci Richardson TX 75083 USA

We consider a non-overlapping domain decomposition approach to approximate the solution of elliptic boundary value problems with high contrast in their coefficients. We propose a method such that initially local solutions subject to Robin boundary conditions in each primal subdomain are constructed with (locally conservative) finite element or finite volume methods. Then, a novel approach is introduced to obtain a (discontinuous) global solution in terms of linear combination of the local subdomain solutions. In the proposed algorithm the computation of local solutions for unions of subdomains are localized at nearest-neighbor subdomain boundaries, thus avoiding the solution of global interface problems. We remove discontinuities in a smoothing step that is defined on a staggered grid or dual subdomains. The resulting algorithm is naturally parallelizable and can be employed as a parallel direct solver, offering great potential for the numerical solution of large problems. In fact, subdomains can be considered small enough to fit well in GPUs and the proposed procedure can handle adaptive (in space) simulations effectively. Numerical simulations are presented and discussed. We demonstrate the effectiveness of the proposed approach with two and three dimensional high contrast and channelized coefficients, that lead to challenging approximation problems. The new procedure, although designed for parallel processing, is also of value for serial calculations. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Elliptic equations combination algorithm Multiscale method Robin boundary condition Parallel computing High contrast media

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Short-term Wind Speed Combined Prediction Model Based on ARIMA-GARCH and Elman

Short-term Wind Speed Combined Prediction Model Based on ARI...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Hanjie Xiao Honglei Tang Jianhua Zhou Business School Huzhou University Huzhou China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

In order to effectively improve the prediction accuracy, short-term wind speed combination prediction model based on ARIMA-GARCH and Elman is proposed. It adopts ARIMA model to linearly predict short-term wind speed and GARCH to improve the non-stationary series heteroscedasticity problems. What's more, Elman neural network is used to nonlinearly predict short-term wind speed. Finally, the minimum weighting method of absolute error is used to determine the optimal linear weight of single model so as to realize short-term wind speed prediction. Case analysis shows that, compared with other prediction models, the combination model not only has significantly improved short-term wind speed prediction accuracy, but provides a certain reference to short-term wind speed prediction in relevant fields.

关键词： ARIMA-GARCH Elman combination algorithm short-term wind speed composite algorithm wind speed short duration Prediction models absolute error

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Summary of Wind Speed Time Series Prediction algorithms

Summary of Wind Speed Time Series Prediction Algorithms

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作者： Xian-hui ZHU Nan SHI Xun-wen SU Pin LV Yue YU School of Electrical and Control Engineering Heilongjiang University of Science and Technology

Accurate prediction of wind speed is an important prerequisite for wind power *** hackle the large number of wind prediction outcomes in recent years,based on simple discussion of the wind power generation principle,wind speed predicted classification was summarized,the characteristics of a single algorithm wind speed forecast model was generalized,and the advantages and disadvantages of typical combination algorithm of wind speed forecast model was trying to scientific evaluated,the prediction effects of wind speed based on numerical weather prediction(NWP) was given *** last,in order to provide a research reference for future the main direction that should pay more attention to predict wind speed is given.

关键词： Wind speed prediction Classification Simple algorithm combination algorithm NWP

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Wind Speed Forecasting Based on Fuzzy-neural Network combination Method 27

Wind Speed Forecasting Based on Fuzzy-neural Network Combina...

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27th Chinese Control and Decision Conference (CCDC)

作者： Li, Shanzhi Wang, Haoping Tian, Yang Shen, Yanyan Aitouche, Abdel Nanjing Univ Sci & Technol Automat Sch Sino French Int J Lab Automat Control & Signal Pr Nanjing 210094 Jiangsu Peoples R China Univ Lille Nord France HEI LaFCAS F-59600 Lille France Univ Lille Nord France HEI LAGIS Labs F-59600 Lille France

ISBN: (纸本)9781479970162

Wind speed forecasting is significant for power system operation. Accuracy and rapidity are essential requirements. This paper is proposed a combination method based on a fuzzy neural network. The combination weight of the proposed hybrid model which combines an Autoregressive Integrated Moving Average (ARIMA) model and a Radial Basis Function Neural Network (RBFNN) model is adjusted online according to the previous error. To validate the proposed method effectiveness and performance, two cases of wind data have been utilized. The first one is a simulation wind case and created by TurbSim code, which is developed by National Renewable Energy Laboratory. The other one is a real-time wind speed case and its wind farm is located in northern Gulf of Mexico. Comparing with the single models of ARIMA, RHFNN, hybrid mean strategy and variance strategy, the result of fuzzy-neural network combination method reduce both mean square and mean absolute errors.

关键词： Wind speed forecasting ARIMA RBF Fuzzy-neural network combination algorithm

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A combination algorithm for Image Denoising and Deblurring

A Combination Algorithm for Image Denoising and Deblurring

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Xu, Yiping Chen, Hanlin Zheng, Kelong SW Univ Sci & Technol Sch Sci Mianyang Sichuan Peoples R China

ISBN: (纸本)9781424437092

In this paper, we propose an efficient combination algorithm of split Bregman method, algebraic multigrid method and Krylov acceleration method for image denoising and deblurring. The split Bregman method is used to convert nonlinear TV model into linear systems. The linear systems with the blur operator are difficult for solving. We apply an algebraic multigrid (AMG) method and Krylov acceleration method to solve the linear systems in the inner iteration. For the linear systems, we have added an auxiliary linear term that significantly stabilizes and accelerates the outer iteration. The inclusion of the linear stabilizing term plays a crucial role in our algorithm. We have also conducted numerical experiments. The results show that our algorithm is efficient and robust. The results of numerical experiments are given and compared with ones of [6], [11].

关键词： Krylov acceleration method algebraic multigrid method auxiliary linear term blur operator combination algorithm differential equations image deblurring image denoising image denoising image restoration inner iteration linear systems nonlinear TV model outer iteration split Bregman method

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Wind Speed Forecasting Based on Fuzzy-neural Network combination Method

Wind Speed Forecasting Based on Fuzzy-neural Network Combina...

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第27届中国控制与决策会议

作者： Shanzhi LI Haoping WANG Yang TIAN Yanyan SHEN Abdel AITOUCHE Sino-French Int.J.Laboratory of Automatic Control and Signal Processing(LaFCAS) Automation SchoolNanjing University of Science & Technology LaFCAS and LAGIS Laboratories HEIUniversity Lille Nord de France

ISBN: (纸本)9781479970186

Wind speed forecasting is significant for power system *** and rapidity are essential *** paper is proposed a combination method based on a fuzzy neural *** combination weight of the proposed hybrid model which combines an Autoregressive Integrated Moving Average(ARIMA) model and a Radial Basis Function Neural Network(RBFNN) model is adjusted online according to the previous *** validate the proposed method effectiveness and performance,two cases of wind data have been *** first one is a simulation wind case and created by TurbSim code,which is developed by National Renewable Energy *** other one is a real-time wind speed case and its wind farm is located in northern Gulf of *** with the single models of ARIMA,RBFNN,hybrid mean strategy and variance strategy,the result of fuzzy-neural network combination method reduce both mean square and mean absolute errors.

关键词： Wind speed forecasting ARIMA RBF Fuzzy-neural network combination algorithm

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