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International Conference on intelligent Computation Technology and Automation, ICICTA

作者： Luan Gao Feng-gang Li Jian Wang Yu Liang Yu Li Anhui Chinese Medical College of Chinese Medicine Clinic Institute Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China

The purpose of this paper is to probe into the rules of medicine compounding for stroke prevention treated by Xin'an physicians by data mining. The method is in two steps. First step is to build the database of the Xin'an physical stroke prevention. Second step is to use the method of association rules contained in professional data mining software and probe into the medicine rules for stroke prevention treated by Xin'an physicians. The result is that Chenpi, Fuling, Gancao, Luxiancao, Xiqiancao, Baijili, and Shinanye which are of the seven common Chinese medical compounding prescriptions can be used for Xin'an physicians' stroke prevention. The conclusion is that we should utilize the eliminating phlegm, calming wind and clearing the channels when Xin'an physicians treat stroke problem.

关键词： Medical diagnostic imaging Association rules Itemsets Diseases Drugs

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Disagreement-based semi-supervised learning method for enterprise credit risk assessment

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ICIC Express Letters 2015年第6期9卷 1671-1676页

作者： Wang, Gang Li, Ningning School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China

The enterprise credit risk assessment problem has long been regarded as an important and widely studied issue in both academia and industry. However, unla-beled data problem is paid less attention to in the credit risk assessment community. To fill in this research gap, one of popular disagreement semi-supervised learning methods, co-training, is introduced to exploit the unlabeled data to improve the enterprise credit risk assessment performance. Co-training allows one to start with just a few labeled instances to produce an initial weak classifier and then use unlabeled data to improve the performance. Experiments based on two credit datasets reveal that co-training SVM&DT (Support Vector Machine & decision Tree) gets the better performance than the compared methods. Based on these results, we can conclude that co-training SVM&DT can be used as an alternative method for enterprise credit risk assessment. © 2015 ICIC International.

关键词： decision trees

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CO-RS: A new method for opinion mining based on semi-supervised learning and ensemble learning

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ICIC Express Letters 2015年第7期9卷 1803-1808页

In recent years many machine learning methods have been proposed for opinion mining and the effectiveness of applying them to opinion mining has also been approved theoretically. However, machine learning methods encounter many problems when they are used in practice. One of the problems is to construct learning systems with strong generalization ability. In this study, we propose, Co-RS, a semi-supervised method for opinion mining based on co-training and Random Subspace. Co-RS tries to get the strong generalization ability by integrating the strength of two machine learning paradigms, i.e., semi-supervised learning and ensemble learning. Moreover, four public opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that Co-RS gets the best result among the compared methods. These results illustrate that Co-RS can be used as a viable method for opinion mining. © 2015, ISSN 1881-803X.

关键词： Sentiment analysis

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Evaluation on regional innovation performance of China's patent data based on two-stage model

Evaluation on regional innovation performance of China's pat...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Su Yang Changyong Liang School of Management Anhui University of Architecture Hefei China School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

ISBN: (纸本)9781467384810

Being paid great attention to its operating performance, patent is the key element for a country or area to gain long-term advantage in various competitions. This paper, based on Chinese patent data, proposes a two-stage model with factor analysis and clustering analysis. It also proposes a patent innovation performance evaluation system in view of the input-output index of twenty patents, giving factor analysis to principal components, combining the various indexes to create new factors through calculating, extracting and aggregating, and then with this new factor score, this article evaluates the patent innovation performance systematically using the clustering analysis. The results show that, obviously, patent modification can promote innovation patent performance, especially the second modification. And at last, this article provides suggestion and development strategies to the improvement of patent system.

关键词： decision support systems Conferences Logistics Informatics

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Developing an evaluation approach for software trustworthiness using combination weights and TOPSIS

Journal of Software

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Journal of Software 2012年第3期7卷 532-543页

作者： Shi, Li Yang, Shan-Lin Li, Kai Yu, Ben-gong School of Management Hefei University of Technology Hefei 230009 China Institute of Computer Network System Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China

Software is everywhere. However, software is not always trustworthy. Confronting the demand of software trustworthiness evaluation, this paper proposes a novel software trustworthiness evaluation approach based on combination weights (CW) and improved TOPSIS methods. The determination of CW relies on experts' judgments and mathematical computation together. FAHP method is used to determine the importance of degree of criteria according to the experts' judgment. To avoid the subjective one-sidedness of weights, entropy weighting method is employed to calculate objective weights. Then, the paper adopts combination weighting method to obtain the CW of evaluation criterion. Next, the CW is applied to the improved TOPSIS method and the trustworthiness rating of software is calculated. The result contributes to provide decision makers more decision information. Finally, the proposed approach is demonstrated with the evaluation of PLM software for an aircraft equipment manufacturer in China, which is followed by the sensitivity analysis and the results illustrate the robustness of the presented evaluation approach. © 2012 ACADEMY PUBLISHER.

关键词： Sensitivity analysis

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Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation

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Energy Procedia 2019年 158卷 6189-6194页

作者： Yaoyao He Yudong Yan Xu Wang Chao Wang School of Management Hefei University of Technology Hefei 230009 China Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China China Institute of Water Resources and Hydropower Research Beijing 100048 China

Accurate and comprehensive forecasting of streamflow plays an important role in the uncertainly analysis of the hydrologic system. It is widely accepted that prediction interval (PI) can provide more precise and detailed information than deterministic forecasting when the uncertainty level of streamflow increases. Support vector regression (SVR) is a supervised learning model for classification and regression analysis based on associated learning algorithms. In this paper, fuzzy information granulation (FIG) is combined with SVR model (FIG-SVR) for uncertainty forecasting of streamflow. On behalf of evaluating the performance of the forecasting results, the evaluation metrics of point forecasting and interval prediction results are introduced. The real streamflow data from the Three Gorges in the Yangtze River are used to validate the proposed method based on the proposed method. The results show that the proposed method provides the high-quality point predictand and PIs, and the uncertainly of streamflow can be well handled.

关键词： Streamflow forecasting Support vector regression Fuzzy information Granulation Triangular fuzzy particles

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A Transfer-Based decision-making Method with Criterion Weight and Reliability Transfer

SSRN

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SSRN 2023年

作者： Jia, Xuefei Chang, Wenjun Liao, Baoyu School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

The accumulated historical data not only brings benefits to social life, but also increases the complexity of decision-making. Data-driven multi-criteria decision-making (D2MCDM) can extract parameter information from historical data to help decision-makers generate acceptable and satisfactory decision solutions. When little historical data in one domain is available, the extracted parameter information may be inaccurate or inconsistency. To deal with this issue, the parameter-based transfer learning is considered to integrate into D2MCDM. The domain with little historical data is called the target domain (TD). One related domain with sufficient data is found, which is called the SD (SD). The sufficient data in the SD can help extract accurate parameter information of the TD. This paper proposes a transfer-based decision-making (TBDM) method with criterion weight and reliability transfer based on the Tri-training algorithm. The Tri-training algorithm is used to construct a data selection stage for the SD, which improves the data similarity between the SD and TD. The data-driven decision-making models are then constructed to learn criterion weights and reliabilities. The proposed TBDM method not only deals with the problem of insufficient data for decision model training, but also improves the acceptability of decision solutions to decision-makers. A case study of the diagnosis of breast lesions is analyzed to demonstrate the advantages of the TBDM method. The UCI datasets are used to conduct the comparative experiments to further validate the advantages of the TBDM method. © 2023, The Authors. All rights reserved.

关键词： Reliability

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3DGS-based Tracking of Articulated Instruments for Instruments-Tissue Interaction Estimation in Robot-assisted Minimally Invasive Surgery

3DGS-based Tracking of Articulated Instruments for Instrumen...

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaojian Li Lili Shen Ling Li Hangjie Mo Xin Wang Xiang Ma Jieyu Zheng School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei China West China Hospital Sichuan University Sichuan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

3D information of surgical scene potentially enhances the accuracy of surgical navigation and facilitate the automation of robot-assisted surgery. However, current research seldom considers the relative 3D position between instruments and tissue. In this paper, the instruments-tissue interaction estimation method is proposed to obtain the relative distance between instruments and tissue. The 3D Gaussian representation of instruments is established preoperatively, making the pose of instruments the only parameter to be learned and updated. Based on this, the instrument pose estimation method is proposed, which applies 3DGS (3D Gaussian Splatting) to the instrument Gaussian model to obtain rendered color images, projection masks, and depth maps. These are aligned with the input images, segmented masks and depth from stereo depth estimation to construct the target loss. By backpropagating through the target loss, the optimal instrument pose is obtained. The L1 loss and smooth loss based on TPS (the Thin Plate Spline) are used to complete the depth of tissue occluded by instruments. The instruments-tissue interaction is estimated by the nearest distance between instrument and tissue. The experimental results indicate an average error of 0.94 mm and an accuracy of 90.40% with an error less than 2mm in estimating relative 3D distance between instruments and tissue.

关键词： Solid modeling Three-dimensional displays Accuracy Minimally invasive surgery Navigation Instruments Pose estimation Real-time systems Splines (mathematics) Robots

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Collaborative optimization of profit and cost of hybrid cloud storage service

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International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 185-196页

作者： Sun, Chao-Ping Zhao, Jing Ruan, Su-Mei School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China School of Business Anhui University of Finance and Economics Bengbu China

Aiming at hybrid cloud storage service (HCSS), this paper conducts research on collaborative optimization of corresponding profits and costs. Profit function of HCSS considers flexibility, extendibility, decrease in investment, improvement on access speed, data sharing, disaster recovery, and green IT, while cost function of HCSS considers data transmission, data storage, and facilities of hardware and software. Based on profit and cost functions of HCSS, a goal programming model is constructed to collaboratively optimize profits and costs of HCSS. The model is validated by making a comparative analysis in two representative contexts. © 2015 SERSC.

关键词： Profitability

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Single-valued neutrosophic entropy and similarity measures to solve supplier selection problems

Single-valued neutrosophic entropy and similarity measures t...

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作者： Jin, Feifei Ni, Zhiwei Chen, Huayou Langari, Reza Zhu, Xuhui Yuan, Hongjun School of Management Hefei University of Technology Hefei Anhui China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei Anhui China Department of Mechanical Engineering Texas AandM University College StationTX United States School of Mathematical Sciences Anhui University Hefei Anhui China School of Statistics and Applied Mathematics Anhui University of Finance and Economics Bengbu Anhui233030 China

The single-valued neutrosophic sets (SVNSs) are useful tools to describe uncertainty and inconsistent information that exist in real world. For SVNSs theory, two important topics are single-valued neutrosophic entropy and single-valued neutrosophic similarity measurer. This paper investigates a multi-attribute decision-making (MADM) method by using single-valued neutrosophic entropy and similarity measure. First, the concepts of single-valued neutrosophic entropy and similarity measure are presented. Then, based on the trigonometric functions (i.e., sine function and cosine function), we introduce two information measure formulas and prove that they satisfy the requirements of the single-valued neutrosophic entropy and similarity measure, respectively. Furthermore, we study the inter-relationship between single-valued neutrosophic entropy and similarity measure. By using Lagrange Multiplier Method and closeness degree, we develop a novel single-valued neutrosophic MADM method. Finally, a numerical example of selecting the desirable supplier is provided, and the comparison with existing approaches is performed to validate the rationality and effectiveness of the proposed method. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Entropy

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