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SSRN 2024年

作者： Yang, Ying Tang, Si Ren, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Accurately identifying high quality online-reviews is beneficial to both consumers and e-commerce platforms. Consumers usually exhibit confirmation bias phenomenon when evaluating the helpfulness of product reviews. However, there has been limited research on simulating and incorporating this information processing bias into predicting multimodal review helpfulness. To address this challenge, we combine confirmation bias theory with deep learning techniques, and propose a hierarchically-trusted multi-view deep learning method for predicting the helpfulness of multimodal reviews. Drawing upon the elaborate likelihood model and confirmation bias theory, multiple views are designed to represent consumers’ initial beliefs, central cues and peripheral cues of multimodal reviews. A hierarchically-trusted multi-view fusion strategy is designed to dynamically aggregate the different views considering their interactions. Experimental results on the *** review dataset demonstrate that the proposed method effectively improves the accuracy of evaluating the values of online reviews. This research contributes to the improvement of platform service operations and enhances consumer experience. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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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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Research on Whole-Link Risk Situational Awareness Index System and Dynamic Risk Pool Supervision 11

Research on Whole-Link Risk Situational Awareness Index Syst...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Sun, Jingchun Deng, Fei Du, Boya School of Management Xi'An Jiaotong University China Key Laboratory of the Ministry of Education for Process Control and Efficiency Engineering China College of Information Engineering Kunming University China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan China

ISBN: (纸本)9781450398039

With the development of society, network security becomes more and more important. It is inevitable to establish the risk index system to effectively monitor the security of network. In the previous study, due to absence of risk indicators, the risk supervising system is not perfect. However, there are few researches on the whole-link risk index system, and the determination of risk indicators lacks basis. Therefore, this paper firstly constructs a new risk index system of situational awareness, which can avoid the omission of risk indicators. Then the sliding window is used to observe this system and the optimal window width is found to determine the dynamic risk pool. Finally, the indicators of dynamic risk pool are used to dynamically monitor the whole link risk index system. The whole technical framework not only proves advantages of the mature risk index system of situational awareness, but also overcomes the disadvantage that the risk index of the whole link cannot be determined, and the dynamic risk pool has the sharing characteristics in the whole process. © 2022 ACM.

关键词： Network security

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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

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

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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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

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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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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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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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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Earth Observation Satellite Scheduling Problem for Multitemporal Revisit Tasks: A Variable Neighborhood Search Algorithm

SSRN

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SSRN 2024年

作者： Xing, Ligang Hu, Xiaoxuan Zhu, Waiming Zhu, Xutong Xia, Wei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Engineering Research Center for Intelligent Management of Aerospace System Hefei230009 China

After natural disasters such as floods and earthquakes, Earth observation satellites (EOSs) often need to revisit affected areas multiple times to acquire multitemporal images. Such observation tasks are referred to as multitemporal revisit tasks. In this paper, we study the Earth observation satellite scheduling problem for multitemporal revisit tasks, which is a combinatorial optimization problem in continuous space. This problem exhibits more complex constraints and is more challenging to solve than the traditional Earth observation satellite scheduling problem. Firstly, we formally define the problem and formulate it with a mixed integer nonlinear programming model. Secondly, we develop a variable neighborhood search algorithm to search for the optimal solution. This algorithm embeds a dynamic greedy heuristic, which can efficiently generate a schedule for satellites. Thirdly, computational experiments demonstrate the efficiency and stability of the algorithm. Moreover, the CPU time of the algorithm increases linearly with both task count and revisit number, indicating good scalability. © 2024, The Authors. All rights reserved.

关键词： Integer programming

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Experimental study on in-situ capture of gaseous heavy metals by calcium-doped kaolinite in furnace

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Journal of Southeast University(English Edition) 2022年第1期38卷 62-69页

作者： Zha Jianrui Huang Yaji Xia Zhipeng Zhu Zhicheng Key Laboratory of Process Optimization and Intelligent Decision of Ministry of Education Hefei University of TechnologyHefei 230009China School of Management Hefei University of TechnologyHefei 230009China School of Energy and Environment Southeast UniversityNanjing 210096China

For revealing the effect of calcium on heavy metal in-furnace capture by kaolinite,gaseous PbCl_(2) and CdCl_(2) adsorptions by calcium-doped kaolinite at high temperatures and their mechanisms were investigated through experiments in a fixed-bed furnace,where factors of temperatures and the mixing proportion of limestone and kaolinite were *** results show that the adsorption efficiency of cadmium by the mixed sorbents is lower than that by single sorbents,mainly due to the consumption of lime or limestone,whereas that of lead varies at different reaction *** kinds of areas,named as Ca-rich zone and Ca-low zone,are found in the sorbents of calcium-doped *** Ca-rich zone tends to lessen the capture efficiencies for the two metals,whereas the Ca-low zone can enhance their *** heterogeneous distributions of calcium have multiple effects on metal sorption,including the diffusion enhancement through the eutectic effect by forming low-melting-point substances,the inhibition by the competitive occupation of reaction sites,and the block of pores caused by sintering at a high temperature.

关键词： adsorption heavy metal high temperature kaolinite mineral reaction

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