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| 1 | Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making显示文摘To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform. | Rao Congjun Xiao Xinping Peng Jin | 2007 | Journal of Systems Engineering and Electronics2007，18，4： | 13 |
| 2 | Further generalized research on the transformationseries of power function x-a*显示文摘 | Congjun Rao Xinping Xiao Xiaoxuan Zhang | 2006 | The Journal of Gray Ssystem2006，3，： | 1 |
| 3 | Procurement decision making mechanism of divisible goods based on multi - attribute auction 显示文摘 | RAO CONGJUN ZHAO YONG MA SHIHUA | 2012 | Electronic Commerce Research and Applications2012，11，4： | 1 |
| 4 | Multi-attribute decision making model based on optimal membership and relative entropy显示文摘To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete,a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy.Firstly,the definitions of the optimal membership and the relative entropy are given.Secondly,for all alternatives,a set of preference weight vectors are obtained by solving a set of linear programming models whose goals are all to maximize the optimal membership.Thirdly,a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined.Based on this optimal weight vector,the algorithm of deviation degree minimization is proposed to rank all the alternatives.Finally,a decision making example is given to demonstrate the feasibility and rationality of this new model. | Rao Congjun Zhao Yong | 2009 | Journal of Systems Engineering and Electronics2009，20，3： | 1 |
| 5 | Information Revelation in Sequential Auctions with Uncertainties About Future Objects显示文摘In many auctions,buyers know beforehand little about objects to be sold in the future.Whether and how to reveal information about future objects is an important decision problem for sellers.In this paper,two objects are sold sequentially and each buyer's valuation for the second object is k times that for the first one,and the true value of k is sellers' private information.The authors identify three factors which affect sellers' revelation strategies: The market's competition intensity which is characterized by the number of buyers,buyers' prior information about the second object,and the difference degree between two objects which is characterized by k.The authors give not only conditions under which revealing information about the second object in advance benefits the seller,but also the optimal releasing amount of information in the market with two sellers and one seller,respectively. | HU Erqin ZHAO Yong RAO Congjun | 2016 | Journal of Systems Science & Complexity2016，29，6： | 0 |
| 6 | Identification of a natural PLA2 inhibitor from the marine fungus Aspergillus sp.c1 for MAFLD treatment that suppressed lipotoxicity by inhibiting the IRE-1a/XBP-1s axis and JNK signaling显示文摘Lipotoxicity is a pivotal factor that initiates and exacerbates liver injury and is involved in the development of metabolic-associated fatty liver disease(MAFLD).However,there are few reported lipotoxicity inhibitors.Here,we identified a natural anti-lipotoxicity candidate,HN-001,from the marine fungus Aspergillus sp.C1.HN-001 dose-and time-dependently reversed palmitic acid(PA)-induced hepatocyte death.This protection was associated with IRE-1a-mediated XBP-1 splicing inhibition,which resulted in suppression of XBP-1s nuclear translocation and transcriptional regulation.Knockdown of XBP-1s attenuated lipotoxicity,but no additional ameliorative effect of HN-001 on lipotoxicity was observed in XBP-1s knockdown hepatocytes.Notably,the ER stress and lipotoxicity amelioration was associated with PLA2.Both HN-001 and the PLA2 inhibitor MAFP inhibited PLA2 activity,reduced lysophosphatidylcholine(LPC)level,subsequently ameliorated lipotoxicity.In contrast,overexpression of PLA2 caused exacerbation of lipotoxicity and weakened the anti-lipotoxic effects of HN-001.Additionally,HN-001 treatment suppressed the downstream pro-apoptotic JNK pathway.In vivo,chronic administration of HN-001(i.p.)in mice alleviated all manifestations of MAFLD,including hepatic steatosis,liver injury,inflammation,and fibrogenesis.These effects were correlated with PLA2/IRE-1a/XBP-1s axis and JNK signaling suppression.These data indicate that HN-001 has therapeutic potential for MAFLD because it suppresses lipotoxicity,and provide a natural structural basis for developing anti-MAFLD candidates. | Yong Rao Rui Su Chenyan Wu Xingxing Chai Jinjian Li Guanyu Yang Junjie Wu Tingting Fu Zhongping Jiang Zhikai Guo Congjun Xu Ling Huang | 2024 | Acta Pharmaceutica Sinica B2024，14，1： | 0 |
| 7 | Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost显示文摘As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible. | Yaling Xu Congjun Rao Xinping Xiao Fuyan Hu | 2023 | Computer Modeling in Engineering & Sciences2023，137，12： | 0 |
| 8 | Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease显示文摘Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results. | Cheng Wang Haoran Zhu Congjun Rao | 2024 | Computer Modeling in Engineering & Sciences2024，138，1： | 0 |
| 9 | Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost显示文摘Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk. | Congjun Rao Mengxi Li Tingting Huang Feiyu Li | 2024 | Computer Modeling in Engineering & Sciences2024，139，4： | 0 |
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