| 1. |
- Karamoozian, Amirhossein, et al.
(författare)
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An Approach for Risk Prioritization in Construction Projects Using Analytic Network Process and Decision Making Trial and Evaluation Laboratory
- 2019
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Ingår i: IEEE Access. - 2169-3536. ; 7, s. 159842-159854
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Tidskriftsartikel (refereegranskat)abstract
- Construction projects always deal with different types of risks such as financial problems, legal factors, availability of resources and other issues during the project lifecycle. According to the type, size and complexity of the project, the number and intensity of risks could be different and many projects cannot reach the project goals due to exposure to multiple risks. This study aims to propose a hybrid DEMATEL-ANP model for risk prioritization in construction projects and the originality of the work comes from its ability to consider interdependencies between risk factors. The first stage of model applied Delphi method to use experts judgments for risk identification. The second stage used Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to define the interdependencies relative intensity among the risks. Afterwards, Analytic Network Process (ANP) method is used in the third stage to assess the relative importance of the risk factors to define risk priorities in project. A case study of oil pipeline project is reported to indicate efficiency and performance of the proposed model. The results indicated that the proposed methodology could successfully reveal the important risk factors and define the interdependencies between them in the case study. On the whole, the proposed methodology can be considered as an efficient approach for risk assessment in construction projects.
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| 2. |
- Karamoozian, Amirhossein, et al.
(författare)
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Risk assessment of renewable energy projects using uncertain information
- 2022
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Ingår i: International Journal of Energy Research. - : Hindawi Limited. - 0363-907X .- 1099-114X. ; 46:13, s. 18079-18099
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Tidskriftsartikel (refereegranskat)abstract
- The majority of countries attempting to switch from conventional energy sources to renewable energy sources have decided to make significant investments in these projects. Considering renewable energy is an industry with a high level of investment expenses, assessing investment risks is critical to making efficient investment strategies. Howbeit, since risk assessment is dependent on expert opinions, uncertainty in the judgment of the consultants should be avoided. In this study, a novel hybrid failure mode and effect analysis approach is proposed to assess the investment risks of renewable energy projects. Results indicated that wind energy is the most appropriate alternative considering technical, marketability, environmental, economic, social aspects. Moreover, the sensitivity analysis is carried out and the robustness and validity of results from the proposed approach are examined. Finally, six different scenarios are considered and the results are interpreted accordingly. The findings of this study will be valuable to authorities, investors, and enterprises involved in the renewable energy projects and evaluating their investment.
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| 3. |
- Luo, Cuicui
(författare)
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A comparison analysis for credit scoring using bagging ensembles
- 2022
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Ingår i: Expert systems (Print). - : Wiley. - 0266-4720 .- 1468-0394. ; 39:2
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a hybrid approach for credit scoring, and the classification performance of this approach is compared with 4 base learners in machine learning. A large credit default swap dataset covering the period from 2006 to 2016 is used to build classifiers and test their performances. The results from this empirical study indicate that the bagging ensemble method can substantially improve individual base learners such as decision tree, multilayer perceptron, and k-nearest neighbours. The performance of support vector machine does not change after applying bagging ensemble. The overall results demonstrate that k-nearest neighbour is more suitable than any other method when dealing with large unbalanced datasets in credit scoring.
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| 4. |
- Luo, Cuicui
(författare)
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A comprehensive decision support approach for credit scoring
- 2020
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Ingår i: Industrial management & data systems. - 0263-5577 .- 1758-5783. ; 120:2, s. 280-290
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this paper is to provide a comprehensive decision support approach in credit risk assessment. Design/methodology/approach A comprehensive decision support approach is proposed for credit scoring and prediction. The predictive performance of the new approach has been investigated by using data including number and text. Findings The results demonstrate that the proposed approach achieves better and more stable classification accuracy than the single classifiers in most cases. Meanwhile, the prediction accuracy of individual classifiers is also improved by the proposed approach. Originality/value This study provides a comprehensive model for credit risk scoring and provides valuable information to the existing literature on credit scoring by using artificial intelligence.
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| 5. |
- Luo, Cuicui, et al.
(författare)
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A deep learning approach for credit scoring using credit default swaps
- 2017
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier BV. - 0952-1976 .- 1873-6769. ; 65, s. 465-470
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Tidskriftsartikel (refereegranskat)abstract
- After 2007-2008 crisis, it is clear that corporate credit scoring is becoming a key role in credit risk management. In this paper, we investigate the performances of credit scoring models applied to CDS data sets. The classification performance of deep learning algorithm such as deep belief networks with Restricted Boltzmann Machines are evaluated and compared with some popular credit scoring models such as logistic regression, multi-layer perceptron and support vector machine. The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. It is found that DBN yields the best performance.
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| 6. |
- Luo, Cuicui, et al.
(författare)
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Environment and economic risk : An analysis of carbon emission market and portfolio management
- 2016
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Ingår i: Environmental Research. - : Elsevier BV. - 0013-9351 .- 1096-0953. ; 149, s. 297-301
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Tidskriftsartikel (refereegranskat)abstract
- Climate change has been one of the biggest and most controversial environmental issues of our times. It affects the global economy, environment and human health. Many researchers find that carbon dioxide (CO2) has contributed the most to climate change between 1750 and 2005. In this study, the orthogonal GARCH (OGARCH) model is applied to examine the time-varying correlations in European CO2 allowance, crude oil and stock markets in US, Europe and China during the Protocol's first commitment period. The results show that the correlations between EUA carbon spot price and the equity markets are higher and more volatile in US and Europe than in China. Then the optimal portfolios consisting these five time series are selected by Mean-Variance and Mean-CVAR models. It shows that the optimal portfolio selected by MV-OGARCH model has the best performance.
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| 7. |
- Pan, Yuchen, et al.
(författare)
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User activity measurement in rating-based online-to-offline (O2O) service recommendation
- 2019
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 479, s. 180-196
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Tidskriftsartikel (refereegranskat)abstract
- The increasing popularity of O2O service make more and more people begin seeking and booking services online. After that, they experience the services in brick-and-mortar stores. This new business model has marketing potential and offer various opportunities to different industries. Consequently, various O2O services starting to appear, which results in difficult service selections for customers. Therefore, in this paper, we proposed a novel rating-based O2O service recommendation model considering user activity. In this method, the traditional similarity estimations are substituted by user activity which can better reflect the differentiations of customers' behavioral characteristics. Therefore, recommendations are more accurate. The experimental results show that proposed method outperforms rating-based methods, including widely used collaborative filtering methods and state-of-the-art matrix methods. In addition, we find the optimal parameter values of our model, and explore the influence of Top-k on rating-based recommendation.
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| 8. |
- Wu, Desheng D., et al.
(författare)
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A Decision Support Approach for Accounts Receivable Risk Management
- 2014
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Ingår i: IEEE Transactions on Systems, Man & Cybernetics. Systems. - 2168-2216 .- 2168-2232. ; 44:12, s. 1624-1632
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Tidskriftsartikel (refereegranskat)abstract
- Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.
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| 9. |
- Wu, Dexiang, et al.
(författare)
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Robust DEA to assess the reliability of methyl methacrylate-hardened hybrid poplar wood
- 2017
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Ingår i: Annals of Operations Research. - : Springer Science and Business Media LLC. - 0254-5330 .- 1572-9338. ; 248:1-2, s. 515-529
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Tidskriftsartikel (refereegranskat)abstract
- We transformed a data envelopment analysis (DEA) optimization model into a robust second-order cone equivalent to immunize against output perturbation in an uncertainty set. The robust DEA framework was then used to assess the effect of a wood hardening treatment using methyl methacrylate (MMA) on selected hybrid poplar clones. Because the performance of MMA-hardened hybrid poplar clones varies across clones, ranking hardened clones is crucial for developing hardening treatments for specific industrial applications. The numerical results demonstrate that the hardening treatment can be optimized by applying the proposed DEA framework to select the best hybrid poplar clone types and the optimal amount of impregnated chemicals.
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