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Cybernetics and Systems
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Volume 52, 2021 - Issue 4
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Research Article

Integrated Artificial Intelligence and Visualization Technique for Enhanced Management Decision in Today’s Turbulent Business Environments

References

  • Avramidis, P., and F. Pasiouras. 2015. Calculating systemic risk capital: A factor model approach. Journal of Financial Stability 16:138–50. doi:10.1016/j.jfs.2015.01.003.
  • Basso, A., F. Casarin, and S. Funari. 2018. How well is the museum performing? A joint use of DEA and BSC to measure the performance of museums. Omega 81:67–84. doi:10.1016/j.omega.2017.09.010.
  • Bermejo, P., J. A. Gámez, and J. M. Puerta. 2014. Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowledge-Based Systems 55:140–7. doi:10.1016/j.knosys.2013.10.016.
  • Bermejo, P., L. Ossa, J. A. Gámez, and J. M. Puerta. 2012. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowledge-Based Systems 25 (1):35–44. doi:10.1016/j.knosys.2011.01.015.
  • Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6):429–44. doi:10.1016/0377-2217(78)90138-8.
  • Charnes, A., W. W. Cooper, and R. M. Thrall. 1986. Classifying and characterizing efficiencies and inefficiencies in data development analysis. Operations Research Letters 5 (3):105–10. doi:10.1016/0167-6377(86)90082-9.
  • Cleofas-Sánchez, L., V. García, A. I. Marqués, and J. S. Sánchez. 2016. Financial distress prediction using the hybrid associative memory with translation. Applied Soft Computing 44:144–52. doi:10.1016/j.asoc.2016.04.005.
  • Delis, M. D., I. Hasan, and E. G. Tsionas. 2014. The risk of financial intermediaries. Journal of Banking & Finance 44:1–12. doi:10.1016/j.jbankfin.2014.03.024.
  • Flannery, M., S. H. Kwan, and M. Nimalendran. 2013. The 2007–2009 financial crisis and bank opaquness. Journal of Financial Intermediation 22 (1):55–84. doi:10.1016/j.jfi.2012.08.001.
  • Frankel, R., J. Jennings, and J. Lee. 2016. Using unstructured and qualitative disclosures to explain accruals. Journal of Accounting and Economics 62 (2–3):209–27. doi:10.1016/j.jacceco.2016.07.003.
  • Geng, R., I. Bose, and X. Chen. 2015. Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research 241 (1):236–47. doi:10.1016/j.ejor.2014.08.016.
  • Hajek, P., V. Olej, and R. Myskova. 2014. Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy 20 (4):721–38. doi:10.3846/20294913.2014.979456.
  • Hancer, E., B. Xue, and M. Zhang. 2018. Differential evolution for filter feature selection based on information theory and feature ranking. Knowledge-Based Systems 140:103–19. doi:10.1016/j.knosys.2017.10.028.
  • Hinton, G. E., and S. T. Roweis. 2002. Stochastic neighbor embedding. In Advances in neural information processing systems. Vol. 15, 833–40.
  • Jenkins, L., and M. Anderson. 2003. A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research 147 (1):51–61. doi:10.1016/S0377-2217(02)00243-6.
  • Jiménez, A., and Á. Herrero. 2019. Selecting Features that Drive Internationalization of Spanish Firms. Cybernetics and Systems 50 (1):25–39. doi:10.1080/01969722.2018.1558012.
  • Jones, J. S., W. Y. Lee, and T. J. Yeager. 2012. Opaque banks, price discovery, and financial instability. Journal of Financial Intermediation 21 (3):383–408. doi:10.1016/j.jfi.2012.01.004.
  • Kamei, T. 1997. Risk management (in Japanese). Tokyo: Dobunkan
  • Khemchandani, R., and S. Chandra 2007. Twin support vector machine for pattern classification. IEEE Transaction of Pattern Analysis Machine Intelligence 29:905–10. doi:10.1109/tpami.2007.1068.
  • Ko, Y. C., H. Fujita, and T. Li. 2017. An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing 52:748–59. doi:10.1016/j.asoc.2016.09.050.
  • Kurgan, L. A., K. J. Cios, and S. Dick. 2006. Highly scalable and robust rule learner: Performance evaluation and comparison. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36 (1):32–53. doi:10.1109/TSMCB.2005.852983.
  • Lang, M., and L. Stice-Lawrence. 2015. Textual analysis and international financial reporting: Large sample evidence. Journal of Accounting and Economics 60 (2–3):110–35. doi:10.1016/j.jacceco.2015.09.002.
  • Li, F. 2012. Discussion of analyzing speech to detect financial misreporting. Journal of Accounting Research 50 (2):393–400. doi:10.1111/j.1475-679X.2012.00451.x.
  • Liang, N., Y. Chen, Y. Zha, and H. Hu. 2015. Performance evaluation of individuals in workgroups with shared outcomes using DEA. INFOR: Information Systems and Operational Research 53:78–89.
  • Li, F., R. Lundholm, and M. Minnis. 2013. A measure of competition based on 10-K filings. Journal of Accounting Research 51 (2):399–436. doi:10.1111/j.1475-679X.2012.00472.x.
  • Li, M., X. Luo, and J. Yang. 2016. Extracting the nonlinear features of motor imagery EEG using parametric t-SNE. Neurocomputing 218:371–81. doi:10.1016/j.neucom.2016.08.083.
  • Liu, S., and I. Lee. 2018. Email sentiment analysis through k-means labeling and support vector machine classification. Cybernetics and Systems 49 (3):181–99. doi:10.1080/01969722.2018.1448242.
  • Maaten, J., and G. Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9:2579–605.
  • Majid, S. M., and S. H. Kassim. 2009. Impact of the 2007 US financial crisis on the emerging equity markets. International Journal of Emerging Markets 4:341–57.
  • Markowitz, H. 1952. Portfolio selection. The Journal of Finance 7 (1):77–91. doi:10.2307/2975974.
  • Mian, A., and A. Sufi. 2009. The consequences of mortgage credit expansion: Evidence from the U.S. mortgage default crisis. The. Quarterly Journal of Economics 124 (4):1449–96. doi:10.1162/qjec.2009.124.4.1449.
  • Mitchell, D. W. 1982. The effects of interest-bearing required reserves on bank portfolio riskiness. The Journal of Financial and Quantitative Analysis 17 (2):209–16. doi:10.2307/2330846.
  • Obstfeld, M. 2012. Financial flows, financial crises, and global imbalances. Journal of International Money and Finance 31 (3):469–80. doi:10.1016/j.jimonfin.2011.10.003.
  • Parkin, D., and B. Hollingsworth. 1997. Measuring production efficiency of acute hospitals in Scotland, 1991–1994: Validity issues in data envelopment analysis. Applied Economics 29 (11):1425–34. doi:10.1080/000368497326255.
  • Peng, X. 2011. TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognition 44 (10–11):2678–92. doi:10.1016/j.patcog.2011.03.031.
  • Peng, X., L. Kong, and D. Chen. 2015. Improvements on twin parametric-margin support vector machine. Neurocomputing 151:857–63. doi:10.1016/j.neucom.2014.10.010.
  • Rastogi, R., P. Saigal, and S. Chandra. 2018. Angle-based twin parametric-margin support vector machine for pattern classification. Knowledge-Based Systems 139:64–77. doi:10.1016/j.knosys.2017.10.008.
  • Sagarra, M., C. Mar-Molinero, and T. Agasisti. 2017. Exploring the efficiency of Mexican universities: Integrating data envelopment analysis and multidimensional scaling. Omega 67:123–33. doi:10.1016/j.omega.2016.04.006.
  • Singh, M., A. Singh, D. Bansal, and S. Sofat. 2019. An analytical model for identifying suspected users on Twitter. Cybernetics and Systems 50 (4):383–404. doi:10.1080/01969722.2019.1588968.
  • Tomar, D., and S. Agarwal. 2015. A comparison on multi-class classification methods based on least squares twin support vector machine. Knowledge-Based Systems 81:131–47. doi:10.1016/j.knosys.2015.02.009.
  • Wang, Z., M. H. Chen, C. L. Chin, and Q. Zheng. 2017. Managerial ability, political connections, and fraudulent financial reporting in China. Journal of Accounting and Public Policy 36 (2):141–62. doi:10.1016/j.jaccpubpol.2017.02.004.
  • Wang, Z., Y. H. Shao, and T. R. Wu. 2013. A GA-based model selection for smooth twin parametric-margin support vector machine. Pattern Recognition 46 (8):2267–77. doi:10.1016/j.patcog.2013.01.023.
  • Wu, I. L., and M. L. Chiu. 2018. Examining supply chain collaboration with determinants and performance impact: Social capital, justice, and technology use perspectives. International Journal of Information Management 39:5–19. doi:10.1016/j.ijinfomgt.2017.11.004.
  • Yazdi, M. R. T., M. M. Mozaffari, S. Nazari-Shirkouhi, and S. M. Asadzadeh. 2018. Integrated fuzzy DEA-ANFIS to measure the success effect of human resource spirituality. Cybernetics and Systems 49 (3):151–69. doi:10.1080/01969722.2018.1448221.
  • Zhou, X., W. Pedrycz, Y. Kuang, and Z. Zhang. 2016. Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Applied Soft Computing 46:424–40. doi:10.1016/j.asoc.2016.04.038.
  • Zhou, Z., E. Placca, Q. Jin, W. Liu, and S. Wu. 2018. Banks efficiency and productivity in Togo after the financial liberalization: A combined Malmquist index approach. INFOR: Information Systems and Operational Research 56:317–31.

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