aita Artificial Intelligence Theory and Applications 2757-9778 İzmir Bakırçay Üniversitesi Artificial Intelligence Applications in Management Information Systems: A Comprehensive Systematic Review with Business Analytics Perspective Çelebi Halil İbrahim SAKARYA UNIVERSITY 04 30 2021 1 1 25 56 04 02 2021 04 15 2021 Copyright © 2021, Artificial Intelligence Theory and Applications 2021 Artificial Intelligence Theory and Applications

The need to solve very complex problems and the desire to model and understand human behavior are the most important factors that trigger artificial intelligence studies. In addition, today, when digitalization has become a necessity with the industrial revolution, the importance of management information systems located at the interface of information, business and industry has increased even more. In this study, which was prepared by taking these two approaches into consideration, it was aimed to examine the use of artificial intelligence techniques in management information systems literature in a multidimensional and systematic manner, and in this direction, a systematic literature review method supported by semi-automatic modern technique such as text mining was proposed. As a result of the literature review, it has been observed that studies in the field of deep learning and swarm intelligence have gained importance in recent years. When evaluated in terms of application, although information system support and information management are at the forefront in the field of informatics, it is thought that there is a shift towards cybercrime and security and fault detection. Similarly, it can be said that there has been a tendency towards environmental factors in other business areas where production and supply chain studies are seen more. In sectoral evaluations, the value of the health sector has increased while manufacturing-oriented areas are ahead as a result of the digital transformation. When all these findings are evaluated together, it is thought that a detailed guide is presented to the academicians and professionals who will work in the field.

 artificial inteligence fuzzy logic neural networks deep learning expert systems genetic algorithms meta heuristics systematic review 1. Elena, C. (2011). Business intelligence. Journal of Knowledge Management, Economics and Information Technology, 1(2), 1-12. 2. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii. 3. Thomas, J., McNaught, J. & Ananiadou, S. (2011). Applications of text mining within systematic reviews, Research Synthesis Methods 4. Bhimani, H. Mention, A.L. & Barlatier, P.J.(2019). Social media and innovation: A systematic literature review and future research directions, Technological Forecasting and Social Change 5. Oke, S. A. (2008). A literature review on artificial intelligence. International journal of information and management sciences, 19(4), 535-570. 6. Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775. 7. Fernandez-Luque, L., & Imran, M. (2018). Humanitarian health computing using artificial intelligence and social media: A narrative literature review. International journal of medical informatics, 114, 136-142. 8. Choudhury, A., & Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR medical informatics, 8(7), e18599. 9. Keding, C. (2020). Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly, 1-44. 10.Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283-314. 11. Aristodemou, L., & Tietze, F. (2018). The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55, 37-51. 12. Rzepka, C., & Berger, B. (2018). User interaction with AI-enabled systems: a systematic review of IS research. 13. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. 14. A. Karami, M. Lundy, F. Webb and Y. K. Dwivedi, "Twitter and Research: A Systematic Literature Review Through Text Mining," in IEEE Access, vol. 8, pp. 67698-67717, 2020, doi:10.1109/ACCESS.2020.2983656. 15. Rekik, R., Kallel, I., Casillas, J., & Alimi, A. M. (2018). Assessing web sites quality: A systematic literature review by text and association rules mining. International journal of information management, 38(1), 201-216. 16. Kedra, J., Radstake, T., Pandit, A., Baraliakos, X., Berenbaum, F., Finckh, A., ... & Gossec, L. (2019). Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations. RMD open, 5(2), e001004. 17. Nascimento, A. M., Vismari, L. F., Molina, C. B. S. T., Cugnasca, P. S., Camargo, J. B., de Almeida, J. R., & Hata, A. Y. (2019). A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety. IEEE Transactions on Intelligent Transportation Systems, 21(12), 4928-4946. 18. Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology. 19. de Sousa, W. G., de Melo, E. R. P., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392. 20. Grover, P., Kar, A. K., & Dwivedi, Y. K. (2020). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 1-37. 21. Jourdan, Z., Rainer, R. K., & Marshall, T. E. (2008). Business intelligence: An analysis of the literature. Information systems management, 25(2), 121-131. 22. Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 102225. 23. Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science, & artificial intelligence: Systems for decision support. Pearson. 24. Shukla, A., Tiwari, R., & Kala, R. (2010). Real life applications of soft computing. CRC press. 25. Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432). 26. Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics & Systems, 29(7), 661-688. 27. Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons. 28. Saikia, P., Baruah, R. D., Singh, S. K., & Chaudhuri, P. K. (2020). Artificial Neural Networks in the domain of reservoir characterization: A review from shallow to deep models. Computers & Geosciences, 135, 104357. 29. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386. 30.Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533-536. 31. Menzies, T., Kocaguneli, E., Turhan, B., Minku, L., & Peters, F. (2014). Sharing data and models in software engineering. Morgan Kaufmann. 32. Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom). 33. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological cybernetics, 43(1), 59-69. 34. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558. 35. Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. 36. Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive science, 9(1), 147-169. 37. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006) 38. Hinton, G. E. (2009). Deep belief networks. Scholarpedia, 4(5), 5947. 39. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chap. Learning Internal Representations by Error Propagation, pp. 318–362. MIT Press, Cambridge, MA, USA (1986). URL http://dl.acm. org/citation.cfm?id=104279.104293 40. Deng, L., Yu, D., & Platt, J. (2012, March). Scalable stacking and learning for building deep architectures. In 2012 IEEE International conference on Acoustics, speech and signal processing (ICASSP) (pp. 2133-2136). IEEE. 41. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial networks. arXiv preprint arXiv:1406.2661. 42. Tripathi, K. P. (2011). A review on knowledge-based expert system: concept and architecture. IJCA Special Issue on Artificial Intelligence Techniques-Novel Approaches & Practical Applications, 4, 19-23. 43. Watson, I., & Marir, F. (1994). Case-based reasoning: A review. Knowledge Engineering Review, 9(4), 327-354. 44. Bäck, T., Fogel, D. B., & Michalewicz, Z. (1997). Handbook of evolutionary computation. Release, 97(1), B1. 45. Holland, J. (1975). Adaptation In Natural and Artificial Systems. University of Michigan Press, Ann Arbor. 46. Soucek, B. (1992). Dynamic, Genetic, and Chaotic Programming: The Sixth-Generation. 47. Yan, X. F., Chen, D. Z., & Hu, S. X. (2003). Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model. Computers & Chemical Engineering, 27(10), 1393-1404. 48. Storn, R., & Price, K. (1996, May). Minimizing the real functions of the ICEC'96 contest by differential evolution. In Proceedings of IEEE international conference on evolutionary computation (pp. 842-844). IEEE. 49. Beyer, H. G., & Schwefel, H. P. (2002). Evolution strategies–a comprehensive introduction. Natural computing, 1(1), 3-52. 50. Hang, N. T. T., & Chikamori, H. (2017). Comparison of efficiency between differential evolution andevolution strategy: application of the LST model to the Be River catchment in Vietnam. Paddy and Water Environment, 15(4), 797-808. 51. Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. 52. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2002). Harmony search optimization: application to pipe network design. International Journal of Modelling and Simulation, 22(2), 125-133. 53. Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989. 54. Coello, C. A. C., & Becerra, R. L. (2003, April). Evolutionary multiobjective optimization using a cultural algorithm. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706) (pp.6-13). IEEE. 55. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. 56. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248. 57. Shah-Hosseini, H. (2009). Optimization with the nature-inspired intelligent water drops algorithm. Evolutionary computation, 57(2), 297-320. 58. Tayarani-N, M. H., & Akbarzadeh-T, M. R. (2008, June). Magnetic optimization algorithms a new synthesis. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (pp. 2659-2664). IEEE. 59. Basu, M. (2011). Artificial immune system for fixed head hydrothermal power system. Energy, 36(1), 606- 612. 60. De Castro, L. N., & Von Zuben, F. J. (1999). Artificial immune systems: Part I–basic theory and applications. Universidade Estadual de Campinas, Dezembro de, Tech. Rep, 210(1). 61. Greensmith, J. (2007). The dendritic cell algorithm (Doctoral dissertation, University of Nottingham, UK). 62. Passino, K. M. (2010). Bacterial foraging optimization. International Journal of Swarm Intelligence Research (IJSIR), 1(1), 1-16. 63. Brezočnik, L., Fister, I., & Podgorelec, V. (2018). Swarm intelligence algorithms for feature selection: a review. Applied Sciences, 8(9), 1521. 64. Colorni, A., Dorigo, M., & Maniezzo, V. (1991, December). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (Vol. 142, pp. 134-142). 65. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1), 108-132. 66. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE. 67. Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343. 68. Yang, X. S. (2009, October). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg. 69. Vukšić, V. B., Ivančić, L., & Vugec, D. S. (2018). A preliminary literature review of digital transformation case studies. International Journal of Computer and Information Engineering, 12(9), 737-742. 70. Laudon, K. C., & Laudon, J. P. (2015). Management information systems. Upper Saddle River: Pearson. 71. Drezner, Z. (2005). A distance based rule for removing population members in genetic algorithms. 4OR, 3(2),109-116. 72. Bijan Fazlollahi, R. V. (2001). A method for generation of alternatives by decision support systems. Journal of Management Information Systems, 18(2), 229-250. 73. Li, T., Feng, S., & Li, L. X. (2001). Information visualization for intelligent decision support systems. Knowledge-Based Systems, 14(5-6), 259-262. 74. Zeleznikow, J., & Nolan, J. R. (2001). Using soft computing to build real world intelligent decision support systems in uncertain domains. Decision Support Systems, 31(2), 263-285. 75. Scott, J. E., & Walczak, S. (2009). Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information & Management, 46(4), 221-232. 76. Evermann, J., Rehse, J. R., & Fettke, P. (2017). Predicting process behaviour using deep learning. Decision Support Systems, 100, 129-140. 77. Kilic, H. S., Zaim, S., & Delen, D. (2014). Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines. Decision Support Systems, 66, 82-92. 78. Kim, Y. (2006). Toward a successful CRM: variable selection, sampling, and ensemble. Decision Support Systems, 41(2), 542-553. 79. Fang, S. K., Shyng, J. Y., Lee, W. S., & Tzeng, G. H. (2012). Exploring the preference of customers between financial companies and agents based on TCA. Knowledge-Based Systems, 27, 137-151. 80. Chu, B. H., Tsai, M. S., & Ho, C. S. (2007). Toward a hybrid data mining model for customer retention. Knowledge-Based Systems, 20(8), 703-718. 81. Huang, B., Liu, N., & Chandramouli, M. (2006). A GIS supported Ant algorithm for the linear feature covering problem with distance constraints. Decision Support Systems, 42(2), 1063-1075. 82. Awad, M. M. (2009). Optimization of GIS analysis using hybrid genetic algorithm. Opsearch, 46(2), 238-245. 83. Palvia, S. C., Sharma, R. S., & Conrath, D. W. (2001). A socio‐technical framework for quality assessment of computer information systems. Industrial Management & Data Systems. 84. Uğuz, H. (2011). A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems, 24(7), 1024-1032. 85. Li, T., Ruan, D., Geert, W., Song, J., & Xu, Y. (2007). A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems, 20(5), 485-494. 86. Zhang, W., Yoshida, T., & Tang, X. (2008). Text classification based on multi-word with support vector machine. Knowledge-Based Systems, 21(8), 879-886. 87. Guan, Y., Wei, Q., & Chen, G. (2019). Deep learning based personalized recommendation with multi-view information integration. Decision Support Systems, 118, 58-69. 88. Choy, K. L., Lee, W. B., Lau, H. C., & Choy, L. C. (2005). A knowledge-based supplier intelligence retrieval system for outsource manufacturing. Knowledge-based systems, 18(1), 1-17. 89. Yang, X., Zhang, M., Dou, H., & Yang, J. (2011). Neighborhood systems-based rough sets in incomplete information system. Knowledge-Based Systems, 24(6), 858-867. 90. Bradley, J. H., Paul, R., & Seeman, E. (2006). Analyzing the structure of expert knowledge. Information & management, 43(1), 77-91. 91. Yang, C. C., & Li, K. W. (2007). An associate constraint network approach to extract multi-lingual information for crime analysis. Decision Support Systems, 43(4), 1348-1361. 92. Li, X., Montazemi, A. R., & Yuan, Y. (2006). Agent-based buddy-finding methodology for knowledge sharing. Information & Management, 43(3), 283-296. 93. Porcel, C., & Herrera-Viedma, E. (2010). Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries. Knowledge-Based Systems, 23(1), 32-39. 94. Nakandala, D., & Lau, H. C. (2012). A novel approach to determining change of caloric intake requirement based on fuzzy logic methodology. Knowledge-Based Systems, 36, 51-58. 95. Ruiz, P. A. P., Kamsu-Foguem, B., & Noyes, D. (2013). Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowledge-Based Systems, 50, 171-186. 96. Barão, A., de Vasconcelos, J. B., Rocha, Á., & Pereira, R. (2017). A knowledge management approach to capture organizational learning networks. International Journal of Information Management, 37(6), 735-740. 97. Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225. 98. Wu, J., Chiclana, F., Fujita, H., & Herrera-Viedma, E. (2017). A visual interaction consensus model for social network group decision making with trust propagation. Knowledge-Based Systems, 122, 39-50. 99. Liu, Y., Liang, C., Chiclana, F., & Wu, J. (2017). A trust induced recommendation mechanism for reaching consensus in group decision making. Knowledge-Based Systems, 119, 221-231. 100. Markus, M. L., Majchrzak, A., & Gasser, L. (2002). A design theory for systems that support emergent knowledge processes. MIS quarterly, 179-212. 101. Nasirzadeh, F., Afshar, A., Khanzadi, M., & Howick, S. (2008). Integrating system dynamics and fuzzy logic modelling for construction risk management. Construction Management and Economics, 26(11), 1197-1212. 102. Gomes, P., Seco, N., Pereira, F. C., Paiva, P., Carreiro, P., Ferreira, J. L., & Bento, C. (2006). The importance of retrieval in creative design analogies. Knowledge-Based Systems, 19(7), 480-488. 103. Beynon, M., Rasmequan, S., & Russ, S. (2002). A new paradigm for computer-based decision support. Decision Support Systems, 33(2), 127-142. 104. Sniezek, J. A., Wilkins, D. C., Wadlington, P. L., & Baumann, M. R. (2002). Training for crisis decisionmaking: Psychological issues and computer-based solutions. Journal of Management Information Systems, 18(4), 147-168. 105. Bien, Z. Z., & Lee, H. E. (2007). Effective learning system techniques for human–robot interaction in service environment. Knowledge-Based Systems, 20(5), 439-456. 106. Arnold, V., Clark, N., Collier, P. A., Leech, S. A., & Sutton, S. G. (2006). The differential use and effect of knowledge-based system explanations in novice and expert judgment decisions. Mis Quarterly, 79-97. 107. Gönül, M. S., Önkal, D., & Lawrence, M. (2006). The effects of structural characteristics of explanations on use of a DSS. Decision support systems, 42(3), 1481-1493. 108. Oztekin, A., Delen, D., Turkyilmaz, A., & Zaim, S. (2013). A machine learning-based usability evaluation method for eLearning systems. Decision Support Systems, 56, 63-73. 109. Zhang, C., Zhang, Y., Xu, W., Ma, Z., Leng, Y., & Guo, J. (2015). Mining activation force defined dependency patterns for relation extraction. Knowledge-Based Systems, 86, 278-287. 110. Bouktif, S., Sahraoui, H., & Ahmed, F. (2014). Predicting stability of open-source software systems using combination of Bayesian classifiers. ACM Transactions on Management Information Systems (TMIS), 5(1), 1-26. 111. Wu, L., Zuo, C., & Zhang, H. (2015). A cloud model based fruit fly optimization algorithm. Knowledge-Based Systems, 89, 603-617. 112. Verma, A., & Kaushal, S. (2014). Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud. International Journal of Grid and Utility Computing, 5(2), 96-106. 113. Liu, Z. Z., Chu, D. H., Jia, Z. P., Shen, J. Q., & Wang, L. (2016). Two-stage approach for reliable dynamic Web service composition. Knowledge-Based Systems, 97, 123-143. 114. Li, D., Deng, L., Lee, M., & Wang, H. (2019). IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. International journal of information management, 49, 533-545. 115. Wei, W., Wang, J., Liang, J., Mi, X., & Dang, C. (2015). Compacted decision tables based attribute reduction. Knowledge-Based Systems, 86, 261-277. 116. Oztekin, A., Pajouh, F. M., Delen, D., & Swim, L. K. (2010). An RFID network design methodology for asset tracking in healthcare. Decision Support Systems, 49(1), 100-109. 117. Corchado, J. M., Bajo, J., De Paz, Y., & Tapia, D. I. (2008). Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Support Systems, 44(2), 382-396. 118. Ahn, H. J., Lee, H. J., Cho, K., & Park, S. J. (2005). Utilizing knowledge context in virtual collaborative work. Decision Support Systems, 39(4), 563-582. 119. Hu, J., Li, T., Wang, H., & Fujita, H. (2016). Hierarchical cluster ensemble model based on knowledge granulation. Knowledge-Based Systems, 91, 179-188. 120. Wang, L., Gopal, R., Shankar, R., & Pancras, J. (2015). On the brink: Predicting business failure with mobile location-based checkins. Decision Support Systems, 76, 3-13. 121. Stanko, M. A. (2016). Toward a theory of remixing in online innovation communities. Information Systems Research, 27(4), 773-791. 122. Bobadilla, J., Ortega, F., Hernando, A., & Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316. 123. Unger, M., Bar, A., Shapira, B., & Rokach, L. (2016). Towards latent context-aware recommendation systems. Knowledge-Based Systems, 104, 165-178. 124. Chan, F. T., & Chong, A. Y. (2012). A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621-630. 125. Duan, Y., Edwards, J. S., & Xu, M. X. (2005). Web-based expert systems: benefits and challenges. Information & Management, 42(6), 799-811. 126. Chiang, W. Y. K., Zhang, D., & Zhou, L. (2006). Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decision Support Systems, 41(2), 514-531. 127. Ngai, E. W., & Wat, F. K. T. (2005). Fuzzy decision support system for risk analysis in e-commerce development. Decision support systems, 40(2), 235-255. 128. Rees, L. P., Deane, J. K., Rakes, T. R., & Baker, W. H. (2011). Decision support for cybersecurity risk planning. Decision Support Systems, 51(3), 493-505. 129. Aldweesh, A., Derhab, A., & Emam, A. Z. (2020). Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems, 189, 105124. 130. Demertzis, K., Tziritas, N., Kikiras, P., Sanchez, S. L., & Iliadis, L. (2019). The next generation cognitive security operations center: adaptive analytic lambda architecture for efficient defense against adversarial attacks. Big Data and Cognitive Computing, 3(1), 6. 131. Ye, M., Wu, X., Hu, X., & Hu, D. (2013). Anonymizing classification data using rough set theory. KnowledgeBased Systems, 43, 82-94. 132. Zhu, D., Li, X. B., & Wu, S. (2009). Identity disclosure protection: A data reconstruction approach for privacypreserving data mining. Decision Support Systems, 48(1), 133-140. 133. Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, 50(2), 491-500. 134. Sinha, A. P., & Zhao, H. (2008). Incorporating domain knowledge into data mining classifiers: An application in indirect lending. Decision Support Systems, 46(1), 287-299. 135. Raman, M. G., Somu, N., Kirthivasan, K., Liscano, R., & Sriram, V. S. (2017). An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine. Knowledge-Based Systems, 134, 1-12. 136. Kavitha, B., Karthikeyan, S., & Maybell, P. S. (2012). An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier. Knowledge-Based Systems, 28, 88-96. 137. Hansen, J. V., Lowry, P. B., Meservy, R. D., & McDonald, D. M. (2007). Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection. Decision Support Systems, 43(4), 1362- 1374. 138. Zhou, L., Burgoon, J. K., Twitchell, D. P., Qin, T., & Nunamaker Jr, J. F. (2004). A comparison of classification methods for predicting deception in computer-mediated communication. Journal of Management Information Systems, 20(4), 139-166. 139. Fuller, C. M., Biros, D. P., & Wilson, R. L. (2009). Decision support for determining veracity via linguisticbased cues. Decision Support Systems, 46(3), 695-703. 140. Elkins, A. C., Dunbar, N. E., Adame, B., & Nunamaker, J. F. (2013). Are users threatened by credibility assessment systems?. Journal of Management Information Systems, 29(4), 249-262. 141. Beebe, N. L., Clark, J. G., Dietrich, G. B., Ko, M. S., & Ko, D. (2011). Post-retrieval search hit clustering to improve information retrieval effectiveness: Two digital forensics case studies. Decision Support Systems, 51(4), 732-744. 142. Horsman, G., Laing, C., & Vickers, P. (2014). A case-based reasoning method for locating evidence during digital forensic device triage. Decision Support Systems, 61, 69-78. 143. Zinovyeva, E., Härdle, W. K., & Lessmann, S. (2020). Antisocial online behavior detection using deep learning. Decision Support Systems, 138, 113362. 144. Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110-122. 145. Xu, Z. (2012). An error-analysis-based method for the priority of an intuitionistic preference relation in decision making. Knowledge-Based Systems, 33, 173-179. 146. Tang, X., Zhuang, L., Cai, J., & Li, C. (2010). Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowledge-Based Systems, 23(5), 486-490. 147. Zhou, F., Yang, S., Fujita, H., Chen, D., & Wen, C. (2020). Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowledge-Based Systems, 187, 104837. 148. Haidong, S., Hongkai, J., Xingqiu, L., & Shuaipeng, W. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 140, 1-14. 149. Han, T., Liu, C., Yang, W., & Jiang, D. (2019). A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 165, 474-487. 150. Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No.w24449). National bureau of economic research. 151. Harrington, S. J., & Guimaraes, T. (2005). Corporate culture, absorptive capacity and IT success. Information and Organization, 15(1), 39-63. 152. Sathish, T., & Jayaprakash, J. (2017). Multi period disassembly-to-order of end-of-life product based on scheduling to maximise the profit in reverse logistic operation. International Journal of Logistics Systems and Management, 26(3), 402-419. 153. Mehralian, G., Rasekh, H. R., Akhavan, P., & Ghatari, A. R. (2013). Prioritization of intellectual capital indicators in knowledge-based industries: Evidence from pharmaceutical industry. International Journal of Information Management, 33(1), 209-216. 154. Emmert-Streib, F. (2021). From the Digital Data Revolution toward a Digital Society: Pervasiveness of Artificial Intelligence. Machine Learning and Knowledge Extraction, 3(1), 284-298. 155. Hrazdil, K., Trottier, K., & Zhang, R. (2013). A comparison of industry classification schemes: A large sample study. Economics Letters, 118(1), 77-80. 156. Thong, N. T. (2015). Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowledge-Based Systems, 74, 133-150. 157. Jones, R. W., Lowe, A., & Harrison, M. J. (2002). A framework for intelligent medical diagnosis using the theory of evidence. Knowledge-Based Systems, 15(1-2), 77-84. 158. Hsu, K. H., Chiu, C., Chiu, N. H., Lee, P. C., Chiu, W. K., Liu, T. H., & Hwang, C. J. (2011). A case-based classifier for hypertension detection. Knowledge-Based Systems, 24(1), 33-39. 159. Galán-Mercant, A., Ortiz, A., Herrera-Viedma, E., Tomas, M. T., Fernandes, B., & Moral-Munoz, J. A. (2019). Assessing physical activity and functional fitness level using convolutional neural networks. KnowledgeBased Systems, 185, 104939. 160. Peng, H., Wang, J., Pérez-Jiménez, M. J., & Riscos-Núñez, A. (2019). Dynamic threshold neural P systems. Knowledge-Based Systems, 163, 875-884. 161. Dag, A., Oztekin, A., Yucel, A., Bulur, S., & Megahed, F. M. (2017). Predicting heart transplantation outcomes through data analytics. Decision Support Systems, 94, 42-52. 162. Alameen, A., & Gupta, A. (2020). Optimization driven deep learning approach for health monitoring and risk assessment in wireless body sensor networks. International Journal of Business Data Communications and Networking (IJBDCN), 16(1), 70-93. 163. Minutolo, A., Esposito, M., & De Pietro, G. (2016). A fuzzy framework for encoding uncertainty in clinical decision-making. Knowledge-Based Systems, 98, 95-116. 164. Shibl, R., Lawley, M., & Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), 953-961.