Data Mining Classification Techniques to Improve Decision-Making Processes
الموضوعات : فصلنامه ریاضی
Ali Ghani Nori Alsaedi
1
,
Mohammad Jalali Varnamkhasti
2
,
Husam Jasim Mohammed
3
,
Mojtaba Aghajani
4
1 - Department of Industrial Management , Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran.
2 - bDepartment of Science, Isfahan Branch, Islamic Azad University, Isfahan, Iran.
3 - Al-Karkh University of Science, Baghdad, Iraq.
4 - Department of Management, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran.
الکلمات المفتاحية: Data mining, Classification, Decision making, Technique, Improve, Processes.,
ملخص المقالة :
This study aims to enhance decision-making processes in dynamic organizational environments by integrating Artificial Intelligence (AI) techniques. It explores AI's potential to improve efficiency, accuracy, and predictability while addressing the ethical concerns, biases, and risks of over-reliance that accompany its implementation. We introduce a novel, domain-independent iterative methodology that leverages data mining classification techniques to analyze large datasets from information systems. This approach identifies specific situations where existing decision-making strategies yield suboptimal results and uses these insights to refine and improve the strategies employed. The methodology has proven effective in augmenting feedback control strategies by employing an inductive machine learning algorithm to uncover areas for enhancement. Our results indicate that initial strategies can be upgraded to achieve comparable levels of cost efficiency, even when accounting for the costs associated with evaluating potential strategies. This study recognizes limitations related to the variability of expert opinions in scenarios characterized by numerous components, which may constrain the optimization scope of the learning system. Future research should consider the application of advanced optimization techniques, such as genetic algorithms, to better establish optimal conditions for improvement. The findings suggest that this methodology can be readily applied within simulation frameworks, allowing organizations to assess changes in control strategies effectively. This facilitates informed resource allocation and improves operational efficiencies across diverse settings. As the adoption of AI in decision-making processes increases, attention to ethical considerations becomes crucial. Our methodology promotes transparency and addresses potential biases, fostering responsible AI use that mitigates negative societal impacts.
[1] A. Agrawal, J. Gans and A. Goldfarb, Prediction, judgment, and complexity: a theory of decision-making and artificial intelligence. In The economics of artificial intelligence: An agenda (2018) 89-110. University of Chicago Press.
[2] S. F. Ahmad, H. Han, M. M. Alam, M. K. Rehmat, M. Irshad, M. Arraño-Muñoz and A. Ariza-Montes, Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10 (1) (2023)1–14. DOI: 10.1057/s41599-023-01787-8.
[3] A. Al-Surmi, M. Bashiri and I. Koliousis, AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14) (2022) 4464-4486. https://doi.org/10.1080/00207543.2021.1966540.
[4] M. E. Balbaa and M. S. Abdurashidova, The Impact of Artifical Inteligent in Decisomn Making a Comprehensive Review EPRA International Journal of Economics, Business and Management Studies (EBMS), 11(2) (2024). 27-38. DOI: 10.36713/epra15747.
[5] I. BaniHani, S. Alawadi and N. Elmrayyan, AI and the decision-making process: a literature review in healthcare, financial, and technology sectors. Journal of Decision Systems, (2024) 1-11, https://doi.org/10.1080/12460125.2024.2349425.
[6] I. V. Buzaev, V. V. Plechev, I. E. Nikolaeva, R. M. Galimova, Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes, Chronic Diseases and Translational Medicine, 2(3) (2016) 166-172. https://doi.org/10.1016/j.cdtm.2016.09.007.
[7] D. Cabrera-Paniagua, C. Vicari, E. Urra, Decision-making system for stock exchange market using artificial emotions, Expert Systems with Applications, 42 (20) (2015) 7070-7083, https://doi.org/10.1016/j.eswa.2015.05.004.
[8] S. Cao, A. Liu and CM. Huang, Designing for Appropriate Reliance: The Roles of AI Uncertainty Presentation, Initial User Decision, and User Demographics in AI-Assisted Decision-Making. arXiv:2401.05612, (2024), https://arxiv.org/abs/2401.05612.
[9] S. Chatterjee and S. Satpathy, Artificial Intelligence in Decision Making System. In ACI@ ISIC (2022) 96-107. https://doi.org/10.1007/11893004_69.
[10] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321–357. https://doi.org/10.1613/jair.953
[11] T. Chen, A fuzzy mid-term single-fab production planning model. Journal of Intelligent Manufacturing 14 (3) (2003) 273–285. https://doi.org/10.1023/A:1024641507765.
[12] B. J. Copeland and D. Proudfoot, The History and Development of Artificial Intelligence. In P. Thagard (Ed.), Philosophy of Psychology and Cognitive Science, (2006). https://doi.org/10.1016/B978-044451540-7/50032-3
[13] T. H. Davenport, and J. G. Harris, Automated decision making comes of age. MIT Sloan Management Review, 46(4) (2005). 83.
[14] K. Dear, Artificial intelligence and decision-making. The RUSI Journal, 164(5) (2019) 18-25.
https://doi.org/10.1080/03071847.2019.1693801.
[15] D. Dennehy, A. Griva, N. Pouloudi, M. Mäntymäki and I. Pappas, Artificial intelligence for decision-making and the future of work, (2023). DOI: 10.1016/j.ijinfomgt. (2022).102574.
[16] C. Dietzmann, Y. Duan, Artificial intelligence for managerial information processing and decision-making in the era of information overload, (2022). DOI: 10.24251/HICSS.2022.720.
[17] L. A. Dobrzański, J. Trzaska, A.D. Dobrzańska-Danikiewicz, (2014). 2.09 - Use of Neural Networks and Artificial Intelligence Tools for Modeling, Characterization, and Forecasting in Material Engineering, Editor(s): Saleem Hashmi, Gilmar Ferreira Batalha, Chester J. Van Tyne, Bekir Yilbas, Comprehensive Materials Processing, Elsevier, 2014, Pages 161-198, https://doi.org/10.1016/B978-0-08-096532-1.00215-6.
[18] R. Douglas, J. Karwowski, C. Bae, A. Draguns and V. Krakovna, Limitations of Agents Simulated by Predictive Models. (2024). arXiv:2402.05829. https://doi.org/10.48550/arXiv.2402.05829A.
[19] A. Dutta, Integrating AI and optimization for decision support: A survey. Decision Support Systems, (1996) 217–226. https://doi.org/10.1016/S0167-9236(96)80001-7.
[20] J. S. Edwards, Y. Duan and P. C. Robins, An analysis of expert systems for business decision making at different levels and in different roles. European Journal of Information Systems, 9(1) (2000). 36–46. https://doi.org/10.1057/palgrave.ejis.3000344.
[21] A. Fadlalla, C. L. Lin, An analysis of the applications of neural networks in finance, Interfaces 31 (4) (2001) 112 – 122. https://doi.org/10.1287/inte.31.4.112.9662.
[22] K. Främling, Decision theory meets explainable AI. In International workshop on explainable, transparent autonomous agents and multi-agent systems (2020) 57-74. Cham: Springer International Publishing.
[23] A.Ghani Nori Alsaedi, M. Jalali Varnamkhasti, H. Jasim Mohammed, M. Aghajani, Integrating Multi-Criteria Decision Analysis with Deep Reinforcement Learning: A Novel Framework for Intelligent Decision-Making in Iraqi Industries, International Journal of Mathematical Modeling & Computations, 2(14) (2024) 171-186
[24] Y. Gil, D. Garijo, D. Khider, C. A. Knoblock, V. Ratnakar, M. Osorio, ... and L. Shu, Artificial intelligence for modeling complex systems: taming the complexity of expert models to improve decision making. ACM Transactions on Interactive Intelligent Systems, 11(2) (2021) 1-49.https://doi.org/10.1145/3453172.
[25] Z. Guo, X. Wong, W.K. Fundamentals of artificial intelligence techniques for apparel management applications, Editor(s): W.K. Wong, Z.X. Guo, S.Y.S. Leung, In Woodhead Publishing Series in Textiles, Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI), Woodhead Publishing, (2013) 13-40, https://doi.org/10.1533/9780857097842.13.
[26] T. Ho, V. Karri, D. Lim, D. Barret, An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car. Int. J. Hydrogen Energy 33 (2008) 3837–3846. https://doi.org/10.1016/j.ijhydene.2008.04.037.
[27] J. Hullman, A. Kaleand J. Hartline, Decision Theoretic Foundations for Experiments Evaluating Human Decisions. arXiv:2401.15106, (2024). https://arxiv.org/abs/2401.15106.
[28] M. J. Jarrahi, Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making, Business Horizons, Volume 61 (4) (2018) 577-586.
[29] H. Jensen, Using neural network for credit scoring. Managerial Finance 18(6) (1992) 15–26. https://doi.org/10.1108/eb013696.
[30] R.D. Johnson, D.L. Stone, and K.M. Lukaszewski, The benefits of eHRM and AI for talent acquisition", Journal of Tourism Futures, 7 (1) (2021) 40-52, https://doi.org/10.1108/JTF-02-2020-0013.
[31] S. Kaggwa, T. F. Eleogu, F. Okonkwo, O. A. Farayola, P. U. Uwaoma and A. Akinoso, AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12) (2024) 423-444. DOI: 10.51244/IJRSI.2023.1012032.
[32] S. Khanna, Artificial intelligence: contemporary applications and future compass, International Dental Journal, 60 (4) (2010) 269-272, https://doi.org/10.1922/IDJ_2422Khanna04.
[33] K. Kim and B. Kim, Decision-making model for reinforcing digital transformation strategies based on artificial intelligence technology. Information,13(5) (2022) 253. https://doi.org/10.3390/info13050253.
[34] F. Klashanov, Artificial Intelligence and Organizing Decision in Construction, Procedia Engineering, 165 (2016) 1016-1020, https://doi.org/10.1016/j.proeng.2016.11.813.
[35] R.J. Kuo, and J.A. Chen, A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm’, Expert Systems with Applications, 26(2) (2004)141–154. https://doi.org/10.1016/S0957-4174(03)00115-5.
[36] P. Langley, Simon H. A. Applications of machine learning and rule induction, Communications of the ACM 38 (11) (1995) 54–64. https://doi.org/10.1145/219717.219768.
[37] A. Lawrynowicz, Production planning and control with outsourcing using artificial intelligence. International Journal of Services and Operations Management, 3 (2) 2007. 193–209,
https://doi.org/10.1504/IJSOM.2007.012137.
[38] B. Layn, Artificial Intelligence in Finance: Transforming Decision-Making and Risk Management for Enhanced Financial Outcomes (2024).
[39] M. Leyer, A. Oberlaender, P. Dootson and M. Kowalkiewicz, Decision-making with artificial intelligence: Towards a novel conceptualization of patterns. In PACIS (2020) 224.
[40] X. Lin, Artificial Intelligence in the Industrial Engineering. Advances in Operation Research and Production Management, 1(2024) 1-6. DOI: 10.54254/3006-1210/direct/0106
[41] I. Lotfi and, A. El Bouhadi, Artificial Intelligence methods: toward a new decision-making tool. Applied Artificial Intelligence, 36(1) (2022) 1992141.
https://doi.org/10.1080/08839514.2021.1992141
[42] Z. Lu, D. Wang and M. Yin, Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making. arXiv:2401.07058, (2024). https://arxiv.org/abs/2401.07058.
[43] T. Marwala, Rational choice and artificial intelligence (2017). arXiv preprint arXiv:1703.10098.
[44] A. Mellit, S.A. Kalogirou, Artificial intelligence techniques for photovoltaic applications: A review. Prog. Energy Combust. Sci. 34 (2008) 574–632. https://doi.org/10.1016/j.pecs.2008.01.001.
[45] A. Mellit, S.A. Kalogirou, L. Hontoria, S. Shaari, Artificial intelligence techniques for sizing photovoltaic systems: A review. Renew. Sustain. Energy Rev. 13 (2009) 406–419. https://doi.org/10.1016/j.rser.2008.01.006.
A. Mellit, S.A. Kalogirou, M. Drif, Application of neural networks and genetic algorithms for sizing of photovoltaic systems. Renew. Energy 35 (2010) 2881–2893, https://doi.org/10.1016/j.renene.2010.04.017.
[46] R. A. Miller, Medical Diagnostic Decision Support Systems-Past, Present, And Future: A Threaded Bibliography and Brief Commentary, J. Am. Med. Informatics Assoc., 1(1) 1994 8–27.
doi: 10.1136/jamia.1994.95236141.
[47] M. K. Mishra, A., Muthulakshnmi, B. R. Mishra and J. M. Sasikala, Optimizing Decision-Making Process In Supply Chain Management Through Intelligent Systems. Migration Letters, 21(6) (2024) 1107-1113. https://doi.org/10.59670/ml.v21iS6.8164.
[48] A. Mishra, , I. A. Sainul, S. Bhuyan, S Deb., D. Sen, and A. K. Deb, (2018). Development of a flexible assembly system using industrial robot with machine vision guidance and dexterous multi-finger gripper. In Precision Product-Process Design and Optimization: Select Papers from AIMTDR (2016) 31-71. Springer Singapore.
[49] M. Mohammed Ridha Naser , M. Jalali Varnamkhasti, H. Jasim Mohammed, M. Aghajani, Artificial Intelligence as a Catalyst for Operational Excellence in Iraqi Industries: Implementation of a Proposed Model, International Journal of Mathematical Modeling & Computations, 2(14) (2024) 101-117
[50] K. Omoteso, The application of artificial intelligence in auditing: Looking back to the future, Expert Systems with Applications, 39 (9) (2012) 8490-8495, https://doi.org/10.1016/j.eswa.2012.01.098.
V. Paliukas, and A. Savanevičiene, Harmonization of rational and creative ˙ decisions in quality management using AI technologies,’’ Econ. Bus., 32(1) (2018) 195–208. https://doi.org/10.2478/eb-2018-0016.
[51] J. Pancras, and K. Sudhir, Optimal marketing strategies for a customer data intermediary. Journal of Marketing Research, 44 (2007) 560–578. https://doi.org/10.1509/jmkr.44.4.560.
[52] D. Paschek, C.T. Luminosu, A. Draghici and A. Mateescu, Artificial intelligence and the way of changing decision-making for business. Management, (2018) 16, 18.
[53] W. Pedrycz, N. Ichalkaranje, G. Phillips-Wren, L.C. Jain, Introduction to computational intelligence for decision making. Intell Decis Mak AI-Based Approach Springe (2008) 97. https://doi.org/10.1007/978-3-540-76829-6_3.
[54] G. Phillips-Wren, AI tools in decision making support systems: a review. International Journal on Artificial Intelligence Tools, 21(2) (2012) 1240005.
[55] T. D. Pilditch, The Reasoning Under Uncertainty Trap: A Structural AI Risk. arXiv:2402.01743. (2024). https://arxiv.org/abs/2402.01743.
[56] L. Richeson, R.A. Zimmermann, K.G. Barnett, Predicting consumer credit performance: can neural networks outperform traditional statistical methods? In: Trippi RR, Turban E (eds) Neural networks in finance and investing. IRWIN, Chicago, (1994) 45–70 https://doi.org/10.1007/s00521-010-0362-z
[57] K. Sadeghi,, D. Ojha, P. Kaur, R. V. Mahto and A. Dhir, Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180 (2024) 114194. DOI: 10.1016/j.dss.2024.114194.
[58] A. Sahay, How to reap Higher profits with Dynamic Pricing. MIT Sloan Management Review (2007).
[59] M. Shin, J. Kim, B Opheusden and T. L. Griffiths, Superhuman artificial intelligence can improve human decision-making by increasing novelty. Proceedings of the National Academy of Sciences, 120(12) (2023). e2214840120. https://doi.org/10.1073/pnas.2214840120.
[60] G. Stalidis, D. Karapistolis, A. Vafeiadis, Marketing Decision Support Using Artificial Intelligence and Knowledge Modeling: Application to Tourist Destination Management, Procedia - Social and Behavioral Sciences, 175 (2015) 106-113. https://doi.org/10.1016/j.sbspro.2015.01.1180.
[61] M. Steyvers, A. Kumar, Three challenges for AI-assisted decision-making. Perspectives on Psychological Science, (2023), 17456916231181102. https://doi.org/10.1177/17456916231181102.
JHM. Tah and V. A. Carr, Proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(4) (2000) 491–500. https://doi.org/10.1080/01446190050024905
[62] K. Takada, Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning. Journal of Evidence-Based Dental Practice, 16(3) (2016) 190-192. doi: 10.1016/j.jebdp.2016.07.002.
[63] J. M. Tenório, A. D. Hummel, F. M. Cohrs, V. L. Sdepanian, I. T. Pisa, H. de F. Marin, Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease, International Journal of Medical Informatics, (80) (11) (2011) 793-802, https://doi.org/10.1016/j.ijmedinf.2011.08.001.
[64] J.C. Trappey, J.L. Hou, K. Hiekata, Collective intelligence modeling, analysis, and synthesis for innovative engineering decision making, Advanced Engineering Informatics, 29 (4) (2015) 757-758, https://doi.org/10.1016/j.aei.2015.09.007.
[65] A. Trunk, H. Birkel and E. Hartmann, On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3) (2020) 875-919. https://doi.org/10.1007/s40685-020-00133-x.
[66] A. L. Venger and V. M. Dozortsev, Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations. Mathematics, 11(24) (2023) 4956. https://doi.org/10.3390/math11244956.
[67] BK. Wong, Y. Selvi, Neural network applications in finance: a review and analysis of literature (1990–1996). Information Manage, 34(3) (1998) 129–139. https://doi.org/10.1016/S0378-7206(98)00050-0
[68] R. Yager, Zadeh, L. An Introduction to Fuzzy Logic Applications in Intelligent Systems (1992). https://doi.org/10.1007/978-1-4615-3640-6.
[69] Y. Yuangping, F. Fred, and W. Michel, Leveraging Missing Ratings to Improve Online Recommendation Systems, Journal of Marketing Research, 43 (2006), 355–65. https://doi.org/10.1509/jmkr.43.3.355.
