Main Article Content

Abstract

There are various generations of decision support systems related to the field of artificial intelligence, and this research paper deals with studying and analyzing the most important applications, tools, and techniques thereof in modern business organizations and e-government. Analyzing and discussing intelligent decision support systems such as neural network systems and technologies, fuzzy logic systems, genetic algorithms, and systems the expert. Study objectives: The main objective of this study is to try to identify and study the applications of artificial intelligence in support of administrative decisions, by achieving the following objectives, defining the concept of the field of artificial intelligence, and the relative importance of each of its components that it includes; Determine the modus operandi of the most prominent known applications of artificial intelligence in solving problems; Define the architecture and structure of AI applications (neural network systems and technologies, fuzzy logic systems, genetic algorithms, and expert systems); Determine the benefits offered by artificial intelligence applications when used. Study structure: Based on the study problem, its objectives, and its importance, the elements of the study can be identified as follows: The first topic: Analyzing the concept of artificial intelligence; the second topic: studying the applications of artificial intelligence in support of administrative decisions in business organizations and e-government. The Approach: The inductive approach was used, through extrapolation and analysis of studies, research, books, and periodicals related to the field of study, to identify the theoretical basis for applications of artificial intelligence in support of administrative decisions in business organizations and e-government

Keywords

Artificial intelligence administrative decisions smart systems business organizations

Article Details

How to Cite
Mohammad Ali Alqudah, Leyla Muradkhanli, & Mohammad Al-Awasa. (2021). Artificial Intelligence Applications That Support: Business Organizations and EGovernment in Administrative Decision. International Journal on Economics, Finance and Sustainable Development, 3(3), 57-72. https://doi.org/10.31149/ijefsd.v3i3.1348

References

  1. Allahverdi, N. (2014). Design of fuzzy expert systems and their applications in some medical areas. International Journal of Applied Mathematics Electronics and Computers, 2(1), 1–8.
  2. Azar, A. T. (2012). Overview of type-2 fuzzy logic systems. International Journal of Fuzzy System Applications (IJFSA), 2(4), 1–28.
  3. Bejarbaneh, B. Y., Bejarbaneh, E. Y., Fahimifar, A., Armaghani, D. J., & Abd Majid, M. Z. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77(1), 345–361.
  4. Chen, S. X., Gooi, H. B., & Wang, M. Q. (2013). Solar radiation forecast based on fuzzy logic and neural networks. Renewable Energy, 60, 195–201.
  5. Chen, Y., & Wang, D. (2017). Forecasting by general type-2 fuzzy logic systems optimized with QPSO algorithms. International Journal of Control, Automation, and Systems, 15(6), 2950–2958.
  6. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  7. Dick, S. (2019). Artificial intelligence.
  8. Egrioglu, E., Aladag, C. H., & Yolcu, U. (2013). Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with Applications, 40(3), 854–857.
  9. Elsheikh, A. H., Sharshir, S. W., Abd Elaziz, M., Kabeel, A. E., Guilan, W., & Haiou, Z. (2019). Modeling of solar energy systems using artificial neural network: A comprehensive review. Solar Energy, 180, 622–639.
  10. Fei, J., & Wang, T. (2019). Adaptive fuzzy-neural-network based on RBFNN control for active power filter. International Journal of Machine Learning and Cybernetics, 10(5), 1139–1150.
  11. Grefenstette, J. J. (2013). Genetic algorithms and their applications: proceedings of the second international conference on genetic algorithms. Psychology Press.
  12. Hossain, M. S., Zander, P., Kamal, M. S., & Chowdhury, L. (2015). Belief‐rule‐based expert systems for evaluation of e‐government: a case study. Expert Systems, 32(5), 563–577.
  13. Hou, S., Fei, J., Chen, C., & Chu, Y. (2019). Finite-time adaptive fuzzy-neural-network control of active power filter. IEEE Transactions on Power Electronics, 34(10), 10298–10313.
  14. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
  15. Junio Guimarães, A., Vitor de Campos Souza, P., Jonathan Silva Araújo, V., Silva Rezende, T., & Souza Araújo, V. (2019). Pruning fuzzy neural network applied to the construction of expert systems to aid in the diagnosis of the treatment of cryotherapy and immunotherapy. Big Data and Cognitive Computing, 3(2), 22.
  16. Kaya, T. (2019). Artificial intelligence driven e-government: the engage model to improve e-decision making. ECDG 2019 19th European Conference on Digital Government, 43.
  17. Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516.
  18. Kramer, O. (2017). Genetic algorithms. In Genetic algorithm essentials (pp. 11–19). Springer.
  19. Kruse, L., Wunderlich, N., & Beck, R. (2019). Artificial intelligence for the financial services industry: What challenges organizations to succeed. Proceedings of the 52nd Hawaii International Conference on System Sciences.
  20. Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041–3046.
  21. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368–375.
  22. Mehtab, S., & Sen, J. (2019). A robust predictive model for stock price prediction using deep learning and natural language processing. Available at SSRN 3502624.
  23. Murmu, S., & Biswas, S. (2015). Application of fuzzy logic and neural network in crop classification: a review. Aquatic Procedia, 4, 1203–1210.
  24. Nagori, V., & Trivedi, B. (2014). Types of expert system: comparative study. Asian Journal of Computer and Information Systems, 2(2).
  25. Nishida, K., Sadamitsu, K., Higashinaka, R., & Matsuo, Y. (2017). Understanding the semantic structures of tables with a hybrid deep neural network architecture. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).
  26. Olivas, F., Valdez, F., Castillo, O., Gonzalez, C. I., Martinez, G., & Melin, P. (2017). Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Applied Soft Computing, 53, 74–87.
  27. Qureshi, N. A., Qureshi, Q. A., Zubair, M., Khan, D., Shah, B., & Marwart, I. (2014). Factors affecting the introduction of ICTs for ‘healthcare decision-making’in hospitals of developing countries. Mediterranean Journal of Medical Sciences Volume, 1(1), 13–20.
  28. Reis, J., Santo, P. E., & Melão, N. (2019). Artificial intelligence in government services: A systematic literature review. World Conference on Information Systems and Technologies, 241–252.
  29. Rocco, I., Arandjelovic, R., & Sivic, J. (2017). Convolutional neural network architecture for geometric matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6148–6157.
  30. Sandkuhl, K. (2019). Putting AI into Context-Method Support for the Introduction of Artificial Intelligence into Organizations. 2019 IEEE 21st Conference on Business Informatics (CBI), 1, 157–164.
  31. Silva Araújo, V. J., Guimarães, A. J., de Campos Souza, P. V., Rezende, T. S., & Araújo, V. S. (2019). Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Machine Learning and Knowledge Extraction, 1(1), 466–482.
  32. Siskos, E., Askounis, D., & Psarras, J. (2014). Multicriteria decision support for global e-government evaluation. Omega, 46, 51–63.
  33. Soto-Hidalgo, J. M., Alonso, J. M., Acampora, G., & Alcalá-Fdez, J. (2018). JFML: a java library to design fuzzy logic systems according to the IEEE std 1855-2016. IEEE Access, 6, 54952–54964.
  34. Turban, E., Whiteside, J., King, D., & Outland, J. (2017). Innovative EC Systems: From E-Government to E-Learning, Knowledge Management, E-Health, and C2C Commerce. In Introduction to Electronic Commerce and Social Commerce (pp. 137–163). Springer.
  35. Wagner, C., & Hagras, H. (2010). Toward general type-2 fuzzy logic systems based on zSlices. IEEE Transactions on Fuzzy Systems, 18(4), 637–660.
  36. Wong, C., Guo, Z. X., & Leung, S. Y. S. (2013). Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Elsevier.