Volume 9 , Issue 2 , PP: 51-64, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mustafa Nazar Dawood 1 * , Mohammed Ayad Alkhafaji 2 , Ahmed Hussian 3 , Hussein Alaa Diame 4 , Naseer Ali Hussien 5 , Sahar Yassine 6 , Venkatesan Rajinikanth 7
Doi: https://doi.org/10.54216/JISIoT.090204
Risk Management is an important task that helps to monitor the business application to eliminate the political, financial, cultural, and social consequences. The organization's risk decision is affected by several characteristics, such as lack of accountability and risk decision-making. The difficulties are resolved by applying the Machine-Learning related Business Decision Making Approach (ML-BDMA). The created framework helps to reduce the difficulties in decision-making while managing the organization's risk. The Business Decision Making process works along with the Optimistic Predictive Techniques (OPT) that are used to identify the risk which leads to attaining the business objective. This process categorizes the risk according to the qualitative characteristics of business data. The system's effectiveness was evaluated using the experimental result in which the system ensures a 98.93% performance rate, 92.25% reliability rate, 93.47% authenticity rate, 91.11% risk management rate, and 97.77% development rate while making a business decision.
Risk management , Business Decision Making (BDM) , Optimistic Predictive Technique (OPT) , reliability rate and development rate.
[1] Abiad, M., Kadry, S., Ionescu, S., &Niculescu, A. (2019). Customers' Perception of Telecommunication Services. FAIMA Business & Management Journal, 7(2), 51-62.
[2] Alazzam, M.B., Basari, A.S.H., Sibghatullah, A.S., Ramli, M.R., Jaber, M.M., and Naim, M.H., 2016. Pilot study of EHRs acceptance in Jordan hospitals by UTAUT2. Journal of Theoretical and Applied Information Technology, 85(3).
[3] Alazab, A., Bevinakoppa, S., &Khraisat, A. (2018, November). Maximizing competitive advantage on E-business websites: A data mining approach. In 2018 IEEE Conference on Big Data and Analytics (ICBDA) (pp. 111-116). IEEE.
[4] Kumar, N. (2020). Call for Special Issue Papers: Internet of Things Data Visualization for Business Intelligence: Deadline for Manuscript Submission: January 15, 2021. Big Data, 8(5), 452-453.
[5] Elhoseny, M., Hassan, M. K., & Singh, A. K. (2020). Special issue on cognitive big data analytics for business intelligence applications: Towards performance improvement.
[6] Chang, C. H. J., & Chou, L. T. L. (2016). Auditor Choice under Client Information Uncertainty. Review of Integrative Business and Economics Research, 5(4), 329-370.
[7] Itskhoki, O., & Moll, B. (2019). Optimal development policies with financial frictions. Econometrica, 87(1), 139-173.
[8] Rahman, A.U., Saeed, M., Mohammed, M.A., Jaber, M.M., and Garcia-Zapirain, B., 2022. A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases. Diagnostics, 12(7).
[9] Ganin, A. A., Quach, P., Panwar, M., Collier, Z. A., Keisler, J. M., Marchese, D., &Linkov, I. (2020). Multicriteria decision framework for cybersecurity risk assessment and management. Risk Analysis, 40(1), 183-199.
[10] Schlüter, F. F., Hetterscheid, E., & Henke, M. (2019). A simulation-based evaluation approach for digitalization scenarios in smart supply chain risk management. Journal of Industrial Engineering and Management Science, 2019(1), 179-206.
[11] Erdogan, S. A., Šaparauskas, J., &Turskis, Z. (2019). A multi-criteria decision-making model to choose the best option for sustainable construction management. Sustainability, 11(8), 2239.
[12] Zhou, J., Deng, L., & Gibson, P. (2020). SMEs' changing perspective on international trade credit risk management in China: a cultural values evolution approach. Asia Pacific Business Review, 1-21.
[13] Rostamzadeh, Reza, Mehdi Keshavarz Ghorabaee, Kannan Govindan, Ahmad Esmaeili, and Hossein Bodaghi Khajeh Nobar. "Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach." Journal of Cleaner Production 175 (2018): 651-669.
[14] Drljevic, N., Aranda, D. A., &Stantchev, V. (2020). Perspectives on risks and standards that affect the requirements engineering of blockchain technology. Computer Standards & Interfaces, 69, 103409.
[15] Jaber, M.M., 2015. Barriers faces telemedicine implementation in the developing countries : toward building iraqi telemedicine framework. ARPN Journal of Engineering and Applied Sciences, 10(4), pp.1562–1567.
[16] Rana, T., Wickramasinghe, D., &Bracci, E. (2019). New development: Integrating risk management in management control systems—lessons for public sector managers. Public Money & Management, 39(2), 148-151.
[17] Hosseini, S. A., &Smadi, O. (2021). How prediction accuracy can affect the decision-making process in the pavement management system. Infrastructures, 6(2), 28.
[18] Poza-Casado, I., Cardoso, V. E., Almeida, R. M., Meiss, A., Ramos, N. M., & Padilla-Marcos, M. Á. (2020). Residential buildings airtightness frameworks: A review of the main databases and setups in Europe and North America. Building and Environment, 107221.
[19] Willumsen, P., Oehmen, J., Stingl, V., &Geraldi, J. (2019). Value creation through project risk management. International Journal of Project Management, 37(5), 731-749.
[20] Fioriti, D., Pintus, S., Lutzemberger, G., &Poli, D. (2020). An economic multi-objective approach to design off-grid microgrids: A support for business decision making. Renewable Energy, 159, 693-704.
[21] De Couck, M., Caers, R., Musch, L., Fliegauf, J., Giangreco, A., &Gidron, Y. (2019). How breathing can help you make better decisions: Two studies on the effects of breathing patterns on heart rate variability and decision-making in business cases. International Journal of Psychophysiology, 139, 1-9.
[22] Nesrine M. Roumieh, Sonia Ahmed, Adopting Risk Management Professional Methodologies as an Effective Strategy to Protect Heritage Sites in Syria, International Journal of BIM and Engineering Science, Vol. 5 , No. 1 , (2022) : 61-72 (Doi : https://doi.org/10.54216/IJBES.050104)
[23] Ahmed Abdelmonem, Development of the hybrid MCDM model for cloud computing adoption strategic management, Fusion: Practice and Applications, Vol. 3 , No. 2 , (2021) : 73-82 (Doi : https://doi.org/10.54216/FPA.030203)
[24] Song, W., & Zhu, J. (2019). A multistage risk decision-making method for normal cloud model considering behaviour characteristics. Applied Soft Computing, 78, 393-406.
[25] Zheng, K., Zhang, Z., Chen, Y., & Wu, J. (2019). Blockchain adoption for information sharing: risk decision-making in the spacecraft supply chain. Enterprise Information Systems, 1-22.
[26] Mohamed Abdel-Basset, Mohamed Elhoseny, Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction, Journal of Cybersecurity and Information Management, Vol. 8 , No. 1 , (2021) : 08-16 (Doi : https://doi.org/10.54216/JCIM.080101)
[27] Rania Abdel Ghaffar, Saad Metawa, A Proposed Framework for Effective Risk Management in Egyptian Sustainable Development Projects, American Journal of Business and Operations Research, Vol. 0 , No. 1 , (2019) : 26-42 (Doi : https://doi.org/10.54216/AJBOR.000102)