2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 11 , Issue 2 , PP: 08-20, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Saif Saad Ahmed 1 * , Anwar Ja’afar M. Jawad 2 , Shorook K. Abd 3 , Aymen Mohammed 4 , Amjed Hameed Majeed 5
Doi: https://doi.org/10.54216/FPA.110201
Because of the proliferation of digital technologies, organizations now have access to previously unimaginable troves of data. In order to make educated choices and generate beneficial results, accurate data analysis and interpretation are essential. The use of data visualization in this context has proven its value. Recent studies found that data visualization increased business owners' drive to make a profit. To aid business owners in evaluating issues related to self-service data resources, a dynamic IoT-based enterprise management framework (IEMF-IDM) was presented. The suggested system uses fusion optimization techniques to maximize the fusion score and enhance decision-making through the use of various models and methods, such as machine learning and fuzzy approaches. Simulation studies in a number of domains, including robots, cloud settings, and multimedia data fusion, attest to the system's efficacy.
Internet of Things , Business Intelligence , Enterprise Management , Data Visualization , Fusion Optimization.
[1] Özemre, M., & Kabadurmus, O. (2020). A big data analytics based methodology for strategic decision making. Journal of Enterprise Information Management.
[2] Nguyen, T. N., Liu, B. H., Nguyen, N. P., & Chou, J. T. (2020, June). Cyber security of smart grid: attacks and defenses. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
[3] Fattahi, R., Tavakkoli-Moghaddam, R., Khalilzadeh, M., Shahsavari-Pour, N., & Soltani, R. (2020). A novel FMEA model based on fuzzy multiple-criteria decision-making methods for risk assessment. Journal of Enterprise Information Management.
[4] Liu, B. H., Pham, V. T., Nguyen, T. N., & Luo, Y. S. (2019). A heuristic for maximizing the lifetime of data aggregation in wireless sensor networks. arXiv preprint arXiv:1910.05310.
[5] Abd Al-Aziz Hosni El-Bagoury , Sundus Naji AL-Aziz , S.S.ASKAR, Social Spider Optimization Algorithm with Gradient Boosting Tree Model for Decision Making in Telemarketing Sector, American Journal of Business and Operations Research, Vol. 7 , No. 1 , (2022) : 09-18 (Doi : https://doi.org/10.54216/AJBOR.070101)
[6] Kranthi Kumar Singamaneni , S V Bharath Kumar Reddy , U Sreenivasulu, A Novel Framework to Enterprise Smart City with IOT and Analytics, Journal of Neutrosophic and Fuzzy Systems, Vol. 1 , No. 1 , (2021) : 37-47 (Doi : https://doi.org/10.54216/JNFS.010104)
[7] Mona Mohamed, A comparative study on Internet of Things (IoT): Frameworks, Tools, Applications and Future directions, Journal of Intelligent Systems and Internet of Things, Vol. 1 , No. 1 , (2020) : 13-39 (Doi : https://doi.org/10.54216/JISIoT.010102)
[8] Naomi A. Bajao, & Jae-an Sarucam. (2023). Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units. Mesopotamian Journal of CyberSecurity, 2023, 22–29. https://doi.org/10.58496/MJCS/2023/005.
[9] Gopal Chaudhary , Smriti Srivastava , Manju Khari, Generative Edge Intelligence for Securing IoTassisted Smart Grid against Cyber-Threats, International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 1 , (2023) : 38-49 (Doi : https://doi.org/10.54216/IJWAC.060104).
[10] Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.
[11] Obrenovic, B., Du, J., Godinic, D., Tsoy, D., Khan, M. A. S., & Jakhongirov, I. (2020). Sustaining enterprise operations and productivity during the COVID-19 pandemic: "Enterprise Effectiveness and Sustainability Model". Sustainability, 12(15), 5981.
[12] Gao, J., Wang, H., & Shen, H. (2020, August). Machine learning based workload prediction in cloud computing. In 2020 29th international conference on computer communications and networks (ICCCN) (pp. 1-9). IEEE.
[13] Nwadigo, O., Naismith, N. N., Ghaffarianhoseini, A., Hoseini, A. G., & Tookey, J. (2020). Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise. Engineering, Construction and Architectural Management.
[14] Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1-21.
[15] Xin, Q., Alazab, M., Díaz, V.G., Montenegro-Marin, C.E. and Crespo, R.G., 2022. A deep learning architecture for power management in smart cities. Energy Reports, 8, pp.1568-1577.
[16] Samiei, E., & Habibi, J. (2020). The mutual relation between Enterprise resource planning and knowledge management: A review. Global Journal of Flexible Systems Management, 21(1), 53-66.
[17] Ramprasad, L., & Amudha, G. (2014, February). Spammer detection and tagging based user generated video search system—A survey. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE.
[18] Han, Y., Deng, Y., Cao, Z., & Lin, C. T. (2020). An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making. Neural Computing and Applications, 32(12), 7641-7659.
[19] Ilmudeen, A., 2022. Artificial Intelligence, Big Data Analytics and Big Data Processing for IoT-Based Sensing Data. In Transforming Management with AI, Big-Data, and IoT (pp. 247-259). Cham: Springer International Publishing.
[20] Challa, S., Das, A. K., Odelu, V., Kumar, N., Kumari, S., Khan, M. K., & Vasilakos, A. V. (2018). An efficient ECC-based provably secure three-factor user authentication and key agreement protocol for wireless healthcare sensor networks. Computers & Electrical Engineering, 69, 534-554.
[21] Beric, D., Havzi, S., Lolic, T., Simeunovic, N., & Stefanovic, D. (2020, March). Development of the MES software and Integration with an existing ERP Software in Industrial Enterprise. In 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-6). IEEE.
[22] Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2019). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing, 10(10), 4151-4166.
[23] Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S.M., Niazi, I.K. and Mehmood, R., 2022. Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective. Electronics, 11(17), p.2676.
[24] Huifeng, W., Shankar, A., & Vivekananda, G. N. (2020). Modelling and simulation of sprinters' health promotion strategy based on sports biomechanics. Connection Science, 1-19.
[25] Huifeng, W., Kadry, S. N., & Raj, E. D. (2020). Continuous health monitoring of sportsperson using IoT devices based wearable technology. Computer Communications, 160, 588-595.
[26] Hnatenko, I., Orlova-Kurilova, O., Shtuler, I., Serzhanov, V., & Rubezhanska, V. (2020). An approach to innovation potential evaluation as a means of enterprise management
improving. International Journal of Supply and Operations Management, 7(1), 112-118.
[27] Tsvetkov, V. Y., Shaytura, S. V., & Sultaeva, N. L. (2020, May). Digital enterprise management in cyberspace. In 2nd International Scientific and Practical Conference "Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth" (MTDE 2020). Advances in Economics, Business and Management Research (Vol. 138, pp. 361-365).
[28] Asaul, A. A., Voynarenko, M., Yemchuk, L., & Dzhulii, L. (2020). New realities of the enterprise management system information support: Economic and mathematical models and cloud technologies. Journal of Information Technology Management, 12(3), 44-60.
[29] Lee, A. H., Chen, S. C., & Kang, H. Y. (2020). A decision-making framework for evaluating enterprise resource planning systems in a high-tech industry. Quality Technology & Quantitative Management, 17(3), 319-336.
[30] Pérez-Castillo, R., Ruiz, F., & Piattini, M. (2020). A decision-making support system for Enterprise Architecture Modelling. Decision Support Systems, 131, 113249.
[31] Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2020). Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management.
[32] Popova, I. V. (2020, February). Management decision-making by the head of the peasant farm enterprise under conditions of uncertainty. In IOP Conference Series: Materials Science and Engineering (Vol. 753, No. 6, p. 062021). IOP Publishing.
[33] Shan, S., Liu, X., Wei, Y., Xu, L., Zhang, B., & Yu, L. (2020). A new emergency management dynamic value assessment model based on social media data: a multiphase decision-making perspective. Enterprise Information Systems, 14(5), 680-709.
[34] Chandra, Y., Triana, R., Wang, G., & Legowo, N. (2020). Utilizing Big Data Framework to Support Decision Making Process: Enterprise Architecture Approach. International Journal, 9(3).
[35] Sun, J., Wang, H., & Chen, J. (2020). Decision-Making of Port Enterprise Safety Investment Based on System Dynamics. Processes, 8(10), 1235.
[36] Khudyakova, T., Zhuravlyov, V., Varkova, N., Aliukov, S., Shmidt, S., & Zhuravlyov, N. (2020). Improving approaches to strategic enterprise management in the context of sustainable development. Sustainability, 12(20), 8375.
[37] Tytenko L. V., Bohdan S. V., Klyuchko O. O., Tymoshenko V. Yu. Software and information support for business analysis in enterprise management. Modern Economics. 2020. No. 20(2020). P. 272-277. DOI: https://doi.org/10.31521/modecon.V20(2020)-42.
