Intelligent Decision Making in IoT-Based Enterprise Management through Fusion Optimization with Deep Learning Models
Saif Saad Ahmed1, Anwar Ja’afar M. Jawad2, Shorook K. Abd3, Aymen Mohammed4, Amjed Hameed Majeed5
1 Al-Turath University College, Baghdad, 10021, Iraq
2 Department of Computer Techniques Engineering, Al-Rafidain University College, Baghdad 10064, Iraq
3 Department of Computer Techniques Engineering, Mazaya University College, Thi Qar, Iraq
4 Department of Medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq
5Business Administration Department, Al- Mustaqbal University College51001 Hillah, Babylon , Iraq
Emails: saif.saad@turath.edu.iq; anwar.jawad@ruc.edu.iq; shurooqkamel7@gmail.com; ayman.mohammed@alfarahidiun.edu.iq; amjed_hameed@uomus.edu.iq
Abstract
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.
Keywords: Internet of Things; Business Intelligence; Enterprise Management; Data Visualization; Fusion Optimization.