Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems
Tri Rijanto1,*, B Santhosh Kumar2, Aws Zuhair Sameen3, Suruchi Pimple4, Swati M. Patil5
1Electrical engineering, Universitas Negeri Surabaya, Indonesia
2Computer science and Engineering, G Pulla Reddy Engineering College, India
3College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq
4Asst.Professor Computer Science Department College Sadabai Raisoni Women's College, Nagpur, Maharashtra, India
5Assistant professor College name: Rajarambapu Institute of Technology, Islampur, ( An Autonomous Institute Affiliated to shivaji University, Kolhapur), India
Email: tririjanto@unesa.ac.id; santhoshkumar.bala@gmail.com; aws.zuhair@uoalfarahidi.edu.iq; suruchi.pimple@raisoni.net; swatim.patil@ritindia.edu
Abstract
We have discovered five novel strategies to enhance data fusion in complex systems. This page provides a comprehensive explanation of these five methodologies. Data may be combined with a list. Examples of techniques include entropy-based data selection and parameter optimization for data fusion. This technique effectively resolves all problems related to merging records. Accurate, rapid, and easily expandable. Ablation studies assess the effectiveness of various techniques. Every process is crucial; omitting anyone would adversely affect the mix. This approach may integrate data from several sources to guarantee accuracy and utility. This facilitates the use of intricate technologies, hence enhancing data integration. The study promotes further inquiry and implementation. These results indicate that using this method might enhance the process of combining data.
Keywords: Anomaly Detection; Data Integration; Data Scalability; Entropy-Based Selection; Fusion Algorithms; Multilevel Integration; Parameter Optimization; Precision; Robustness.