1
Lecturer/Prof. Associate, Politeknik Manufaktur Bandung, Indonesia
(yul_erdani@yahoo.com)
2
Assistant Professor, Computer Science and Engineering, Baderia Global Institute of Engineering and Management, Jabalpur MP, India
(ankitkdubey@yahoo.com)
3
College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq
(aws.zuhair@uoalfarahidi.edu.iq)
4
Centre for Interdisciplinary Research in Business and Technology, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
(sood.orp@chitkara.edu.in)
5
Professor GITAM School of Business GITAM University, Hyderabad Telangana, India
(rramanch@gitam.edu)
6
Department of Management Information System Dhofar University Salalah, Oman
(mkhan@du.edu.om)
Abstract :
The research article "Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed" proposes integrating different machine learning methods to improve data fusion. The suggested method uses an ensemble learning strategy, a deep learning-based fusion model, SVMs for data combining, CNNs for image and time-series data combining, and RNNs for time-series data combining. For best efficiency, each algorithm is carefully constructed utilizing mathematical concepts. Deep learning shines on complicated datasets, whereas the ensemble approach, which uses several models, is more accurate. CNN handles visual data better than RNN does sequence data. However, SVM shines in multidimensional domains. These reliable and adaptive solutions can tackle various data fusion difficulties. This approach outperforms others in processing speed, accuracy, precision, memory, and F1-score. Finding a balance between computer complexity and human satisfaction enhances dependability, data duplication, and quality. This novel technique transforms machine learning-powered data fusion. Another benefit is better data integration in complicated systems.
Keywords :
Convolutional Neural Networks; Data Fusion; Deep Learning; Ensemble Learning; Machine Learning; Recurrent Neural Networks; Support Vector Machines; Time-Series Data; User Satisfaction.
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| Style | # |
|---|---|
| MLA | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan. "Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed." Fusion: Practice and Applications, Vol. 15, No. 2, 2024 ,PP. 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |
| APA | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan. (2024). Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed. Journal of Fusion: Practice and Applications, 15 ( 2 ), 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |
| Chicago | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan. "Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed." Journal of Fusion: Practice and Applications, 15 no. 2 (2024): 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |
| Harvard | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan. (2024). Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed. Journal of Fusion: Practice and Applications, 15 ( 2 ), 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |
| Vancouver | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan. Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed. Journal of Fusion: Practice and Applications, (2024); 15 ( 2 ): 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |
| IEEE | Yuliadi Erdani, Ankit Kumar Dubey, Aws Zuhair Sameen, Saksham Sood, Ramanchi Radhika, Mohammad Ahmar Khan, Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed, Journal of Fusion: Practice and Applications, Vol. 15 , No. 2 , (2024) : 208-220 (Doi : https://doi.org/10.54216/FPA.150219) |