2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 2 , PP: 208-220, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Yuliadi Erdani 1 * , Ankit Kumar Dubey 2 , Aws Zuhair Sameen 3 , Saksham Sood 4 , Ramanchi Radhika 5 , Mohammad Ahmar Khan 6
Doi: https://doi.org/10.54216/FPA.150219
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.
Convolutional Neural Networks , Data Fusion , Deep Learning , Ensemble Learning , Machine Learning , Recurrent Neural Networks , Support Vector Machines , Time-Series Data , User Satisfaction.
[1] Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, "Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0," Information Fusion, vol. 50, pp. 92–111, 2019.
[2] J. Dalzochio, R. Kunst, E. Pignaton et al., "Machine learning and reasoning for predictive maintenance in Industry 4.0: current status and challenges," Computers in Industry, vol. 123, Article ID 103298, 2020.
[3] D. Pathak and R. Kashyap, "Neural correlate-based E-learning validation and classification using convolutional and Long Short-Term Memory networks," Traitement du Signal, vol. 40, no. 4, pp. 1457-1467, 2023. [Online]. Available: https://doi.org/10.18280/ts.400414
[4] R. Kashyap, "Stochastic Dilated Residual Ghost Model for Breast Cancer Detection," J Digit Imaging, vol. 36, pp. 562–573, 2023. [Online]. Available: https://doi.org/10.1007/s10278-022-00739-z
[5] D. Bavkar, R. Kashyap, and V. Khairnar, "Deep Hybrid Model with Trained Weights for Multimodal Sarcasm Detection," in Inventive Communication and Computational Technologies, G. Ranganathan, G. A. Papakostas, and Á. Rocha, Eds. Singapore: Springer, 2023, vol. 757, Lecture Notes in Networks and Systems. [Online]. Available: https://doi.org/10.1007/978-981-99-5166-6_13
[6] Omar A. abd Alwahab, Using method of Nadaraya-Watson kernel regression to detection outliers in multivariate data fusion, Journal of Fusion: Practice and Applications, Vol. 10 , No. 2 , (2023) : 75-85 (Doi : https://doi.org/10.54216/FPA.100207.
[7] S. Zuo, H. Zhao, N. Jiang et al., "Multisensor information fusion based on machine learning for real applications in human activity recognition: state-of-the-art and research challenges," Information Fusion, vol. 80, pp. 241–265, 2022.
[8] Irina V. Pustokhina, Blockchain technology in the international supply chains, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 16-25 (Doi : https://doi.org/10.54216/IJWAC.010103)
[9] D. Santos, E. Pinto, D. David da Silva, C. Hummel do Amaral, E. Inácio Fernandes-Filho, and R. Luís Silva Dias, "A Machine Learning approach to reconstruct cloudy affected vegetation indices imagery via data fusion from Sentinel-1 and Landsat 8," Computers and Electronics in Agriculture, vol. 194, Article ID 106753, 2022.
[10] P. Fu, J. Wang, X. Zhang, L. Zhang, and R. X. Gao, "Dynamic routing-based multimodal neural network for multisensory fault diagnosis of induction motor," Journal of Manufacturing Systems, vol. 55, no. 4, pp. 264–272, 2020.
[11] D. Zhao and Z. Li, "The impact of manufacturer's encroachment and nonlinear production cost on retailer's information sharing decisions," Annals of Operations Research, vol. 264, no. 1-2, pp. 499–539, 2018.
[12] He, X. Cao, and Y. Hua, "Data fusion-based sustainable digital twin system of intelligent detection robotics," Journal of Cleaner Production, vol. 280, no. 2021, Article ID 124181, 2021.
[13] J. G. Kotwal, R. Kashyap, and P. M. Shafi, "Artificial Driving based EfficientNet for Automatic Plant Leaf Disease Classification," Multimed Tools Appl, 2023. [Online]. Available: https://doi.org/10.1007/s11042-023-16882-w
[14] V. Roy et al., “Detection of sleep apnea through heart rate signal using Convolutional Neural Network,” International Journal of Pharmaceutical Research, vol. 12, no. 4, pp. 4829-4836, Oct-Dec 2020.
[15] R. Kashyap, "Machine Learning, Data Mining for IoT-Based Systems," in Research Anthology on Machine Learning Techniques, Methods, and Applications, Information Resources Management Association, Ed. IGI Global, 2022, pp. 447-471. [Online]. Available: https://doi.org/10.4018/978-1-6684-6291-1.ch025
[16] S. Khan, S. Nazir, I. García-Magariño, and A. Hussain, "Deep learning-based urban big data fusion in smart cities: towards traffic monitoring and flow-preserving fusion," Computers & Electrical Engineering, vol. 89, no. 2021, Article ID 106906, 2021.
[17] X. Chen, F. Kong, Y. Fu et al., "A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model," The International Journal of Advanced Manufacturing Technology, vol. 117, no. 3-4, pp. 707–727, 2021.
[18] H. P. Sahu and R. Kashyap, "FINE_DENSEIGANET: Automatic medical image classification in chest CT scan using Hybrid Deep Learning Framework," International Journal of Image and Graphics [Preprint], 2023. [Online]. Available: https://doi.org/10.1142/s0219467825500044
[19] S. Stalin, V. Roy, P. K. Shukla, A. Zaguia, M. M. Khan, P. K. Shukla, A. Jain, "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, vol. 2021, Article ID 2942808, 11 pages, 2021. [Online]. Available: https://doi.org/10.1155/2021/2942808
[20] S. M. Shahandashti, S. N. Razavi, L. Soibelman et al., "Data-fusion approaches and applications for construction engineering," Journal of Construction Engineering and Management, vol. 137, no. 10, pp. 863–869, 2011.
