1
Department of medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq
(m.altaee@alfarahidiuc.edu.iq)
2
Computer Communications Engineering Department, Alrafidain University College
(anwar.jawad@ruc.edu.iq)
3
Department of Computer Engineering techniques, Alturath University college, Baghdad, Iraq
(mohammed.jalil@turath.edu.iq)
4
Department of Computer Engineering techniques, mazaya University college, Thi Qar, Iraq
(noor.sami34@gmail.com)
5
Radiological Techniques Department, Al- Mustaqbal University College, 51001 Hilla, Iraq
(zaid.saad@uomus.edu.iq)
Abstract :
The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.
Keywords :
Cyber-Physical Systems; Healthcare; Multi-Level Fusion Optimization; Computer Vision Technology; Artificial Neural Network.
References :
[1] Alsufyani, A., Alotaibi, Y., Almagrabi, A. O., Alghamdi, S. A., & Alsufyani, N. (2021). Optimized intelligent data management framework for a cyber-physical system for computational applications. Complex & Intelligent Systems, 1-13.
[2] Xiong, H., Jin, C., Alazab, M., Yeh, K. H., Wang, H., Gadekallu, T. R. R., ... & Su, C. (2021). On the Design of Blockchain-based ECDSA with Fault-tolerant Batch Verication Protocol for Blockchain-enabled IoMT. IEEE Journal of Biomedical and Health Informatics.
[3] Ahmed N. Al-Masri , Hamam Mokayed, Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition, Journal of Intelligent Systems and Internet of Things, Vol. 4 , No. 1 , (2021) : 26-40 (Doi : https://doi.org/10.54216/JISIoT.040102).
[4] Israa Ezzat Salem, Mijwil, M., Alaa Wagih Abdulqader, Marwa M. Ismaeel, Anmar Alkhazraji, & Anas M. Zein Alaabdin. (2022). Introduction to The Data Mining Techniques in Cybersecurity . Mesopotamian Journal of CyberSecurity, 2022, 28–37. https://doi.org/10.58496/MJCS/2022/004
[5] Preeti Baderiya , Chetan Gupta , Shivendra Dubey, A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks, International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 2 , (2023) : 34-42 (Doi : https://doi.org/10.54216/IJWAC.060203)
[6] Nazerdeylami, A., Majidi, B., & Movaghar, A. (2021). Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems. Ocean & Coastal Management, 200, 105478.
[7] He, Y., Nie, B., Zhang, J., Kumar, P.M. and Muthu, B., 2021. Fault detection and diagnosis of cyber-physical system using the computer vision and image processing. Wireless Personal Communications, pp.1-20.
[8] Gurjanov, A.V., Babenkov, V.I., Shukalov, A.V., Zharinov, I.O. and Zharinov, O.O., 2021, April. Total quality control of the cyber-physical production using machine vision technologies. In Journal of Physics: Conference Series (Vol. 1889, No. 5, p. 052014). IOP Publishing.
[9] Tsafack, N., Sankar, S., Abd-El-Atty, B., Kengne, J., Jithin, K. C., Belazi, A., ... & Abd El-Latif, A. A. (2020). A new chaotic map with dynamic analysis and encryption application in internet of health things. IEEE Access, 8, 137731-137744.
[10] Lins, R.G., de Araujo, P.R.M. and Corazzim, M., 2020. In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems. Robotics and computer-integrated manufacturing, 61, p.101859.
[11] Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1-21.
[12] Samuel, R. D. J., & Kanna, B. R. (2019). Tuberculosis (TB) detection system using deep neural networks. Neural Computing and Applications, 31(5), 1533-1545.
[13] Ezhilmaran, D., & Adhiyaman, M. (2017). Fuzzy approaches and analysis in image processing. In Advanced Image Processing Techniques and Applications (pp. 1-31). IGI Global.
[14] Hiremath, P. N., Armentrout, J., Vu, S., Nguyen, T. N., Minh, Q. T., & Phung, P. H. (2019, November). MyWebGuard: toward a user-oriented tool for security and privacy protection on the web. In International Conference on Future Data and Security Engineering (pp. 506-525). Springer, Cham.
[15] Ali, M.H., Jaber, M.M., Alfred Daniel, J., Vignesh, C.C., Meenakshisundaram, I., Kumar, B.S. and Punitha, P., 2023. Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks. Multimedia Tools and Applications, pp.1-24.
[16] 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.
[17] Lv, Z., Chen, D., Lou, R., & Alazab, A. (2021). Artificial intelligence for securing industrial-based cyber–physical systems. Future generation computer systems, 117, 291-298.
[18] Hou, R., Jeong, S., Lynch, J.P. and Law, K.H., 2020. Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors. Transportation research part c: emerging technologies, 111, pp.547-571.
[19] 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.
[20] Janarthanan, R., Doss, S., & Baskar, S. (2020). Optimized unsupervised Deep learning assisted reconstructed coder in the on-nodule wearable sensor for Human Activity Recognition. Measurement, 108050.
[21] https://doi.org/10.1016/j.measurement.2020.108050
[22] Thakur, S., Chakraborty, A., De, R., Kumar, N., & Sarkar, R. (2021). Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model. Computers & Electrical Engineering, 91, 107044.
[23] Nazerdeylami, A., Majidi, B., & Movaghar, A. (2021). Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems. Ocean & Coastal Management, 200, 105478.
[24] Farajzadeh-Zanjani, M., Hallaji, E., Razavi-Far, R., & Saif, M. (2021). Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems. Neurocomputing, 440, 101-110.
[25] Nguyen, G. N., Le Viet, N. H., Elhoseny, M., Shankar, K., Gupta, B. B., & Abd El-Latif, A. A. (2021). Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model. Journal of Parallel and Distributed Computing, 153, 150-160.
[26] Fiaidhi, J., & Mohammed, S. (2021). Virtual care for cyber–physical systems (VH_CPS): NODE-RED, community of practice and thick data analytics ecosystem. Computer Communications, 170, 84-94.
[27] Ali, M.H., Jaber, M.M., Abd, S.K., Alkhayyat, A. and Jasim, A.D., 2022. Artificial Neural Network-Based Medical Diagnostics and Therapeutics. International Journal of Pattern Recognition and Artificial Intelligence.
[28] Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.
[29] Ali, M.H., Jaber, M.M., Abd, S.K., Alkhayyat, A. and Jameel, H.A., 2022. Model for wireless image correlation assisted by sensors based on 3D display technology. Optik, 268, p.169794.
[30] Adnan, M.M., Rahim, M.S.M., Al-Jawaheri, K., Ali, M.H., Waheed, S.R. and Radie, A.H., 2020, September. A survey and analysis on image annotation. In 2020 3rd International Conference on Engineering Technology and its Applications (IICETA) (pp. 203-208). IEEE.
| Style | # |
|---|---|
| MLA | Mustafa Altaee, Anwar Ja’afar M. Jawad , Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi. "Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection." Fusion: Practice and Applications, Vol. 11, No. 2, 2023 ,PP. 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |
| APA | Mustafa Altaee, Anwar Ja’afar M. Jawad , Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi. (2023). Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection. Journal of Fusion: Practice and Applications, 11 ( 2 ), 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |
| Chicago | Mustafa Altaee, Anwar Ja’afar M. Jawad , Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi. "Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection." Journal of Fusion: Practice and Applications, 11 no. 2 (2023): 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |
| Harvard | Mustafa Altaee, Anwar Ja’afar M. Jawad , Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi. (2023). Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection. Journal of Fusion: Practice and Applications, 11 ( 2 ), 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |
| Vancouver | Mustafa Altaee, Anwar Ja’afar M. Jawad , Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi. Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection. Journal of Fusion: Practice and Applications, (2023); 11 ( 2 ): 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |
| IEEE | Mustafa Altaee, Anwar Ja’afar M. Jawad, Mohammed A. Jalil, Noor Sami, Zaid Saad Madhi, Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection, Journal of Fusion: Practice and Applications, Vol. 11 , No. 2 , (2023) : 62-75 (Doi : https://doi.org/10.54216/FPA.110205) |