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                    "to": "ref14"
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                    "to": "ref21"
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                    "to": "ref22"
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                    "from": null,
                    "to": "ref26"
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                    "from": null,
                    "to": "Ganesan, T., Devarajan, M. V., & Yalla, R. K. M. K. (2019). Performance analysis of genetic algorithms, Monte Carlo methods, and Markov models for cloud-based scientific computing. International Journal of Applied Science, Engineering and Management, 13(1), 17. ISSN 2454-9940"
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                    "to": "Parthasarathy, K., & Ayyadurai, R. (2019). IoT-driven visualization framework for enhancing business intelligence, data quality, and risk management in corporate financial analytics. International Journal of HRM and Organizational Behavior, 7(3)"
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                    "to": "Gudivaka, R. K., Gudivaka, R. L., & Gudivaka, B. R. (2019). Robotics-driven swarm intelligence for adaptive and resilient pandemic alleviation in urban ecosystems: Advancing distributed automation and intelligent decision-making processes. International Journal of Modern Electronics and Communication Engineering (IJMECE), 7(4), 9. ISSN 2321-2152"
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                    "to": "Lupton, M. (2018). Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends Med, 18(4), 100147"
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