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Optimizing connectivity: a novel AI approach to assess transmission levels in optical networks

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Abstract

Introducing a novel approach for assessing connectivity in dynamic optical networks, we propose the quantum-driven particle swarm-optimized self-adaptive support vector machine (QPSO-SASVM) model. By integrating quantum computing and machine learning, this advanced framework offers enhanced convergence and robustness. Tested against a network simulation with 187 nodes and 96 DWDM channels, QPSO-SASVM outperforms traditional benchmarks such as LSTM, Naive method, E-DLSTM, and GRU. Evaluation using metrics such as signal-to-noise ratio, ROC curve, RMSE, and R2 consistently demonstrates superior predictive accuracy and adaptability. These results underscore QPSO-SASVM as a powerful tool for precise and reliable prediction in dynamic optical network environments.

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Contributions

Mehaboob Mujawar helped in revision work. S.Manikandan was a paper writer. Monica Kalbande worked in research problem and objectives. Puneet Kumar Aggarwal helped in the research design, selecting methods, and collecting and analyzing data. Nallam Krishnaiah was responsible for writing the initial manuscript, including introduction, methods, and results sects. Yasin Genc supervised the research and ensured the overall success of the project.

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Correspondence to S. Manikandan.

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Mujawar, M., Manikandan, S., Kalbande, M. et al. Optimizing connectivity: a novel AI approach to assess transmission levels in optical networks. J Supercomput 80, 26568–26588 (2024). https://doi.org/10.1007/s11227-024-06410-4

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