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

Mujawar M., Manikandan S., Kalbande M., Aggarwal P. K., Krishnaiah N., Genc Y.

Journal of Supercomputing, cilt.80, sa.18, ss.26568-26588, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 80 Sayı: 18
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s11227-024-06410-4
  • Dergi Adı: Journal of Supercomputing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.26568-26588
  • Anahtar Kelimeler: Artificial intelligence, Optical networks, Quality of transmission, Quantum-driven particle swarm-optimized self-adaptive support vector machine (QPSO-SASVM)
  • Gazi Üniversitesi Adresli: Evet

Özet

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.