Showing 1,101 - 1,120 results of 5,488 for search 'decision three algorithm', query time: 0.16s Refine Results
  1. 1101

    Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Sani I. Abba, Jamil Hussain, Soo-Mi Choi

    Published 2025-07-01
    “…The results of this study demonstrated that integrating swarm-based methods with machine learning boosting algorithms significantly enhanced FSM accuracy. The results of this study, as a non-structural approach, can help managers and decision-makers in flood management and control.…”
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  2. 1102

    Rapid identification of the authenticity of iron rod yam by in-situ mass spectrometry based on random forest algorithm by ZHONG Hengyan, CHEN Chun, OUYANG Yongzhong, ZHOU Lin, GUO Weiqing

    Published 2024-11-01
    “…The accuracy of the training set and detection set reached 100% when the number of decision trees was 25. HS-APCI-MS combined with RF algorithm had a significant identification effect on TG, and the classification effect of RF was superior to that of PCA.ConclusionAtmospheric pressure chemical ionization mass spectrometry, combined with the RF algorithm, can rapidly and non-destructively identify TG and FTG, providing a new technical method for authenticating TG.…”
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  3. 1103

    Algorithm for Managing Patients Hospitalized in the Surgical Wards Due to COVID-19-Related Acute Abdominal Pathology by D. G. Buribayev, D. B. Tulyaganov, Kh. E. Anvarov

    Published 2024-10-01
    “…The application of the developed algorithm will optimize the management of patients with COVID-19-related acute surgical pathology, guarantee rapid decision-making while minimizing the risk of infection, reduce the risk of complications and adverse outcomes.…”
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    Soft actor-critic algorithm and improved GNN model in secure access control of disaggregated optical networks by Zhenqian Zhao, Yuhe Wang

    Published 2025-08-01
    “…Key findings include: (1) Threat Detection: GESAC achieves an F1-score of 0.915–0.931 in identifying physical-layer attacks such as wavelength eavesdropping and cross-domain privilege escalation, with a false positive rate as low as 0.7%. (2) Resource Optimization: Compared to greedy strategies, GESAC improves wavelength utilization variance by up to 58.9% and reduces end-to-end latency standard deviation by up to 57.7% under high-load conditions. (3) Policy Robustness: In scenarios involving topological mutations, the model increases Pareto frontier coverage by over 100% and reduces policy entropy decay rate by more than 65%, indicating strong robustness. (4) Scalability: At a scale of 100,000 network nodes, GESAC achieves a single-step decision latency of just 25.6µs and significantly reduces communication overhead, demonstrating excellent scalability. …”
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