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Showing 1 - 20 results of 38 for search '(( binary more swarm optimization algorithm ) OR ( total sample bayesian optimization algorithm ))', query time: 0.59s Refine Results
  1. 1

    MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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  3. 3

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

    Published 2022
    “…Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. …”
  4. 4

    Bayesian network for BMV_OD model. by Xinchi Dong (18554525)

    Published 2024
    “…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”
  5. 5

    Bayesian network for BMV_C1 model. by Xinchi Dong (18554525)

    Published 2024
    “…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”
  6. 6

    Bayesian network for BMV_C3 model. by Xinchi Dong (18554525)

    Published 2024
    “…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”
  7. 7

    Bayesian network for BMV_C2 model. by Xinchi Dong (18554525)

    Published 2024
    “…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”
  8. 8

    Table_1_bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.docx by Yupeng Li (507508)

    Published 2023
    “…In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. …”
  9. 9

    Results of network meta-analysis. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  10. 10

    Results of network meta-analysis. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  11. 11

    Results of network meta-analysis. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  12. 12

    Results of network meta-analysis. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  13. 13

    Study flowchart. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  14. 14

    Risk of bias graph. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  15. 15

    Results of network meta-analysis. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  16. 16

    Characteristics of included studies. by Kaiyu Zhang (198799)

    Published 2023
    “…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”
  17. 17

    Summary of LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
  18. 18

    SHAP analysis for LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
  19. 19

    Comparison of intrusion detection systems. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
  20. 20

    Parameter setting for CBOA and PSO. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”