Latent state estimations for experimental data with interruptions for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 2. حسب Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 2. حسب Samiul Alam (18563323)

Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

P-value on CEC-2017(Dim = 30). حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Memory storage behavior. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Elite search behavior. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Description of the datasets. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

S and V shaped transfer functions. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

S- and V-Type transfer function diagrams. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Collaborative hunting behavior. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Friedman average rank sum test results. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

IRBMO vs. variant comparison adaptation data. حسب Chenyi Zhu (9383370)

"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"

Parameter settings. حسب Yang Cao (53545)

"…<div><p>Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. …"

MCLP_quantum_annealer_V0.5 حسب Anonymous Anonymous (4854526)

"…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …"

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... حسب Ramsés Noguez Imm (6658637)

An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. حسب Zhengdong Wang (35299)

datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf حسب Jan Tönshoff (10192709)

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

"…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. …"

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

"…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. …"