The confidence change of the binary image after iteration. by Dingming Yang (8505990)

Data sample for the total number of airline passengers at six airports (general directorate of state airports authority, 2019), approximate-area measurements, and average number of... by Emre Demir (6190242)

Failure Samples and Fitting Results. by Dongyan Wang (195482)

Deterministic-optimal policy in the EGT-model. by MingYi Wang (2807581)

Plots of steady-state frequency control. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Plots of steady-state voltage control. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Plots of steady-state input trajectory. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Two-dimensional benchmark test-functions. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Block diagram of autonomous microgrid. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Thirty-dimensional benchmark test-functions. by Salisu Mohammed (772274)

“…The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. …”

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

Iteration diagram of genetic algorithm. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Genetic algorithm flow chart. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Sample of train service network. by Jiren CAO (20442214)

Results of genetic algorithm tuning parameters. by Ke Peng (2220973)

“…The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. …”

Importance ranking and 10-fold cross-validation of the analyzed variables using a random forest model to determine the optimal number of variables. by Xin Luo (428648)

General overview of the total model. by Xueliang Guo (4797057)

Sample screening flowchart. by Meng Cao (105914)

The nitrogen content of the leaf samples. by Yongmei Li (227480)

Algorithm for MFISTA-VA [30]. by Faisal Najeeb (20542714)

“…GRASP uses Temporal Total Variation (TV) norm as a sparsity transform to promote sparsity among multi-coil MRI data and Nonlinear Conjugate Gradient (NL-CG) algorithm to obtain an optimal solution. …”