MSE for ILSTM algorithm in binary classification. by 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. …”

The number of iterations to reach convergence (iters) and the total CPU time in seconds (CPU(s)) for different images of different sizes by using our algorithm and the THC algorith... by Jintao Song (12245209)

The number of iterations to reach convergence (iters) and the total CPU time in seconds (CPU(s)) for different images of different sizes by using our algorithm and the ZTC algorith... by Jintao Song (12245209)

The number of iterations to reach convergence (iters) and the total CPU time in seconds (CPU(s)) for different images of different sizes by using our algorithm and the ZTC algorith... by Jintao Song (12245209)

Tools applied to the research process. by Weixiang Zhang (10520078)

Flowchart of the proposed algorithm. by Md. Rafiqul Islam (403453)

“…Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO). Though HHO effectively solved some engineering optimization problems, is sensitive to parameter settings and may converge slowly, potentially getting trapped in local optima. …”

Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

“…We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. …”

An example of the Kernighan-Lin algorithm. by Md. Rafiqul Islam (403453)

“…Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO). Though HHO effectively solved some engineering optimization problems, is sensitive to parameter settings and may converge slowly, potentially getting trapped in local optima. …”

An example of the Kernighan-Lin algorithm. by Md. Rafiqul Islam (403453)

“…Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO). Though HHO effectively solved some engineering optimization problems, is sensitive to parameter settings and may converge slowly, potentially getting trapped in local optima. …”

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)

“…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. …”

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. …”

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. …”

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel (19843282)

Homomorphic binary tree. by Jianhua He (341366)

Flowchart of the ITSO algorithm. by Wenjie Wang (413433)

“…This paper proposes a Multi - load Balancing Control Strategy (MLBS) based on the Improved Tuna Swarm Optimization (ITSO) algorithm. …”

Total time comparison. by Mohammed Hlayel (20455634)

Image10_Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms.PNG by R. Lalhmachhuana (19684261)

Image13_Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms.PNG by R. Lalhmachhuana (19684261)

Image2_Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms.JPEG by R. Lalhmachhuana (19684261)