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. …”

Optimized process of the random forest algorithm. by Hongxia Li (493545)

“…Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. …”

The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

The weight optimization process. by Guomei Cui (20721578)

“…The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. …”

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…By integrating a heuristic search mechanism, the algorithm can quickly converge to potential high utility patterns and effectively reduce redundant computational processes. …”

Sample of the comparison of inference given by the adaboost algorithm and the real data. by Raphael Souza de Oliveira (12647110)

The Search process of the genetic algorithm. by Wenguang Li (6528113)

“…The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model’s predictive accuracy. …”

Statistics of GI data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

Subjects:

Statistics of HEWL data processing using different algorithms at various input CTTDs. by Ki Hyun Nam (5813819)

Subjects:

Simulated solution and the original spectral data of the actual water sample. by Wang hongmei (22598690)

Relationship between the three key elements in the sampling process. by Xiaochen He (455300)

Parameter optimization results of the RF model improved by FCM clustering. by Xini Fang (20861990)

Three sensor sample data with noise data. by Tang Ruipeng (20141334)

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. …”

Description of optimal model parameters. by Libo Liu (552605)

Comparative analysis of off-policy PPO algorithm and on-policy algorithm on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)

The construction process of the CGO prediction model. by Wenbing Shi (5806160)

Temperature sensor 2 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)

Temperature sensor 1 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)

Temperature sensor 3 sample data and data curve after noise reduction. by Tang Ruipeng (20141334)