Illustration of two joints dynamical model of the human body in standing position. by Hedyeh Jafari (9667127)

Subjects:

The loss curve for model training. by Jie Fang (306330)

“…The pointer network with an encoder and decoder structure is taken as the basic network for the deep reinforcement learning algorithm. A model-free reinforcement learning algorithm is designed to train network parameters to optimize the packing sequence. …”

, MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

Cuff-less Blood Pressure Measurement based on Four-wavelength PPG Signals by Liang yongbo (4822017)

“…<a href="https://www.mdpi.com/2079-6374/8/4/101" target="_blank"><b>Link</b></a></p><p dir="ltr">[12] Xuhao Dong Ziyi Wang, Liangli Cao, Zhencheng Chen*, <b>Yongbo Liang*</b>. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals [J]. …”

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. …”

Video_1_Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.MP4 by Binbin Su (640741)

KNN algorithm flowchart. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

MGA algorithm flowchart. by Guilian Feng (18530806)

“…In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. …”

LSTM model validation results. by Yao Hu (3479972)

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. …”

Visualization of Residuals across different models applied in our research. by Md Junayed Hossain (22615268)

Subjects:

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. …”

Structure of DBN model. by Saraswathi M. (19353597)

Subjects:

AMI, ARI and NMI accuracy on two real-world networks. by Yasamin Tabatabaee (18095491)

Performance comparison of four optimization methods. by Xuexing Du (20624042)

Experiment 5: Clustering networks that are only partially clusterable. by Yasamin Tabatabaee (18095491)

Structure diagram of LSTM cell model. by Yao Hu (3479972)

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. …”

Intelligent risk assessment model diagram. by Yao Hu (3479972)

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. …”

LSTM model training accuracy verification. by Yao Hu (3479972)

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. …”

LSTM model training stability verification. by Yao Hu (3479972)

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. …”