Process flow diagram of CBFD. by Indhumathi S. (19173013)

1d-MSCNN + GRU model process. by Jianpeng Zhang (528185)

“…Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. …”

The details of the control group. by Yule Sun (16015342)

“…Five state-of-the-art evolutionary algorithms are used in the control group. Finally, experimental results demonstrate that the DMBBPSO can provide high precision results for single-objective optimization problems.…”

The flowchart of DMBBPSO. by Yule Sun (16015342)

“…Five state-of-the-art evolutionary algorithms are used in the control group. Finally, experimental results demonstrate that the DMBBPSO can provide high precision results for single-objective optimization problems.…”

T2-Flair model training process using all data. by Fengning Liang (20927493)

T2-Flair model training process using all data. by Fengning Liang (20927493)

Data_Sheet_1_Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm.PDF by Chaeun Lee (11912042)

Image2_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.EPS by Xiaohu Zhao (175664)

Image3_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.PNG by Xiaohu Zhao (175664)

Image4_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.PNG by Xiaohu Zhao (175664)

Image1_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.EPS by Xiaohu Zhao (175664)

Image5_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.EPS by Xiaohu Zhao (175664)

Image6_A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring.EPS by Xiaohu Zhao (175664)

Integration of multiple models into RfaH sequence design leads to reduction of sequence bias and variance. by Lu Hong (399882)

Paeameter ranges and optimal values. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks by Eric Yanchenko (3266514)

“…In this work, we propose <math><mrow>BOPIM</mrow></math>, a Bayesian Optimization (BO) algorithm for IM on temporal networks. …”

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

Table_1_Optimal Reopening Pathways With COVID-19 Vaccine Rollout and Emerging Variants of Concern.pdf by Yanyu Xiao (682443)

“…<p>We developed a stochastic optimization technology based on a COVID-19 transmission dynamics model to determine optimal pathways from lockdown toward reopening with different scales and speeds of mass vaccine rollout in order to maximize social economical activities while not overwhelming the health system capacity in general, hospitalization beds, and intensive care units in particular. …”

Nomenclature table. by Jianhua He (341366)

“…The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. …”