Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

Visualization of hyperparameter optimization results. by Jiayue Zhang (7832669)

Optimal parameters of the machine learning algorithm. by Novel Chandra Das (19742953)

Visualization of Comparative Performance Results Across Datasets for Different Path Planning Models. by Yan Chen (4308)

User interface of the visualization-assisted design software for planting mechanism. by Jian Kang (118630)

Details of 23 basic benchmark functions. by Ying Li (38224)

Comparison of different model algorithms. by Meiling Shi (13148944)

“…Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.…”

Visualization of Ablation Study Results for Different Model Configurations on TartanAir, NuScenes, and AirSim Datasets. by Yan Chen (4308)

Model assumptions for mass distribution campaign and proCCM intervention scenarios. by Mauricianot Randriamihaja (9091488)

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics by Gennady Gorin (8466255)

Table_1_A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.DOCX by Peiyuan Tian (6428570)

“…This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. …”

Visual comparison of TinyPerson. by Ju Liang (4277062)

Algorithm flowchart of OFEO. by Wenshun Sheng (21485393)

“…<div><p>A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed to address the issue of target ID transformation and loss caused by the increase of practical background complexity. …”

Image1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

Image2_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

Image4_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

Image5_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.jpg by Wisnu Ananta Kusuma (9276182)

Image3_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.JPEG by Wisnu Ananta Kusuma (9276182)

Evaluation metrics (accuracy, precision, recall, F1-score) comparing the proposed model with other models (related to Table 4). by Islam Zada (21755819)

DataSheet1_Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.docx by Wisnu Ananta Kusuma (9276182)