Training process. by Ning Zhao (84707)

Hierarchical Optimization on large models. by Nisha Ann Viswan (20371285)

Quantitative experimental results of the present algorithm and other methods. by Tingting Liu (267387)

Dropout optimization model framework structure diagram. by Peng Yang (296696)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. by Tingting Liu (267387)

An optimal solution for the HFS instance. by Xiang Tian (4369285)

“…Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

“…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…”

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-01. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Proposed architecture testing phase. by Yasir Khan Jadoon (21433231)

“…The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. …”

Flat optimization method. by Nisha Ann Viswan (20371285)

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design by Shu-Sen Chen (1777108)

“…Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. …”

Process of chromosome selection. by Xiaoxuan Wu (4455202)

Schematic diagram of the Dropout optimization. by Peng Yang (296696)