Training process. by Ning Zhao (84707)

Library of the 55 different classification and regression machine-learning algorithms used by the ensemble predictor SuperLearner (SL.library) in the CBDA 2.0 implementation. by Simeone Marino (81051)

Summary of classification algorithms. by Stephen R. Piccolo (8367780)

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

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Summary of feature-selection algorithms. by Stephen R. Piccolo (8367780)

Quantitative experimental results of the present algorithm and other methods. 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-05. by Tingting Liu (267387)

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

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)

Relative classification performance per combination of feature-selection and classification algorithm. by Stephen R. Piccolo (8367780)

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. …”

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…In response to these challenges, this work presents a method to fine-tune a genetic algorithm for CAMD. The proposed method builds on the COSMO-CAMD framework that utilizes a genetic algorithm for solving optimization-based molecular design problems and COSMO-RS for predicting physical properties of molecules. …”

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…In response to these challenges, this work presents a method to fine-tune a genetic algorithm for CAMD. The proposed method builds on the COSMO-CAMD framework that utilizes a genetic algorithm for solving optimization-based molecular design problems and COSMO-RS for predicting physical properties of molecules. …”

Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features. by Stephen R. Piccolo (8367780)