Overall rankings of optimization algorithms. by Máté Mohácsi (20469514)

“…<p>Statistics of the ranks achieved by individual optimization algorithms on the different benchmarks involving multiple error components (Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g001" target="_blank">1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g004" target="_blank">4</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g005" target="_blank">5</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g006" target="_blank">6</a>) according to the final error (A) and convergence speed (B). …”

Overall rankings of single-objective optimization algorithms. by Máté Mohácsi (20469514)

“…<p>Statistics of the ranks achieved by single-objective optimization algorithms on the six different benchmarks (Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g001" target="_blank">1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g006" target="_blank">6</a>) according to the final error (A) and convergence speed (B). …”

MEA-BP neural network algorithm flowchart. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Computation results of similar cases. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

The results of fitting input fluxes and initial concentrations of key molecular species in a subcellular biochemical network model. by Máté Mohácsi (20469514)

“…(C) Plot showing the evolution of the cumulative minimum error during the optimization. (D) Box plot representing the distribution of the final error scores over 10 independent runs of each algorithm. …”

Feature attribute weight cloud map. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

CBR problem-solving process. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

MEA subpopulation convergence processes. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Cloud model evaluation values for each indicator. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

On-site emergency response in mine B. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Parameter settings. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Weight values of basic characteristic attributes. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Emergency response procedure for coal mine B. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

MEA-BP neural network training process curve. by Dongling Ma (1269888)

“…A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. …”

Fusion effectiveness of 6 groups of images. by Yandong Liu (11893664)

“…Evaluation metrics indicate that the fusion outcomes obtained using our method achieve optimal values in 66.7% of cases, with sub-optimal and higher values accounting for 80.9%, significantly surpassing the performance of traditional single fusion methods.…”

Overall flowchart of Multi-agent fusion model. by Yandong Liu (11893664)

“…Evaluation metrics indicate that the fusion outcomes obtained using our method achieve optimal values in 66.7% of cases, with sub-optimal and higher values accounting for 80.9%, significantly surpassing the performance of traditional single fusion methods.…”

Fusion performance of 3 groups fusion structures. by Yandong Liu (11893664)

“…Evaluation metrics indicate that the fusion outcomes obtained using our method achieve optimal values in 66.7% of cases, with sub-optimal and higher values accounting for 80.9%, significantly surpassing the performance of traditional single fusion methods.…”

Fusion rule set. by Yandong Liu (11893664)

“…Evaluation metrics indicate that the fusion outcomes obtained using our method achieve optimal values in 66.7% of cases, with sub-optimal and higher values accounting for 80.9%, significantly surpassing the performance of traditional single fusion methods.…”

The relevant code used in this study. by Jiaxin Jiang (10656134)

“…Our study provides a theoretical foundation for the future targeted regulation of fructose metabolism in colorectal cancer patients, while simultaneously optimizing dietary guidance and therapeutic care for colorectal cancer patients in the context of the COVID-19 pandemic.…”

Representative flux distribution for aerobic growth on (A) glucose, (B) pyruvate, (C) succinate, (D) L-lactate, (E) glycerol, and (F) L-glutamate for optimal growth with maximum al... by Evangelia Vayena (20861768)