Incremental Inverse Design of Desired Soybean Phenotypes حسب Joseph Zavorskas (19761296)

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Mean convergence graphs of the evolution in the performance function and its terms through generations in thirty executions of each algorithm. حسب Roberto Castro-Medina (21484990)

Mathematical analysis process of sliding mode speed controller design based on traditional EAL. حسب Haoran Cui (7235234)

Developing an Integrated Framework to Guide the Ecological Design of Electrical and Electronic Equipment حسب Guo-Guo Liu (21625651)

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Flowchart of MOSRS algorithm. حسب Vahid Goodarzimehr (21343124)

Performance comparison of different loss functions. حسب Junjie Lu (160350)

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Hardware design for IoT-based irrigation system. حسب Gourab Saha (8987405)

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Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. حسب Panfei Li (22379086)

"…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…"

EASP-AODV algorithm for route discovery at the intermediate node. حسب Tibebu Legesse (21030014)

Functional enrichment and correlation analysis of hub DE-SRGs. حسب Xihao Shen (20347942)

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Comparison of different algorithms. حسب Jiandong Qiu (20389944)

Sensor value scenario for fuzzy logic algorithm. حسب Gourab Saha (8987405)

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Sensor value scenario for average value algorithm. حسب Gourab Saha (8987405)

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Hyper parameter settings for ML Algorithms. حسب R. S. Jayaram (22139155)

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. حسب Yuanchen Zhao (12905580)

"…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …"

The average training time for different kernel functions. حسب Jialiang Zhang (1574704)

Pseudo-code of DMDDPG algorithm. حسب Guohua Cao (697580)

"…First, we analyze the stages of hole-seeking, alignment, and insertion in the shaft-hole assembly process, based on a comprehensive study of the interactions between shafts and holes. Next, a reward function is designed by integrating the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm. …"

Genetic algorithm flowchart. حسب Junjie He (700642)

"…Then, the epsilon constraint method was used to address the multi-objective model, followed by designing the genetic algorithm based on the problem characteristics for solutions. …"

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)