IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

Streamfunction calculated by SA with different hyperparameters. by Takuro Matsuta (21608505)

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SA solutions using a series of spins without iteration. by Takuro Matsuta (21608505)

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QA solutions using a series of spins without iteration. by Takuro Matsuta (21608505)

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QA and SA solutions by truncated spectral expansion when by Takuro Matsuta (21608505)

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The benchmark functions. by Shuhui Yu (1535500)

Changes in Punishment Function. by Huiyue Xu (12022388)

Quantum Chemistry-Guided Machine Learning for Accelerated Design of CO₂‑Solubilizing Deep Eutectic Solvents by Dingkai Hu (16277547)

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MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

Model performance over epochs. by Ayesha Khalid (7843483)

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Evaluation metrics on test data. by Ayesha Khalid (7843483)

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The cost function Φ(<i>x</i>, <i>w</i>) versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³.... by Limin Ma (556873)

Ablation study visualization results. by Xiaozhou Feng (2918222)

“…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Experimental parameter configuration. by Xiaozhou Feng (2918222)

“…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

FLMP-YOLOv8 identification results. by Xiaozhou Feng (2918222)

“…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”