Parameters selected. by Ali Abbas Abbod (22386097)

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Pedal Scent Marking and Sniffing Behaviors of Brown-Bears, (a) Pedal Marks in an Area. by Ali Abbas Abbod (22386097)

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Performance results of the YOLOv10s model on the Arson Detection Dataset. by Ali Abbas Abbod (22386097)

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Visualized results for a single arson scene, showing (A) the original image, (B) YOLO v10 bounding‑box, and (C) Grad‑CAM heatmap overlay. by Ali Abbas Abbod (22386097)

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Visual comparison of YOLOv10 arson detection results. by Ali Abbas Abbod (22386097)

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Description and recommended ranges of key YOLO hyperparameters. by Ali Abbas Abbod (22386097)

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Presents a comparison between our study, Abbod et al. and Singh et al. by Ali Abbas Abbod (22386097)

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A flowchart for methodology. by Ali Abbas Abbod (22386097)

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GWO Strategy [36]. by Ali Abbas Abbod (22386097)

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Architecture of YOLOv10 [24]. by Ali Abbas Abbod (22386097)

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Evolution of training losses and evaluation metrics (precision, recall, mAP@0.5, mAP@0.5:0.95) over 100 epochs. by Ali Abbas Abbod (22386097)

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Ablation studies of the CNN-BiGRU model optimized via the GWO algorithm across LendingClub Loan Data and German Credit Data. The evaluation metrics include MAE, MAPE, RMSE, and MSE... by Yetong Fang (21433414)

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Comparison of feature subset sizes for different competitor algorithms, and the top-ranked results are shown in bold. by Genliang Li (5816264)

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Comparison of classification error rates for different competitor algorithms, with the top-ranked results shown in bold. by Genliang Li (5816264)

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Ricker seismic profile. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

Noise reduction on testing sets from STEAD. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

SNR comparison of real-field seismic profile. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

The flowchart of GWO-VMD method. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

The 147th single trace. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”