Overlap between predicted box and ground truth box. حسب LinNa Li (5665885)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code حسب Goutami Sooda (20714729)

The architecture of the box office prediction model. حسب Shenglan Tang (707472)

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IoU measures the overlap between predicted bounding boxes and ground truth boxes. حسب Yang Zhang (30734)

Box plot analysis schematic diagram. حسب Wei Su (89824)

Data and code resources. حسب Sam Hall-McMaster (10343795)

Process followed to develop and validate the MAMELI algorithm. حسب Valentina Bollati (106778)

Iteration curve of the optimization process. حسب Meijun Shang (22806461)

"…The measured data showed good agreement with the FEA predictions, with deviations of 5.2% for steel beam stress and 3.7% for foundation box deflection. …"

The effectiveness of the XGBoost algorithm. حسب Shenglan Tang (707472)

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Key features of the XGBoost algorithm. حسب Shenglan Tang (707472)

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The working principle of the XGBoost algorithm. حسب Shenglan Tang (707472)

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Box plots of the fitness function across independent runs. حسب Jin Wang (29560)

The parameter setting of the evolutionary neural network. حسب Wenbing Shi (5806160)

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The genome coding scheme. حسب Wenbing Shi (5806160)

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Visualized XGBoost simulations for all four metaheuristics regarding the box plot. حسب Jin Wang (29560)

Box plot the selected feature for weekends and weekdays in the dataset. حسب Mohd Sakib (20565228)

Stress Nephogram of the Splitting Specimen Process. حسب Nan Ru (9594384)

Flowchart of Random Aggregate Placement Process. حسب Nan Ru (9594384)

Comparison of different optimization algorithms. حسب Hang Zhao (143592)

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The optimal number of clusters identified using the GMM clustering algorithm. حسب Dinh K. Tang (20469082)