Comparison of PSNR values of different algorithms. by Xiaojun Wang (50332)

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Changes in FID values of different algorithms. by Xiaojun Wang (50332)

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Comparison of SSIM values of different algorithms. by Xiaojun Wang (50332)

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Changes in loss values for different algorithms. by Xiaojun Wang (50332)

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Different steps of the experiment and data processing. by Isabel Barradas (20614853)

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The overview of the ML algorithms’ flowchart. by Yasemin Ayaz Atalan (21989402)

“…Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. …”

Model 1 performance for each fold of training and testing. by Jamison H. Burks (21029607)

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Model 2 performance for each fold of training and testing. by Jamison H. Burks (21029607)

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A. Receiver-operator curve (ROC) for the prediction of class 0 (area above range is lower the next day). by Jamison H. Burks (21029607)

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List of features used in the XGBoost Model. by Jamison H. Burks (21029607)

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Demographic information of Dexcom Type 2 diabetes cohort. by Jamison H. Burks (21029607)

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Left. Calibration curves for 3-label classification in Model 1 (statistical features only). Right. Calibration curves for 3-label classification in Model 2 (Model 1 features + temp... by Jamison H. Burks (21029607)

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Genetic algorithm flowchart. by Wenguang Li (6528113)

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Overview of the C&CG method. by Tianhe Sun (21145341)

“…The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. …”

Intelligent decision-making exercise training. by Qiong Wu (84327)

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Phases of proposed algorithm. by Muhammad Abdullah (3357692)

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Summary of the metrics of the different trained algorithms that performed best in the Testing process for each dataset. by Jaime B. Matas-Bustos (21772430)

Ethics of algorithmic invasiveness in beauty apps: an online experimental survey of public perspectives by Yves Saint James Aquino (12311423)

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Overview of the experimental design and data processing. by Keisuke Otaki (14853535)