Estimated processing times for peptides based on molecular weight and the used visualization approach. by Paula Cifuentes (22050278)

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Analysis process. by Jie Chu (293795)

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A data processing scheme for spatially explicit soil spectral calibration. by José L. Safanelli (20552654)

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Data Sheet 1_Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China.docx by Hanyu Zhang (1425250)

“…Introduction<p>The unmanned aerial vehicle -based light detection and ranging (UAV-LiDAR) can quickly acquire the three-dimensional information of large areas of vegetation, and has been widely used in tree species classification.</p>Methods<p>UAV-LiDAR point clouds of Populus alba, Populus simonii, Pinus sylvestris, and Pinus tabuliformis from 12 sample plots, 2,622 tree in total, were obtained in North China, training and testing sets were constructed through data pre-processing, individual tree segmentation, feature extraction, Non-uniform Grid and Farther Point Sampling (NGFPS), and then four tree species were classified efficiently by two machine learning algorithms and two deep learning algorithms.…”

Dynamic window based median filtering algorithm. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

State-of-the-Art Skin Disease Classification Using Ensemble Learning and Advanced Image Processing by Nirupama (22148684)

“…The proposed method involves extensive data collection from the Skin disease image dataset, Skin Disease Dataset, and 33k skin disease dataset. …”

FastICA algorithm and feature point selection. by Huanyu Yang (6683780)

“…The sub-block selection algorithm sorts and filters sub-blocks based on the average pixel difference, reconstructing the input data to ensure accurate separation of melanin and hemoglobin. …”

Proposing sequence genetic distances with a locally greedy algorithm. by Hannah Waddel (21520023)

The weight optimization process. by Guomei Cui (20721578)

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Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data by Gabriel Sinclair (10688582)

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Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data by Gabriel Sinclair (10688582)

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Comparison of algorithm performance aesults. by Chunjuan Li (7890182)

“…This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. …”

Data supporting figures and tables in Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams by IKENNA CLEMENT EKEKE (20995226)

“…</li><li>Reproducing automated wiring design evaluations using pathfinding algorithms.</li></ul><p></p>…”

Confusion matrix for Random Forest algorithm (3000 Number of trees) highlights the classification accuracy across different classes. by Sadia Nazim (21440115)

The evaluation scale used in the analytical hierarchical process. by Ahmed S. Abuzaid (5088119)

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Flow chart for data acquisition and processing. by Micanaldo Ernesto Francisco (18723706)

“…Data from 2009 to 2012 were used for training, and data from 2013 were used for model validation with quantitative and qualitative dengue variables. …”

Processing time for the different pipeline steps. by Marc Weitz (8131560)

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Improved random forest algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

K-means++ clustering algorithm. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

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