Adjusting the process. by Hui Yang (91136)

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Indicator analysis of integrated models. by Hui Yang (91136)

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UC division situation. by Hui Yang (91136)

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Comparison of deep partitioning models. by Hui Yang (91136)

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Projection weight distribution. by Hui Yang (91136)

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Horizontal comparison of models. by Hui Yang (91136)

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Adaptive model BD rate analysis. by Hui Yang (91136)

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Index analysis of deep partitioning strategy. by Hui Yang (91136)

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The denoising performance of the algorithm in this article. by Jing Xiang (267561)

Grid positions of biclusters across three datasets for used biclustering algorithms. by Muhammad Waqar (500788)

Metabolites identified and extracted ion chromatogram of Semaglutide using MiM algorithm. by Paula Cifuentes (22050278)

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Prediction of transmission rates , for two VOIs in five regions using the VOCs-INN algorithm. by Wenxuan Li (2566450)

Impact of temperature on price and load. by Nasir Nauman (22272299)

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Relationship between ISO-NE Price and Load. by Nasir Nauman (22272299)

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Comparison of forecasting errors for ISO-NE. by Nasir Nauman (22272299)

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Impact of Wind Speed on Price and Load. by Nasir Nauman (22272299)

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Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Using LU-Net model to remove gaussian noise. by Hai Huang (140371)

“…This network captures the characteristics of input signals through an encoder, reconstructs denoised signals using a decoder, and utilizes LSTM layers and skip connections to preserve the temporal coherence and spatial details of the signals, thereby achi-eving the purpose of denoising. …”

Using LU-Net model to remove permutation noise. by Hai Huang (140371)

“…This network captures the characteristics of input signals through an encoder, reconstructs denoised signals using a decoder, and utilizes LSTM layers and skip connections to preserve the temporal coherence and spatial details of the signals, thereby achi-eving the purpose of denoising. …”

Comparison of results based on CycleGAN method. by Xiaojun Wang (50332)

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