Table 2_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.docx by Yi-Ran Wang (5938265)

“…This limitation overlooks the unique gene interaction patterns within individual cells, potentially compromising the accuracy of cell type classification. We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …”

IDWE_CHM (2000–2023) by Hao Chen (11770646)

“…These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. …”

SPAAHP by Sanglin Zhao (20599835)

“…The nonlinear mapping between system classification and highway characteristics is pivotal. …”

MViT2025 by Linjun He (22524461)

“…Second, a 7×7 dynamic partitioning template together with a boundary compensation algorithm jointly optimizes dense structural representation and resolution adaptability. …”

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

“…It also incorporates an enhanced Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), which uses attention layers, layer normalization, and skip connections in the discriminator to improve the realism of synthetic minority-class samples. A dynamic attention-based ensemble (DA_Ensemble) comprising Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Feedforward Neural Network (FNN) models is employed to boost classification performance. …”

Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine by Canyi Chen (13532043)

“…First, we demonstrate that our generalized ADMM algorithm achieves linear convergence with a straightforward implementation. …”

Model code by Waltraud Schulze (20695250)

“…The latter provides insights into the inherent texture complexity of the image data due to the lossless coding. For classification of leaf surfaces from tree species based on the computed features, we utilized the k-nearest neighbors (kNN) algorithm with k=3. …”

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx by Jun Chen (4238)

“…We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm’s training metric matrix. The metric matrix obtained from training is then applied to perform a mapping transformation on the input data for the KNN classifier, thereby reducing the distance between similar samples and enhancing KNN classification performance. …”

Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery by Xiaohui Huang (458909)

“…To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). …”

Gear_fault_feature_image_data.zip by Jeongjun Lee (21074354)

“…<p dir="ltr">This dataset provides the original feature image used in the study “Development of Feature Images Considering Fault Characteristics for Gearbox Condition Classification.” The image was constructed based on vibration signals collected from a gearbox system under multiple fault conditions.…”

Data Sheet 1_A multimodal travel route recommendation system leveraging visual Transformers and self-attention mechanisms.pdf by Zhang Juan (11780753)

“…To effectively merge these two modalities, a self-attention mechanism fuses the visual features and sequential encodings, thoroughly accounting for their interdependencies. Based on this fused representation, a classification or regression model is trained using real travel datasets to recommend optimal travel routes.…”

IDWE_CHM (NRT_L) by Hao Chen (11770646)

“…These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. …”

IDWE_CHM (NRT_F) by Hao Chen (11770646)

“…These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. …”

Image 2_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Table 2_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Image 3_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Table 1_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Image 1_Integrative multi-omics analysis and experimental validation identify molecular subtypes, prognostic signature, and CA9 as a therapeutic target in oral squamous cell carcin... by Yun Zhao (48978)

“…</p>Methods<p>Multi-omics data from TCGA cohort was analyzed using consensus clustering algorithms for subtype classification. Based on the classification, a multi-omics cancer subtyping signature (MSCC) model was constructed using machine learning methods. …”

Integrating AI and OR for investment decision-making in emerging digital lending businesses: a risk-return multi-objective optimization approach by Vajiheh Torkian (21244732)

“…</p> <p>The NSGA-II algorithm is used to optimize the portfolio model.</p> <p>A sensitivity analysis is used to evaluate the investment amounts.…”

Hyperspectral Camouflage Detection Dataset and Codes by Qiran Wang (22051883)

“…Hyperspectral data of camouflage fabrics and natural grass (389.06–1005.10 nm) were acquired and preprocessed using principal component analysis, standard normal variate transformation, Savitzky–Golay filtering, and derivative-based enhancement. The Sample set Partitioning based on joint X–Y distance (SPXY) algorithm was applied to improve representativeness of training subsets, and several classifiers were constructed, including SVM, RF, KNN, CNN, and ResNet. …”