Flowchart of algorithm principles. by Wenjuan Zhou (115285)

“…First, the Lasso algorithm and Pearson correlation coefficient method are applied to screen key multi-source features from wind turbine operation and maintenance data, quantifying their dynamic correlations with power output. …”

Load data or power demand. by M. Nasrallah (19066121)

Evaluation of model aggregation algorithms. by JiaMing Wang (334865)

“…Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. …”

Comparison of homomorphic encryption algorithms. by JiaMing Wang (334865)

“…Finally, the paper designs a deep learning model based on two-dimensional convolutional neural networks and bidirectional gated recurrent units (2DCNN-BIGRU) to handle incomplete data features and missing labels in network traffic data. …”

Comparison of the optimal objective function values obtained by three different algorithms under different scale test data sets. by Meilin Zhu (688698)

Countries and data availability period. by Angélica C. G. Santos (21553405)

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…Although various pruning strategies have been proposed to enhance mining efficiency, they cannot adaptively adjust based on the characteristics of the data distribution, making them difficult to apply widely across different datasets. …”

Temporal response patterns determined by the GMM clustering algorithm across varying primary stimulus parameters (amplitude or onset ramp velocity) of all test stimuli. by Dinh K. Tang (20469082)

Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers by Seunghyun Lee (1372719)

“…Motivated by this, we propose interpretable deep generative models for rich data types with discrete latent layers, called <i>Deep Discrete Encoders</i> (DDEs). …”

Image 1_Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm.tif by Xuehui Fan (10762662)

Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

“…Considering the increased detection range of EDARM and the requirements for computational efficiency, this paper presents a 2.5-dimensional (2.5D) finite element method (FEM). By leveraging the symmetry of simulated signals in the spectral domain, the algorithm reduces computation time by 50%, significantly enhancing computational efficiency while preserving accuracy. …”

Study area and conceptual diagram of algorithm implementation in one community health worker catchment. by Mauricianot Randriamihaja (9091488)

Subjects:

The workflow of Algorithm 2 with the example in by Badhan Das (7434362)

Statistical comparison of model performance. by Amin Ullah (12015113)

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The pre-processing of CTG raw signals [26]. by Amin Ullah (12015113)

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CTG imbalance dataset distribution. by Amin Ullah (12015113)

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The architecture of the proposed framework. by Amin Ullah (12015113)

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Quadratic velocity elements. by Rui Sun (177521)

“…To enhance the precision of the proposed UB finite element method using a reduced element count, this study implements a mesh adaptation algorithm grounded in plastic dissipation. …”

Quadratic velocity elements. by Rui Sun (177521)

“…To enhance the precision of the proposed UB finite element method using a reduced element count, this study implements a mesh adaptation algorithm grounded in plastic dissipation. …”

Random Forest and Gradient Boosting algorithms. by Yvan J. Garcia-Lopez (20783591)