Comparison results of different optimization methods. حسب Yin Luo (160903)

الموضوعات:

Comparison of prediction result of models on decomposed power load time series in Dataset 1. حسب Yin Luo (160903)

الموضوعات:

A conceptual diagram illustrating the aggressive and migratory behaviors of the black-winged kite. حسب Yin Luo (160903)

الموضوعات:

Optimized parameters. حسب Yin Luo (160903)

الموضوعات:

Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf حسب Sanglin Zhao (20915732)

Adelic Fractal Cosmogenesis Recursive Number-Theoretic Scaffolding of Spacetime Geometry and Cosmic Observables حسب Julian Del Bel (21360938)

Adjacency Matrix Decomposition Clustering for Human Activity Data حسب Martha Barnard (7450217)

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data حسب Jiuyun Hu (13792760)

"…<p>We propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. …"

State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the differen... حسب Antonio Quintero-Rincón (21087716)

BiLSTM model structure diagram [30]. حسب Yuye Zou (22806476)

"…The model employs a sophisticated three-phase methodology: (1) decomposition through Variational Mode Decomposition (VMD) to extract multiple intrinsic mode functions (IMFs) from the original time series, effectively capturing its nonlinear and complex patterns; (2) optimization using a Chaotic Particle Swarm Optimization (CPSO) algorithm to fine-tune the Bi-directional Long Short-Term Memory (BiLSTM) network parameters, thereby improving both predictive accuracy and model stability; and (3) integration of predictions from both high-frequency and low-frequency components to generate comprehensive final forecasts. …"

VMD-CPSO-BiLSTM network structure. حسب Yuye Zou (22806476)

"…The model employs a sophisticated three-phase methodology: (1) decomposition through Variational Mode Decomposition (VMD) to extract multiple intrinsic mode functions (IMFs) from the original time series, effectively capturing its nonlinear and complex patterns; (2) optimization using a Chaotic Particle Swarm Optimization (CPSO) algorithm to fine-tune the Bi-directional Long Short-Term Memory (BiLSTM) network parameters, thereby improving both predictive accuracy and model stability; and (3) integration of predictions from both high-frequency and low-frequency components to generate comprehensive final forecasts. …"

Seasonal adjustment of time series observed at mixed frequencies using singular value decomposition with wavelet thresholding حسب Shiyuan He (11282556)

"…A novel Alternating Direction Method of Multiplier (ADMM) algorithm that can handle the non-smooth penalty and the manifold structure of the parameter space is developed for efficient computation. …"

Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China حسب Jingjing Liu (305549)

"…We employed the Louvain algorithm and SVD decomposition to examine the spatiotemporal patterns of population movement. …"

Distribution of the median Root Mean Square Error (RMSE) values for pollen/station by imputation method. حسب Sofia Tagliaferro (20291213)

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation حسب Hong Zhu (109912)

"…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. …"

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation حسب Hong Zhu (109912)

"…To address this challenge, we introduce a graph algorithm-based explicit denoising approach that defines user-controlled operations for removing oscillatory reaction patterns, including combination and separation, isomerization, and node contraction. …"

S4 Dataset - حسب Jaipriya D. (20571339)

"…However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. …"

S3 Dataset - حسب Jaipriya D. (20571339)

"…However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. …"

S1 Dataset - حسب Jaipriya D. (20571339)

"…However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. …"

S2 Dataset - حسب Jaipriya D. (20571339)

"…However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. …"