Overall block diagram of proposed EMD-PCNN work. 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. …"

Confusion matrix of the proposed EMD-PCNN method. 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. …"

Timing diagram of BCI competition IV dataset 2b. 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. …"

Statistics of model identification results. حسب Tong Zhou (3770)

"…In this experiment, the principal component analysis (PCA) algorithm was used to examine the overall distribution and grouping of the samples after initial characterizing the 3D fluorescence spectrum of PRR using the alternating trilinear decomposition (ATLD) algorithm. …"

Confusion matrix of model prediction results. حسب Tong Zhou (3770)

"…In this experiment, the principal component analysis (PCA) algorithm was used to examine the overall distribution and grouping of the samples after initial characterizing the 3D fluorescence spectrum of PRR using the alternating trilinear decomposition (ATLD) algorithm. …"

PRR information table of different origin. حسب Tong Zhou (3770)

"…In this experiment, the principal component analysis (PCA) algorithm was used to examine the overall distribution and grouping of the samples after initial characterizing the 3D fluorescence spectrum of PRR using the alternating trilinear decomposition (ATLD) algorithm. …"

Comparison of model parameter settings. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

Structure of CNN. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

Flowchart of the SVMD-CNN-BiLSTM-A model. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

The decomposed components of corn. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

One basic unit of LSTM. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

Structure of BiLSTM network. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

The decomposed components of wheat. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

Daily corn and wheat future price series. حسب Kaixuan Sun (4739976)

"…The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. …"

Computational efficiency of the DySCo framework. حسب Giuseppe de Alteriis (20846230)

Quantum-Consistent Adelic Integration and Structure of Egyptian Fractions حسب Del Bel, Julian (21403013)

Data Sheet 1_Integrating problem-based learning and computational thinking: cultivating creative thinking in primary education.docx حسب Wancen Ji (22274758)

"…A four-week “Unmanned Supermarket” project was designed, incorporating CT skills such as problem decomposition, pattern recognition, and algorithm design. …"

A data binning-based multi-task prediction method for earthquake casualty prediction حسب Xiaowen Wei (12259334)

"…This paper proposes a data binning-based multi-task prediction method that enahnces forecasting through task decomposition and adaptive estimator optimization. A data binning tree algorithm first partitions samples into bins with minimal target dispersion by identifying shared attribute patterns. …"

Table 1_Integrating structured and unstructured data for livestock price forecasting: a sustainability study from South Korea.docx حسب Yifan Zhu (417526)

"…SASD framework, which systematically decomposes complex livestock price time series into trend, seasonal, and residual components, improving the forecasting accuracy by isolating seasonal patterns and irregular fluctuations. Additionally, we develop a Korean-language sentiment lexicon using an improved Term Frequency–Inverse Document Frequency (ITF-IDF) algorithm, enabling morpheme-level sentiment analysis for better sentiment extraction in Korean contexts. …"

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. حسب Huiwen Li (17705280)

"…</p><p dir="ltr">SOC20 and SOC20-100 maps in the Qinling Mountains with a spatial resolution of 1 km × 1 km during the 1980s were extracted from our previous SOC datasets, which were generated by a machine learning algorithm (Li et al., 2022b). The spatial patterns of the two SOC maps are shown in Fig. 1b. …"