LSTM model’s equations. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Parameter’s interpretation. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

The models’ training parameters. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Association point and relationship. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. by Huamei Li (8815955)

Spatial characterization and cell-type-specific genes in mouse brain tissue. by Huamei Li (8815955)

Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions. by Huamei Li (8815955)

BootCellNet2 gives clustering that comply more with CITE-seq results. by Yutaro Kumagai (481168)

Overall framework design. by Matthew Yit Hang Yeow (20721206)

“…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”

Gamma distribution of reuse. by Matthew Yit Hang Yeow (20721206)

“…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”

Top 5 correlated features based on reuse. by Matthew Yit Hang Yeow (20721206)

“…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”

Features with the top importance score. by Matthew Yit Hang Yeow (20721206)

“…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …”

Heatmap of pre-post interview changes in system insights, health equity thinking, & healthy equity action. by Travis R. Moore (19532635)

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. …”

S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

“…The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. …”

The architecture of the SE-multi-input CNN model. by Bin Zheng (45359)

“…The fusion strategy combines feature maps via bicubic interpolation and element-wise summation to maintain spatial integrity. …”

Confusion matrix for the Multi-input CNN model. by Bin Zheng (45359)

“…The fusion strategy combines feature maps via bicubic interpolation and element-wise summation to maintain spatial integrity. …”

Confusion matrices for single-input CNN models. by Bin Zheng (45359)

“…The fusion strategy combines feature maps via bicubic interpolation and element-wise summation to maintain spatial integrity. …”

Confusion matrix for the Multi-input CNN model. by Bin Zheng (45359)

“…The fusion strategy combines feature maps via bicubic interpolation and element-wise summation to maintain spatial integrity. …”

Steps to the final sample. by Vittorio Nicoletta (21367878)

“…<div><p>One major obstacle to advancing research and treatment for major psychiatric disorders is their substantial within-diagnosis heterogeneity in patient lifetime trajectories. Adapted research methods such as cluster analysis to define subgroups of patients are currently used. …”