Investigating the trend of land use class changes. by Abdolhossein Boali (19996390)

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Class imbalance on GMSC dataset. by Seongil Han (20490232)

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Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Class distribution balanced through SMOTE oversampling. by Seongil Han (20490232)

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Class distribution balanced through NearMiss undersampling. by Seongil Han (20490232)

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Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate... by Hua Huo (1818133)

Linear discriminant analysis (LDA) for effect size (LEfSe) analysis of endophytic bacterial communities associated with <i>Synsepalum dulcificum</i> among the three phenotypic grou... by Rabiath F. R. Adigoun (14189090)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”

AI KRegression by CHIN-CHENG CHU (20718575)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

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Performance enhancements of classification models using the undersampling method (NearMiss). by Seongil Han (20490232)

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Features on GMSC dataset used in NATE. by Seongil Han (20490232)

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Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset. by Seongil Han (20490232)

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Searching space for hyperparameters in Table 7. by Seongil Han (20490232)

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GMSC dataset (IR: Imbalance Ratio). by Seongil Han (20490232)

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Evaluation of benchmark and optimal model performance with resampling techniques. by Seongil Han (20490232)

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Local and global interpretability of NATE. by Seongil Han (20490232)

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