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. …”

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

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)

Supplementary file 1_Spatiotemporal monitoring in beidagang wetland using Landsat time-series images and Google Earth Engine during 2000–2022.docx by Xinyue Zhang (271120)

“…Therefore, we developed a Decision Tree (DT)-based classification method, incorporating water frequency (WF) and vegetation frequency (VF) calibrated with field observations, to monitor wetland dynamics using Landsat-5/7/8/9 time-series images (2000–2022) and Google Earth Engine (GEE). …”

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)

Data Sheet 1_A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers.pdf by Ephrem Tibebe Mekonnen (19710493)

“…The methodology involves two major phases: (1) training and evaluating deep-learning-based time series classification models, and (2) extracting parameterized primitive events, such as increasing, decreasing, local max and local min, from each instance of the evaluation set and clustering such events to extract prototypical ones. …”

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

Germination speed(A), Germination rate (B), Mean germination Time(MGT) (C), Germination Vigor Index (GVI) (D), and Germination index(GI) (E), of M. stenopetala seeds under the effe... by Nadia Khater (22553166)

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Change from full-scale to reduced model in sum (across all time points and grids) of squared error between simulation and MC scan data as a function of threshold elevation. by Monica E. Shapiro (22622240)

Change from full-scale to reduced model in sum (across all time points and grids) of squared error between model and MC scan data as a function of the number of clusters used in th... by Monica E. Shapiro (22622240)

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

The Brazil nut: A. tree, B. fruit, C. opened ripe fruit, and D. shelled nuts; and açai palm: E. palm, F. harvesting, and G. picking drupes from panicles. by Paul Mathäss (21587593)

RF Tree and Confusion Matrix for Random Forest. by Gaurav Sandeep Dave (21519884)

Feature engineering. by Youli Jiang (15249070)

Changes in growth characteristics (A = shoot fresh weight; B = shoot dry weight; C = root fresh weight; D = root dry weight) of M. stenopetala under drought conditions.... by Nadia Khater (22553166)

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Changes in growth characteristics (A = shoot length; B = root length; C = collar diameter; D = number of leaves) of M. stenopetala under drought conditions. by Nadia Khater (22553166)

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Seed germination kinetics of M. stenopetala under the effect of different PEG concentrations. by Nadia Khater (22553166)

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