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. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Data Sheet 1_Large language models for closed-library multi-document query, test generation, and evaluation.docx by Claire Randolph (19747105)

“…Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. …”

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

“…<b>Method:</b> Thirty-five deidentified BICAMS protocols, including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test–II (CVLT-II), and Brief Visuospatial Memory Test–Revised (BVMT-R), were independently scored by two trained human raters and ChatGPT-4.5. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

Cross-lagged panel model consistency test based on physical activity, aggressive behavior and Chinese language learning. by Zixia Bu (21475019)

Class-wise performance on the test set. by Muhammad Zubair (728141)

Means for STEM competence for pre- and post-test. by Esther Kim (2996622)

Means for global competence pre- and post-test scores. by Esther Kim (2996622)

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)

SHAP interpretation of the optimal machine learning model. by Wentao Yang (205781)

Subjects:

Confusion Metrics: Presenting classification results on the test set. by Muhammad Zubair (728141)

Training Data/Validation/Test. by Mudhafar Jalil Jassim Ghrabat (22177655)

“…The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. Every model was subjected to individual testing. …”

Comprehensive evaluation of machine-learning models in the training cohort. by Wentao Yang (205781)

Subjects:

Salt chemotaxis learning assay. by Masahiro Tomioka (430683)

The Ishihara test for colourblindness [41] encompasses stimuli that can determine the degree and type of colourblindness a person has. by Marcel F. Hinss (14675457)

Testing set error. by Xiangjuan Liu (618000)

“…Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. Finally, the Beluga Whale Optimization (BWO)-tuned STL-PCA-BWO-BiLSTM hybrid model delivered optimal performance on test sets (RMSE = 0.22, MAE = 0.16, MAPE = 0.99%, ), exhibiting 40.7% higher accuracy than unoptimized BiLSTM (MAE = 0.27). …”

The optimized GPR approach’s cross-plotting for the testing dataset. by Fahd Saeed Alakbari (10701871)

The optimized GPR approach’s errors histogram of the testing dataset. by Fahd Saeed Alakbari (10701871)

Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreas... by Eric Wang (30587)