Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx حسب 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. حسب Kerol Djoumessi (21349992)

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

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

Largest changes in influenza vaccine rates, Pre- vs. Post-COVID-19. حسب Tyler B. Nofzinger (21560244)

الموضوعات:

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? حسب 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. حسب Muhammad Idrees (92773)

The top 10 putative metabolites with the largest negative fold change between normal and thin cows. حسب Bhuripit Saraphol (20855573)

The top 10 putative metabolites with the largest positive fold change between normal and thin cows. حسب Bhuripit Saraphol (20855573)

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

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

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

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

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

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

الموضوعات:

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

Training Data/Validation/Test. حسب 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. حسب Wentao Yang (205781)

الموضوعات:

Salt chemotaxis learning assay. حسب Masahiro Tomioka (430683)

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

Testing set error. حسب 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). …"