The attraction force generated by the attractor set on the robot. حسب Long Di (9977453)

Parameter sets generated through Latin hypercube sampling. حسب Alexandra M. Campbell (3284094)

Raw data files for IMC data sets. حسب Kafayat Yusuf (22104155)

Classification model parameter settings. حسب Chao Tang (10925)

Parameter setting table. حسب Bo Tong (2138632)

PCA-CGAN model parameter settings. حسب Chao Tang (10925)

Minimal data set. حسب Jingru Zhou (766195)

Summary of codes generated from MAXQDA software. حسب Awole Seid (3839965)

Generated spline library. حسب Zhe Hu (787283)

"…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. Field experiments demonstrate that the predicted values from the LSTM model closely align with the measured values, maintaining short-term shape error prediction accuracy within 3 mm. …"

Data set used for analysis. حسب Florian Simon (5387228)

ActAs and CoT prompt strategies do not result in decreased error rate for tasks with increasing complexity. حسب Jacqueline A. Jansen (14894276)

Self-correction prompt strategy results in decreased error rate for all tasks with increasing complexity. حسب Jacqueline A. Jansen (14894276)

fileCont prompt strategy results in decreased error rate for some tasks with increasing complexity. حسب Jacqueline A. Jansen (14894276)

Model Set Test for Prais–Winsten AR(1) Regression. حسب Muhummad Khairul Islam (21955544)

AI KRegression حسب CHIN-CHENG CHU (20718575)

Correlation between each MSTM and each generative parameter. حسب Fabiano Baroni (234173)

Accuracy of multiple classifiers with a deep-learning-based feature set حسب Afira Aslam (21433666)

Model parameter setting. حسب Yuxuan Liu (349237)

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

Discrete phase parameter setting. حسب Yuxuan Liu (349237)