The confidence curve of a binary image. by Dingming Yang (8505990)

The confidence change of the binary image after iteration. by Dingming Yang (8505990)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

The pseudo-code of the WOA-LSTM-PID model. by Dayu Zhang (710629)

Coding schematic. by Dongyan Wang (195482)

Chromosome coding. by Junjie He (700642)

IKM Pseudo – code. by Yingbo Mao (22522192)

ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

Testcases and code. by Min Tian (438923)

Pseudo-code for the study design model. by Jianpeng Zhang (528185)

GA pseudo-code. by Jianpeng Zhang (528185)

GuacaMol: Benchmarking Models for de Novo Molecular Design by Nathan Brown (421008)

“…However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. …”

GuacaMol: Benchmarking Models for de Novo Molecular Design by Nathan Brown (421008)

“…However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. …”

Stimuli to test the coherency threshold for surround suppression. by Elnaz Nemati (21402730)

Data_Sheet_1_Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.pdf by Alper Yegenoglu (12659710)

Data_Sheet_1_Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.pdf by Alper Yegenoglu (12659710)

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)

Training Code Smell Detection Models with Cross-Project Information: An Empirical Assessment by Manuel De Stefano (8634399)

“…In this paper, we propose an empirical study on the matter. Focusing on three code smell types, i.e., Blob, Complex Class, and Spaghetti Code, whose data are collected from a manually built dataset of 15 open-source projects, we address three main research angles:</div><div>(1) How a cross-project training strategy affects the performance of code smell detection models with respect to a within-project strategy; (2) What is the impact of filtering techniques, which have the goal of only selecting the</div><div>relevant data from external sources to avoid issues due to data heterogeneity; and (3) How the application of local learning affects the performance of the models. …”