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

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

Coding schematic. حسب Dongyan Wang (195482)

Chromosome coding. حسب Junjie He (700642)

Homomorphic binary tree. حسب Jianhua He (341366)

IKM Pseudo – code. حسب Yingbo Mao (22522192)

Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

Testcases and code. حسب Min Tian (438923)

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm حسب Hussein Ali Bardan (21976208)

"…This strategy </p><p dir="ltr">not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, </p><p dir="ltr">leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection </p><p dir="ltr">process and CNNs for image classification, the proposed approach reduces computational costs while increasing </p><p dir="ltr">classification accuracy. …"

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

GA pseudo-code. حسب Jianpeng Zhang (528185)

GuacaMol: Benchmarking Models for de Novo Molecular Design حسب 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 حسب 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. …"

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization حسب Sandeep Jagonda Patil (22048337)

"…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…"

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

Schematic representation of semi-automated algorithm for organoids analyzing. حسب Anna Demchenko (18449439)

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

Data_Sheet_1_Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.pdf حسب 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. حسب Cihun-Siyong Alex Gong (11874066)

Training Code Smell Detection Models with Cross-Project Information: An Empirical Assessment حسب 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. …"