Search for Correlations Between the Results of the Density Functional Theory and Hartree–Fock Calculations Using Neural Networks and Classical Machine Learning Algorithms by Saadiallakh Normatov (20682324)

Functions in the package CTNote, their categories, and their descriptions. by Gabriel J. Odom (11867720)

AODV algorithm for route selection at node destination. by Tibebu Legesse (21030014)

Model training accuracy for Random Forest (RF), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) algorithms by adding variables based on the correlation results. by Mohsen Yoosefzadeh-Najafabadi (6917399)

Convergence of average SIR in the proposed algorithm. by Fadhil Mukhlif (8411451)

Convergence of average power in the proposed algorithm. by Fadhil Mukhlif (8411451)

Flowchart of MOSRS algorithm. by Vahid Goodarzimehr (21343124)

Parselmouth for bioacoustics: automated acoustic analysis in Python by Yannick Jadoul (11498813)

“…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …”

Comparison of different algorithms. by Jiandong Qiu (20389944)

<b>Inappropriate application of mapping algorithms results in length-dependent gene abundances in metagenomic analysis</b> by Wenkai Teng (17150785)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”

Convergence graphs of the ten algorithms for the five functions from the CEC 2014 suite. by Juan Yao (293170)

Convergence curve of shift function. by Yong Gao (29929)

<b>（代码）</b><b>一种新的VMD优化策略ASSA-WFEE及其在滚动轴承故障诊断中的应用</b> by Xiaohan Xu (17426229)

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

EASP-AODV algorithm for route discovery at the intermediate node. by Tibebu Legesse (21030014)