Optimization algorithms performance comparison across benchmark functions. by Sundreen Asad Kamal (20390818)

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

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)

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)

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

Images of partial benchmark functions. by ZeSheng Lin (20501356)

Subjects:

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Predicting the Mutagenic Activity of Nitroaromatics Using Conceptual Density Functional Theory Descriptors and Explainable No-Code Machine Learning Approaches by Andrés Halabi Diaz (20798460)

“…This study integrates conceptual density functional theory (CDFT) descriptors with explainable no-code machine learning (ML) models to predict NA mutagenicity based on Ames test results. …”

(a-b) The LASSO algorithm was used for hyperparameter tuning and identification of the important predictive features. by Devi Soorya Narayana Sasikumar (22331743)

Subjects:

Cell-type marker genes and functional enrichment analysis. by Hailin Li (453832)

Subjects:

Concept of the Loss function in the case of Supervised Machine Learning and Physics Machine Learning approach. by Akshansh Mishra (21001499)

Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. by Mahmoudreza Moein (22127153)

S and V shaped transfer functions. by Chenyi Zhu (9383370)

Subjects:

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

Subjects:

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional by Ryan Stocks (16867476)

“…Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. …”

Multidimensional test results of BWEMFO and MFO on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

Subjects: