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:

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

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. …”

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

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

Subjects:

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

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

Subjects:

Statistical results of various algorithms. by ZeSheng Lin (20501356)

Subjects:

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

Functional analysis of anoikis-related genes. by Yufeng He (5673119)

Subjects:

The structure of genetic algorithm (GA). by Ali Akbar Moosavi (17769033)

“…Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. …”

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

Subjects:

PSO parameters and objective function. by André G. Coimbra (21354135)

GA parameters and objective function. by André G. Coimbra (21354135)

Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

Subjects:

Cost function calculated by QA with different hyperparameters. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Cost function calculated by SA with different hyperparameters. by Takuro Matsuta (21608505)

Subjects: “…currently available algorithms…”

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”