Relationship between the three key elements in the sampling process. by Xiaochen He (455300)

GA-XGBoost optimization process diagram. by Ke Peng (2220973)

Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization by Jian-Feng Cai (498405)

Binary contingency table. by Lu Xiao (314030)

Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

Region division of traditional SVPWM algorithm. by Jun Li (6494)

Small area control of the conventional algorithm. by Jun Li (6494)

Parameter estimation process and performance. by Gennady Gorin (8466255)

General overview of the total model. by Xueliang Guo (4797057)

RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

Summary by recording: Annotations, training features, and optimal parameters. by Rytis Paškauskas (20404817)

Optimal contrast analysis with heterogeneous variances and budget concerns by Show-Li Jan (3538697)

“…The corresponding computer algorithms are developed to implement the recommended power and sample size calculations for optimal contrast analysis.…”

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

“…By integrating Latent Encoder Coupled Generative Adversarial Network (LEGAN) optimized with Binary Emperor Penguin optimizer (BEPO), the scheme enhances routing efficiency and security. …”

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

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Details underlying our iterative forward sampling algorithm using MCTS. by Gwangho Lee (11027565)

“…<p>The process (third iteration in terms of total <i>N</i>) illustrates the internal process of our sampling algorithm. …”

IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. …”

QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

“…Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”

Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

“…The sensor had detection limits of 100 ppb for NH₃ and 1 ppm for creatinine. Clinically, a total of 96 exhaled breath samples, half from 39 CKD patients and the other half from 48 healthy controls were collected and analyzed. …”