Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rastrigin function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

A Chemoproteomic Approach for System-Wide and Site-Specific Uncovering of Functional Protein N‑Glycosylation by Guoli Wang (59357)

“…However, there is still a considerable delay in systematic research on the functional significance of these modifications. …”

Parameters to configure the multi-staged vascularization using the PDCCO algorithm. by Eduardo G. Zilves (22209247)

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

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

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

Nash equilibrium payoffs (_<i>i</i>) with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Immune microenvironment (A) Comparison of immune cell infiltration between the high-risk and low-risk groups using the ssGSEA algorithm. by Chengting Wu (21485612)

Table 2_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…The MCODE algorithm was used to identify key modules, and CytoHubba was applied to determine hub genes. …”

Table 1_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…The MCODE algorithm was used to identify key modules, and CytoHubba was applied to determine hub genes. …”

Table 3_Uncovering differential gene expression between mtRNA-positive and -negative osteosarcoma cells: implications beyond mitochondrial function.xlsx by Xiaoyuan Meng (22261234)

“…The MCODE algorithm was used to identify key modules, and CytoHubba was applied to determine hub genes. …”

Weighting Function Fitting Process Diagram. by Jiahui Gan (22793753)

Flow chart diagram of quantum hash function. by Sultan H. Almotiri (14029251)

“…However, in the pursuit of complexity, vulnerabilities may be introduced inadvertently, posing a substantial danger to software security. Our study addresses five major components of the quantum method to overcome these challenges: lattice-based cryptography, fully homomorphic algorithms, quantum key distribution, quantum hash functions, and blind quantum algorithms. …”

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

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. …”

The process of constructing a functional network. by Wen-Wei Shao (21162477)

Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes. by Wenjun Zhao (644283)

“…<p>Computational time for each algorithm as functions of (1) number of genes, with a fixed number of 1200 cells per time point (left); or (2) number of cells per time point, with a fixed number of 100 genes.…”