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)

Subjects:

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform by Zhehong Ai (17937324)

Subjects: “…customized multiobjective functions…”

Test functions. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. …”

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Fitness comparison on test function. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. …”

Biological functions and network analysis of highly interrelated genes selected by ADC across modalities. by Qunlun Shen (11668968)

R-squared comparison of test function. by Kejia Liu (5699651)

“…The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. …”

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Coarse-fine optimization algorithm. by Chao Ma (207385)

“…First, analyzes the current methods for evaluating image sharpness and proposes an improved Tenengrad function for the focused scene that could extract gradient information from the image in several directions and improve sharpness evaluation. …”

Detailed information of benchmark functions. by Guangwei Liu (181992)

“…The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. …”

Sensitivity analysis of the TV-BayesOpt algorithm performance at tracking a periodic temporal drift when the period of the drift is offset from the covariance function period anticipated by the TV-BayesOpt algorithm. by John E. Fleming (8533956)

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

Subjects:

Types of training algorithms. by Caixia Yan (6647072)

Smooth function of sample size. by Muhammad Atif (2575789)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

Subjects:

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Flowchart for Algorithm 2. by Sagvan Y. Musa (17797611)

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. …”