Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

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Hyper parameter settings for ML Algorithms. by R. S. Jayaram (22139155)

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)

“…Whiskers show the lowest and highest values within 1.5 × <i>IQR</i> from the first and third quartiles, respectively. …”

Classification algorithm implemented in SPARTA. by Baptiste Ruiz (20285217)

Comparison of different algorithms. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

The average training time for different kernel functions. by Jialiang Zhang (1574704)

Pseudo-code of DMDDPG algorithm. by Guohua Cao (697580)

“…First, we analyze the stages of hole-seeking, alignment, and insertion in the shaft-hole assembly process, based on a comprehensive study of the interactions between shafts and holes. Next, a reward function is designed by integrating the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm. …”

Genetic algorithm flowchart. by Junjie He (700642)

“…Then, the epsilon constraint method was used to address the multi-objective model, followed by designing the genetic algorithm based on the problem characteristics for solutions. …”

Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

The convergence diagram of the coefficients of the objective function for the capacity of 1 MW. by Mohammad Hassan Tanha (19832141)

Subjects: “…refrigeration optimization algorithm…”

IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

Parameterization of the feature selection algorithm. by Chenyi Zhu (9383370)

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Comparison of metaheuristic algorithm parameterization. by Chenyi Zhu (9383370)

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

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VEP annotation of the aSNPs listed in S1 Table. by Rongxin Zhang (1618159)

“…<div><p>G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. …”

Observations that influence the accuracy of the algorithm. by Rabnawaz Khan (6509084)

Elementary effects (EE) results for the generation of villous vessels within a placentone employing the input parameter ranges from Table 1. by Diana C. de Oliveira (9220729)

Results of Wilcoxon rank-sum test and Friedman test for different algorithms. by Yanzhao Gu (21192659)

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