Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Table_1_Vestibular assessment in children with sensorineural hearing loss: diagnostic accuracy and proposal for a diagnostic algorithm.DOCX by Max Gerdsen (13825351)

Parameters of the genetic algorithm. by Jian Ma (170138)

Running memory of the algorithms. by Yanju Zhang (437336)

Data set of nameplate exposure of electrical equipment. by Hao Wu (65943)

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Test results of different training methods. by Hao Wu (65943)

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Schematic diagram of subpixel convolution. by Hao Wu (65943)

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Training results of data sets with different sizes. by Hao Wu (65943)

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PS-UNet++ model structure. by Hao Wu (65943)

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Comparison of training results of data sets with different sizes. by Hao Wu (65943)

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Network structure diagram of UNet++. by Hao Wu (65943)

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Ablation test results of the data set of electrical equipment nameplate exposure. by Hao Wu (65943)

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Sub-pixel convolution upsampling module. by Hao Wu (65943)

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CPU runtime of each algorithm: (a) Running Time Comparison of Different Algorithms on F1-F10 and (b) Running Time Comparison of Different Algorithms on F11-F20. by Ruyi Dong (9038174)

Tour construction for a weighted graph with a probability function p ∈ [0,1]. by Matheus Sant’Ana Lima (9937809)

Mean convergence graphs of the evolution in the performance function and its terms through generations in thirty executions of each algorithm. by Roberto Castro-Medina (21484990)

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx by Ekaterina Mikhaylets (17865407)

“…<p>The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. …”

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)

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