<i>E</i>₁(<i>m</i>)~<i>m</i> and <i>E</i>₂(<i>m</i>)~<i>m</i> curves for the observed displacement data series of the PL13-3. by Mingjun Li (178925)

Subjects:

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set? by Minyi Su (1425976)

Subjects:

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

List of feature selection methods used in the voting algorithm. by Mahmoud Zeydabadinezhad (12289570)

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Comparison of Algorithms 1, 2 and 3 using Examples 1, 5, and 6. by Sagvan Y. Musa (17797611)

Acurracy comparison of CCA and FBCCA algorithms. by Hossein Hamidi Shishavan (19688384)

Classification parameters of the proposed three classifiers. by Ali Ahmed (2567584)

Subjects:

datasheet1_PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine.docx by Bing Li (43671)

“…Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). …”

Data_Sheet_1_Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people.docx by Shuojia Wang (3823360)

“…The Chinese version of the Mini-Mental State Examination (MMSE) was used to examine cognitive function. Six machine learning algorithms (including a neural network model) and an ensemble method were trained on data split 2/3 for training and 1/3 testing. …”

Illustration of the DLNN used in this study, including 10 inputs, three hidden layers, and one output variable. by Tuan Anh Pham (1346049)

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…Functional groups are widely used in organic chemistry, because they provide a rationale to analyze physicochemical and reactivity properties. …”

Accuracy of the support vector machines for different proportion of test samples and different length of nodes used for training. by Abeer Aljohani (18497914)

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Data Sheet 1_Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms.docx by Ming Xie (420493)

“…By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. …”

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

Improved A* algorithm flowchart. by Peiying Li (797714)

Subjects:

Table_6_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xls by Pamela Vernocchi (791125)

Table_1_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)

Table_7_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)

Table_8_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.csv by Pamela Vernocchi (791125)

Table_2_Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors.xlsx by Pamela Vernocchi (791125)