Performance metrics comparison of various algorithms. by Parthiban Aravamudhan (15338781)

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Overall accuracy comparison for different algorithms. by Parthiban Aravamudhan (15338781)

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Data fitting and inference of the time-dependent parameters by TDINN algorithm for multiple waves of COVID-19 infection in Liaoning province. by Mengqi He (5753525)

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Reward function related parameters. by Honglei Pang (22693724)

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Visualizing the scatter plot of the drought (red) versus no-drought (green) and the support vectors (black) using the SVM section 4 classification algorithm. by J. Emmanuel Johnson (9579567)

Table_1_Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.DOCX by Gaoyan Zhang (450697)

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Details of the 30 benchmark functions for IEEE CEC2017. by Yan Wei (32168)

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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Statistic results between the proposed algorithm and other metaheuristic algorithms. by ZeSheng Lin (20501356)

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

Comparison of classification accuracy across different machine learning models using the objective function metric. by Amal H. Alharbi (21755906)

Visualization of the RITSS algorithm. by Julian Hecker (758791)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

MWOA-BiLSTM machine fault detection process. by Yi-Qiang Xia (20161326)

Supervised Machine Learning Algorithms for Predicting Rate Constants of Ozone Reaction with Micropollutants by Yajuan Shi (5575703)

“…In this work, several supervised machine learning (ML) algorithms, including multiple linear regression (MLR), support vector machine with radial basis function kernels (SVM-RBF), decision tree (DT), random forest (RF), and deep neutral network (DNN) methods, were used to develop quantitative structure–property relationship (QSPR) models for the estimation of log <i>k</i>_O3. …”

3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

Data_Sheet_1_Combined brain network topological metrics with machine learning algorithms to identify essential tremor.docx by Qin Li (36669)

“…The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.…”

Flowchart of the specific incarnation of the BO algorithm used in the experiments. by Lisa Laux (9367681)

Multi-scale t kernel function under different parameters. by Wen Bo Liu (11559222)

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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)

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