Mean and standard deviation of MLP classifier performance across different training algorithms with cross-validation. by Mohammad Hossein Hadizadeh Isfahani (22634016)

Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf by Luciana Erika Yaginuma (10477013)

“…In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

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

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

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Water consumption due to average value and fuzzy logic algorithm. by Gourab Saha (8987405)

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Crop yield prediction accuracy using different ML algorithms. by Gourab Saha (8987405)

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IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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Accuracy of the support vector machines for different proportion of test samples. by Abeer Aljohani (18497914)

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Multidimensional test results of BWEMFO and MFO on IEEE CEC 2017 test functions. by Jingjing Ma (419752)

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Automated Analysis of Astrocyte Cell Connectivity After Laser Ablation Using Machine Learning and Path-Finding Algorithms by Connor Lee (22327243)

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms by Hossein Mashhadimoslem (11456548)

“…In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO₂ adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO₂ adsorption performance as a function of characteristics and adsorption isotherm parameters. …”

S and V shaped transfer functions. by Chenyi Zhu (9383370)

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The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Differential expression of DSRGs, PPI, and functional enrichment analysis for DSRGs in AMI. by Nan Huang (464390)

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

Functional enrichment analyses of Co-genes. by Qingcong Zheng (11169094)