Data Sheet 1_Hybrid machine learning algorithms accurately predict marine ecological communities.pdf حسب 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 حسب Tháfanys S. Travezani (20609398)

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

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

Multimodal reference functions. حسب Ruiyu Zhan (21602031)

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

The benchmark functions. حسب Shuhui Yu (1535500)

Study proposed algorithm. حسب Ainhoa Pérez-Guerrero (21377457)

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"

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

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

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

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

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

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Gillespie algorithm simulation parameters. حسب Nicholas H. Vitale (20469289)

"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"

Scheduling time of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Convergence speed of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases حسب Maru Song (22593561)

"…Crucially, we propose fitness functions based on approximate measures of the wave function compactness, which enable inexpensive genetic algorithm searches. …"

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

الموضوعات:

Unraveling the gender-specific molecular landscape of lung squamous cell carcinoma progression حسب Ayushi Dwivedi (20658343)

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

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms حسب 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. …"