Comparison in terms of the selected features. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

Iterative chart of control factor. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

Details of 23 basic benchmark functions. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

Related researches. حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

S1 Dataset - حسب Ying Li (38224)

"…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …"

Analysis and design of algorithms for the manufacturing process of integrated circuits حسب Sonia Fleytas (16856403)

Image_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... حسب Huangwei Wei (17011266)

Table_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.XLS... حسب Huangwei Wei (17011266)

Image_2_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... حسب Huangwei Wei (17011266)

UK Biobank data wrangling statistics and results. حسب Simeone Marino (81051)

Iterative Registration and Clustering (IREC) Algorithm: The input image stack (<i>S</i>) is first aligned and grouped using registration and clustering (REC) based on an initial re... حسب Creighton M. Lisowski (22756444)

The data processing and analysis pipeline. حسب Xin Zhang (35492)

Image5_Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.tif حسب Viacheslav V. Danilov (11151426)

Image3_Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.tif حسب Viacheslav V. Danilov (11151426)

Image1_Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.tif حسب Viacheslav V. Danilov (11151426)

Image2_Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.tif حسب Viacheslav V. Danilov (11151426)

Image4_Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization.tif حسب Viacheslav V. Danilov (11151426)

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity حسب George S. Watts (7962206)

"…Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. …"

DataSheet1_Relative Populations and IR Spectra of Cu38 Cluster at Finite Temperature Based on DFT and Statistical Thermodynamics Calculations.doc حسب Carlos Emiliano Buelna-García (12162653)

An Example of a WPT-MEC Network. حسب Hend Bayoumi (22693738)

"…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"