The optimal individual corresponding load impedance of different algorithms. by Jun Sun (48981)

Machine learning process. by Zhanjie Jing (13955448)

The specific process of joint simulation between MOEA/D-ND algorithm and HFSS. by Jun Sun (48981)

The average fitness value results of three algorithms under optimized algebraic changes. by Jun Sun (48981)

MOEA/D-ND algorithm flowchart. by Jun Sun (48981)

“…Therefore, an improved multi-objective optimization algorithm proposed by decomposition is introduced to optimize its matching network to achieve overall optimization design of power amplifiers. …”

Comparison of solution quality of three algorithms. by Jun Sun (48981)

IGD evolution trends of four algorithms on test functions. by Jun Sun (48981)

Comparison results of actual operational efficiency of different algorithms. by Jun Sun (48981)

Comparison of parameter ranges used for Latin hypercube sampling (LHS) in [29] and genetic algorithms (GA) in the present work. by Jyotika Bahuguna (729510)

IGD values of different algorithms under different standard test functions. by Jun Sun (48981)

Comparison of the different parameters in previous work. by Murad Al-Rajab (10661360)

Comparison of fitness values of different algorithms in the 10th, 20th, and 60th generations. by Jun Sun (48981)

The mean (standard deviation) results of GD and SP after running the same algorithm 20 times under different testing problems. by Jun Sun (48981)

AI-Enhanced Methods in Autonomous Systems: Large Language Models, DL Techniques, and Optimization Algorithms by I. de Zarza (17378452)

SHAP value feature influences of individual model predictions of unplanned C-section for high and low risk patients from the <i>t</i>_<i>early</i> prediction scenario. by Karl W. Schulz (14309461)

SHAP value influence of CDC inputs for the top 20 most impactful features using XGBoost on training set: (A) <i>t</i>_<i>early</i> scenario variables, (B) <i>t</i><sub><i... by Karl W. Schulz (14309461)

Variables from CDC vital statistics data considered for machine-learning models to classify mode of delivery for two prediction scenarios: <i>t</i>_<i>early</i> variables... by Karl W. Schulz (14309461)

Delivery mode classification performance using varying number of input features from public CDC data and machine-learning models using tenfold cross-validation of the training set... by Karl W. Schulz (14309461)

Schematic representation of the end-to-end occupational stress detection system architecture, illustrating the workflow from data acquisition through preprocessing, feature selecti... by Mohammad Junayed Hasan (21464229)

Classifier performance of XGBoost model trained using births from 2011 thru 2013 and evaluated on subsequent years 2014 thru 2017. by Karl W. Schulz (14309461)