The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

Parameter optimization results of the RF model improved by FCM clustering. by Xini Fang (20861990)

Optimized model prediction performance. by Guang Tu (22054865)

DATA. by Yang Yu (4292)

“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”

The robustness test results of the model. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. …”

Architecture of proposed system model. by Nanavath Kiran Singh Nayak (22184565)

System model of the proposed method. by Nanavath Kiran Singh Nayak (22184565)

Flowchart of simple ant colony algorithm. by Yang Yu (4292)

“…Based on a comprehensive assessment of service transmission reliability and time costs, a route satisfaction evaluation function model has been developed. Utilizing this model, an enhanced Risk-Time Ant Colony Optimization (RT-ACO) routing algorithm is proposed, which builds upon the traditional ant colony algorithm. …”

Construction process of RF. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. …”

Reducing positional uncertainty by He Wu (22584128)

Data Sheet 1_TBESO-BP: an improved regression model for predicting subclinical mastitis.pdf by Kexin Han (10933209)

“…The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.…”

Data used in this study. by Qinghua Li (398885)

“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”

DEM error verified by airborne data. by Qinghua Li (398885)

“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”

Error of ICESat-2 with respect to airborne data. by Qinghua Li (398885)

“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”

S1 Data - by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Iteration curve of the optimization process. by Meijun Shang (22806461)

“…The load-bearing mechanism of the proposed steel platform was analyzed theoretically, and finite element analysis (FEA) was employed to evaluate the stresses and deflections of key members. A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf by Mikhail Ya Yadgarov (18543769)

Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" by Ziyu Lin (9151064)

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…The DLCM defines four soil carbon pools, categorized based on their location within the soil profile and their decomposition rates. The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. …”

The prediction error of each model. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”