Cleavage time as a function of scaled fiber diameter. حسب Roukayatou R. Ouedraogo (20460980)

RMSE values of different algorithms for different bands in the "Kherson" region. حسب YueSheng Jiang (19267984)

RMSE values of different algorithms for different bands in the "PY" region. حسب YueSheng Jiang (19267984)

Median fiber cleavage time (black) and the fraction of successful runs (red) for different parameter values. حسب Roukayatou R. Ouedraogo (20460980)

Elapsed time of algorithms on benchmark functions. حسب Muhammad Zubair Rehman (11243478)

"…<p>Elapsed time of algorithms on benchmark functions.</p>…"

Comparison between different PSO algorithms on function 4–6. حسب Lai Xu (1828831)

Comparison between different PSO algorithms on function 1–3. حسب Lai Xu (1828831)

Comparison between different PSO algorithms on function 7–8. حسب Lai Xu (1828831)

Average function evaluation times of the three optimization algorithms. حسب Shuhui Yu (1535500)

"…<p>Average function evaluation times of the three optimization algorithms.…"

Similarity between the allegiance matrices of the multiplex SC-FC graph, the FC graph alone, and the SC graph alone, as a function of the topological scale for the group. حسب Arian Ashourvan (6685232)

Average CPU time (s) of all the referenced algorithm on benchmark function. حسب Aihua Guo (13267712)

"…<p>Average CPU time (s) of all the referenced algorithm on benchmark function.…"

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

"…<div><p>This paper aims to solve the scheduling optimization problem in the emergency management of long-distance natural gas pipelines, with the goal of minimizing the total scheduling time. To this end, the objective function of the minimum total scheduling time is established, and the relevant constraints are set. …"

Completion times for different algorithms. حسب Jianbin Zheng (587000)

"…The algorithm employs recurrent neural networks to capture and process historical information. …"

Comparison of the running time(in ms) of the three algorithms. حسب Yaling Zhang (1687540)

Run times of two algorithms. حسب Gourab Saha (8987405)

"…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. The LSTM model is used for healthy vegetation area forecasting highlighting the changes of the vegetation area over time. …"

RMSE results. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the Kherson Area Visual Assessment. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Work flow chart. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Experimental data. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"

Results of the PY area visual assessment. حسب YueSheng Jiang (19267984)

"…To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. …"