The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

Subjects:

Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Comparison between simulations for all algorithms (success rate <i>versus</i> number of swarm particles). by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Fraction of stripes considered as a function of aspect ratio cut-off value for individual stripe measurements. by Pratik Mullick (12851011)

Subjects:

Overall concept and specific components for developing the cloud based solution. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Cycle GAN approach for performing style transfer between healthy and pathological coronary angiography images slices. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Sample envisaged detection of the distal point (white circle) for a right coronary artery image. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Approach for artificial intelligence based virtual post-percutaneous coronary intervention fractional flow reserve (FFR) computation. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Fig 1 - by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Graphic processing unit instance orchestration on the cloud. by Vlad Ploscaru (13767257)

Subjects: “…artificial intelligence algorithms…”

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

“…<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. …”

RMSE results. by 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. by 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. by 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. by 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. by 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. …”

Main parameters of braking system. by Honglei Pang (22693724)

Subjects:

Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... by Honglei Pang (22693724)

Subjects: