Performance metrics under different noise levels. by Jia Li (160557)

Depreciated value of used sailboats by year. by Zhanni Huang (20577481)

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”

Global used ship transaction size. by Zhanni Huang (20577481)

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”

Overview of MitoTracer algorithm. by Xuexin Yu (733842)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Confusion matrix of the proposed algorithm and state of the art (part 1). by Meshari Alazmi (452881)

Confusion matrix of the proposed algorithm and state of the art (part 2). by Meshari Alazmi (452881)

Effect images of CLAHE algorithm. by Yang Gao (18005)

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption by Xuyang Li (11431426)

“…We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. Subsequently, the metamaterials with the specific platform stress values (from 0.015 to 4.05 MPa) and specific energy absorptions (from 0.049 to 23.377 J/g) can be inversely designed with the aid of the artificial neural networks and genetic algorithms to pinpoint optimized parameters from a 181-dimensional space. …”

<i>RMSE</i> comparison using all investigated methods in Table 3: MSX-60 module. by Ziad M. Ali (12516532)

Subjects:

The flowchart of GWO-VMD method. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

Image processing algorithm. by Madison Shoraka (22171205)

Comparison of Multiple Runs of Same Algorithm on the Same Dataset (UNSW-NB15). by Muhammad Rehan Naeem (21432852)

Template- and subject-Level QSM and T1w contrasts with regional segmentation overlays. by Fahad Salman (6150395)