Example measurement and analysis units for landscape elements. by Jaewon Han (8660295)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models by Yasemin Simsek Turker (22340676)

Risk element category diagram. by Yao Hu (3479972)

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

, MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

Block diagram of the SSNMF algorithm for source separation. by Balamurali Krishna Ponukumati (19864775)

Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

Comparison of different algorithms. by Jiandong Qiu (20389944)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…It addresses the limitations of the hydrophone coprime array in utilizing all array elements’ information and mitigates the interference of ocean noise in shallow waters, which impairs the accuracy and resolution of target direction estimation. …”

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

<b>Discrimination of gold deposit types based on big data analysis and machine learning techniques</b> by Lihao Feng (20837036)

Subjects:

Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…It addresses the limitations of the hydrophone coprime array in utilizing all array elements’ information and mitigates the interference of ocean noise in shallow waters, which impairs the accuracy and resolution of target direction estimation. …”

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone by Michael J. Pasterski (11726741)

“…We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithmsk-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. …”

Flowchart of the subspace fitting algorithm for interpolated coprime arrays. by Chuanxi Xing (20141665)

Comparison of ablation study. The deep model which consists of MOCOA-VMD and attention scheme achieves the best accuracy. by Hang Zhao (143592)

Elements: A Sustainable, Resource-Efficient Cyberinfrastructure for Notebook Interactive ML Training Workloads by Yue Cheng (22087328)

Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction by Mohamad A. Raja (19640297)

Subjects:

Coprime array with interpolated array elements. by Chuanxi Xing (20141665)

“…It addresses the limitations of the hydrophone coprime array in utilizing all array elements’ information and mitigates the interference of ocean noise in shallow waters, which impairs the accuracy and resolution of target direction estimation. …”