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Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review
- Alalawi, Khalid, Athauda, Rukshan, Chiong, Raymond
- Arifuzzaman, Md., Kim, Ho Sung
- Bartlett, John M. S., Ahmed, Ikhlaaq, Brookes, Cassandra L., Forbes, John F., Viale, Giuseppe, Cuzick, Jack, Dowsett, Mitchell, Rea, Daniel W., Regan, Meredith M., Sestak, Ivana, Mallon, Elizabeth A., Dell'Orto, Patrizia, Thürlimann, Beat, Seynaeve, Caroline, Putter, Hein, Van de Velde, Cornelis J. H.
Predicting the Level of Safety Performance Using an Artificial Neural Network
- Boateng, Emmanuel Bannor, Pillay, Manikam, Davis, Peter
Monitoring of an encapsulated embankment for the validation of a dimensionless model for soil swelling
- Buzzi, O., Fityus, S., Kingsland, R., Aryal, S., Russell, G.
- Carter, Gregory, Milner, Allison, McGill, Katie, Pirkis, Jane, Kapur, Nav, Spittal, Matthew J.
Slope Unit Extraction and Landslide Susceptibility Prediction Using Multi-scale Segmentation Method
- Chang, Zhilu, Huang, Faming, Jiang, Shuihua, Zhang, Yinlang, Zhou, Chuangbing, Huang, Jinsong
Consensus acceleration of multi-agent systems via model prediction
- Chen, Zhiyong, Zhang, Hai-Tao
Craving as a predictor of treatment outcomes in heavy drinkers with comorbid depressed mood
- Connolly, Jennifer M., Kavanagh, David J., Baker, Amanda L., Kay-Lambkin, Frances J., Lewin, Terry J., Davis, Penelope J., Quek, Lake-Hui
Utility of risk-status for predicting psychosis and related outcomes: evaluation of a 10-year cohort of presenters to a specialised early psychosis community mental health service
- Conrad, Agatha M., Lewin, Terry J., Sly, Ketrina A., Schall, Ulrich, Halpin, Sean A., Hunter, Mick, Carr, Vaughan J.
Prediction of game performance in Australian football using heart rate variability measures
- Cornforth, David, Campbell, Piers, Nesbitt, Keith, Robinson, Dean, Jelinek, Herbert F.
Predicting the hydrogen release ability of LiBH₄-based mixtures by ensemble machine learning
- Ding, Zhao, Chen, Zhiqian, Ma, Tianyi, Lu, Chang-Tien, Ma, Wenhui, Shaw, Leon
- Ernest, Kissi, Theophilus, Adjei-Kumi, Amoah, Peter, Emmanuel, Boateng Bannor
A theory for mathematical framework and fatigue damage function for the S-N plane
A validity framework theory and fatigue damage function for an S–N plane
Risk of secondary progressive multiple sclerosis: a longitudinal study
- Fambiatos, Adam, Jokubaitis, Vilija, Grand'Maison, Francois, Grammond, Pierre, Sola, Patricia, Ferraro, Diana, Alroughani, Raed, Terzi, Murat, Hupperts, Raymond, Boz, Cavit, Lechner-Scott, Jeannette, Pucci, Eugenio, Horakova, Dana, Bergamaschi, Roberto, Van Pesch, V, Ozakbas, S, Granella, F, Turkoglu, R, Iuliano, G, Spitaleri, D, McCombe, P, Solaro, C, Slee, M, Kubala Havrdova, Eva Kubala, Ampapa, R, Soysal, A, Petersen, T, Sanchez-Menoyo, JL, Verheul, F, Prevost, J, Sidhom, Y, Van Wijmeersch, B, Vucic, S, Cristiano, E, Trojano, Maria, Saladino, ML, Deri, N, Barnett, M, Olascoaga, J, Moore, F, Skibina, O, Gray, O, Fragoso, Y, Yamout, B, Shaw, C, Prat, Alexandre, Singhal, B, Shuey, N, Hodgkinson, S, Altintas, A, Al-Harbi, T, Csepany, T, Taylor, B, Hughes, J, Jun, J-K, van der Walt, A, Girard, Marc, Spelman, T, Butzkueven, H, Kalincik, T, Duquette, Pierre, Lugaresi, Alessandra, Izquierdo, Guillermo
Offsite construction skills prediction: A conceptual model
- Ginigaddara, Buddhini, Perera, Srinath, Feng, Yingbin, Rahnamayiezekavat, Payam
Measuring social competence, task competence and self-protection in an organisational context
Nonlinear dynamical analysis of noisy time series
- Heathcote, A., Elliot, David
Service usage and vascular complications in young adults with type 1 diabetes
- James, Steven, Perry, Lin, Gallagher, Robyn, Lowe, Julia, Dunbabin, Janet, McElduff, Patrick, Acharya, Shamasunder, Steinbeck, Katherine
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