APA (7th ed.) Citation

Amirruddin, A. D., Muharam, F. M., Ismail, M. H., Tan, N. P., & Ismail, M. F. (2022). Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles. Elsevier.

Chicago Style (17th ed.) Citation

Amirruddin, Amiratul Diyana, Farrah Melissa Muharam, Mohd Hasmadi Ismail, Ngai Paing Tan, and Mohd Firdaus Ismail. Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive Boosting Algorithms for Classifying Imbalanced Datasets of Nutrient and Chlorophyll Sufficiency Levels of Oil Palm (Elaeis Guineensis) Using Spectroradiometers and Unmanned Aerial Vehicles. Elsevier, 2022.

MLA (9th ed.) Citation

Amirruddin, Amiratul Diyana, et al. Synthetic Minority Over-Sampling Technique (SMOTE) and Logistic Model Tree (LMT)-adaptive Boosting Algorithms for Classifying Imbalanced Datasets of Nutrient and Chlorophyll Sufficiency Levels of Oil Palm (Elaeis Guineensis) Using Spectroradiometers and Unmanned Aerial Vehicles. Elsevier, 2022.