Extreme Learning Machines

Capstone Papers

Top cited papers (250+ citations, 2018-2024) ranked by citation count.

#	Paper	Citations
1	Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets Yifan Peng, Shankai Yan, Zhiyong Lu 2019	797
2	An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Sadeq Oleiwi Sulaiman, Ravinesh C. Deo et al. Journal of Hydrology 2018	665
3	Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection Iftikhar Ahmad, Mohammad Basheri, Muhammad Iqbal et al. IEEE Access 2018	632
4	Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China Junliang Fan, Wenjun Yue, Lifeng Wu et al. Agricultural and Forest Meteorology 2018	507
5	Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM Hui Liu, Xiwei Mi, Yanfei Li Energy Conversion and Management 2018	465
6	A review on extreme learning machine Jian Wang, Siyuan Lu, Shuihua Wang‎ et al. Multimedia Tools and Applications 2021	445
7	A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification Abdu Gumaei, Mohammad Mehedi Hassan, Md. Rafiul Hassan et al. IEEE Access 2019	432
8	Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks Xiaoyu Li, Lei Zhang, Zhenpo Wang et al. Journal of Energy Storage 2018	418
9	Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine Zhuyun Chen, Konstantinos Gryllias, Weihua Li Mechanical Systems and Signal Processing 2019	363
10	A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement Mahdi Shariati, Mohammad Saeed Mafipour, Behzad Ghahremani et al. Engineering With Computers 2020	334