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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Trends in extreme learning machines: A review
Gao Huang, Guang-Bin Huang, Shiji Song, et al.
Neural Networks (2014) Vol. 61, pp. 32-48
Closed Access | Times Cited: 1632

Showing 1-25 of 1632 citing articles:

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
John Ball, Derek T. Anderson, Chee Seng Chan
Journal of Applied Remote Sensing (2017) Vol. 11, Iss. 04, pp. 1-1
Open Access | Times Cited: 643
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) Vol. 569, pp. 387-408
Closed Access | Times Cited: 631
Advanced Spectral Classifiers for Hyperspectral Images: A review
Pedram Ghamisi, Javier Plaza, Yushi Chen, et al.
IEEE Geoscience and Remote Sensing Magazine (2017) Vol. 5, Iss. 1, pp. 8-32
Open Access | Times Cited: 584
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Hydrology (2020) Vol. 585, pp. 124670-124670
Closed Access | Times Cited: 531
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Soujanya Poria, Erik Cambria, Newton Howard, et al.
Neurocomputing (2015) Vol. 174, pp. 50-59
Closed Access | Times Cited: 491
A review on neural networks with random weights
Weipeng Cao, Xizhao Wang, Zhong Ming, et al.
Neurocomputing (2017) Vol. 275, pp. 278-287
Closed Access | Times Cited: 440
A survey towards an integration of big data analytics to big insights for value-creation
Mandeep Kaur Saggi, Sushma Jain
Information Processing & Management (2018) Vol. 54, Iss. 5, pp. 758-790
Closed Access | Times Cited: 438
Artificial Intelligence in Healthcare: Review and Prediction Case Studies
Guoguang Rong, Arnaldo Mendez, Elie Bou Assi, et al.
Engineering (2020) Vol. 6, Iss. 3, pp. 291-301
Open Access | Times Cited: 426
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Ravinesh C. Deo, Ameer A. Hilal, et al.
Advances in Engineering Software (2017) Vol. 115, pp. 112-125
Closed Access | Times Cited: 367
A programmable diffractive deep neural network based on a digital-coding metasurface array
Che Liu, Qian Ma, Zhangjie Luo, et al.
Nature Electronics (2022) Vol. 5, Iss. 2, pp. 113-122
Open Access | Times Cited: 357
A review of neural networks in plant disease detection using hyperspectral data
Kamlesh Golhani, Siva K. Balasundram, Ganesan Vadamalai, et al.
Information Processing in Agriculture (2018) Vol. 5, Iss. 3, pp. 354-371
Open Access | Times Cited: 353
Local Receptive Fields Based Extreme Learning Machine
Guang-Bin Huang, Zuo Bai, Chamara Kasun Liyanaarachchi Lekamalage, et al.
IEEE Computational Intelligence Magazine (2015) Vol. 10, Iss. 2, pp. 18-29
Closed Access | Times Cited: 342
Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine
Zhuyun Chen, Konstantinos Gryllias, Weihua Li
Mechanical Systems and Signal Processing (2019) Vol. 133, pp. 106272-106272
Open Access | Times Cited: 328
A review on extreme learning machine
Jian Wang, Siyuan Lu, Shuihua Wang‎, et al.
Multimedia Tools and Applications (2021) Vol. 81, Iss. 29, pp. 41611-41660
Open Access | Times Cited: 326
The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting
Enrico Camporeale
Space Weather (2019) Vol. 17, Iss. 8, pp. 1166-1207
Open Access | Times Cited: 312
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Othman Jaafar, Ravinesh C. Deo, et al.
Journal of Hydrology (2016) Vol. 542, pp. 603-614
Closed Access | Times Cited: 295
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Hui Liu, Chao Chen, Xinwei Lv, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 328-345
Closed Access | Times Cited: 256
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
Yu Zhang, Yu Wang, Guoxu Zhou, et al.
Expert Systems with Applications (2017) Vol. 96, pp. 302-310
Closed Access | Times Cited: 249
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105444-105444
Open Access | Times Cited: 246
Hyperspectral image reconstruction by deep convolutional neural network for classification
Yunsong Li, Weiying Xie, Huaqing Li
Pattern Recognition (2016) Vol. 63, pp. 371-383
Closed Access | Times Cited: 245
Machine learning applications in minerals processing: A review
John T. McCoy, Lidia Auret
Minerals Engineering (2018) Vol. 132, pp. 95-109
Closed Access | Times Cited: 241
Autoencoder and Its Various Variants
Junhai Zhai, Sufang Zhang, Junfen Chen, et al.
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2018), pp. 415-419
Closed Access | Times Cited: 219
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
Ravinesh C. Deo, Mukesh Tiwari, Jan Adamowski, et al.
Stochastic Environmental Research and Risk Assessment (2016) Vol. 31, Iss. 5, pp. 1211-1240
Closed Access | Times Cited: 214
Application of Artificial Neural Networks for Catalysis: A Review
Hao Li, Zhien Zhang, Zhijian Liu
Catalysts (2017) Vol. 7, Iss. 10, pp. 306-306
Open Access | Times Cited: 213
Concrete dam deformation prediction model for health monitoring based on extreme learning machine
Fei Kang, Jia Liu, Junjie Li, et al.
Structural Control and Health Monitoring (2017) Vol. 24, Iss. 10, pp. e1997-e1997
Open Access | Times Cited: 208
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