Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation
Chan-Uk Yeom, Keun-Chang Kwak
Energies (2017) Vol. 10, Iss. 10, pp. 1613-1613
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
Shadfar Davoodi, Hung Vo Thanh, David A. Wood, et al.
Separation and Purification Technology (2023) Vol. 316, pp. 123807-123807
Closed Access | Times Cited: 45
A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score
Eliana Vivas, Héctor Allende‐Cid, Rodrigo Salas
Entropy (2020) Vol. 22, Iss. 12, pp. 1412-1412
Open Access | Times Cited: 97
Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM
Taiyong Li, Zijie Qian, Ting He
Complexity (2020) Vol. 2020, pp. 1-20
Open Access | Times Cited: 71
Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
Meysam Rajabi, Omid Hazbeh, Shadfar Davoodi, et al.
Journal of Petroleum Exploration and Production Technology (2022) Vol. 13, Iss. 1, pp. 19-42
Open Access | Times Cited: 54
Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
Shadfar Davoodi, Mohammad Mehrad, David A. Wood, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106459-106459
Closed Access | Times Cited: 34
Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
Shadfar Davoodi, Mohammad Mehrad, David A. Wood, et al.
International Journal of Rock Mechanics and Mining Sciences (2023) Vol. 170, pp. 105546-105546
Closed Access | Times Cited: 28
Comparing of deep neural networks and extreme learning machines based on growing and pruning approach
Kemal Akyol
Expert Systems with Applications (2019) Vol. 140, pp. 112875-112875
Closed Access | Times Cited: 57
Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms
Mohammad Farsi, Nima Mohamadian, Hamzeh Ghorbani, et al.
Natural Resources Research (2021) Vol. 30, Iss. 5, pp. 3455-3481
Closed Access | Times Cited: 54
Modeling Energy Demand—A Systematic Literature Review
Paul Verwiebe, Stephan Seim, Simon Burges, et al.
Energies (2021) Vol. 14, Iss. 23, pp. 7859-7859
Open Access | Times Cited: 51
Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
Sina Rashidi, Mohammad Mehrad, Hamzeh Ghorbani, et al.
Journal of Petroleum Science and Engineering (2021) Vol. 202, pp. 108425-108425
Closed Access | Times Cited: 47
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
Abouzar Rajabi Behesht Abad, Seyedmohammadvahid Mousavi, Nima Mohamadian, et al.
Journal of Natural Gas Science and Engineering (2021) Vol. 95, pp. 104210-104210
Closed Access | Times Cited: 47
Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data
Meysam Rajabi, Saeed Beheshtian, Shadfar Davoodi, et al.
Journal of Petroleum Exploration and Production Technology (2021) Vol. 11, Iss. 12, pp. 4375-4397
Open Access | Times Cited: 44
Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms
Mohammad Mehrad, Ahmad Ramezanzadeh, Mahdi Bajolvand, et al.
Journal of Petroleum Science and Engineering (2022) Vol. 212, pp. 110254-110254
Closed Access | Times Cited: 34
A review on short‐term load forecasting models for micro‐grid application
V. Y. Kondaiah, B. Saravanan, Sanjeevikumar Padmanaban, et al.
The Journal of Engineering (2022) Vol. 2022, Iss. 7, pp. 665-689
Open Access | Times Cited: 27
Pattern extraction and structured characterization for electricity consumption profiles in different types of buildings
Yi Dai, Shuo Liu, Hao Li, et al.
Energy and Buildings (2025), pp. 115598-115598
Closed Access
Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting
Pedro Bento, José Pombo, M.R.A. Calado, et al.
Energies (2021) Vol. 14, Iss. 21, pp. 7378-7378
Open Access | Times Cited: 36
Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms
Amirhossein Sheykhinasab, Amir Ali Mohseni, Arash Barahooie Bahari, et al.
Journal of Petroleum Exploration and Production Technology (2022) Vol. 13, Iss. 2, pp. 661-689
Open Access | Times Cited: 22
Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea
Hye-Suk Yi, Sangyoung Park, Kwang‐Guk An, et al.
International Journal of Environmental Research and Public Health (2018) Vol. 15, Iss. 10, pp. 2078-2078
Open Access | Times Cited: 41
Developing a New Model for Drilling Rate of Penetration Prediction Using Convolutional Neural Network
Morteza Matinkia, Amirhossein Sheykhinasab, Soroush Shojaei, et al.
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 9, pp. 11953-11985
Closed Access | Times Cited: 20
Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir
Saeed Beheshtian, Meysam Rajabi, Shadfar Davoodi, et al.
Marine and Petroleum Geology (2022) Vol. 142, pp. 105772-105772
Closed Access | Times Cited: 19
A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield
Farshad Jafarizadeh, Babak Larki, Bamdad Kazemi, et al.
Petroleum (2022) Vol. 9, Iss. 3, pp. 468-485
Open Access | Times Cited: 18
A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting
Wei Sun, Chongchong Zhang
Energies (2018) Vol. 11, Iss. 5, pp. 1282-1282
Open Access | Times Cited: 28
A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM
Jinwoong Park, Eenjun Hwang
Sensors (2021) Vol. 21, Iss. 22, pp. 7697-7697
Open Access | Times Cited: 22
An improved short term load forecasting with ranker based feature selection technique
Siva Sankari Subbiah, C. Jayakumar
Journal of Intelligent & Fuzzy Systems (2020) Vol. 39, Iss. 5, pp. 6783-6800
Closed Access | Times Cited: 20
RGB-D based human action recognition using evolutionary self-adaptive extreme learning machine with knowledge-based control parameters
Preksha Pareek, Ankit Thakkar
Journal of Ambient Intelligence and Humanized Computing (2021) Vol. 14, Iss. 2, pp. 939-957
Closed Access | Times Cited: 16
Page 1 - Next Page

Scroll to top