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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:

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains
Polash Dey, Emam Hossain, Md. Ishtiaque Hossain, et al.
Algorithms (2021) Vol. 14, Iss. 8, pp. 251-251
Open Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Machine learning with Belief Rule-Based Expert Systems to predict stock price movements
Emam Hossain, Mohammad Shahadat Hossain, Pär‐Ola Zander, et al.
Expert Systems with Applications (2022) Vol. 206, pp. 117706-117706
Closed Access | Times Cited: 33
Harnessing Deep Learning and Technical Indicators for Enhanced Stock Predictions of Blue-Chip Stocks on the Indonesia Stock Exchange (IDX)
B. Yudi Dwiandiyanta, Rudy Hartanto, Ridi Ferdiana
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 1, pp. 20348-20357
Open Access
Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
Gaurav Narkhede, Anil Hiwale, Bharat Tidke, et al.
Algorithms (2023) Vol. 16, Iss. 1, pp. 52-52
Open Access | Times Cited: 10
Smart Water Meter Based on Deep Neural Network and Undersampling for PWNC Detection
Marco Carratù, Salvatore Dello Iacono, Giuseppe Di Leo, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 10
Ionospheric TEC prediction using hybrid method based on ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) deep learning model over India
S. Nath, Bornali CHETİA, S. Kalita
Advances in Space Research (2022) Vol. 71, Iss. 5, pp. 2307-2317
Open Access | Times Cited: 15
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
Rekha Ks, Sabu MK
PeerJ Computer Science (2022) Vol. 8, pp. e1158-e1158
Open Access | Times Cited: 14
A new approach to forecasting Islamic and conventional oil and gas stock prices
حسن حیدری, Oluwasegun B. Adekoya, Muhammad Mahdi Rashidi, et al.
International Review of Economics & Finance (2024) Vol. 96, pp. 103513-103513
Closed Access | Times Cited: 1
Prediction of Stock Market Using LSTM-RNN Model
Nimesh Raj
(2023), pp. 623-628
Closed Access | Times Cited: 4
Segmentation and classification of brain tumour using LRIFCM and LSTM
K. S. Neetha, Dayanand Lal Narayan
Multimedia Tools and Applications (2024) Vol. 83, Iss. 31, pp. 76705-76730
Closed Access | Times Cited: 1
A Comparative Study of Machine Learning and Neural Network Models in Short-term Market Prediction
Fayez Abu-Ajamieh
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Face Mask Detection in the Era of COVID-19: A CNN-Based Approach
Noortaz Rezoana, Mohammad Shahadat Hossain, Karl Andersson
Lecture notes in networks and systems (2022), pp. 3-15
Closed Access | Times Cited: 5
Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review
Cristiane Orquisa Fantin, Eli Hadad
Journal of Computer and Communications (2022) Vol. 10, Iss. 12, pp. 29-50
Open Access | Times Cited: 5
Deep Learning in Stock Market: Techniques, Purpose, and Challenges
Zericho R. Marak, Anand J. Kulkarni, Sarthak Sengupta
(2024), pp. 1-21
Closed Access
Stock Market Prediction with Artificial Intelligence Techniques in Recession Times
David Valle-Cruz, Vanessa Fernández-Cortez, Asdrúbal López‐Chau, et al.
Communications in computer and information science (2024), pp. 246-263
Closed Access
Recurrent Neural Network-based Closing Index Prediction of Indian Software Industry Stocks
Chiradeep Mukherjee, Arindam Chakraborty, Subhalaxmi Chakraborty, et al.
(2024), pp. 1-6
Closed Access
Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
Hossein Zolfagharinia, Mehdi Najafi, Shamir Rizvi, et al.
Algorithms (2024) Vol. 17, Iss. 6, pp. 234-234
Open Access
A Study of the Impact of Feature Selection on the Accuracy of Short-term Crude Oil Price Prediction
Fayez Abu-Ajamieh
Research Square (Research Square) (2024)
Open Access
DEA-RNNs: An Ensemble Approach for Portfolio Selection in the Thailand Stock Market
Mojtaba Safari, Nawapon Nakharutai, Phisanu Chiawkhun, et al.
Studies in systems, decision and control (2024), pp. 453-467
Closed Access
Deep Learning in Stock Market: Techniques, Purpose, and Challenges
Zericho R. Marak, Anand J. Kulkarni, Sarthak Sengupta
(2024), pp. 577-597
Closed Access
Improving the Performance of Long Short Term Memory and Gated Recurrent Unit for Predicting the Composite Stock Price Index
Rahardian Fajar Nugroho, Sutikno Sutikno, Eko Adi Sarwoko, et al.
(2024), pp. 444-449
Closed Access
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Sahil Agarwal, Bosco Paul Alapatt, Akhil M. Nair, et al.
(2024), pp. 1074-1079
Closed Access
Return forecasting of sub-new stocks via ADARNN Trained with Mixed Historical Data
Lieping Zhang, Peng Chen, Hailing He, et al.
(2024), pp. 67-72
Closed Access
A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture
Xiaojun Ye, Beixi Ning, Pengyuan Bian, et al.
Communications in computer and information science (2023), pp. 12-24
Closed Access | Times Cited: 1
Stock Price Prediction: Impact of Volatility on Model Accuracy
Juan Parada-Rodriguez, Ixent Galpin
Communications in computer and information science (2023), pp. 58-73
Closed Access
Improve Short-Term Stock Price Forecasts Through Deep Learning Algorithms
Jitesh Kumar Meena, Rohitash Kumar Banyal
Lecture notes in networks and systems (2023), pp. 203-212
Closed Access
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