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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:

Time series k -means: A new k -means type smooth subspace clustering for time series data
Xiaohui Huang, Yunming Ye, Liyan Xiong, et al.
Information Sciences (2016) Vol. 367-368, pp. 1-13
Closed Access | Times Cited: 128

Showing 1-25 of 128 citing articles:

Deep long short-term memory networks for nonlinear structural seismic response prediction
Ruiyang Zhang, Zhao Chen, Su Chen, et al.
Computers & Structures (2019) Vol. 220, pp. 55-68
Open Access | Times Cited: 384
Machine learning toward advanced energy storage devices and systems
Tianhan Gao, Wei Lu
iScience (2020) Vol. 24, Iss. 1, pp. 101936-101936
Open Access | Times Cited: 135
Collaborative multi-depot logistics network design with time window assignment
Yong Wang, Shuanglu Zhang, Xiangyang Guan, et al.
Expert Systems with Applications (2019) Vol. 140, pp. 112910-112910
Closed Access | Times Cited: 78
The Geothermal Artificial Intelligence for geothermal exploration
Jaime Moraga, Şebnem Düzgün, Mahmut Çavur, et al.
Renewable Energy (2022) Vol. 192, pp. 134-149
Open Access | Times Cited: 40
Efficient seismic fragility analysis method utilizing ground motion clustering and probabilistic machine learning
Jia‐Yi Ding, De‐Cheng Feng, Emanuele Brunesi, et al.
Engineering Structures (2023) Vol. 294, pp. 116739-116739
Closed Access | Times Cited: 24
Streamflow prediction in ungauged catchments through use of catchment classification and deep learning
Miao He, S. S. Jiang, Liliang Ren, et al.
Journal of Hydrology (2024) Vol. 639, pp. 131638-131638
Closed Access | Times Cited: 10
Proposal of a Machine Learning Approach for Traffic Flow Prediction
Mariaelena Berlotti, Sarah Di Grande, Salvatore Cavalieri
Sensors (2024) Vol. 24, Iss. 7, pp. 2348-2348
Open Access | Times Cited: 9
Convolutional autoencoder-based ground motion clustering and selection
Yiming Jia, Mehrdad Sasani
Soil Dynamics and Earthquake Engineering (2025) Vol. 191, pp. 109240-109240
Open Access | Times Cited: 1
A new distance with derivative information for functional k-means clustering algorithm
Yinfeng Meng, Jiye Liang, Fuyuan Cao, et al.
Information Sciences (2018) Vol. 463-464, pp. 166-185
Closed Access | Times Cited: 80
Decentralized Clustering by Finding Loose and Distributed Density Cores
Yewang Chen, Shengyu Tang, Lida Zhou, et al.
Information Sciences (2016) Vol. 433-434, pp. 510-526
Open Access | Times Cited: 65
Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast
Fatemeh Najibi, Dimitra Apostolopoulou, Eduardo Alonso
International Journal of Electrical Power & Energy Systems (2021) Vol. 130, pp. 106916-106916
Open Access | Times Cited: 50
Road network evolution in the urban and rural United States since 1900
Keith Burghardt, Johannes Uhl, Kristina Lerman, et al.
Computers Environment and Urban Systems (2022) Vol. 95, pp. 101803-101803
Open Access | Times Cited: 35
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
Junchen Jin, Dingding Rong, Tong Zhang, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 9, pp. 16185-16196
Closed Access | Times Cited: 34
A lane-changing risk profile analysis method based on time-series clustering
Tianyi Chen, Xiupeng Shi, Yiik Diew Wong
Physica A Statistical Mechanics and its Applications (2020) Vol. 565, pp. 125567-125567
Closed Access | Times Cited: 47
Clustering sequential navigation patterns inmultiple‐sourcereading tasks with dynamic time warping method
Qiwei He, Francesca Borgonovi, Javier Suárez‐Álvarez
Journal of Computer Assisted Learning (2022) Vol. 39, Iss. 3, pp. 719-736
Open Access | Times Cited: 25
Modeling nonlinear flutter behavior of long‐span bridges using knowledge‐enhanced long short‐term memory network
Tao Li, Teng Wu
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 38, Iss. 11, pp. 1504-1519
Closed Access | Times Cited: 16
Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives
Yaliang Zhao, Samwel K. Tarus, Laurence T. Yang, et al.
Information Sciences (2019) Vol. 515, pp. 132-155
Closed Access | Times Cited: 40
Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment
Chunxiao Ning, Yazhou Xie
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 2, pp. 165-185
Open Access | Times Cited: 12
Cost-effective sizing method of Vehicle-to-Building chargers and energy storage systems during the planning stage of smart micro-grid
Ziliang Wei, Geng Yang, Hao Tang, et al.
eTransportation (2024) Vol. 21, pp. 100343-100343
Closed Access | Times Cited: 4
Lithium-Ion Cell Screening With Convolutional Neural Networks Based on Two-Step Time-Series Clustering and Hybrid Resampling for Imbalanced Data
Chengbao Liu, Jie Tan, Heyuan Shi, et al.
IEEE Access (2018) Vol. 6, pp. 59001-59014
Open Access | Times Cited: 36
Logging-data-driven lithology identification in complex reservoirs: an example from the Niuxintuo block of the Liaohe oilfield
Zuochun Fan, Hu Changhao, Shu Jiang, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
Machine Learning for Battery Energy Storage System (BESS)
S. Elango
Advances in computational intelligence and robotics book series (2025), pp. 169-202
Closed Access
Unsupervised Contrastive Learning for Time Series Data Clustering
Cao Bo, Qinghua Xing, Ke Yang, et al.
Electronics (2025) Vol. 14, Iss. 8, pp. 1660-1660
Open Access
Time Series and Similarity Measures
Zoltan Geler, Vladimir Kurbalija, Miloš Radovanović, et al.
Intelligent systems reference library (2025), pp. 9-93
Closed Access
A contrastive clustering loss function increases class-balanced in time series classification
Chaomin Wu, Xu Cheng, Hao Wang
Expert Systems with Applications (2025) Vol. 283, pp. 127493-127493
Closed Access
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