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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:

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Francisco Ordóñez, Daniel Roggen
Sensors (2016) Vol. 16, Iss. 1, pp. 115-115
Open Access | Times Cited: 2269

Showing 1-25 of 2269 citing articles:

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al.
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 4693
Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, et al.
Data Mining and Knowledge Discovery (2019) Vol. 33, Iss. 4, pp. 917-963
Open Access | Times Cited: 2542
Deep learning for sensor-based activity recognition: A survey
Jindong Wang, Yiqiang Chen, Shuji Hao, et al.
Pattern Recognition Letters (2018) Vol. 119, pp. 3-11
Open Access | Times Cited: 1667
Deep Learning in Mobile and Wireless Networking: A Survey
Chaoyun Zhang, Paul Patras, Hamed Haddadi
IEEE Communications Surveys & Tutorials (2019) Vol. 21, Iss. 3, pp. 2224-2287
Open Access | Times Cited: 1496
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, et al.
Electronics (2019) Vol. 8, Iss. 3, pp. 292-292
Open Access | Times Cited: 1369
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mehdi Mohammadi, Ala Al‐Fuqaha, Sameh Sorour, et al.
IEEE Communications Surveys & Tutorials (2018) Vol. 20, Iss. 4, pp. 2923-2960
Open Access | Times Cited: 1256
Deep Multimodal Learning: A Survey on Recent Advances and Trends
Dhanesh Ramachandram, Graham W. Taylor
IEEE Signal Processing Magazine (2017) Vol. 34, Iss. 6, pp. 96-108
Closed Access | Times Cited: 788
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
Henry Friday Nweke, Teh Ying Wah, Mohammed Ali Al-Garadi, et al.
Expert Systems with Applications (2018) Vol. 105, pp. 233-261
Closed Access | Times Cited: 772
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
Andrey Ignatov
Applied Soft Computing (2017) Vol. 62, pp. 915-922
Closed Access | Times Cited: 710
LSTM-CNN Architecture for Human Activity Recognition
Kun Xia, Jianguang Huang, Hanyu Wang
IEEE Access (2020) Vol. 8, pp. 56855-56866
Open Access | Times Cited: 620
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
Ziqi Zhang, David Robinson, Jonathan Tepper
Lecture notes in computer science (2018), pp. 745-760
Closed Access | Times Cited: 570
Sensor-based and vision-based human activity recognition: A comprehensive survey
L. Minh Dang, Kyungbok Min, Hanxiang Wang, et al.
Pattern Recognition (2020) Vol. 108, pp. 107561-107561
Closed Access | Times Cited: 529
Remembering history with convolutional LSTM for anomaly detection
Weixin Luo, Wen Liu, Shenghua Gao
2022 IEEE International Conference on Multimedia and Expo (ICME) (2017), pp. 439-444
Closed Access | Times Cited: 518
Advancing Biosensors with Machine Learning
Feiyun Cui, Yun Yue, Yi Zhang, et al.
ACS Sensors (2020) Vol. 5, Iss. 11, pp. 3346-3364
Closed Access | Times Cited: 501
Deep Recurrent Neural Networks for Human Activity Recognition
Abdulmajid Murad, Jae-Young Pyun
Sensors (2017) Vol. 17, Iss. 11, pp. 2556-2556
Open Access | Times Cited: 449
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Yu Guan, Thomas Plötz
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2017) Vol. 1, Iss. 2, pp. 1-28
Open Access | Times Cited: 447
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
Sen Qiu, Hongkai Zhao, Nan Jiang, et al.
Information Fusion (2021) Vol. 80, pp. 241-265
Closed Access | Times Cited: 439
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Monika A. Myszczynska, Poojitha N. Ojamies, Alix M.B. Lacoste, et al.
Nature Reviews Neurology (2020) Vol. 16, Iss. 8, pp. 440-456
Closed Access | Times Cited: 426
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare
Polina Mamoshina, Lucy O. Ojomoko, Yury Yanovich, et al.
Oncotarget (2017) Vol. 9, Iss. 5, pp. 5665-5690
Open Access | Times Cited: 411
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Hongsong Wang, Liang Wang
(2017)
Open Access | Times Cited: 409
Deep, convolutional, and recurrent models for human activity recognition using wearables
Nils Hammerla, Shane Halloran, Thomas Plötz
arXiv (Cornell University) (2016), pp. 1533-1540
Closed Access | Times Cited: 405
Interacting with Soli
Saiwen Wang, Jie Song, Jaime Lien, et al.
(2016)
Open Access | Times Cited: 376
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Daniela Micucci, Marco Mobilio, Paolo Napoletano
Applied Sciences (2017) Vol. 7, Iss. 10, pp. 1101-1101
Open Access | Times Cited: 376
Smartphone Sensors for Health Monitoring and Diagnosis
Sumit Majumder, M. Jamal Deen
Sensors (2019) Vol. 19, Iss. 9, pp. 2164-2164
Open Access | Times Cited: 360
Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope
Anoop Singh, Asha Sharma, Aamir Ahmed, et al.
Biosensors (2021) Vol. 11, Iss. 9, pp. 336-336
Open Access | Times Cited: 358
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