The DOI matches its Crossref record.
Mining Intelligence and Knowledge Exploration: Second International Conference, MIKE 2014, Cork, Ireland, December 10-12, 2014. Proceedings
Overview
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“As such, autoencoders are most commonly used for feature extraction and dimensionality reduction [120,122,123,124,125,126,133,134,135,136,137,138,139,140,141].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [125]
In the citing paper
“The result reveals that the stacked autoencoder has the highest accuracy of 92.16% and provides a 7% advantage over traditional methods with hand-crafted features.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [125]
Citations
Cited by
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
Soleimani, E., & Nazerfard, E. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.056 · Cited by 146
Identifying machine learning techniques for classification of target advertising
Choi, J.-A., & Lim, K. (2020) · ICT Express · doi:10.1016/j.icte.2020.04.012 · Cited by 139
Cruz, E. F., & Rosado da Cruz, A. M. (2023) · Cleaner and Responsible Consumption · doi:10.1016/j.clrc.2023.100138 · Cited by 42
Metadata
- Authors
- Prasath, R., O’Reilly, P., & Kathirvalavakumar, T.
- Affiliations
- University College Cork (IE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4251398444
- Cited by
- 39
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 3 · 2020: 9 · 2019: 3 · 2018: 2 · 2017: 1 · 2016: 4
- FWCI
- 1.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- knowledge exploration, data mining, knowledge discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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