The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.
Early fire detection using convolutional neural networks during surveillance for effective disaster management
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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
“For instance, the outcomes of the Mivia dataset experiment achieved an accuracy of 95.32%, outperforming another CNN-based system [132] that achieved 94.39% accuracy.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [132]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Region-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912
A survey on wireless multimedia sensor networks
Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2006) · Computer Networks · doi:10.1016/j.comnet.2006.10.002 · Cited by 2,310
Livescu, K., Bezman, A., Borges, N., Yung, L.-Y., Çetin, Ö., Frankel, J., King, S., Magimai.-Doss, M., Chi, X., & Lavoie, L. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 2,145
Flagged first, then by citations
2 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- D. Guha-Sapir, F. Vos, R. Below, S. Penserre, Annual disaster statistical review 2015: the numbers and trends, 2015. http://www.cred.be/sites/default/files/ADSR_2015.pdf.
Cited by
Sorted by citations
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 895
A review of machine learning applications in wildfire science and management
Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. (2020) · Environmental Reviews · doi:10.1139/er-2020-0019 · Cited by 945
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018) · IEEE Access · doi:10.1109/access.2018.2812835 · Cited by 556
A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing
Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., & Grammalidis, N. (2020) · Sensors · doi:10.3390/s20226442 · Cited by 547
“In [56], the authors combined AlexNet as a baseline architecture and the internet of multimedia things (IoMT) for fire detection and disaster management.”
BackgroundEfficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., & Baik, S. W. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2830099 · Cited by 539
“, can 34 be detected at early stages, and the appropriate authority 35 can be autonomously informed [6], [7].”
Background
Metadata
- Authors
- Muhammad, K., Ahmad, J., & Baik, S. W.
- Affiliations
- Sejong University (KR)
- Corresponding author
- Baik, Sung Wook
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 288
- Pages
- 30-42
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2780222614
- Cited by
- 558
- References
- 68
- Citations by year
- 2026: 40 · 2025: 62 · 2024: 78 · 2023: 120 · 2022: 87 · 2021: 60 · 2020: 51 · 2019: 43 · 2018: 17
- FWCI
- 44.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF) (2016R1A2B4011712)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire dynamics and safety research
- Keywords
- early fire detection, CCTV surveillance, convolutional neural networks, fine-tuning, disaster management, dynamic channel selection, cognitive radio networks, autonomous response
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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