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
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“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]
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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
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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
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