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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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Multi-grade brain tumor classification using deep CNN with extensive data augmentation
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Convolutional Neural Networks Based Fire Detection in Surveillance Videos
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A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing
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“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].”
BackgroundApplications of artificial intelligence for disaster management
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Muhammad, K., Khan, S., Ser, J. D., & de Albuquerque, V. H. C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2995800 · Cited by 411
“These challenges include privacy preservation [149], the computational complexity of deep learning models [150],”
BackgroundImage fire detection algorithms based on convolutional neural networks
Li, P., & Zhao, W. (2020) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2020.100625 · Cited by 377
Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100303 · Cited by 375
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“…successful and widely used DL algorithms, has produced significant improvements in the latest studies in areas such as disaster damage detection (Muhammad et al. 2018; Vetrivel et al. 2018), remotely sensed image classification (Liu and Abd-Elrahman 2018; Zhang et al. 2018), and landslide…”
MethodMachine Learning in Disaster Management: Recent Developments in Methods and Applications
Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020020 · Cited by 319
“[34] presented an early fire detection methodology using fine-tuned CNN for closed-circuit television (CCTV) surveillance cameras.”
BackgroundMethodEfficient Fire Detection for Uncertain Surveillance Environment
Muhammad, K., Khan, S., Elhoseny, M., Ahmed, S. H., & Baik, S. W. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2897594 · Cited by 311
“[23] presented a CNN-based early fire detection method for surveillance networks using two benchmark datasets.”
MethodInfluentialSudhakar, S., Vijayakumar, V., Kumar, C. S., Priya, V. S., Ravi, L., & Subramaniyaswamy, V. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.10.007 · Cited by 294
Forest fire and smoke detection using deep learning-based learning without forgetting
Easwaramoorthy, S. V., Cho, J., Subramanian, M., & Naren, O. S. (2023) · Fire Ecology · doi:10.1186/s42408-022-00165-0 · Cited by 284
Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review
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Fire Sensing Technologies: A Review
Gaur, A., Singh, A. P., Kumar, A., Kulkarni, K. S., Lala, S., Kapoor, K., Srivastava, V., Kumar, A., & Mukhopadhyay, S. C. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2894665 · Cited by 238
Recent Advances in Sensors for Fire Detection
Khan, F., Xu, Z., Sun, J., Khan, F. M., Ahmed, A., & Zhao, Y. J. (2022) · Sensors · doi:10.3390/s22093310 · Cited by 219
A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems
Abid, F. (2020) · Fire Technology · doi:10.1007/s10694-020-01056-z · Cited by 212
Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment
Khan, S., Muhammad, K., Mumtaz, S., Baik, S. W., & de Albuquerque, V. H. C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2896120 · Cited by 198
“In video-based fire detection systems [10]–[12]”
Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM
Ullah, A., Muhammad, K., Ser, J. D., Baik, S. W., & de Albuquerque, V. H. C. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2881943 · Cited by 191
Fire-YOLO: A Small Target Object Detection Method for Fire Inspection
Zhao, L., Zhi, L., Zhao, C., & Zheng, W. (2022) · Sustainability · doi:10.3390/su14094930 · Cited by 188
“The authors of [2] propose an early fire detection framework using fine-tuned Convolutional Neural Networks (CNN), but the model has a high computational cost.”
BackgroundMethodUllah, A., Muhammad, K., Haq, I. U., & Baik, S. W. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.01.029 · Cited by 186
de Venâncio, P. V. A. B., Lisboa, A. C., & Barbosa, A. V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07467-z · Cited by 183
“To deal with this problem, convolutional network architectures were precisely designed for efficient fire recognition [23, 27–30].”
BackgroundMethodInfluentialA Survey on an Emerging Area: Deep Learning for Smart City Data
Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2907718 · Cited by 167
“In fact, it has been applied in a limited number of small-scale studies such as fire detection [70] and parking lot detection [71].”
BackgroundMethodInfluentialA comprehensive survey of multi-view video summarization
Hussain, T., Muhammad, K., Ding, W., Lloret, J., Baik, S. W., & de Albuquerque, V. H. C. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107567 · Cited by 159
Edge Video Analytics for Public Safety: A Review
Zhang, Q., Sun, H., Wu, X., & Zhong, H. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2925910 · Cited by 156
“Moreover, several CNN-based flame detection models [120], [121] have been proposed without image segmentation.”
MethodDeepFire: A Novel Dataset and Deep Transfer Learning Benchmark for Forest Fire Detection
Khan, A. H., Hassan, B., Khan, S., Ahmed, R., & Abuassba, A. O. (2022) · Mobile Information Systems · doi:10.1155/2022/5358359 · Cited by 165
Edge deep learning in computer vision and medical diagnostics: a comprehensive survey
Xu, Y., Khan, T. M., Song, Y., & Meijering, E. H. W. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11033-5 · Cited by 187
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Deep neural networks for global wildfire susceptibility modelling
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Afzal, S., Maqsood, M., Nazir, F., Khan, U. M., Aadil, F., Awan, K. M., Mehmood, I., & Song, O.-Y. (2019) · IEEE Access · doi:10.1109/access.2019.2932786 · Cited by 149
“PROPOSED METHODOLOGY Deep Learning has seen immense work recently in numerous studies especially in computer-vision [36]–[38], image processing [39], [40], authentication system [41] and speech recognition [42], [43].”
BackgroundLi, S., Yan, Q., & Liu, P. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3016431 · Cited by 151
“AlexNet [40] has good performance for fire detection in normal scenes, which was proven in [27], [32], [33].”
BackgroundMethodResultInfluentialWildfire detection using transfer learning on augmented datasets
Sousa, M. J., Moutinho, A., & Almeida, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112975 · Cited by 147
Edge Intelligence-Assisted Smoke Detection in Foggy Surveillance Environments
Muhammad, K., Khan, S., Palade, V., Mehmood, I., & de Albuquerque, V. H. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2915592 · Cited by 129
“Due to this reason, several fire detection systems are recently developed and significant research efforts are spent for further improvement [5], [6].”
BackgroundA Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
CNN-based anti-spoofing two-tier multi-factor authentication system
Sajjad, M., Khan, S., Hussain, T., Muhammad, K., Sangaiah, A. K., Castiglione, A., Esposito, C., & Baik, S. W. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.015 · Cited by 126
“The recent success of the deep convolutional neural networks in he field of image classification [23,24] , as well as in object recog-”
BackgroundSerious games in science education: a systematic literature
Ullah, M., Amin, S. U., Munsif, M., Yamin, M. M., Safaev, U., Khan, H., Khan, S. H., & Ullah, H. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.02.001 · Cited by 145
A Brief Review of Machine Learning Algorithms in Forest Fires Science
Alkhatib, R., Sahwan, W., Alkhatieb, A., & Schütt, B. (2023) · Applied Sciences · doi:10.3390/app13148275 · Cited by 139
Information-Guided Flame Detection Based on Faster R-CNN
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“[14]–[17] trained different CNN structures, such as AlexNet, GoogLeNet, and MobileNet, and analyzed the effects of different methods on the dataset [9], achieving a performance boost (84% to 92%).”
MethodArtificial neural network approaches for disaster management: A literature review
Guha, S., Jana, R. K., & Sanyal, M. K. (2022) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2022.103276 · Cited by 126
Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark
Yar, H., Hussain, T., Agarwal, M., Khan, Z. A., Gupta, S. K., & Baik, S. W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3207006 · Cited by 118
“As discussed in Section II, plain CNN architectures, including LeNet-5 [14], AlexNet [33], VGG [1], [26], and the light-weight CNN (LW-CNN) [3], achieved considerably better performances compared to TML-based methods.”
MethodResultFire detection in video surveillances using convolutional neural networks and wavelet transform
Huang, L., Liu, G., Wang, Y., Yuan, H., & Chen, T. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104737 · Cited by 115
A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets
Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access · doi:10.1109/access.2019.2955387 · Cited by 114
“lance field dealing with implementation of algorithms to perform traditional techniques of image processing such as motion detection [24], [26], [57], [58], object detection [17], [25], [31], [57], [59]– [71], [99], object or event classification [16], [20], [34], [35], [54], [66], [144], [145],”
MethodEfficient CNN based summarization of surveillance videos for resource-constrained devices
Muhammad, K., Hussain, T., & Baik, S. W. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.08.003 · Cited by 112
Wang, S., Sun, J., Mehmood, I., Pan, C., Chen, Y., & Zhang, Y. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5130 · Cited by 112
FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT ApplicationsPreprint
Jadon, A., Omama, M., Varshney, A., Ansari, M. S., & Sharma, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11922 · Cited by 178
“We observed from the datasets used in the past approaches [20]–[23] that currently there is a scarcity of a diverse fire dataset.”
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Hashemzadeh, M., & Zademehdi, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.019 · Cited by 100
Efficient attention based deep fusion CNN for smoke detection in fog environment
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Perspectives of big experimental database and artificial intelligence in tunnel fire research
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Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs
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“However, the abovementioned deep-learning-based models improved the fire detection accuracy, with minimum false alarms, but the complexity and size of the model are comparatively large, that is, 238MB [23].”
BackgroundMethodObject Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces
Liu, G., Liu, S., Muhammad, K., Sangaiah, A. K., & Doctor, F. (2018) · IEEE Access · doi:10.1109/access.2018.2834916 · Cited by 88
“An IVS generally includes five parts, which are video sequence acquisition [3], target detection, tracking, abnormal detection [4-6] and data analysis [7, 8].”
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[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments
Yar, H., Hussain, T., Khan, Z. A., Koundal, D., Lee, M. Y., & Baik, S. W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5195508 · Cited by 80
“In the experiments, the model is trained over 30 epochs using an SGD optimizer. training and validation accuracy is presented in Figure 5 classification report of the proposed model over test data is given in Table 3, where the precision, recall, and F1-score of fire class are 0.97, 0.98, and 0.97, respectively, and the…”
BackgroundMethodInfluentialRetractedAn Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks
Kizilkaya, B., Ever, E., Yatbaz, H. Y., & Yazıcı, A. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3473037 · Cited by 83
“Studies such as References [21, 36, 43] mainly consider fire detection in various environments.”
BackgroundMethodResultInfluentialRandomly Initialized CNN with Densely Connected Stacked Autoencoder for Efficient Fire Detection
Khan, Z. A., Hussain, T., Ullah, F. U. M., Gupta, S. K., Lee, M. Y., & Baik, S. W. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105403 · Cited by 79
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Development of early fire detection model for buildings using computer vision-based CCTV
Ahn, Y., Choi, H., & Kim, B. S. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105647 · Cited by 81
Real-time detection of flame and smoke using an improved YOLOv4 network
Wang, Y., Hua, C., Ding, W., & Wu, R. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02060-8 · Cited by 75
“The early detection of fire is extremely more important than anything else to minimize loss of lives and property damage [1].”
BackgroundAn Effective Attention-based CNN Model for Fire Detection in Adverse Weather Conditions
Yar, H., Ullah, W., Khan, Z. A., & Baik, S. W. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.10.019 · Cited by 74
Fire and smoke detection from videos: A literature review under a novel taxonomy
Gragnaniello, D., Greco, A., Sansone, C., & Vento, B. (2024) · Expert systems with applications · doi:10.1016/j.eswa.2024.124783 · Cited by 71
Conflux LSTMs Network: A Novel Approach for Multi-View Action Recognition
Ullah, A., Muhammad, K., Hussain, T., & Baik, S. W. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.12.151 · Cited by 73
A convolutional neural network-based flame detection method in video sequence
Zhong, Z., Wang, M., Shi, Y., & Gao, W. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1319-4 · Cited by 72
“Therefore, fire is one of the common happening events, whose detection at early detection of fire is extremely more important than anything else to minimize loss of lives and property damage [3].”
BackgroundHosseini, A., Hashemzadeh, M., & Farajzadeh, N. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101638 · Cited by 76
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An efficient fire and smoke detection algorithm based on an end-to-end structured network
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MS-FRCNN: A Multi-Scale Faster RCNN Model for Small Target Forest Fire Detection
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“In the early stage of a wildfire, the flame area is small, and the spread is slow, which is the best time to fight it [6–8].”
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“ious resource-constrained platforms, such as raspberry pi and smartphones for IoT applications [17]–[19].”
BackgroundA Review on Forest Fire Detection Techniques: A Decadal Perspective
Chowdary, V., Gupta, M. K., & Singh, R. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.12.17876 · Cited by 65
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An Improved Approach for Fire Detection using Deep Learning Models
Dua, M., Kumar, M., Charan, G. S., & Ravi, P. S. (2020) · International Conference on Industry 4.0 Technology (I4Tech) · doi:10.1109/i4tech48345.2020.9102697 · Cited by 66
“In [6], authors explained that CNN is deep learning algorithm which takes images as input and allocate weights in order to differentiate from others.”
BackgroundAn efficient deep learning architecture for effective fire detection in smart surveillance
Yar, H., Khan, Z. A., Rida, I., Ullah, W., Kim, M. J., & Baik, S. W. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104989 · Cited by 64
Deep learning based inverse model for building fire source location and intensity estimation
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Fire Sensor and Surveillance Camera-Based GTCNN for Fire Detection System
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“Traditional fire alarm systems make use of multiple sensors, including heat, temperature, and smoke [2], [3], [4].”
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Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis
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Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review
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A Video Based Fire Smoke Detection Using Robust AdaBoost
Wu, X., Lu, X., & Leung, H. (2018) · Sensors · doi:10.3390/s18113780 · Cited by 61
“[33] propose an adaptive prioritization mechanism for fire smoke detection.”
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Fire Detection Using Multi Color Space and Background Modeling
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The Use of Artificial Intelligence in Disaster Management - A Systematic Literature Review
Nunavath, V., & Goodwin, M. (2019) · International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) · doi:10.1109/ict-dm47966.2019.9032935 · Cited by 55
An open flame and smoke detection dataset for deep learning in remote sensing based fire detection
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“Nevertheless, under intricate real-world scenarios, flame or smoke displays various attributes, including flickering, gradual growth, irregularity, color variation, and various light intensities (Muhammad, Ahmad, and Baik 2018).”
AlphaMEX: A smarter global pooling method for convolutional neural networks
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Kollem, S., Reddy, K. R., Prasad, C. R., Chakraborty, A., Ajayan, J., Sreejith, S., Bhattacharya, S., Joseph, L. M. I. L., & Janapati, R. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22870 · Cited by 54
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Xie, W., Zeng, Y., Zhang, X., Wong, H. Y., Zhang, T., Wang, Z., Wu, X., Shi, J., Huang, X., Xiao, F., et al. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103117 · Cited by 52
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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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