@online{Automaticdifferentiationpackage_,
  title = {Automatic Differentiation Package - Torch.Autograd — {{PyTorch}} 2.9 Documentation},
  url = {https://docs.pytorch.org/docs/stable/autograd.html},
  urldate = {2025-11-26},
  langid = {english}
}

@inproceedings{Bridle_ProbabilisticInterpretationFeedforward_1990,
  title = {Probabilistic {{Interpretation}} of {{Feedforward Classification Network Outputs}}, with {{Relationships}} to {{Statistical Pattern Recognition}}},
  booktitle = {Neurocomputing},
  author = {Bridle, John S.},
  editor = {Soulié, Françoise Fogelman and Hérault, Jeanny},
  date = {1990},
  pages = {227--236},
  publisher = {Springer},
  location = {Berlin, Heidelberg},
  doi = {10.1007/978-3-642-76153-9_28},
  abstract = {We are concerned with feed-forward non-linear networks (multi-layer perceptrons, or MLPs) with multiple outputs. We wish to treat the outputs of the network as probabilities of alternatives (e.g. pattern classes), conditioned on the inputs. We look for appropriate output non-linearities and for appropriate criteria for adaptation of the parameters of the network (e.g. weights). We explain two modifications: probability scoring, which is an alternative to squared error minimisation, and a normalised exponential (softmax) multi-input generalisation of the logistic non-linearity. The two modifications together result in quite simple arithmetic, and hardware implementation is not difficult either. The use of radial units (squared distance instead of dot product) immediately before the softmax output stage produces a network which computes posterior distributions over class labels based on an assumption of Gaussian within-class distributions. However the training, which uses cross-class information, can result in better performance at class discrimination than the usual within-class training method, unless the within-class distribution assumptions are actually correct.},
  isbn = {978-3-642-76153-9},
  langid = {english},
  keywords = {Boltzmann Machine,Class Label,Hide Markov Model,Posterior Distribution,Statistical Pattern Recognition}
}

@article{ChoiEtAl_ExperimentalStudyOptimum_2017,
  title = {An {{Experimental Study}} on the {{Optimum Installation}} of {{Fire Detector}} for {{Early Stage Fire Detecting}} in {{Rack-Type Warehouses}}},
  author = {Choi, Ki Ok and Hong, Sung Ho and Kim, Dong Suck and Choi, Don Mook},
  date = {2017-03-01},
  doi = {10.5281/zenodo.1130004},
  url = {https://zenodo.org/records/1130004},
  urldate = {2025-11-27},
  abstract = {Rack type warehouses are different from general buildings in the kinds, amount, and arrangement of stored goods, so the fire risk of rack type warehouses is different from those buildings. The fire pattern of rack type warehouses is different in combustion characteristic and storing condition of stored goods. The initial fire burning rate is different in the surface condition of materials, but the running time of fire is closely related with the kinds of stored materials and stored conditions. The stored goods of the warehouse are consisted of diverse combustibles, combustible liquid, and so on. Fire detection time may be delayed because the residents are less than office and commercial buildings. If fire detectors installed in rack type warehouses are inadaptable, the fire of the warehouse may be the great fire because of delaying of fire detection. In this paper, we studied what kinds of fire detectors are optimized in early detecting of rack type warehouse fire by real-scale fire tests. The fire detectors used in the tests are rate of rise type, fixed type, photo electric type, and aspirating type detectors. We considered optimum fire detecting method in rack type warehouses suggested by the response characteristic and comparative analysis of the fire detectors.},
  langid = {english},
  keywords = {Fire detector,rack,response characteristic,warehouse.},
  file = {C:\Users\03765\Zotero\storage\I3WXBP2M\Choi et al. - 2017 - An Experimental Study on the Optimum Installation of Fire Detector for Early Stage Fire Detecting in.pdf}
}

@article{DengEtAl_IndoorFireDetection_2023,
  title = {An {{Indoor Fire Detection Method Based}} on {{Multi-Sensor Fusion}} and a {{Lightweight Convolutional Neural Network}}},
  author = {Deng, Xinwei and Shi, Xuewei and Wang, Haosen and Wang, Qianli and Bao, Jun and Chen, Zhuming},
  date = {2023-12-07},
  journaltitle = {Sensors (Basel)},
  volume = {23},
  number = {24},
  eprint = {38139534},
  eprinttype = {pubmed},
  pages = {9689},
  issn = {1424-8220},
  doi = {10.3390/s23249689},
  abstract = {Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky-Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1\%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.},
  langid = {english},
  pmcid = {PMC10747019},
  keywords = {embedded platform,fire numerical simulation,indoor fire detection,sensor data fusion,time-series imaging},
  file = {C:\Users\03765\Zotero\storage\TCZ96CZR\Deng et al. - 2023 - An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural.pdf}
}

@article{DengEtAl_LargeSpaceFireDetection_2025,
  title = {Large-{{Space Fire Detection Technology}}: {{A Review}} of {{Conventional Detector Limitations}} and {{Image-Based Target Detection Techniques}}},
  shorttitle = {Large-{{Space Fire Detection Technology}}},
  author = {Deng, Li and Wu, Siqi and Zou, Shuang and Liu, Quanyi},
  date = {2025-09},
  journaltitle = {Fire},
  volume = {8},
  number = {9},
  pages = {358},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2571-6255},
  doi = {10.3390/fire8090358},
  url = {https://www.mdpi.com/2571-6255/8/9/358},
  urldate = {2025-11-27},
  abstract = {With the rapid development of large-space buildings, their fire risk has become increasingly prominent. Conventional fire detection technologies are often limited by spatial height and environmental interference, leading to false alarms, missed detections, and delayed responses. This paper reviews 83 publications to analyze the limitations of conventional methods in large spaces and highlights the advantages of and current developments in image-based fire detection technology. It outlines key aspects such as equipment selection, dataset construction, and target recognition algorithm optimization, along with improvement directions including scenario-adaptive datasets, model enhancement, and adaptability refinement. Research demonstrates that image-based technology offers broad coverage, rapid response, and strong anti-interference capability, effectively compensating for the shortcomings of conventional methods and providing a new solution for early fire warning in large spaces. Finally, future prospects are discussed, focusing on environmental adaptability, algorithm efficiency and reliability, and system integration, offering valuable references for related research and applications.},
  langid = {english},
  keywords = {algorithm improvement,fire detection,image processing,large space},
  file = {C:\Users\03765\Zotero\storage\AUDRDKPT\Deng et al. - 2025 - Large-Space Fire Detection Technology A Review of Conventional Detector Limitations and Image-Based.pdf}
}

@article{DesikanEtAl_DempsterShaferEmpoweredMachine_2025,
  title = {Dempster {{Shafer-Empowered Machine Learning-Based Scheme}} for {{Reducing Fire Risks}} in {{IoT-Enabled Industrial Environments}}},
  author = {Desikan, Jayameena and Singh, Sushil Kumar and Jayanthiladevi, A. and Singh, Saurabh and Yoon, Byungun},
  date = {2025},
  journaltitle = {IEEE Access},
  volume = {13},
  pages = {46546--46567},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2025.3550413},
  url = {https://ieeexplore.ieee.org/document/10921730},
  urldate = {2025-11-27},
  abstract = {In high-risk IoT-enabled Industrial Environments such as oil and gas industries, early and accurate fire detection is crucial to prevent catastrophic damage. Traditional fire detection systems that rely on single IoT sensors, like heat or smoke detectors, are prone to inaccuracies and delayed responses, particularly in noisy or complex industrial environments. This research proposes an advanced fire prediction approach aiming to enhance decision-making accuracy with uncertain or incomplete fire sensor data in an edge computing IoT complex industrial environment that integrates multiple supervised machine learning algorithms for each sensor types and Dempster-Shafer theory (DST) with multi-sensor fusion. By combining data from multiple different types of sensors (RGB, thermal, gas, smoke, and flame), the proposed architecture enhances the reliability of fire prediction and detection as each sensor detects different parameters of a fire and this ensures every parameter is considered for fire detection ensuring early detection and it reduces false positives. By preprocessing sensor data and by combining machine learning algorithms (MobileNet, EfficientNet, Random Forest, SVM, and Decision Trees) where each ML processes different sensor data types, this supports providing more accurate data to DST. The D-S theory helps in handling uncertainty by fusing conflicting multi-sensor data to generate a more reliable decision. The results demonstrate that this multi-integrated methodology proposed significantly outperforms traditional methods in predicting and detecting fires early and accurately, even under challenging and complex environmental conditions of Oil and Gas Industries. The goal of this research contribute to the development of advanced fire prediction protocols offering a robust solution for managing and mitigating fire risks in dynamic, data-rich IoT environments. Predicting fire and reducing Fire in a critical oil and gas IoT industrial environment remains a critical challenge. The proposed method aims to enhance fire prediction and detection accuracy through algorithms developed using sensor preprocessing, machine learning, and Dempster Shafer fusion handling uncertain and varying multi-sensor data and demonstrates real-time, data-driven decision-making in critical industrial environments. The results show an accuracy of 98.2\%, a precision of 98.5\%, and a recall of 98.3\% significantly confirming the prediction over other models.},
  keywords = {Accuracy,Computational modeling,Convolutional neural networks,Dempster-Shafer,Gas detectors,Gas industry,industry environment,IoT,machine learning,Machine learning,Machine learning algorithms,Petroleum industry,sensor fusion,Sensor fusion,Sensors},
  file = {C:\Users\03765\Zotero\storage\ITH2HU26\Desikan et al. - 2025 - Dempster Shafer-Empowered Machine Learning-Based Scheme for Reducing Fire Risks in IoT-Enabled Indus.pdf}
}

@article{GragnanielloEtAl_FLAMEfiredetection_2025,
  title = {{{FLAME}}: Fire Detection in Videos Combining a Deep Neural Network with a Model-Based Motion Analysis},
  shorttitle = {{{FLAME}}},
  author = {Gragnaniello, Diego and Greco, Antonio and Sansone, Carlo and Vento, Bruno},
  date = {2025-03-01},
  journaltitle = {Neural Comput \& Applic},
  volume = {37},
  number = {8},
  pages = {6181--6197},
  issn = {1433-3058},
  doi = {10.1007/s00521-024-10963-z},
  url = {https://doi.org/10.1007/s00521-024-10963-z},
  urldate = {2025-11-24},
  abstract = {Among the catastrophic natural events posing hazards to human lives and infrastructures, fire is the phenomenon causing more frequent damages. Thanks to the spread of smart cameras, video fire detection is gaining more attention as a solution to monitor wide outdoor areas where no specific sensors for smoke detection are available. However, state-of-the-art fire detectors assure a satisfactory Recall but exhibit a high false-positive rate that renders the application practically unusable. In this paper, we propose FLAME, an efficient and adaptive classification framework to address fire detection from videos. The framework integrates a state-of-the-art deep neural network for frame-wise object detection, in an automatic video analysis tool. The advantages of our approach are twofold. On the one side, we exploit advances in image detector technology to ensure a high Recall. On the other side, we design a model-based motion analysis that improves the system’s Precision by filtering out fire candidates occurring in the scene’s background or whose movements differ from those of the fire. The proposed technique, able to be executed in real-time on embedded systems, has proven to surpass the methods considered for comparison on a recent literature dataset representing several scenarios. The code and the dataset used for designing the system have been made publicly available by the authors at (https://mivia.unisa.it/large-fire-dataset-with-negative-samples-lfdn/).},
  langid = {english},
  keywords = {Fire detection,Object detection,Object tracking,Video classification},
  file = {C:\Users\03765\Zotero\storage\Y8YRXZVT\Gragnaniello et al. - 2025 - FLAME fire detection in videos combining a deep neural network with a model-based motion analysis.pdf}
}

@inproceedings{HeEtAl_DeepResidualLearning_2016,
  title = {Deep {{Residual Learning}} for {{Image Recognition}}},
  booktitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  date = {2016-06},
  pages = {770--778},
  issn = {1063-6919},
  doi = {10.1109/CVPR.2016.90},
  url = {https://ieeexplore.ieee.org/document/7780459/},
  urldate = {2025-11-28},
  abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC \& COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.},
  eventtitle = {2016 {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  keywords = {Complexity theory,Degradation,Image recognition,Image segmentation,Neural networks,Training,Visualization},
  file = {C:\Users\03765\Zotero\storage\XF6LNFLD\He et al. - 2016 - Deep Residual Learning for Image Recognition.pdf}
}

@inproceedings{KergEtAl_Untanglingtradeoffsrecurrence_2020,
  title = {Untangling Tradeoffs between Recurrence and Self-Attention in Artificial Neural Networks},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Kerg, Giancarlo and Kanuparthi, Bhargav and ALIAS PARTH GOYAL, Anirudh Goyal and Goyette, Kyle and Bengio, Yoshua and Lajoie, Guillaume},
  date = {2020},
  volume = {33},
  pages = {19443--19454},
  publisher = {Curran Associates, Inc.},
  url = {https://proceedings.neurips.cc/paper_files/paper/2020/hash/e2065cb56f5533494522c46a72f1dfb0-Abstract.html},
  urldate = {2025-11-28},
  abstract = {Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role in model optimization and computation, and rely on considerable memory and computational resources that scale poorly. In this work, we present a formal analysis of how self-attention affects gradient propagation in recurrent networks, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies by establishing concrete bounds for gradient norms. Building on these results, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. While providing guarantees to avoid vanishing gradients, we use simple numerical experiments to demonstrate the tradeoffs in performance and computational resources by efficiently balancing attention and recurrence. Based on our results, we propose a concrete direction of research to improve scalability of attentive networks.},
  file = {C:\Users\03765\Zotero\storage\H5WEZGB8\Kerg et al. - 2020 - Untangling tradeoffs between recurrence and self-attention in artificial neural networks.pdf}
}

@article{KhanEtAl_RecentAdvancesSensors_2022,
  title = {Recent {{Advances}} in {{Sensors}} for {{Fire Detection}}},
  author = {Khan, Fawad and Xu, Zhiguang and Sun, Junling and Khan, Fazal Maula and Ahmed, Adnan and Zhao, Yan},
  date = {2022-01},
  journaltitle = {Sensors},
  volume = {22},
  number = {9},
  pages = {3310},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {1424-8220},
  doi = {10.3390/s22093310},
  url = {https://www.mdpi.com/1424-8220/22/9/3310},
  urldate = {2025-11-27},
  abstract = {Fire is indeed one of the major contributing factors to fatalities, property damage, and economic disruption. A large number of fire incidents across the world cause devastation beyond measure and description every year. To minimalize their impacts, the implementation of innovative and effective fire early warning technologies is essential. Despite the fact that research publications on fire detection technology have addressed the issue to some extent, fire detection technology still confronts hurdles in decreasing false alerts, improving sensitivity and dynamic responsibility, and providing protection for costly and complicated installations. In this review, we aim to provide a comprehensive analysis of the current futuristic practices in the context of fire detection and monitoring strategies, with an emphasis on the methods of detecting fire through the continuous monitoring of variables, such as temperature, flame, gaseous content, and smoke, along with their respective benefits and drawbacks, measuring standards, and parameter measurement spans. Current research directions and challenges related to the technology of fire detection and future perspectives on fabricating advanced fire sensors are also provided. We hope such a review can provide inspiration for fire sensor research dedicated to the development of advanced fire detection techniques.},
  langid = {english},
  keywords = {fire detection,flame,gas,heat,sensor,smoke},
  file = {C:\Users\03765\Zotero\storage\64JWIU8A\Khan et al. - 2022 - Recent Advances in Sensors for Fire Detection.pdf}
}

@article{KimEtAl_EarlyFireDetection_2024,
  title = {Early {{Fire Detection System}} by {{Using Automatic Synthetic Dataset Generation Model Based}} on {{Digital Twins}}},
  author = {Kim, Hyeon-Cheol and Lam, Hoang-Khanh and Lee, Suk-Hwan and Ok, Soo-Yol},
  date = {2024-01},
  journaltitle = {Applied Sciences},
  volume = {14},
  number = {5},
  pages = {1801},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2076-3417},
  doi = {10.3390/app14051801},
  url = {https://www.mdpi.com/2076-3417/14/5/1801},
  urldate = {2025-11-27},
  abstract = {Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space.},
  langid = {english},
  keywords = {digital twin smart city,early fire detection,object detection,particle system,synthetic learning data},
  file = {C:\Users\03765\Zotero\storage\IPECPBTF\Kim et al. - 2024 - Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital T.pdf}
}

@article{KimEtAl_RealTimeFireClassification_2024,
  title = {Real-{{Time Fire Classification Models Based}} on {{Deep Learning}} for {{Building}} an {{Intelligent Multi-Sensor System}}},
  author = {Kim, Youngchan and Heo, Yoseob and Jin, Byoungsam and Bae, Youngchul},
  date = {2024-09},
  journaltitle = {Fire},
  volume = {7},
  number = {9},
  pages = {329},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2571-6255},
  doi = {10.3390/fire7090329},
  url = {https://www.mdpi.com/2571-6255/7/9/329},
  urldate = {2025-11-27},
  abstract = {Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.},
  langid = {english},
  keywords = {deep learning,fire detection algorithms,fire hazards,fire sensing system,real-time fire detection,time-series classification},
  file = {C:\Users\03765\Zotero\storage\IXXFGIFN\Kim et al. - 2024 - Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor.pdf}
}

@article{LiEtAl_RealTimeEarlyIndoor_2023,
  title = {Real-{{Time Early Indoor Fire Detection}} and {{Localization}} on {{Embedded Platforms}} with {{Fully Convolutional One-Stage Object Detection}}},
  author = {Li, Yimang and Shang, Jingwei and Yan, Meng and Ding, Bei and Zhong, Jiacheng},
  date = {2023-01},
  journaltitle = {Sustainability},
  volume = {15},
  number = {3},
  pages = {1794},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2071-1050},
  doi = {10.3390/su15031794},
  url = {https://www.mdpi.com/2071-1050/15/3/1794},
  urldate = {2025-11-27},
  abstract = {Fire disasters usually cause significant damage to human lives and property. Thus, early fire detection and localization in real time are crucial in minimizing fire disasters and reducing ecological losses. Studies of convolution neural networks (CNNs) show their capabilities in image processing tasks such as image classification, visual recognition, and object detection. Using CNNs for fire detection could improve detection accuracy. However, the high computational cost of CNNs requires an extensive training model size, making it difficult to deploy to resource-constrained edge devices. Moreover, the large size of the training model is challenging for real-time object detection. This paper develops a real-time early indoor fire-detection and -localization system that could be deployed on embedded platforms such as Jetson Nano. First, we propose a fully convolutional one-stage object detection framework for fire detection with real-time surveillance videos. The combination of backbone, path aggregation network, and detection head with generalized focal loss is used in the framework. We evaluate several networks as backbones and select the one with balanced efficiency and accuracy. Then we develop a fire localization strategy to locate the fire with two cameras in the indoor setting. Results show that the proposed architecture can achieve similar accuracy compared with the Yolo framework but using one-tenth of the model size. Moreover, the localization accuracy could be achieved within 0.7 m.},
  langid = {english},
  keywords = {convolutional neural networks,embedded systems,fire detection},
  file = {C:\Users\03765\Zotero\storage\SVZ3MFXB\Li et al. - 2023 - Real-Time Early Indoor Fire Detection and Localization on Embedded Platforms with Fully Convolutiona.pdf}
}

@article{NazirEtAl_EarlyFireDetection_2022,
  title = {Early {{Fire Detection}}: {{A New Indoor Laboratory Dataset}} and {{Data Distribution Analysis}}},
  shorttitle = {Early {{Fire Detection}}},
  author = {Nazir, Amril and Mosleh, Husam and Takruri, Maen and Jallad, Abdul-Halim and Alhebsi, Hamad},
  date = {2022-02},
  journaltitle = {Fire},
  volume = {5},
  number = {1},
  pages = {11},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2571-6255},
  doi = {10.3390/fire5010011},
  url = {https://www.mdpi.com/2571-6255/5/1/11},
  urldate = {2025-11-27},
  abstract = {Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal on paperboard, cardboard or clothing. Important data such as humidity, temperature, MQ139, Total Volatile Organic Compounds (TVOC) and eCO2 were collected using sensor devices. These datasets will be extremely valuable to researchers in the machine learning and data science communities interested in pursuing novel advanced statistical and machine learning techniques and methods for developing early fire detection systems. The analysis of the collected data demonstrates the possibility of using eCO2 and TVOC reading levels for early detection of smoldering fires. The experimental setup was based on Low-Power Wireless Area Networks (LPWAN), which can be used to reliably deliver fire-related data over long ranges without depending on the status of a cellular or WiFi Network.},
  langid = {english},
  keywords = {early fire detection,indoor fire analysis,machine learning,non-image fire analysis,realistic indoor experiments},
  file = {C:\Users\03765\Zotero\storage\GSCCNZSR\Nazir et al. - 2022 - Early Fire Detection A New Indoor Laboratory Dataset and Data Distribution Analysis.pdf}
}

@inproceedings{PascanuEtAl_difficultytrainingrecurrent_2013,
  title = {On the Difficulty of Training Recurrent Neural Networks},
  booktitle = {Proceedings of the 30th {{International Conference}} on {{Machine Learning}}},
  author = {Pascanu, Razvan and Mikolov, Tomas and Bengio, Yoshua},
  date = {2013-05-26},
  pages = {1310--1318},
  publisher = {PMLR},
  issn = {1938-7228},
  url = {https://proceedings.mlr.press/v28/pascanu13.html},
  urldate = {2025-11-28},
  abstract = {There are two widely known issues with properly training recurrent neural networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section.},
  eventtitle = {International {{Conference}} on {{Machine Learning}}},
  langid = {english},
  file = {C:\Users\03765\Zotero\storage\NZ2GZTY3\Pascanu et al. - 2013 - On the difficulty of training recurrent neural networks.pdf}
}

@article{QianEtAl_OmniDimensionalDynamicConvolution_2023,
  title = {Omni-{{Dimensional Dynamic Convolution Meets Bottleneck Transformer}}: {{A Novel Improved High Accuracy Forest Fire Smoke Detection Model}}},
  shorttitle = {Omni-{{Dimensional Dynamic Convolution Meets Bottleneck Transformer}}},
  author = {Qian, Jingjing and Lin, Ji and Bai, Di and Xu, Renjie and Lin, Haifeng},
  date = {2023-04},
  journaltitle = {Forests},
  volume = {14},
  number = {4},
  pages = {838},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {1999-4907},
  doi = {10.3390/f14040838},
  url = {https://www.mdpi.com/1999-4907/14/4/838},
  urldate = {2025-11-27},
  abstract = {The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage of a wide diffusion range that is not easily obscured. Therefore, timely detection of forest fire smoke with better real-time detection for early warnings of forest fires wins valuable time for timely firefighting and also has great significance and applications for the development of forest fire detection systems. However, existing forest fire smoke detection methods still have problems, such as low detection accuracy, slow detection speed, and difficulty detecting smoke from small targets. In order to solve the aforementioned problems and further achieve higher accuracy in detection, this paper proposes an improved, new, high-accuracy forest fire detection model, the OBDS. Firstly, to address the problem of insufficient extraction of effective features of forest fire smoke in complex forest environments, this paper introduces the SimAM attention mechanism, which makes the model pay more attention to the feature information of forest fire smoke and suppresses the interference of non-targeted background information. Moreover, this paper introduces Omni-Dimensional Dynamic Convolution instead of static convolution and adaptively and dynamically adjusts the weights of the convolution kernel, which enables the network to better extract the key features of forest fire smoke of different shapes and sizes. In addition, to address the problem that traditional convolutional neural networks are not capable of capturing global forest fire smoke feature information, this paper introduces the Bottleneck Transformer Net (BoTNet) to fully extract global feature information and local feature information of forest fire smoke images while improving the accuracy of small target forest fire target detection of smoke, effectively reducing the model’s computation, and improving the detection speed of model forest fire smoke. Finally, this paper introduces the decoupling head to further improve the detection accuracy of forest fire smoke and speed up the convergence of the model. Our experimental results show that the model OBDS for forest fire smoke detection proposed in this paper is significantly better than the mainstream model, with a computational complexity of 21.5 GFLOPs (giga floating-point operations per second), an improvement of 4.31\% compared with the YOLOv5 (YOLO, you only look once) model mAP@0.5, reaching 92.10\%, and an FPS (frames per second) of 54, which is conducive to the realization of early warning of forest fires.},
  langid = {english},
  keywords = {BoTNet,forest fire smoke detection,ODConv,SimAM,YOLOv5},
  file = {C:\Users\03765\Zotero\storage\6W4YF93X\Qian et al. - 2023 - Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer A Novel Improved High Accuracy Fo.pdf}
}

@article{RahmanEtAl_EconomicCostsResidential_2023,
  title = {Economic {{Costs}} of {{Residential Fires}}: {{A Systematic Review}}},
  shorttitle = {Economic {{Costs}} of {{Residential Fires}}},
  author = {Rahman, Fahmida Saadia and Tannous, Wadad Kathy and Avsar, Gulay and Agho, Kingsley Emwinyore and Ghassempour, Nargess and Harvey, Lara A.},
  date = {2023-10-18},
  journaltitle = {Fire},
  volume = {6},
  number = {10},
  pages = {399},
  issn = {2571-6255},
  doi = {10.3390/fire6100399},
  url = {https://www.mdpi.com/2571-6255/6/10/399},
  urldate = {2025-11-25},
  abstract = {Globally, most fire-related deaths and injuries occur in residential areas. The aim of this systematic review is to report on the economic costs of residential fires from a societal perspective. Five databases (MEDLINE, EMBASE, EconLit, CINAHL, and Scopus) and grey literature were searched to identify studies that report economic or societal costs of residential fires with data from 1978 to 2021. There were no restrictions on study design. A narrative synthesis was undertaken based on the societal and economic costs reported for each included study. Seven studies from the United States, Canada, Australia, and Kuwait reported costs of residential fires. The costs of injuries and deaths were between USD 12 million and USD 5 billion, and between USD 75 million and USD 26 billion, respectively. The costs of treatment ranged from USD 0.3 million to USD 551 million, lost productivity from USD 12 million to USD 4 billion, and property damage from USD 8 million to USD 10 billion. This systematic review provides the most comprehensive evidence to date on the economic costs of residential fires. This study would offer insights into the effects of residential fires on diverse economic agents and aid in community fire prevention messaging and incentives.},
  langid = {english}
}

@article{RahmanEtAl_Realtimesmokefire_2024,
  title = {Real-Time Smoke and Fire Detection Using You Only Look Once v8-Based Advanced Computer Vision and Deep Learning},
  author = {Rahman, Shakila and Jamee, Syed muhammad Hasnat and Rafi, Jakaria Khan and Juthi, Jafrin Sultana and Sajib, Abdul Aziz and Uddin, Jia},
  date = {2024-12-01},
  journaltitle = {International Journal of Advances in Applied Sciences},
  volume = {13},
  number = {4},
  pages = {987--999},
  issn = {2722-2594},
  doi = {10.11591/ijaas.v13.i4.pp987-999},
  url = {https://ijaas.iaescore.com/index.php/IJAAS/article/view/21220},
  urldate = {2025-11-27},
  abstract = {Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperforming some baseline models- YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2 in the precision, recall, and mean average precision (mAP) metrics. The proposed method achieves 68.3\% precision, 54.6\% recall, 60.7\% F1 score, and 57.3\% mAP. Integrating YOLOv8 in fire and smoke detection systems can significantly improve response times, enhance the ability to mitigate fire outbreaks, and potentially save lives and property. This research advances fire detection systems and establishes a precedent for applying deep learning techniques to critical safety applications, pushing the boundaries of innovation in public safety.},
  langid = {english},
  file = {C:\Users\03765\Zotero\storage\JRGIVCNE\Rahman et al. - 2024 - Real-time smoke and fire detection using you only look once v8-based advanced computer vision and de.pdf}
}

@article{RajendranChenniappan_comprehensivesurveyseveral_2024,
  title = {A Comprehensive Survey on Several Fire Management Approaches in Wireless Sensor Networks},
  author = {Rajendran, Swetha and Chenniappan, Navaneethan},
  date = {2024-04-01},
  journaltitle = {Bulletin of Electrical Engineering and Informatics},
  volume = {13},
  number = {2},
  pages = {947--954},
  issn = {2302-9285},
  doi = {10.11591/eei.v13i2.5833},
  url = {https://beei.org/index.php/EEI/article/view/5833},
  urldate = {2025-11-27},
  abstract = {The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.},
  langid = {english},
  keywords = {Clustering,Data aggregation,Fire management,Malicious cognitive radio,Security,Wireless sensor networks},
  file = {C:\Users\03765\Zotero\storage\EEGCKDWV\Rajendran und Chenniappan - 2024 - A comprehensive survey on several fire management approaches in wireless sensor networks.pdf}
}

@article{RichardsonEtAl_hiddencostwildfires_2012,
  title = {The Hidden Cost of Wildfires: {{Economic}} Valuation of Health Effects of Wildfire Smoke Exposure in {{Southern California}}},
  shorttitle = {The Hidden Cost of Wildfires},
  author = {Richardson, Leslie A. and Champ, Patricia A. and Loomis, John B.},
  date = {2012-01},
  journaltitle = {JFE},
  volume = {18},
  number = {1},
  pages = {14--35},
  issn = {11046899},
  doi = {10.1016/j.jfe.2011.05.002},
  url = {https://www.nowpublishers.com/article/Details/JFE-0192},
  urldate = {2025-11-25},
  langid = {english}
}

@article{RoyEtAl_L2regularizeddeep_2022,
  title = {L2 Regularized Deep Convolutional Neural Networks for Fire Detection},
  author = {Roy, Sanjiban Sekhar and Goti, Vatsal and Sood, Aditya and Roy, Harsh and Gavrila, Tania and Floroian, Dan and Paraschiv, Nicolae and Mohammadi-Ivatloo, Behnam},
  date = {2022-06-09},
  journaltitle = {Journal of Intelligent \& Fuzzy Systems},
  volume = {43},
  number = {2},
  pages = {1799--1810},
  publisher = {SAGE Publications},
  issn = {1064-1246},
  doi = {10.3233/JIFS-219281},
  url = {https://doi.org/10.3233/JIFS-219281},
  urldate = {2025-11-27},
  abstract = {Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87\% of train accuracy, 71\% of validation accuracy and 70\% of test accuracy after running 10 epochs.},
  langid = {english},
  file = {C:\Users\03765\Zotero\storage\F34IJKBL\Roy et al. - 2022 - L2 regularized deep convolutional neural networks for fire detection.pdf}
}

@article{RumelhartEtAl_Learningrepresentationsbackpropagating_1986,
  title = {Learning Representations by Back-Propagating Errors},
  author = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
  date = {1986-10},
  journaltitle = {Nature},
  volume = {323},
  number = {6088},
  pages = {533--536},
  issn = {1476-4687},
  doi = {10.1038/323533a0},
  url = {https://www.nature.com/articles/323533a0},
  urldate = {2025-11-26},
  abstract = {We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.},
  langid = {english},
  keywords = {Humanities and Social Sciences,multidisciplinary,Science}
}

@article{SharmaKanwal_Intelligentvideobasedfire_2025,
  title = {Intelligent Video-Based Fire Detection: {{A}} Novel Dataset and Real-Time Multi-Stage Classification Approach},
  shorttitle = {Intelligent Video-Based Fire Detection},
  author = {Sharma, Himani and Kanwal, Navdeep},
  date = {2025-05-01},
  journaltitle = {Expert Systems with Applications},
  volume = {271},
  pages = {126655},
  issn = {0957-4174},
  doi = {10.1016/j.eswa.2025.126655},
  url = {https://www.sciencedirect.com/science/article/pii/S0957417425002775},
  urldate = {2025-11-24},
  abstract = {Fire incidents pose significant threats to life and property, necessitating the development of reliable and efficient fire detection systems. Real-time fire flame identification in camera video sequences may provide early warning to guarantee a quick response to potentially disastrous fire threats. Numerous computer vision-based fire detection techniques have been proposed that may produce high detection rates but sometimes at excessive false alarms. Moreover, the existing approaches mainly struggle with inefficiency in real-time detection, limited adaptability to diverse scenarios, and lack of comprehensive datasets. To address these issues, the present research proposes an intelligent video based fire detection approach and a novel dataset, designed to capture diverse fire scenarios. The proposed approach utilizes different feature extraction techniques along with MobileNet-V2 that outperforms existing techniques. Experimental results demonstrate the superior performance, achieving an accuracy of 99.13\% and effectively localizing and classifying fire stages while having lower computational complexity. Although, the proposed technique is validated using standard dataset videos; however, verifying the effectiveness of research techniques become challenging because of the scarcity of video based fire datasets for evaluation. To address this challenge, a new Video Library for Fire Frame Detection (VLFFD) dataset has also made available by this research paper. The proposed dataset includes 100 native videos in Full-HD and Ultra-HD resolution and is acquired with different cameras. Each video has a duration of 3 to 18 s and is recorded at 30 frames per second. Finally, the paper presents comparative analysis to highlight the significant contributions of the proposed technique and dataset, establishing them as valuable resources for advancing fire detection research and practical applications in smart surveillance.},
  keywords = {Computer vision,Deep learning,Flame stage classification,MobileNet-V2,Video based fire detection,VLFFD dataset},
  file = {C\:\\Users\\03765\\Zotero\\storage\\4IYUIFEP\\Sharma und Kanwal - 2025 - Intelligent video-based fire detection A novel dataset and real-time multi-stage classification app.pdf;C\:\\Users\\03765\\Zotero\\storage\\BHJPNPIN\\S0957417425002775.html}
}

@article{SunEtAl_forestfiredetection_2024,
  title = {A Forest Fire Detection Method Based on Improved {{YOLOv5}}},
  author = {Sun, Zukai and Xu, Ruzhi and Zheng, Xiangwei and Zhang, Lifeng and Zhang, Yuang},
  date = {2024-12-16},
  journaltitle = {SIViP},
  volume = {19},
  number = {2},
  pages = {136},
  issn = {1863-1711},
  doi = {10.1007/s11760-024-03680-6},
  url = {https://doi.org/10.1007/s11760-024-03680-6},
  urldate = {2025-11-24},
  abstract = {With the continuous intensification of global climate change, forest fires have become a significant threat to natural ecosystems and human society. The automatic fire detection system plays a crucial role in the early discovery of forest fires. Current YOLOv5-based fire detection methods encounter several significant challenges: low accuracy and high miss rates in complex backgrounds, inefficiency in real-time applications, and difficulty in detecting small targets, particularly in the early stages of a fire. To address these issues, we propose a forest fire detection method based on improved YOLOv5, aimed at achieving efficient real-time monitoring in resource-constrained environments. First, we add the Convolutional Block Attention Module to improve channel and spatial attention, enhancing the detection of small fire features essential for early detection. Next, we integrate a small target detection layer and the Ghost module into YOLOv5. The small target layer boosts sensitivity to small fire areas, while the Ghost module reduces computational load and parameters, improving feature extraction without sacrificing performance. Finally, we use the SIOU loss function to accelerate model convergence, enhancing overall detection efficiency and precision. Experimental results show that the proposed method achieves an mAP of 88.3\% on the Yang et al. dataset, which improves the mAP by 0.9\% compared to other YOLOv5-based methods on the same dataset. Model parameter size decreased by 2.8\%. On our forest fire detection dataset, the proposed method achieves an mAP of 79.1\%. Compared to the YOLOv5s model, this represents a 3.7\% improvement in mAP. Model parameter size decreased by 2.3\%.},
  langid = {english},
  keywords = {CBAM,Fire detection,Ghost,SIOU,Small target detection,YOLOv5s},
  file = {C:\Users\03765\Zotero\storage\7PADMCUW\Sun et al. - 2024 - A forest fire detection method based on improved YOLOv5.pdf}
}

@article{SungEtAl_ConstructingLightweightFire_2025,
  title = {Constructing a {{Lightweight Fire}} and {{Smoke Detection Through}} the {{Improved GhostNet Architecture}} and {{Attention Module Mechanism}}},
  author = {Sung, Wen-Tsai and Griha Tofik Isa, Indra and Tseng, Ching-Hsiang and Hsiao, Sung-Jung},
  date = {2025},
  journaltitle = {IEEE Access},
  volume = {13},
  pages = {46794--46809},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2025.3550539},
  url = {https://ieeexplore.ieee.org/document/10924243},
  urldate = {2025-11-27},
  abstract = {Fire disasters have a serious impact on human life and property. Fire detection methods have been developed as early detection in preventing fire disasters, including implementing object detection models. However, the challenges in fire detection are random object shapes and complex backgrounds, which cause the model to be unable to detect fire optimally. In addition, to support practical application, the model must be efficient in memory-consuming. To address these issues, this study proposed FSP-YOLO as a fire detection algorithm that integrates the YOLOv8 model with the improved GhostNet (iGhostNet) and the attention module that can detect three classes, including fire, smoke, and people. The improvement of iGhostNet is implementing coordinate attention module and trained integrated learning, which has an impact on increasing accuracy and reducing computational costs. With this improvement, the proposed fire detection model could gain outstanding performance, reducing computational costs, and a lightweight architecture. Furthermore, to validate the performance of FSP-YOLO, several comparative experiments have been conducted, including single and multiple module experiments, evaluation under different YOLOv8, experiments with other object detection algorithms, and experiments in practical application. From these experiments, FSP-YOLO represents the optimal performance, which results in mAP0.5 of 0.977, precision of 0.952, and recall of 0.973. Based on weight or model size, compared to the other fire detection algorithms such as YOLOFM, FireRPG, Fine Tuned YOLOv8, Improved YOLOv8, YOLOv7-Fire, YOLOv9, and Improved-YOLOv7, FSP-YOLO has a lighter size of 53.18 MB. Therefore, FSP-YOLO is reliable in accuracy but reduces the computational cost.},
  keywords = {Accuracy,Adaptation models,Attention module mechanism,Biological system modeling,Computational efficiency,Computational modeling,Deep learning,Disasters,Edge computing,Feature extraction,fire-smoke detection,lightweight YOLOv8,Object detection,the improved GhostNet},
  file = {C:\Users\03765\Zotero\storage\E6I8ZZPI\Sung et al. - 2025 - Constructing a Lightweight Fire and Smoke Detection Through the Improved GhostNet Architecture and A.pdf}
}

@article{SungTsai_Datafusionmultisensor_2012,
  title = {Data Fusion of Multi-Sensor for {{IOT}} Precise Measurement Based on Improved {{PSO}} Algorithms},
  author = {Sung, Wen-Tsai and Tsai, Ming-Han},
  date = {2012-09-01},
  journaltitle = {Computers \& Mathematics with Applications},
  series = {Advanced {{Technologies}} in {{Computer}}, {{Consumer}} and {{Control}}},
  volume = {64},
  number = {5},
  pages = {1450--1461},
  issn = {0898-1221},
  doi = {10.1016/j.camwa.2012.03.092},
  url = {https://www.sciencedirect.com/science/article/pii/S0898122112002970},
  urldate = {2025-11-27},
  abstract = {This work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications.},
  keywords = {Data fusion,Internet of things,Particle swarm optimization,RFID,Wireless sensor network},
  file = {C:\Users\03765\Zotero\storage\TYA6ZER2\S0898122112002970.html}
}

@inproceedings{VaswaniEtAl_Attentionallyou_2017,
  title = {Attention Is All You Need},
  booktitle = {Proceedings of the 31st {{International Conference}} on {{Neural Information Processing Systems}}},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Łukasz and Polosukhin, Illia},
  date = {2017-12-04},
  series = {{{NIPS}}'17},
  pages = {6000--6010},
  publisher = {Curran Associates Inc.},
  location = {Red Hook, NY, USA},
  abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.},
  isbn = {978-1-5108-6096-4},
  file = {C:\Users\03765\Zotero\storage\HFJ4MYHC\Vaswani et al. - 2017 - Attention is all you need.pdf}
}

@article{VenkataiahEtAl_IOTBasedFire_2023,
  title = {{{IOT Based Fire Detection}} and {{Surveillance System}}},
  author = {Venkataiah, C. and Sireesha, R. and Raju, K. and Rao, Y. Mallikarjuna and Jayamma, Manjula},
  date = {2023},
  pages = {19--26},
  doi = {10.5281/zenodo.10089553},
  url = {https://zenodo.org/records/10089553},
  urldate = {2025-11-27},
  abstract = {The prevention of property loss due to fire accidents, both unintentional and intentional, requires the early detection of fire in homes. The ability to detect a fire can be crucial since it could be the difference between life and death. Because fires can start anywhere and at any moment, having a fire alarm system helps to keep your family safe. Some people do not believe that a fire alarm system is necessary. They simply think that they will be able to smell the fire and escape quickly. Nowadays, a house burns down in under 60 seconds on average. So, by the time you smell the fire and try to flee, the house has probably already been completely consumed by the fire. A network of connected and reachable objects is known as the Internet of Things (IoT). Any actual object, from a toaster to an automobile, can be referred to as a "Thing" in the Internet of Things. We can alter or gather data from these gadgets by connecting them to the internet. In this paper, we will employ a wide range of sensors to locate a fire and notify the watchman and emergency personnel of its location. It goes into great detail regarding the roles played by each module and how elaborately they are implemented. The use of IoT technology in relation to fire detection systems is also covered.},
  langid = {english},
  file = {C:\Users\03765\Zotero\storage\S6QZ2KYB\Venkataiah et al. - IOT Based Fire Detection and Surveillance System.pdf}
}

@dataset{Vorwerk_IndoorFireDataset_2024,
  title = {Indoor {{Fire Dataset}} with {{Distributed Multi-Sensor Nodes}} ({{Industrial Hall}})},
  author = {Vorwerk, Pascal},
  namea = {{Otto Von Guericke Universitat Magdeburg}},
  nameatype = {collaborator},
  date = {2024-09-16T06:35:40Z},
  publisher = {Mendeley Data},
  doi = {10.17632/YGHYKZM4KM.1},
  url = {https://data.mendeley.com/datasets/yghykzm4km/1},
  urldate = {2025-11-26},
  abstract = {The dataset contains 4 incipient fire scenarios (wood, candles, cable, lunts) along with 8 different nuisance scenarios (smoke from a fog machine, deodorant, ethanol, CO release, exhaust gases, green waste, welding, and abrasive cutting) carried out in a (10 x 22 x 8)m\textasciicircum 3 industrial hall without ventilation. Each scenario was repeated 3 times in random with background sequences in between to reduce the influence of prehistory. The dataset consists of 248,502 rows and 18 columns and is structures as a continuous multivariate time series. Each row represents the sensor measurements (CO2, CO, H2, humidity, particulate matter of different sizes, air temperature, VOC and UV) from a unique sensor node position ("Sensor\_ID") in the industrial hall at a specific timestamp. The columns correspond to the sensor measurements and include additional labels: a scenario-specific label ("scenario\_label"), a binary label ("anomaly\_label") distinguishing between "Normal" (background) and "Anomaly" (fire or nuisance scenario), a "fire\_label" intersecting the anomalies into fire-relevant or non-fire-relevant anomalies and a progress label ("progress\_label") that allows for dividing the events into sub-sequences based on ongoing physical sub-processes. The "Sensor\_ID" column can be utilized to access data from different sensor node positions.},
  keywords = {Application of Sensors,Data Fusion Multiple Sensor,Data Mining,Fire Detection,Machine Learning,Multivariate Analysis,Stream,Time Series,Time Series Prediction}
}

@article{WangEtAl_ExperimentalStudyEarly_2024,
  title = {Experimental {{Study}} on {{Early Fire Smoke Characteristics}} in a {{High-Volume Space}}: {{A Fire Detection Perspective}}},
  shorttitle = {Experimental {{Study}} on {{Early Fire Smoke Characteristics}} in a {{High-Volume Space}}},
  author = {Wang, Li and Zhang, Xi and Li, Liming and Li, Boning and Mei, Zhibin},
  date = {2024-09},
  journaltitle = {Fire},
  volume = {7},
  number = {9},
  pages = {298},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2571-6255},
  doi = {10.3390/fire7090298},
  url = {https://www.mdpi.com/2571-6255/7/9/298},
  urldate = {2025-11-27},
  abstract = {High-volume space structures are characterized by high combustible loads, rapid fire development, difficulty in firefighting, and potential building collapse risks, making early fire detection particularly crucial. The effectiveness of early fire detection technologies relies on their ability to adapt to the characteristics of smoke-dominant combustion products in the protected space. However, there is a lack of targeted research on the characteristics of the smoke generated during the early low-power stages of fires in high-volume spaces, which has not supported the development of early fire detection technologies for such environments. To address this, this paper presents an experimental study that, for the first time, collects data on fire smoke parameters such as temperature, velocity, concentration, and particle size at heights ranging from 6.5 m to 18.5 m using lightweight sensors. The study analyzes the characteristic and correlations of these parameters and their impact on early fire detection in high-volume spaces for the first time, presenting variation patterns in the plume velocity and particle size distribution of early fire smoke with height. It identifies three patterns of particle size distribution, contrasting with previous studies, and offers a qualitative explanation for these findings. This research enhances the understanding of early fire smoke signals in large spaces and offers valuable insights for developing more accurate and efficient fire detection strategies and technologies.},
  langid = {english},
  keywords = {early fire,experimental study,high-volume space,particle size distribution,plume velocity,smoke characteristics},
  file = {C:\Users\03765\Zotero\storage\52IXPJAB\Wang et al. - 2024 - Experimental Study on Early Fire Smoke Characteristics in a High-Volume Space A Fire Detection Pers.pdf}
}

@article{WangEtAl_openflamesmoke_2025,
  title = {An Open Flame and Smoke Detection Dataset for Deep Learning in Remote Sensing Based Fire Detection},
  author = {Wang, Ming and Yue, Peng and Jiang, Liangcun and Yu, Dayu and Tuo, Tianyu and Li, Jian},
  date = {2025-03-04},
  journaltitle = {Geo-spatial Information Science},
  volume = {28},
  number = {2},
  pages = {511--526},
  publisher = {Taylor \& Francis},
  issn = {1009-5020},
  doi = {10.1080/10095020.2024.2347922},
  url = {https://doi.org/10.1080/10095020.2024.2347922},
  urldate = {2025-11-24},
  abstract = {The early warning of fires is pivotal for preventing substantial economic losses and ecological damage. However, the enhancement of fire model generalization performance still faces challenges due to limitations in existing fire datasets, such as image quantity and heterogeneity. Taking the advantage of rapid advancements in remote sensing technology and artificial intelligence, we introduce the Flame And Smoke Detection Dataset (FASDD), a pioneering collection comprising over 120,000 heterogeneous images covering various fire scenarios, aiming to promote the evolution of fire detection models. FASDD serves as a challenging benchmark for object detection tasks and features three sub-datasets: FASDD\_CV, FASDD\_UAV, and FASDD\_RS. These sub-datasets comprise cross-domain images from ground-based, airborne, and spaceborne sensors, respectively. Extensive experiments using advanced Swin Transformer models demonstrate satisfactory fire detection performance, with mAP scores of 84.9\%, 89.7\%, and 74.0\% on the respective sub-datasets. Notably, the application case in wildfire localization highlights the proficiency of FASDD\_RS models for monitoring and tracking forest fires. Trained on FASDD\_RS, FASDD\_UAV, and FASDD\_CV, deep learning models can be individually deployed on edge devices including satellites, drones, and terrestrial sensors, thus empowering collaborative fire warning within the space-air-ground integrated observation network. Additionally, a standardized procedure for data preparation, data procedure, and quality assurance is established, providing off-the-shelf annotations with four common configurations to facilitate machine learning model training. FASDD stands as a pivotal benchmark, providing valuable resources for training and evaluating deep learning fire detection models, especially Large Vision Models (LVM). It holds the potential to accelerate research progress in urban firefighting or forest fire detection, providing vital security measures for early fire warning and emergency response tasks.},
  keywords = {Active fire detection,fire benchmark dataset,object detection,remote sensing,space-air-ground integration},
  file = {C:\Users\03765\Zotero\storage\NZ6EGNZ2\Wang et al. - 2025 - An open flame and smoke detection dataset for deep learning in remote sensing based fire detection.pdf}
}

@book{WorldHealthOrganization_Burnpreventionsuccess_2011,
  title = {Burn prevention: success stories lessons learned},
  shorttitle = {Burn prevention},
  author = {{World Health Organization}},
  date = {2011},
  publisher = {World Health Organization},
  location = {Geneva},
  isbn = {978-92-4-150118-7},
  langid = {und},
  pagetotal = {1},
  annotation = {OCLC: 1389577534}
}

@article{WuEtAl_MultiSensorDataFusion_2021,
  title = {Multi-{{Sensor Data Fusion Algorithm}} for {{Indoor Fire Early Warning Based}} on {{BP Neural Network}}},
  author = {Wu, Lesong and Chen, Lan and Hao, Xiaoran},
  date = {2021-02},
  journaltitle = {Information},
  volume = {12},
  number = {2},
  pages = {59},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2078-2489},
  doi = {10.3390/info12020059},
  url = {https://www.mdpi.com/2078-2489/12/2/59},
  urldate = {2025-11-27},
  abstract = {Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32\%.},
  langid = {english},
  keywords = {back propagation neural network,fire detection time,multi-sensor data fusion,trend extraction},
  file = {C:\Users\03765\Zotero\storage\ASGAERP7\Wu et al. - 2021 - Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network.pdf}
}

@article{XiaoEtAl_HybridFeatureFusionBased_2023,
  title = {Hybrid {{Feature Fusion-Based High-Sensitivity Fire Detection}} and {{Early Warning}} for {{Intelligent Building Systems}}},
  author = {Xiao, Shengyuan and Wang, Shuo and Ge, Liang and Weng, Hengxiang and Fang, Xin and Peng, Zhenming and Zeng, Wen},
  date = {2023-01},
  journaltitle = {Sensors},
  volume = {23},
  number = {2},
  pages = {859},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {1424-8220},
  doi = {10.3390/s23020859},
  url = {https://www.mdpi.com/1424-8220/23/2/859},
  urldate = {2025-11-27},
  abstract = {High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96\% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.},
  langid = {english},
  keywords = {D-S evidence theory,early fire warning,hybrid feature fusion,intelligent building system},
  file = {C:\Users\03765\Zotero\storage\K6BC8YEC\Xiao et al. - 2023 - Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Buildi.pdf}
}

@article{YangEtAl_Realtimeintelligentfire_2025,
  title = {Real-Time Intelligent Fire Identification and Early Warning Method Based on Campus Surveillance Video},
  author = {Yang, Kexue and Zhao, Jing and Li, Jixian and Xia, Chen},
  date = {2025},
  journaltitle = {ce/papers},
  volume = {8},
  number = {2},
  pages = {2005--2012},
  issn = {2509-7075},
  doi = {10.1002/cepa.3284},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cepa.3284},
  urldate = {2025-11-27},
  abstract = {In recent years, campus fire incidents have become frequent, significantly impacting campus safety. Meanwhile, intelligent fire detection and early warning methods have been proposed and applied in forest fire prevention and urban building fire prevention. This paper addresses the issue of campus fires by first proposing a fire detection model based on object detection algorithms, trained with fire sample data. The trained model achieved an accuracy of 94\% and a recall rate of 92\%. Next, executable files were created to connect campus video data with the program, facilitating intelligent and convenient campus fire early warning through the collaborative work of monitoring devices, displays, and servers. Finally, different fire prevention measures were proposed for different areas of the campus.},
  langid = {english},
  keywords = {Campus fire,intelligent fire detection,object detection},
  file = {C\:\\Users\\03765\\Zotero\\storage\\GNTMYMB9\\Yang et al. - 2025 - Real-time intelligent fire identification and early warning method based on campus surveillance vide.pdf;C\:\\Users\\03765\\Zotero\\storage\\XLVLHQK9\\cepa.html}
}

@article{ZervasEtAl_Multisensordatafusion_2011,
  title = {Multisensor Data Fusion for Fire Detection},
  author = {Zervas, E. and Mpimpoudis, A. and Anagnostopoulos, C. and Sekkas, O. and Hadjiefthymiades, S.},
  date = {2011-07-01},
  journaltitle = {Information Fusion},
  series = {Special {{Issue}} on {{Information Fusion}} in {{Future Generation Communication Environments}}},
  volume = {12},
  number = {3},
  pages = {150--159},
  issn = {1566-2535},
  doi = {10.1016/j.inffus.2009.12.006},
  url = {https://www.sciencedirect.com/science/article/pii/S1566253509001006},
  urldate = {2025-11-27},
  abstract = {Fire is a common disastrous phenomenon that constitutes a serious threat. The SCIER (Sensor and Computing Infrastructure for Environmental Risks is partially funded by the European Community through the FP6 IST Program. The work presented in this paper expresses the ideas of the authors and not necessarily the whole SCIER consortium.) project envisages the deployment of Wireless Sensor Networks at the “Urban–Rural-Interface” (URI) aiming to the detection, monitoring and crisis management of such natural hazards. One of its primary objectives is the development of an advanced multisensor data fusion scheme which feeds a CUSUM sequential test used in the early detection of fires. Reasoning about the probability of fire in a geographical area covered by temperature, humidity and vision sensors is achieved through Evidential Reasoning (Dempster–Shafer theory).},
  keywords = {Cusum,Dempster–Shafer theory,Evidence combination rules,Fire detection,Multisensor data fusion},
  file = {C:\Users\03765\Zotero\storage\MQGQSQ25\S1566253509001006.html}
}
