# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Artificial-Intelligence-Based Networks for Fire Prevention/Monitoring/Simulation
Considering that learning is integral in AI-based systems for fire detection, the activity of naive Bayes classifier-feedforward neural network (FFNN) systems has to be augmented by gradient descent (GD) learning algorithms [30], which might introduce new challenges, especially in cases when it is impractical to integrate GD systems. After considering the cost benefits of naive Bayes classifier-feedforward neural network (FFNN) systems, with embedded gradient descent (GD), it is clear that the AI network is appropriate in wireless-sensor-based systems for fire detection. The main advantage is cost and accuracy and unique architectures. In theory, FFNNs can be deployed across multiple locations, especially in settings with distinct requirements and sensor capabilities. The capabilities are reinforced by the fact that wireless sensor networks act as isolated events, where each node can make an independent decision. The outcomes documented by [PERSON] et al. [30] concerning WSNs were collaborated by [PERSON] et al. [32]. In the latter case, the computing challenges attributed to the integration of AI into WSNs were resolved using the nearest neighbor (k-NN) algorithm, capable of surmounting attributes that are inappropriate based on the proximity of the attributes with small computational  
Figure 3: FFNN architecture [30]. values [32]. However, it remains unclear whether K-NN algorithms could offset the demanding maintenance and installation and limited energy capacity. Considering that existing AI networks have practical constraints in real-life settings, [PERSON] [21] proposed the development of an ant colony algorithm for fire safety simulations. Similar to other networks, the system integrated aspects of IoT and AI in the dynamic modeling, monitoring, and control of fire-fighting infrastructures such as fire hydrants, water sprinklers, fire extinguishers, safety evacuation signs, smoke and temperature sensors. In contrast to the previous model, the probability for success in the ant colony algorithm is reinforced by intelligent optimization. The model focuses on intelligent optimization and the establishment of the shortest possible route for dynamic evacuation. Even though the simulation findings suggest that the model would be useful in finding the best possible evacuation routes in line with the ant colony algorithm, the practical utility of the model can be challenged in farm environments, especially in livestock holding areas. Farm animal's response to fire hazards is dissimilar to humans. In addition, the architectures of barns are unique compared to buildings that house humans. AI Networks for Fire Prevention, Insurance Claims, and Future Manufacturing/Value Addition of Agricultural Commodities  
The fire risks in silos are relevant to agricultural warehouses used to process or value-add agricultural commodities. [PERSON] and [PERSON] [53] state that the settlement for fire claims is a lengthy process, and in the meantime, normal operations would be impacted or even halted in worst-case scenarios [53]. The settlement of multiple fire claims might also translate to higher insurance premiums. This means that farm fires have a broader impact on the insurance industry. The need for reliable and intelligent fire safety systems for agricultural structures, silos, and warehouses is augmented by the immense prospects of intelligent technologies. For example, the smart city model envisioned by [PERSON] et al. [54] and [PERSON] et al. [43] features a new generation of intelligent manufacturing (NGIM) in warehouses and technologies of cyberphysical systems (CPSs) to bridge the gaps between humans and machines and bypass non-digital-native HR. The distinct capabilities cannot be optimized without addressing the risk of regular fire hazards.