## 3 UAV Computing
### AI for UAV and SAR  
During and after disasters, search and rescue activities necessitate a great deal of work and money. Locating injured and missing people quickly helps rescuers and medical professionals optimize their efforts. This has the potential to save lives and money. Emer  
Figure 3: UAV computing-assisted disaster area. gent AI demonstrates real-time detection based on CNN. UAVs can investigate collected series of photos and send back results in real-time by combining the high-performance detection and classification skills offered by emergent AI approaches with the exploration abilities of the UAVs. The advancement of AI-assisted UAVs allows for detecting injured and trapped people while flying, with the accurate and timely transmission of information to ground stations to guide rescuers and medical professionals to victims' locations. The authors of [47] deliberated on how UAVs may enhance their processing capabilities by using emergent AI-based detectors. The proposed system can identify people in real-time, and the coordinates of such locations are communicated to the ground station. The authors of [48] implemented a pattern recognition approach to build a methodology for estimating UAV position. The correlation of SAR UAV and optical Quickbird satellite-segmented pictures was used to determine placement. UAVs in natural disaster response are currently primitive at best, with a slew of barriers in the way. To build a framework that allows many UAVs to interact and adapt as mission conditions change, AI approaches are used. As a result, UAVs must be adaptable and capable of real-time environment sensing and online autonomous decision making, with AI providing superior decision-making. Furthermore, AI can increase SAR's knowledge of natural hazards by analyzing vast amounts of data from various sources and allowing proactive rather than reactive disaster risk reduction activities. As a result, deploying AI to improve knowledge of all phases of disasters is a crucial goal, which may be accomplished by speeding up the development of SAR-safe algorithms. There are explainable AI techniques that provide futuristic UAV technology prospects that may be used to identify, analyze, and reduce disaster risks, and to provide better monitoring. To avoid, minimize, and manage all types of risks, there is a need to make informed and timely choices. The application of AI in the decision-making process has shown enormous potential. Figure 4 depicts the artificial neural network model. An input layer of ni nodes, hidden levels of nm nodes, and an output layer of Y nodes make up the ANN. We have NI = 7 nodes in this paper. Furthermore, there are several input parameters to be taken into consideration. \[\bar{x}=(x_{1},x_{2},\ldots,x_{s})\]  
where \(x_{1}\), and \(x_{2}\) are the parameters that should be considered for the artificial neural network. The artificial neural network output is \(Y\). The artificial neural network with two hidden layer \(a\) and \(b\) neurons, respectively, can be expressed by:  
\[Y_{\sigma,r}=Z_{0}\left(\sum_{k=1}^{a}w_{2}^{k\ell}\left(Z_{2}^{h}\left(\sum_{ j=1}^{b}w_{1}^{jk}\left(Z_{1}^{h}\left(\sum_{i=1}^{s}w_{in}^{ij}x_{i}^{r} \right)\right)\right)\right)\right) \tag{1}\]  
where \(x_{i}^{r}\) represents the \(i^{th}\) element of the \(r^{th}\) input parameter, and \(Z_{n}^{h}\) is the \(n^{th}\) layer activation function. The results of the testing step reveal whether or not the model is correct. If this is the case, it is ready to accept the cell information as input for the point of interest and the coordinates of the UAV computing for the required purpose. The Mean Square Error (\(MSE\)) model is commonly used to optimize artificial neural network weights and training. This is demonstrated by  
\[MSE=\frac{1}{D_{train}}\sum_{p=1}^{D_{train}}\left[\left|d_{m}-Y_{\sigma,r} \right|\right]^{2}. \tag{2}\]The performance of the procedure is validated using appropriate error indicators and the test set.