## 5 AI-Based Microwave NDT Algorithms  
The scientific community has made various efforts to develop algorithms to analyze data, extract relevant features, and interpret information to detect and characterize material defects or anomalies in MNDT applications. The selection of an algorithm is determined by the MNDT method, alongside the nature and characteristics of the material being inspected. Furthermore, the algorithm's objective is to solve the inverse scattering problem (ISP) associated with the MNDT technique. In ISPs, the fields are measured, and the goal is to estimate the location, shape, and/or the electrical properties of a specimen or a defect. ISPs are mathematically complex due to the nonlinearity of the problem and the fact it is ill-posed [132]. Non-linearity arises due to the mathematical relation between the electromagnetic fields and the specimen. The fact that the problem is ill-posed is due to its non-uniqueness and instability. Here, non-uniqueness means that the same problem might have multiple solutions. Moreover, instability means that a small variation in the measured fields might result in a large variation in the solutions. There are various optimization algorithms utilized in the MNDT literature to solve the inverse scattering problem [133; 134; 135; 136; 137; 138; 139; 140], but due to its complexity, more recent works are utilizing artificial intelligence (AI), in particular machine learning (ML). This trend highlights a growing emphasis in MNDT on harnessing the potential of advanced computational methodologies for enhancing precision and efficiency [141]. Moreover, the value of using AI models stems from their ability to identify minor signal fluctuations to automate crack detection in scanned surfaces [141]. Next, some research works related to utilizing AI in MNDT are given. [PERSON] et al. [141] presented the application of an AI model designed to detect sub-millimeter cracks on metallic surfaces, utilizing a waveguide sensor equipped with an SRR. This approach was evaluated using a metal plate featuring multiple cracks, with the experimental results of this procedure demonstrating promising accuracy in categorizing the given cracks. The outcomes obtained with AI models confirm the feasibility of incorporating them into standard MNDT techniques, with the accuracy rates exceeding 99% [141]. Further, a novel microwave-based NDT approach that utilized the k-means unsupervised machine learning algorithm for identifying defects in GFRPs was presented in [142]. The proposed method involved scanning a composite material using an open-ended RWG operating across a frequency range from 18 to 26.5 GHz, utilizing 101 frequency points [142]. The system was shown to identify defects as small as 1 mm, with the results demonstrating a significant improvement in defect detection capability, making it a promising method for composite inspection. [PERSON] et al. [62; 63] used the collected CSI data from Wi-Fi signals to train convolutional neural networks (CNNs) to perform regression and classification tasks. In [62], a CNN was utilized to classify construction materials into different types. Furthermore, regression models were used to estimate material parameters such as thickness and water content. Further, in [63], CNN models were trained to classify concrete mixtures based on their compositions. [PERSON] et al. [143] proposed a novel deep neural network (DNN), attention-UNet (ATTN-UNet), to solve the electromagnetic inverse scattering problem in microwave imaging. Their proposed architecture builds upon the traditional UNet architecture first presented in [144] by incorporating attention mechanisms, which allows their model to focus on more significant aspects of the input over others. Moreover, the outputs of the DNN are colormaps representing the electrical properties of the specimen. The ATTN-UNet architecture was trained on noiseless synthetic data collected using targets from the MNIST dataset [145] before being tested on experimental data from the University of Manitoba repository.