## 6 The Role of AI and Its Subsets, ML, and DL in Artifact Analysis
This allows for the classification of visitors based on eye movements, enabling data-driven management and personalized exhibition recommendations, thereby enhancing museum planning efficiency. AI has led to numerous studies on CH and photography, focusing on damage monitoring, creating high-fidelity 3D models, and enhancing heritage image analysis through computer vision. [PERSON] et al. [189] present a DL workflow for accurately retrieving and estimating the positioning and orientation of historical images, particularly of altered or destroyed buildings. [PERSON] et al. [198] explored a virtual tour application for CH, developed using DL for hand gesture recognition and photogrammetry with reality capture software to create 3D models of an alleyway in the Sassi of Matera, Italy. User testing indicated that the prototype, which included hand gestures, was intuitive and enhanced immersion in the interactive platform. [PERSON] et al. [199] utilized pre-trained CNN models to classify architectural heritage images with high accuracy. [PERSON] et al. [200] proposed a CNN-based data augmentation approach for classifying augmented CH images, outperforming traditional models. The Convolutional Neural Network Attention Retrieval (CNNAR) framework was used to classify and retrieve diaspora Chinese architectural heritage images in Jiangmen, China, achieving an accuracy of 98.3% and a mean Average Precision (mAP) of 76.6%. It employs transfer learning and a fusion attention mechanism for improved feature extraction. [PERSON] et al. [201] investigated ML and DL techniques for classifying fresco fragments in the DAFNE dataset, highlighting their effectiveness in both binary and multi-class classification for reconstructing damaged frescoes despite challenges such as irregular shapes and color changes. Recent studies have examined ML techniques for damage identification in structural health monitoring (SHM) [202]. [PERSON] et al. [203] present a classification scheme that focuses on pre-processing, feature extraction, and pattern recognition. [PERSON] et al. [204] demonstrate that CNN and support vector machines (SVM) effectively classify damage severity in heritage buildings, with CNN slightly outperforming SVM. Reference [205] reports on a DL-based system that effectively classifies surface damages on wooden heritage structures, achieving high detection rates. [PERSON] et al. [206] present a hybrid CNN-SVM method for assessing paint defects in heritage buildings, demonstrating high accuracy and precision. Reference [207] employs a region-based CNN within a heritage building information modeling (HBIM) framework to automate defect-recognition in historical buildings. These approaches enhance automated damage assessment, thereby improving heritage management and preservation. Reference [208] explores the application of CNNs for detecting archaeological remains in aerial, satellite, and LiDAR imagery. Additionally, DL has been utilized as a substitute for XRF to estimate the elemental composition of iron and copper artifacts through stereo microscopy images, providing a non-invasive and cost-effective alternative for pre-restoration analysis [209]. DL algorithms detect physical damage on artwork surfaces using activation maps to identify issues such as cracks, blisters, and detachments [210]. Algorithms like Region-based Convolutional Neural Networks (R-CNN) [209] and Mask R-CNN have been developed to address this issue, resulting in more accurate, efficient, and unbiased DL models. CNNs can assist in reconstructing 3D models from 2D images, which is valuable for virtual exhibitions and preservation efforts. By analyzing images over time, CNNs can effectively predict when artifacts may require conservation efforts due to visual deterioration. Additionally, Mask R-CNN networks significantly improve both the efficiency and accuracy of photogrammetric modeling, demonstrating impressive recall rates in the simplification of backgrounds for 3D models [211].