## 4 Related Work in the ML and DL Domain
### Eye Tracking-Based Diagnosis of ASD
This suggests that visual representations can instead capture gaze motion details well. The study also looked at relationships between eye movement patterns and the degree of autism. Children with diagnoses of ASD, as well as generally developing kids, participated in the study; medical professionals confirmed the diagnosis. They used a threshold of 200 points to ensure optimal visual clarity in the scan path representations. In 2022, [PERSON] et al. [112] presented three AI approaches, ML, DL, and hybrid, for the early detection of autism. The first technique combined features taken from grey-level co-occurrence matrix (GLCM) and local binary pattern (LBP) methods using neural networks (FFNNs and ANNs). The second technique extracts deep features from CNN models that have already been trained, such as GoogleNet and ResNet-18. The third strategy, known as GoogleNet + SVM and ResNet-18 + SVM, is a hybrid approach that combines ML and DL methods such as (SVM, GoogleNet, and ResNet-18). The study also used image preprocessing and feature extraction using eye-tracking routes. The main contributions of their study are the unique hybrid strategy and the thorough examination of machine learning and deep learning techniques for eye-tracking ASD detection. Using visible eye-tracking scan path (ETSP) pictures, [PERSON] and [PERSON] [113] analyzed ML approaches to determine the optimal model for autism prediction. They used a publicly accessible dataset of 547 ETSPs from 328 usually developing and 219 autistic children to test three conventional models, including a DNN classifier. To avoid overfitting, they used picture augmentation. The study aimed to replace subjective manual screenings with an objective and trustworthy way of diagnosing autism utilizing 2D ETSP pictures. While boosted DT (BDT), deep SVM, decision jungle (DJ), and DNN functioned as classifiers, principal component analysis (PCA) and CNN were employed for feature extraction. They assessed performance using AUC, specificity, sensitivity, PPV, and NPV. [PERSON] et al. [114] utilized cutting-edge technology based on eye tracking and deep-learning algorithms such as LSTM, CNN-LSTM, Bi-LSTM, and GRU networks. They trained these models using an eye tracking dataset specifically curated for ASD research. In performance evaluations, LSTM outperformed the other models in terms of accuracy. Moreover, in 2023, [PERSON] et al. [115] aimed to detect ASD using eye tracking based on deep learning techniques. The researcher integrated eye tracking data with the Chaotic Butterfly Optimization (CBO) method to enhance the diagnosis accuracy using the ETASD-CBODL framework. It analyzes the patterns using the visualization of eye-tracking scanpaths image dataset in ASD. The ETASD-CBODL approach uses Inception v3 for feature extraction and U-Net for segmentation to find regions of interest for feature extraction. More recently, [PERSON] et al. [109] presented T-CNN-ASD, which analyzes ASD based on eye tracking data to categorize subjects into typically developing (TD) and ASD groups. The researchers utilized quantitative eye movement analysis to study attentional processes. It is a potential technique for establishing biomarkers in clinical experiments for ASD because of its high accuracy, cost, and ease of use. This framework compared the abnormal visual attention patterns displayed in children with ASD and those in TD children. The T-CNN-ASD model has two hidden layers comprising 300 and 150 neurons, respectively, and it was subjected to a 20% dropout rate during a 10-fold cross-validation process. A summary of the studies related to the eye tracking data is presented in Table 4.