## 4 Related Work in the ML and DL Domain
### EEG-Based Diagnosis of ASD  
EEG is a noninvasive method for evaluating electrical activity in the brain, making it crucial for clarifying the neurophysiological underpinnings of ASD. An EEG allows us to investigate the brain and detect minute electrical vibrations. This method of measuring brain activity and displaying it as wave patterns is painless. Epilepsy and sleep disturbances are among the illnesses that doctors identify with EEGs. Moreover, ASD can benefit from this technology if AI technologies are introduced in the diagnosis workflow. EEG recordings include intricate patterns that may be analyzed by AI techniques such as ML and DL, which allow the identification of ASD-specific patterns [52; 116]. They search for indicators such as wave frequency, the electrical interactions between various brain areas, and the brain's response to stimuli. AI can increase the precision of ASD diagnosis, particularly in the critical early stages, by identifying these minimal variations. Even better, AI can grow and learn over time. Large-scale EEG data analysis improves its ability to recognize ASD patterns. Because of this, AI techniques, such as ML and DL, can evaluate EEG data to identify abnormal brainwave patterns and connectivity and are valuable tools for medical professionals and researchers, providing a noninvasive and ever more trustworthy means of diagnosing and comprehending ASD [117; 118; 119]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{4}{*}{[110]} & Eye Tracking Subject-Experiment (ETS-E) dataset, Public & RF, C4.5 DT, PART, and headforward (FFNN) & Accuracy: & 93.45\% & Limited data, restricted generalizability, and handcrafted features. \\ \hline \multirow{4}{*}{[111]} & Self-collected data from 59 children: Private & CNN & Accuracy: & 71.00\% & Limited sample size, unsatisfactory accuracy, and lack of model comparison. \\ \cline{2-6}  & Early Screening ASD dataset, Eye-Tracking Scan Path (ETSP) images & Boosted DT (BDT), Deep SVM, and Decision Jungle (DJ), DNN & Sensitivity: & 93.00\% & Lack of feature interpretation, missing accuracy metric, and incomplete performance evaluation. \\ \hline \multirow{4}{*}{[113]} & Eye-Tracking Scanpaths in ASD dataset, ASD eye-tracking scanpaths images of ASD and developing patients & ANN, FFNN, GoogleNet, ResNet-18, Hybrid GoogleNet + SVM, Hybrid GoogleNet + SVM, Hybrid ResNet-18 + SVM & Accuracy: & 97.60\% & Missing feature interpretation, lack of dataset comparison, specificity: & 97.60\% & Missing feature interpretation, lack of dataset comparison, and incomplete research findings. \\ \hline \multirow{4}{*}{[114]} & Eye tracking dataset & LSTM, CNN-LSTM, BiLSTM, GRU & Accuracy: & 98.33\% & \multirow{4}{*}{Demographic imbalance.} \\  & & LSTM, CNN-LSTM, BiLSTM, GRU & Sensitivity: & 97.25\% & \multirow{4}{*}{Class imbalance, no proper preprocessing.} \\  & & BiLSTM, GRU & Specificity: & 98.94\% & \multirow{4}{*}{Demographic imbalance.} \\  & & F1-Score: & 98.70\% & & \\ \cline{1-1} \cline{5-5}  & Visualization of Eye-Tracking Scanpaths image dataset & U-net, InceptionV3, LSTM & Accuracy: & 99.29\% & \multirow{4}{*}{Class imbalance, no proper specificity.} \\  & & LSTM & Sensitivity: & 99.29\% & \\ \cline{1-1} \cline{5-5}  & & & Specificity: & 99.29\% & \\ \hline \multirow{4}{*}{[109]} & Eye-Tracking Scanpath Image Dataset & CNN & Accuracy: & 95.59\% & \multirow{4}{*}{Limited data Sample, no data augmentation.} \\  & & CNN & Sensitivity: & 77.60\% & \\ \cline{1-1} \cline{5-5}  & & & Specificity: & 79.91\% & \\ \cline{1-1} \cline{5-5}  & & & F1-Score: & 78.73\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary of works using AI techniques for detecting ASD through eye tracking data. For instance, [PERSON] et al. [120] investigated many deep convolutional architectures for ASD diagnosis, emphasizing good classification accuracy and automated feature extraction.