## 4 Related Work in the ML and DL Domain
### EEG-Based Diagnosis of ASD
Their study assesses models including AlexNet, DenseNet, SqueezeNet, ShuffleNet, VGGNet, InceptionNet, and ResNet based on precision, recall, and accuracy. Using _k_-fold cross-validation, ResNet50 outperformed the other models based on specificity and F1-Score. Therefore, the study suggests that ResNet50 be used to diagnose ASD from EEG waves. EEG data were obtained from 10 normally growing and autistic children collected using the internationally recognized 10-20 electrode system. At the same time, they were respecting ethical standards and getting parental agreement. The Helsinki Ethical Principles and ICMR Ethical Guidelines were adhered to by the procedure. [PERSON] et al. [53] investigated the classification of ASD in children utilizing statistical methods and ML approaches to EEG data. The authors performed retrospective research using statistical methods and machine-learning approaches, employing clinical EEG recordings from two patient groups aged from 2.08 to 5.92 years. The Wilcoxon rank-sum test with FDR correction and the Friedman test were two statistical procedures. A logistic regression model with L2 regularization was utilized for machine learning approaches using cross-validation optimization to assess the model's performance. In the same year, [PERSON] et al. [121] presented sensor-based techniques for early detection of ASD using EEG signals. The study emphasized energy-efficient signal modification for feature extraction and classification using machine learning techniques. The complexity of brain activity was measured using multiscale entropy (MSE), which was calculated using digital Haar wavelet decomposition. The three ML classifiers examined were SVM, logistic regression, and decision trees. More recently, [PERSON] et al. [122] evaluated the effectiveness of many deep learning models, such as AlexNet, VGG16, and ResNet50, in diagnosing ASD using EEG data utilizing five cepstral coefficient characteristics. One of the main contributions is using cepstral coefficients to create spectrograms during the processing stage. According to experimental data, the maximum accuracy is obtained when AlexNet is combined alongside Linear Frequency Cepstral Coefficients (LFCC). In addition, [PERSON] et al. [123] utilized several pre-trained models, such as AlexNet, SqueezeNet, and MobileNetV2, to classify ASD based on EEG data. The EEG signals are segmented into five-second intervals, and power spectral density (PSD) is used to transform the signals into greyscale spectrograms. The researcher used hybrid models that combined DL models with traditional machine learning models such as SVM, kNN, and DT. According to the results, SqueezeNet increased accuracy, and performance is further improved when combined with the SVM classifier. Additionally, in 2024, [PERSON] and [PERSON] [124] presented a weighted ensemble model based on DL for the classification of ASD. They used STFT to extract features from EEG recordings and convert them into 2D time-frequency spectrograms. The dataset included EEG data from 17 participants, 12 with ASD and 5 TD participants. EEG data was recorded with Ag/AgCl electrodes. An ensemble model is formed by combining three CNN models, and a grid search is used to build the final weighted ensemble. We provide a summary of these studies in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{4}{*}{[120]} & Self collected EEG & AlexNet, DenseNet, & Accuracy: & 81.00\% \\  & Signal Data from 10 & SqueezeNet, & Precision: & 92.00\% \\ \cline{1-1}  & Children: Private & ShuffleNet,VGGNet, & Recall: & 91.00\% \\ \cline{1-1}  & & InceptionNet, and & Specificity: & 100.00\% \\ \cline{1-1}  & & ResNet & F1-Score: & 91.00\% \\ \hline \end{tabular}
\end{table} 
Table 5: A summary of works using AI techniques for detecting ASD through EEG data.