## 5 Discussion on Challenges and Open Issues  
The effective identification of ASD faces several challenges such as including the heterogeneity of ASD traits, the limited availability of labeled datasets, and the high cost of traditional diagnostic methods. One major hurdle is the variability in behavioral and physiological markers among individuals, which complicates the development of universally applicable diagnostic models. Additionally, data collection often relies on resource-intensive clinical setups, making early detection inaccessible in underserved regions. The integration of specific AI algorithms and IoT sensor systems offers promising solutions to these challenges. The integration of AI, IoT, and sensor-based technologies has the potential to revolutionize ASD diagnostics, providing unprecedented precision, scalability, and adaptability. These advanced methodologies enable the identification of behavioral, physiological, and neurological markers through real-time, non-invasive, and continuous monitoring across diverse settings. By leveraging these innovations, researchers and clinicians can move beyond traditional observational methods, ensuring earlier and more reliable diagnoses that lay the groundwork for tailored interventions. Among the algorithms evaluated, RNNs stand out for their ability to process sequential data, such as speech patterns or time-series sensor inputs. By analyzing temporal relationships within data, RNNs have shown promising results in ASD diagnostics, particularly in identifying communication challenges and behavioral irregularities. Similarly, ensemble methods like XGBoost and support vector machines (SVMs) excel in specific applications, with XGBoost performing well in microbiome-based analyses and SVM effectively analyzing head movement data. Deep learning models such as Xception also demonstrate exceptional performance in facial emotion recognition, making them highly suitable for real-time applications. Together, these algorithms offer robust, high-accuracy solutions for diverse diagnostic needs. In terms of sensors and IoT systems, wearable devices equipped with EEG and motion sensors effectively capture critical ASD markers, including stress levels and motor patterns. IoT-based frameworks like IoT-LAS and edge computing systems enhance the diagnostic process by enabling real-time data collection and processing in natural environments. These technologies offer practical, scalable, and efficient tools for ASD diagnosis, facilitating seamless integration into both clinical and everyday settings. By combining advanced algorithms like RNNs with cutting-edge IoT systems, ASD diagnostics can achieve greater precision, accessibility, and adaptability, significantly improving outcomes for individuals with ASD. While the application of AI, IoT, sensor-based technologies shows significant promise in advancing ASD diagnosis, several challenges and open issues must be addressed. One major limitation lies in the diversity and generalizability of the datasets used to develop these tools. Many studies rely on relatively homogeneous participant groups, limiting the applicability of these diagnostic methodologies in diverse cultural, demographic, and socioeconomic contexts. Future research must focus on creating more inclusive and representative datasets to improve the robustness and effectiveness of these technologies. This limitation highlights the need for more inclusive datasets to ensure that the proposed technologies can be effectively applied in diverse contexts. One potential approach to addressing the challenges of limited dataset representativeness is for researchers to explore synthetic dataset generation and data augmentation techniques. These methods can artificially enhance data diversity by introducing controlled variations in existing datasets, thereby improving representativeness without requiring extensive new data collection.