Detecting Childhood Austistic Spectrum Disorder And Enhancing Their Reading Skills

This research study uses advanced machine learning techniques for the early diagnosis of Autism Spectrum Disorder (ASD) in children and the improvement of reading skills. ASD is a neurodevelopmental disorder that affects the communication, social interaction, and sensory processing in children. It uses behavioral analysis, speech processing, and multimodal data including facial expressions, gestures, and physiological signals and integrates BILSTM, CNN (VGG16), Variational Autoencoders, and Deep Belief Networks for the early detection and diagnosis. Personalized, gamified reading programs are built to the pace of the child’s learning and include real-time feedback and multi-sensory learning strategies. Emotional and social skill development occurs through emotion recognition and virtual social simulations. Parents and educators have dashboards for tracking progress and long-term development is predicted through predictive analytics. Moreover, learning activities are also disseminated through a special YouTube channel, and the rules are e-mailed to the family members so that support and guidance could be continued with the child. Such an integrated approach benefits the child with ASD cognitively, emotionally, and socially.