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Full Description
The integration of AI in detecting autism holds promise for early intervention, personalized treatment plans, enhanced accuracy, and improved outcomes for individuals on the autism spectrum.
The book discusses the use of AI in detecting autism spectrum disorder (ASD). AI analyses behavioral patterns, speech, and cognitive responses. Machine learning algorithms are used to identify potential markers of ASD. AI can process vast amounts of data, including social interactions, facial expressions, and language nuances to help arrive at a diagnosis that would otherwise not be possible. AI-driven tools like natural language processing, computer vision, and predictive analytics provide objective assessments. These assessments complement traditional diagnostic methods, offer more accurate and efficient evaluations. Further, AI assists data analysis models in identifying unnoticeable behavioral signals that might normally escape detection by human observation, enhancing the precision of ASD diagnosis. Covering AI applications in detection, diagnosis, and life support, the book highlights how technology is reshaping autism care and research.
Contents
Preface. Section 1: AI based early detection and diagnosis models. 1. AI Based Early Detection and Diagnosis Model. 2. Advancements in Early Diagnosis and Screening of Autism Spectrum Disorder. 3. Analyzing the Optimized Features Requirement and Data Mining Techniques in Heart Disease Prediction. Section 2: ML models for behaviour analysis. 4. Advanced Patient Monitoring and Alert System with Instant Care Recommendation System using ML. Section 3: AI models for facial recognition. 5. Implementation of Machine learning Algorithm for Weed Detection. 6. AI Models for Facial Recognition in Autism Detection: A Comprehensive Review and Analysis. Section 4: Autism diagnosis through speech analysis. 7. Ethical Consideration in Autism Detection using AI. 8. Machine Learning Approaches for Detecting Autism through Chromosomal Aberration. 9. Analysis of Artificial Intelligence Techniques for Autism Detection. 10. Artificial Intelligence and Machine Learning in Detecting Autism: Transforming Diagnosis and Care. Section 5: Genetic and biomarker data analysis and diagnosis. 11. Harnessing Explainable AI and Multi-Omics Biomarkers for Precision Autism Diagnosis: From Data Acquisition to Clinical Decision Support. 12. Genetic and Biomarker Data Analysis and Diagnosis. 13. Single-Nucleus RNA Sequencing Data for Cell-Type-Specific Genev Prioritization and Predictive Modeling of Autism Spectrum Disorder. Section 6: Clinical data analysis model. 14. Machine Learning-Based Predictive Models for Medical Data Analysis: A Case of Early Diabetic Prediction. 15. Optimizing Heart Disease prediction through Machine Learning and Synthetic Data Augmentation using GANs. Section 7: AI tools for early prediction. 16. Uses of Generative AI for SAP HANA Data Management. 17. Artificial Intelligence and Robotics in Healthcare: A Comprehensive Overview. Section 8: AI based screening tools for parental support & other applications. 18. Blind Guide System with Voice Assistance. 19. Personalized Curriculum Adaptation: Using Emotion Responses to Tailor Education for Autistic Students. 20. Liver Disease Classification in Diverse Patient Population using Ensemble Technique.
