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.
Table of 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.
About the Author :
Ram Kumar Chenthur Pandian is the Director IQAC and Professor ECE at Sri Krishna College of Technology (An Autonomous Institution), Coimbatore since 2023. He has over 10 years of teaching and research experience in department of BME, Dr NGP Institute of Technology, Coimbatore, India (2016 to 2023) and prior to that, he was with SNS College of Engineering. He was a visiting faculty at Anna University, Coimbatore. He completed his UG degree in ECE at SRM Easwari Engineering College, Chennai in 2010 and PG degree in Communication System at Sri Krishna College of Engineering and Technology, Coimbatore in the year 2012, PhD in Wireless Sensor networks at Anna University in 2017. His experience includes quality assurance, patent analyst, innovation culture, international collaborations, and audit. His interests include Sensor Networks, Biomedical Instrumentation, Digital Electronics and AI in Wearable Devices. He has published 6 books, around 80 papers in national and international conference proceedings, and almost 50 papers in major journals. He has filed a patent in wireless sensor networks.
Shanmuga Raju Sekar is an Assistant Professor in the Department of Computer Science and Engineering (IoT) at Sri Krishna College of Technology. He holds a Bachelor of Engineering in Electronics and Communication Engineering and a Master of Engineering in VLSI Design, both from Anna University. With over a decade of teaching and research experience, Shanmuga Raju specializes in AI in healthcare, VLSI design, IoT applications, and cyber physical systems. He has contributed to developing innovative methods in power and delay-optimized VLSI implementations and has explored applying AI techniques in system design. Shanmuga Raju has published extensively in reputed journals and is actively involved in guiding students in interdisciplinary research. His current research interests include IoT-enabled systems, data analytics and system design.
Subrata Chowdhury is an Associate Professor in the Department of the Computer Science of Engineering of Sreenivasa Institute of Technology and Management. He is been working in the IT Industry for more than 5 years in the R&D developments, and has handled many projects in the industry. He has handled projects related to AI, Blockchains and the Cloud Computing for many global clients. He has authored 4 books and edited 2 volumes with acclaimed publishers. He has participated in the Organizing committee, Technical Programmed Committee and Guest Speaker for more than 10 conference and the webinars. He has also reviewed and evaluated more than 50 papers from the conferences, journals, book chapters and scientific articles in AI, Data Science, IoT, Blockchain and Cloud Computing for acclaimed publishers. He is the Associate Editor for the Journal of Engineering and other journals. He has taken parts in the Workshops, Webinars, FDPs. He has published more than 30 papers and copyrights and patents. He has been awarded by various science societies for his contributions in the R&D.
Muhammad Rukunuddin Ghalib is a highly accomplished professional with a strong educational background and extensive experience in academia and research. He holds a Bachelor's, Master's, and PhD in Computer Engineering from Anna University in India. Dr. Ghalib has been associated with renowned institutions such as Vellore Institute of Technology (VIT) University and Anna University in India. At VIT, he was Assistant Director, International Relations, where he played a role in establishing academic and research collaborations with universities in Oceania countries, primarily in Australia and New Zealand, as well as several European institutions. His expertise extends to working on Erasmus+ Staff and Student exchange programs. He currently holds the position of Associate Professor at De Montfort University (DMU), Dubai, an off-campus branch of De Montfort University (DMU) Leicester, UK. He has published over 70 peer-reviewed research articles, spanning SCI and Scopus indexed journals, as well as contributions to book chapters and numerous conference proceedings.
Dr. Ghalib is an active IEEE senior member from the R10 region. Additionally, he holds memberships/life memberships in professional societies. His has forged partnerships with researchers across various domains, with a focus on healthcare. Dr. Ghalib's research interests encompass Data Science Technology, Cyber Security, IoT & Bioinformatics, Machine Learning, and Artificial Intelligence.
Dr. Kassian T.T. AMESHO is an accomplished and globally recognized professional known for his expertise as an analyst and researcher. With a track record of successfully resolving complex problems, he brings valuable insights and solutions to diverse challenges. Driven by a commitment to excellence, he thrives in demanding roles, both domestically and internationally, collaborating effectively with multidisciplinary teams to achieve shared objectives. Dr. AMESHO holds two PhDs, one in Environmental Engineering and another in Business Administration, with a focus on technology and innovation management for competitive advantage. His PhD in Environmental Engineering emphasized sustainability science and engineering. With over 40 scientific articles, more than 20 international conference presentations, and 5 book chapters, he has contributed significantly to the body of knowledge in his field. Currently, Dr. AMESHO serves as a senior research fellow at the INRAE - French National Research Institute for Agriculture, Food and Environment, in the division of Agropolymer Engineering and Emerging Technologies (IATE). In addition, he is a distinguished book editor, contributing to the advancement of research and knowledge dissemination.
