Kassian T.T. Amesho – författare

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.Key Features:Investigates recent research on AI based autism diagnosis modelsExplores various ML models for diagnosis using behaviour analysis, speech analysisReviews various AI models for clinical data analysisInvestigates early prediction toolsExplores tools for assisting and parental care

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.Key Features: Investigates recent research on AI based autism diagnosis models Explores various ML models for diagnosis using behaviour analysis, speech analysis Reviews various AI models for clinical data analysis Investigates early prediction tools Explores tools for assisting and parental care

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.Key Features: Investigates recent research on AI based autism diagnosis models Explores various ML models for diagnosis using behaviour analysis, speech analysis Reviews various AI models for clinical data analysis Investigates early prediction tools Explores tools for assisting and parental care

Solid waste management has become a significant global concern, particularly in maintaining and preserving value in solid waste resources. Local socio-economic disparities create various demands for sustainable solid waste management, leading to a wide range of waste reduction, recovery, and recycling solutions. This highlights the importance of accurately interpreting new policies within the framework of the circular economy. Accordingly, the title of this book is Solid Waste Management: Sustainable Environmental Practices and the Circular Economy. This book contains chapters on contemporary research that focuses on innovative technologies, concepts, models, and tools for sustainable solid waste management, as sustainable environmental practices and the circular economy have garnered increasing interest. This volume seeks to enhance scientific potential in the field of sustainable waste management by publishing cutting-edge theoretical and experimental studies that will advance our understanding of green technologies for sustainable solid waste management systems, aligned with the principles of sustainable environmental practices and the circular economy.