Karthik Ramamurthy – författare

Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.. Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains. Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role. Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems

Adaptive AI in Sensor Informatics: Methods, Applications, and Implications explores the growing need for efficient, interpretable, and reliable adaptive AI systems tailored to wireless sensor networks. The book highlights how adaptive AI strengthens collaboration between humans and artificial intelligence by enabling transparent decision-making processes. Aimed at academics, professionals, and students, it provides an accessible yet thorough guide to understanding the intersection of adaptive AI and sensor informatics, focusing on practical implementation and the development of models that are both trustworthy and user-friendly. Readers will gain insight into the essential role adaptive AI plays in advancing wireless sensor networks across various sectors.The book also examines the unique challenges and opportunities that arise when deploying adaptive AI in real-world sensor environments. It offers actionable advice for designing AI models that comply with regulations and support user confidence, especially in areas such as healthcare, environmental monitoring, smart cities, and industrial automation.* Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains* Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role* Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems

Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.Consolidates scattered information on deep learning techniques in neuroimaging data analysis, facilitating access for researchers, practitioners, and studentsExplores deep learning algorithms applied to various neuroimaging data types, including EEG, MRI, and PET scansHighlights methodologies like CNNs and RNNsIncludes real-world case studies that demonstrate how deep learning enhances research and clinical applications, such as identifying biomarkers for Alzheimer's disease and stroke

Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer's disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts. - Consolidates scattered information on deep learning techniques in neuroimaging data analysis, facilitating access for researchers, practitioners, and students- Explores deep learning algorithms applied to various neuroimaging data types, including EEG, MRI, and PET scans- Highlights methodologies like CNNs and RNNs- Includes real-world case studies that demonstrate how deep learning enhances research and clinical applications, such as identifying biomarkers for Alzheimer's disease and stroke

Today's generative AI has been marked by the advent of neural networks, inspired by the human brain, which are trained to recognize patterns in a dataset. Once the network is trained, it can make decisions or predictions without being programmed to perform tasks. Generative AI learns from a set of data without explicit instructions and can create and generate new digital content such as text, audio and art. Recent models are beginning to overcome challenges such as computational power, data quality and training stability.This edited book on generative AI presents novel perspectives, approaches and methodologies, as well as security, ethical and legal considerations, and future trends. Topics and technologies covered include generative transformers and text generation models, generative models for human-like speech synthesis, generative AI for image synthesis, data synthesis for privacy protection, and exploration of the impact of generative AI in fields including industry 4.0, astronomy, and brain tumour detection. Two chapters offer perspectives on ethics and legality.Generative AI Unleashed: Advancements, transformative applications and future frontiers will serve as a valuable resource for researchers, engineers, advanced students and lecturers operating in the domains that are significantly impacted by generative AI.

There are a growing number of challenges in handling medical data in order to provide an effective healthcare service in real-time. Bridging the gap between patient expectations and their experiences needs effective collaboration and connectivity across the healthcare ecosystem. The success of joined-up care relies on patient data being shared between all active stakeholders, including hospitals, outreach workers, and GPs. All these needs and challenges pave the way for the next trend of development in healthcare - healthcare 4.0.This book covers the state-of-the-art approaches in AI, IOT, cloud, big data, deep learning, and blockchain for building intelligent healthcare 4.0 systems, which provide effective healthcare services in real-time.The editors consider the benefits and challenges of immersive technologies and mixed reality systems for physical and mental health conditions, and outline and discuss the trending technologies supporting the internet of medical things, patient-centred care, assisted medical diagnoses, and electronic medical records.Technologies for Healthcare 4.0: From AI and IoT to blockchain is essential reading for researchers, scientists, engineers, designers and advanced students in the fields of computer science, computer vision, pattern recognition, machine learning, imaging, feature engineering, IOT, AI, signal processing, blockchain and big data for healthcare and those in adjacent fields.