Anindya Nag – författare

Carbon Nanotube-Based Sensors: Fabrication, Characterization, and Implementation highlights the latest research and developments on carbon nanotubes (CNTs) and their applications in sensors and sensing systems. It offers an overview of CNTs, including their synthesis, functionalization, characterization, and toxicology. It then delves into the fabrication and various applications of CNT-based sensors.FEATURES Defines the significance of different forms of CNT-based sensors synthesized for diverse engineering applications and compares the feasibility of their generation Helps readers evaluate different types of fabrication techniques to generate CNTs and their subsequent sensing Discusses fabrication of low-cost, efficient CNTs-based sensors that can be used for diverse applications and sheds light on synthesis methods for a range of printing techniques Highlights challenges and advances in security-related issues using CNTs-based sensorsThis book is aimed at researchers in the fields of materials and electrical engineering who are interested in the development of sensor technology for industrial, biomedical, and related applications.

Carbon Nanotube-Based Sensors: Fabrication, Characterization, and Implementation highlights the latest research and developments on carbon nanotubes (CNTs) and their applications in sensors and sensing systems. It offers an overview of CNTs, including their synthesis, functionalization, characterization, and toxicology. It then delves into the fabrication and various applications of CNT-based sensors.FEATURES Defines the significance of different forms of CNT-based sensors synthesized for diverse engineering applications and compares the feasibility of their generation Helps readers evaluate different types of fabrication techniques to generate CNTs and their subsequent sensing Discusses fabrication of low-cost, efficient CNTs-based sensors that can be used for diverse applications and sheds light on synthesis methods for a range of printing techniques Highlights challenges and advances in security-related issues using CNTs-based sensorsThis book is aimed at researchers in the fields of materials and electrical engineering who are interested in the development of sensor technology for industrial, biomedical, and related applications.

This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.

Pressure sensors have become a pivotal part of sensing technology due to their high accuracy, low power consumption and low response time to detect the amount of force/pressure needed to conduct an operation.Pressure induced Flexible Sensors: Fabrication Characterization and Application highlights the research done on the fabrication and implementation of different pressure-induced flexible sensors. It provides a detailed overview of the different types of pressure sensors used in healthcare, industrial and environmental sectors. It encompasses the entire spectrum from fabrication to application of the pressure sensors used in the current scenario. The book categorizes the sensors by their operating mechanisms and practical uses, with clear explanations of key working principles including piezoresistive, piezoelectric, capacitive, and triboelectric technologies. The also authors showcase breakthrough developments in multimodal and biodegradable sensing, pushing the boundaries of what's possible in sensor design. Finally, the challenges and remedies of current pressure sensors are examined and explained.This book is aimed at graduate level students and researchers in the fields of electrical engineering and materials science engineering who are interested in the development of sensor technology for industrial, biomedical, and related applications.

Transforming Global Healthcare: The Role of AI and IoT in Modern Medical Systems explores the transformative potential of the integration of technologies such as AI, Big Data, IoT, and Blockchain in the healthcare sector. This comprehensive volume aims to illustrate how these advanced technologies can address sustainability challenges, improve decision-making processes, and enhance overall healthcare delivery. By mapping existing knowledge and forecasting future trends, the book provides a detailed roadmap for sustainable healthcare development.Key features include:– Explores AI, big data, IoT, and blockchain in healthcare in-depth.– Provides actionable strategies for energy management, waste disposal, and green supply chains– Provides in-depth coverage of real-time health monitoring, predictive analytics, robotic surgery, and AI medical imaging.– Provides insights into preventing drug counterfeiting, managing pharmaceutical cold chains, and optimizing supply chains.– Comprises a comparative analysis of global healthcare systems, telemedicine evolution, and future digital health trends.

This book comprehensively explores the intersection between AI and neurology, focusing on the application of generative models in diagnosing, treating, and managing neurodegenerative diseases. It covers the principles of generative AI and its roles in neuroimaging, biomarker identification, medication development, and tailored treatment options. It examines how advanced AI approaches are leveraged to increase the detection and treatment of neurodegenerative illnesses. The concepts are supported by case studies and examples.Features:Focuses on the emerging field of generative models in neurology.Presents an interdisciplinary approach that bridges AI and medical applications.Examines the ability of neural diffusion models to generate genuine data to enhance healthcare AI.Discusses tools for the adaptation and augmentation of medical images and healthcare data domains.Includes case studies and practical impacts.This book is aimed at graduate students and researchers in biomedical and computer engineering, artificial intelligence, and health informatics.

The sensors were processed using laser cutting and 3D printing techniques, and the sensors developed were employed in a number of healthcare, environmental and industrial applications, including: monitoring of physiological movements, respiration, salinity and nitrate measurement, and tactile sensing.

This book investigates the fabrication of different types of flexible sensors and their subsequent implementation for energy-harvesting applications.

This book investigates the fabrication of different types of flexible sensors and their subsequent implementation for energy-harvesting applications.

This book offers a thorough examination of the revolutionary capabilities of quantum computing in the context of Industry 6.0, with a specific emphasis on its use in disaster management. The aim of this proposed book is to clarify how quantum computing, in conjunction with other Industry 6.0 technologies, might profoundly transform our comprehension, preparedness, and response to natural disasters. In the era of Industry 6.0, there is a pressing need for creative solutions to tackle the increasing difficulties caused by natural disasters. This proposed book explores the distinctive characteristics and capacities of quantum computing that make it especially suitable for improving disaster management procedures. The proposed book examines the potential of quantum algorithms to enhance resource allocation, enhance forecasting precision, and facilitate real-time decision-making in the context of rapidly changing crisis scenarios. This proposed book proposes a comprehensive strategy for catastrophe management that is adaptable, robust, and efficient by utilizing quantum computing in conjunction with other advanced technologies.This proposed book offers a comprehensive analysis of the specific ways in which quantum computing can be utilized in different areas of disaster management. It covers topics such as risk assessment, early warning systems, and infrastructure resilience. By examining real-world case studies and examples, readers can acquire valuable insights into the practical implementation and effectiveness of quantum-powered crisis management solutions, showcasing their potential impact. This proposed book acknowledges the ethical consequences of implementing sophisticated technologies in disaster management. It focuses on important ethical and societal factors, including data privacy, algorithmic bias, and fair access to technology. The aim is to ensure that quantum-powered solutions prioritize ethical principles and cater to the requirements of all communities. This proposed book provides readers with a clear understanding of the potential areas for future study, innovation, and collaboration in the field of quantum-powered crisis management systems.

This book delves into the fundamental concepts, methodologies, and practical implementations of feature fusion, providing valuable perspectives on how merging several data aspects might augment the decision-making skills of artificial intelligence. Feature fusion is inherently connected to the advancement of intelligent solutions from medical data as it enables the incorporation of various and complementary data sources to construct more advanced AI models. Within the medical domain, data manifests in diverse formats, including electronic health records (EHRs), medical imaging, genomic data, and real-time sensor metrics. Although each of these data kinds offers distinct perspectives, they may have limitations in terms of their breadth or depth when considered independently. The application of feature fusion enables the integration of diverse data sources into a unified model, hence improving the AI's capacity to detect patterns, make precise predictions, and produce significant insights. The fusion process facilitates the development of intelligent solutions that exhibit enhanced reliability and effectiveness by using a more extensive reservoir of knowledge. For example, an artificial intelligence system that combines imaging data with clinical history might enhance the precision of disease diagnosis, forecast patient outcomes, and suggest tailored treatment strategies. Feature fusion is the crucial factor in unleashing the complete capabilities of medical data, enabling artificial intelligence to provide intelligent solutions that not only enhance the provision of healthcare but also stimulate advancements in medical research and practice. The proposed book explores the advanced notion of feature fusion within the field of artificial intelligence, with a particular emphasis on its implementation in physiological data. The integration of many data sources is crucial in the development of more precise, dependable, and understandable AI models as the healthcare industry becomes more data-driven.

This book represents a unique and comprehensive resource for understanding the intersection of advanced artificial intelligence (AI) and neurology. By focusing on graph neural networks (GNNs), the book addresses a crucial gap in the current literature, providing valuable insights into the analysis and interpretation of complex brain networks and neurological data. Intended for a diverse audience, including clinicians, scientists, researchers, and students, it demystifies the complexities of GNNs and their applications in neurology. For clinicians and healthcare practitioners, the book illustrates how GNNs can enhance diagnostic accuracy, inform personalized treatment plans and predict disease progression. This leads to improved patient outcomes and a deeper understanding of neurological conditions such as Alzheimer's, Parkinson's, multiple sclerosis and epilepsy. Researchers will find the book particularly valuable as it delves into the methodologies and technical aspects of GNNs, showcasing their ability to handle diverse data sources including genetic, imaging and clinical information. By integrating these datasets, GNNs reveal hidden patterns and biomarkers, offering new avenues for research and potential therapeutic targets.A Guide to Graph Neural Networks for Neurological Disorders addresses the challenge of missing data, a common issue in neurological research, and demonstrates how GNNs can manage and mitigate these gaps. For students, both undergraduate and postgraduate, the book serves as an educational tool, providing clear explanations and practical examples that make complex concepts accessible. It equips the next generation of neuroscientists and data scientists with the knowledge and skills needed to contribute to this rapidly evolving field. The book aims to provide a foundational understanding of GNNs, demonstrate their practical applications in neurology, and inspire further research and innovation. By bridging the gap between AI and medical practice, the book empowers readers to leverage cutting-edge technology in the quest to understand and treat neurological illnesses, ultimately enhancing the quality of care and advancing the field of neuroscience.

This book showcases the most recent developments in the application of artificial intelligence to cardiology and medical imaging, with an emphasis on precise diagnosis, early prediction, and patient-centered care. In order to overcome clinical data ambiguity and enhance confidence in automated systems, it presents innovative frameworks that combine deep learning, fuzzy graph neural networks, metaheuristic optimization, and explainable AI. This book bridges the gap between state-of-the-art research and practical healthcare applications by covering a wide range of techniques, including CNNs, RNNs, residual networks, federated learning, and multimodal learning. As a research reference and a manual for implementing AI-driven healthcare solutions, it provides useful tools, datasets, and methodologies that foster innovation in precision medicine and medical decision-making. It is designed for researchers, clinicians, and students.