Akansha Singh – författare

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide.

The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intelligence systems for healthcare. The book focuses on using Artificial Intelligence to improve diagnosis, prevent diseases, and personalize patient care. It addresses potential drawbacks, like reduced human interaction and ethical concerns, offering solutions for ethical and transparent Artificial Intelligence use in medicine. Across eight chapters, the book explores Artificial Intelligence's current status, its importance, and associated risks in healthcare. It explains designing reliable Artificial Intelligence for healthcare, tackling biases, and safeguarding patient privacy in the age of big data. The legal and regulatory landscape is also covered. One chapter is dedicated to showcasing real-world examples of responsible Artificial Intelligence in healthcare, highlighting best practices. The book concludes by summarizing key takeaways and discussing future challenges. "Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection" is a valuable resource for healthcare professionals, policymakers, computer scientists, and ethicists concerned about Artificial Intelligence's ethical and societal impact on medicine.Gives insights into the responsible and explainable use of Artificial Intelligence in healthcare and explore the challenges and opportunities for promoting ethical and transparent practices in this fieldOffers the solution to strike a balance between patient privacy and data exchangeProvides concrete advice on how to create trustworthy, accountable, and transparent Artificial Intelligence systemsExplains the moral and social effects of Artificial intelligence in healthcare and suggests ways to encourage its ethical application

Multimodal Data Fusion in Healthcare: AI Approaches for Precision Diagnosis explores the transformative potential of AI in modern medicine by integrating diverse data sources such as medical imaging, genomics, EHRs, and wearable sensors. It highlights how AI technologies are revolutionizing healthcare systems through personalized and proactive diagnostics. The book covers cutting-edge methodologies, real-world applications, and the challenges of multimodal data fusion. Topics include AI-driven diagnostics, precision medicine, real-time patient monitoring, and the integration of clinical, genomic, and wearable data, providing both theoretical foundations and practical insights. This book is essential for healthcare professionals, data scientists, and engineers, offering clear frameworks for integrating diverse data types. It addresses crucial issues like data interoperability, privacy, and technical constraints, providing practical solutions. It serves as an invaluable reference for understanding and applying AI advancements in diagnostic precision and personalized medicine.Explores cutting-edge AI methodologies and their real-world applications in healthcare diagnosticsProvides comprehensive frameworks for integrating multimodal data, including medical imaging, genomics, EHRs, and wearable sensorsAddresses critical issues such as data interoperability, privacy, and technical constraints, offering practical solutions for academic and clinical settings

Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.

MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAIN The unique aspect of this book is that its focus is the convergence of machine learning, IoT, and blockchain in a single publication. Blockchain technology and the Internet of Things (IoT) are two of the most impactful trends to have emerged in the field of machine learning. Although there are a number of books available solely on the subjects of machine learning, IoT and blockchain technology, no such book has been available which focuses on machine learning techniques for IoT and blockchain convergence until now. Thus, this book is unique in terms of the topics it covers. Designed as an essential guide for all academicians, researchers, and those in industry who are working in related fields, this book will provide insights into the convergence of blockchain technology and the IoT with machine learning. Highlights of the book include: Examines many industries such as agriculture, manufacturing, food production, healthcare, the military, and ITSecurity of the Internet of Things using blockchain and AIDeveloping smart cities and transportation systems using machine learning and IoTAudience The target audience of this book is professionals and researchers (artificial intelligence specialists, systems engineers, information technologists) in the fields of machine learning, IoT, and blockchain technology.

BLOCKCHAIN and DEEP LEARNING for SMART HEALTHCARE The book discusses the popular use cases and applications of blockchain technology and deep learning in building smart healthcare. The book covers the integration of blockchain technology and deep learning for making smart healthcare systems. Blockchain is used for health record-keeping, clinical trials, patient monitoring, improving safety, displaying information, and transparency. Deep learning is also showing vast potential in the healthcare domain. With the collection of large quantities of patient records and data, and a trend toward personalized treatments. there is a great need for automated and reliable processing and analysis of health information. This book covers the popular use cases and applications of both the above-mentioned technologies in making smart healthcare. Audience Comprises professionals and researchers working in the fields of deep learning, blockchain technology, healthcare & medical informatics. In addition, as the book provides insights into the convergence of deep learning and blockchain technology in healthcare systems and services, medical practitioners as well as healthcare professionals will find this essential reading.

Stay ahead of the curve in urban innovation with this essential guide that provides a comprehensive roadmap for federated learning and blockchain to build secure, intelligent, and efficient smart city ecosystems. As cities grow smarter, the demand for secure, decentralized, and privacy-preserving technologies is greater than ever. This book explores how federated learning and blockchain are transforming urban landscapes by enabling intelligent, secure, and efficient systems. By combining the power of decentralized machine learning with the transparency and security of blockchain, this book provides a roadmap for tackling challenges in urban mobility, energy management, public safety, and healthcare, delving into theoretical frameworks, architectural designs, security considerations, and real-world case studies to illustrate the impact of these technologies. This book serves as a comprehensive guide for researchers, industry professionals, and policymakers seeking to understand, implement, and innovate within smart city ecosystems. Readers will find the volume: Explores the synergy between federated learning and blockchain, offering cutting-edge solutions for smart city challenges;Addresses critical issues of data privacy, decentralized AI, and secure digital infrastructure in urban environments;Features practical case studies on smart transportation, energy management, healthcare, and governance;Provides a forward-looking perspective on how emerging technologies will shape the cities of tomorrow.Audience Academics, researchers, industry professionals, and policymakers working in the fields of artificial intelligence, machine learning, blockchain, IoT, cybersecurity, smart city planning, and urban technology development.

This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide.The authors explore the convergence of IoT and deep learning to enable things to communicate, share information, and coordinate decisions. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Chapters look at assistive devices in healthcare, alerting and detection devices, energy efficiency in using IoT, data mining for gathering health information for individuals with autism, IoT for mobile applications, and more. The text also offers mathematical and conceptual background that presents the latest technology as well as a selection of case studies.

This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide.The authors explore the convergence of IoT and deep learning to enable things to communicate, share information, and coordinate decisions. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Chapters look at assistive devices in healthcare, alerting and detection devices, energy efficiency in using IoT, data mining for gathering health information for individuals with autism, IoT for mobile applications, and more. The text also offers mathematical and conceptual background that presents the latest technology as well as a selection of case studies.

An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing.The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.

Aquatic plants are plants that grow in and around water and have structural, functional, and economical roles in an aquatic ecosystem. This book covers biology and the importance of aquatic plants in food, health, and biofuel industries. This book also discusses their role in water treatment and resource recovery. This book also enlightens the readers about the different types and uses of aquatic plants. Additionally, this will provide highlights on the anatomy of aquatic plants, anatomical adaptations of aquatic plants, life forms of the aquatic plants, evolution, distribution and diversity of aquatic plants, cellular biology, physiology, and morphological adaptions of the aquatic plants. This book also sheds light on the future economic and environmental impacts of microalgal technology. This book also mentions the impact of changing the global environment on the water plants, environmental impacts of the microalgae technology, impacts to aquatic biodiversity, and terrestrial impacts. This book is an important resourse material for students of undergraduate, postgraduate and research scholars working the field of hydroplant ecosystem. This book has been designed to suit the knowledge and pursuit of the researcher and scholars and to empower them with various aspects of the aquatic plants, so that they are updated with the information.

Over the course of the last few decades, the incorporation of genetics, genomics, proteomics, and statistics into the field of bioinformatics has had a significant impact on both the field of biology and the field of medicine. The primary application for it is in assisting with the modeling, forecasting, and analysis of enormous amounts of multidimensional biological data utilizing the most recent and cutting-edge computational technologies. The majority of life science students and researchers do not have the skills necessary to make effective use of this sophisticated technology, which is why bioinformatics is not yet widely incorporated in academic courses despite the fact that it holds enormous promise. This book was written after realizing that there was a void in the market for a resource that could provide students and researchers with prior knowledge of the biosciences with a place to begin their studies. The book is an excellent resource that students and young professionals can use in their pursuit of knowledge on this ever-changing topic.

The book Genetics Basics: Exploring DNA & Heredity offers an engaging and comprehensive introduction to the fundamental principles of genetics, tailored for students, enthusiasts, and professionals in the field of genetics. The book begins by tracing the historical milestones and key figures that have shaped the field of genetics, highlighting how the study of heredity has transformed our understanding of life. It delves into the structure and function of DNA, elucidating the double-helix model and the role of nucleotides as the blueprint of life. The principles of heredity are explored through Mendelian genetics, including dominant and recessive traits, followed by an examination of more complex genetic phenomena such as codominance, incomplete dominance, and sex-linked inheritance. The genetic code is unpacked, detailing the processes of transcription and translation that convert DNA information into functional proteins. Additionally, the book addresses gene mutations, their impact on genetic diversity, and the mechanisms of DNA repair that maintain genetic stability. The final chapters connect genetics to the cell cycle, illustrating how genetic information is accurately transmitted during cell division. Overall, this book serves as a foundational resource, demystifying the complexities of genetics and fostering a deeper appreciation for the genetic mechanisms that underpin biological diversity and inheritance.

Explainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways.Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems.This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI.

This book stands at the forefront of AI research, offering a comprehensive examination of multimodal generative technologies. A core highlight of this work is its detailed discourse on integrating visual and textual models, laying out state-of-the-art techniques for creating cohesive, multimodal AI systems.

Several advanced crop breeding technologies like, marker-assisted backcross breeding, marker-assisted recurrent selection, genomic assisted breeding, haplotype breeding and genome editing technologies have been introduced and employed for rice productivity improvement.