Nidhi Srivastava – författare

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource on the challenges and mitigation strategies involved in their design. The book's authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models. In essence, the goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety.Covers foundational concepts, advanced theories, and real-world applications, ensuring readers gain a thorough understanding of AI/ML as it applies to Safety-Critical SystemsPresents both the risks and advantages of implementing machine learning techniques in Safety-Critical SystemsPresents machine learning techniques in Safety-Critical Systems across domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many othersDemonstrates how to meet legal certification and regulatory requirements for the appropriate ML models

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality?Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality?Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

Oxidative stress is an imbalance caused by the body's failure to effectively eliminate reactive products generated from the formation of reactive oxygen species (ROS) during cellular metabolism. ROS, reactive nitrogen species (RNS), and RSS are the three primary categories of oxidants produced within the body.This book summarizes the challenges and limitations of targeting oxidative stress. In addition, the chapters discuss the impact of different oxidants and oxidative stress in different fields of human disease, especially as a global therapeutic to treat various chronic disease managed with drugs. Furthermore, this book explores a wide range of promising approaches and explores the mechanism behind the oxidative stress based on pharmacological properties and highlights its application in the medical field. It serves as a resource for graduates, clinical researchers, industry professionals, and anyone working on antioxidant activity of enzymes or understanding the role of oxidative stress in diseases like cancer.

The edited book is a consolidated handbook on quantum computing that covers quantum basic science and mathematics to advanced concepts and applications of quantum computing and quantum machine learning applied to diverse domains.

The edited book is a consolidated handbook on quantum computing that covers quantum basic science and mathematics to advanced concepts and applications of quantum computing and quantum machine learning applied to diverse domains.

Additionally, the book includes studies related to the potential of microbial nanomaterials for novel cancer therapies, revolutionizing diagnostics and imaging, sustainable agriculture, plant disease control, toxicant mitigation, nanobiosensing and water purification.