khullar shikha (62 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: new. Paperback. Single-paradigm AI solutions no longer sufficiently address the complexity, scale, and dynamics of modern intelligent systems. When deployed in isolation, traditional paradigms often encounter limitations related to scalability, energy efficiency, latency, interpretability, or robustness. The future of AI lies in integration through hybrid architectures that unify complementary strengths across computational, algorithmic, and infrastructural domains. Hybrid systems promote adaptive coordination between modalities, infrastructures, and learning paradigms. Further exploration may reveal how hybridization enables systems to respond dynamically to environmental feedback and evolving workloads. Hybrid AI Architectures for Intelligent Systems explores the conceptual foundations, architectural innovations, performance optimizations, and future directions of hybrid AI systems. It examines how hybrid AI redefines current frameworks for future intelligent systems. This book covers topics such as cyber threat detection, edge computing, and smart cities, and is a useful resource for engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. As societies become increasingly dependent on interconnected digital systems, ensuring their resilience and security is essential for economic stability, public trust, and continuity of critical services. Addressing evolving cyber threats, system failures, and data vulnerabilities helps protect sensitive information while enabling organizations to adapt and recover during disruptions. A multidisciplinary approach to secure digital ecosystems supports innovation without sacrificing reliability, privacy, or scalability. Strengthening these digital foundations ultimately benefits individuals, businesses, and governments by fostering safer, more trustworthy, and sustainable digital environments in an increasingly complex technological landscape. Resilient Privacy-Preserving Mechanisms for Digital Identity Management provides a comprehensive platform that integrates theoretical frameworks, innovative strategies, and practical applications aimed at enhancing the resilience and security of modern digital infrastructures. By drawing upon multidisciplinary perspectives, it addresses critical challenges in ensuring reliability, adaptability, and protection in digital systems. Covering topics such as identity management, network security, and privacy, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, technology developers, IT professionals, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. As artificial intelligence (AI) systems confront the limits of classic computing, emerging models that integrate neuromorphic AI with quantum computing create a compelling path forward. Neuromorphic tools inspire the dynamics of the human brain, while quantum computing promises speedups for certain classes of complex optimization and probabilistic problems. By uniting these models, hybrid neuromorphic-quantum models aim to overcome efficiency and learning constraints that challenge each approach in isolation. Emerging Hybrid Models for Neuromorphic AI and Quantum Computing explores two technological revolutions, AI, powered by intricate networks of artificial neurons, and quantum computing, which harnesses the counterintuitive principles of quantum mechanics. It examines the future of computation within sophisticated hybrid models. Covering topics such as AI, quantum computing, and hybrid models, this book is an excellent resource for software developers, technology developers, business organizations, academicians, researchers, and graduate students. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. Single-paradigm AI solutions no longer sufficiently address the complexity, scale, and dynamics of modern intelligent systems. When deployed in isolation, traditional paradigms often encounter limitations related to scalability, energy efficiency, latency, interpretability, or robustness. The future of AI lies in integration through hybrid architectures that unify complementary strengths across computational, algorithmic, and infrastructural domains. Hybrid systems promote adaptive coordination between modalities, infrastructures, and learning paradigms. Further exploration may reveal how hybridization enables systems to respond dynamically to environmental feedback and evolving workloads. Hybrid AI Architectures for Intelligent Systems explores the conceptual foundations, architectural innovations, performance optimizations, and future directions of hybrid AI systems. It examines how hybrid AI redefines current frameworks for future intelligent systems. This book covers topics such as cyber threat detection, edge computing, and smart cities, and is a useful resource for engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. As societies become increasingly dependent on interconnected digital systems, ensuring their resilience and security is essential for economic stability, public trust, and continuity of critical services. Addressing evolving cyber threats, system failures, and data vulnerabilities helps protect sensitive information while enabling organizations to adapt and recover during disruptions. A multidisciplinary approach to secure digital ecosystems supports innovation without sacrificing reliability, privacy, or scalability. Strengthening these digital foundations ultimately benefits individuals, businesses, and governments by fostering safer, more trustworthy, and sustainable digital environments in an increasingly complex technological landscape. Resilient Privacy-Preserving Mechanisms for Digital Identity Management provides a comprehensive platform that integrates theoretical frameworks, innovative strategies, and practical applications aimed at enhancing the resilience and security of modern digital infrastructures. By drawing upon multidisciplinary perspectives, it addresses critical challenges in ensuring reliability, adaptability, and protection in digital systems. Covering topics such as identity management, network security, and privacy, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, technology developers, IT professionals, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. Single-paradigm AI solutions no longer sufficiently address the complexity, scale, and dynamics of modern intelligent systems. When deployed in isolation, traditional paradigms often encounter limitations related to scalability, energy efficiency, latency, interpretability, or robustness. The future of AI lies in integration through hybrid architectures that unify complementary strengths across computational, algorithmic, and infrastructural domains. Hybrid systems promote adaptive coordination between modalities, infrastructures, and learning paradigms. Further exploration may reveal how hybridization enables systems to respond dynamically to environmental feedback and evolving workloads. Hybrid AI Architectures for Intelligent Systems explores the conceptual foundations, architectural innovations, performance optimizations, and future directions of hybrid AI systems. It examines how hybrid AI redefines current frameworks for future intelligent systems. This book covers topics such as cyber threat detection, edge computing, and smart cities, and is a useful resource for engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. As societies become increasingly dependent on interconnected digital systems, ensuring their resilience and security is essential for economic stability, public trust, and continuity of critical services. Addressing evolving cyber threats, system failures, and data vulnerabilities helps protect sensitive information while enabling organizations to adapt and recover during disruptions. A multidisciplinary approach to secure digital ecosystems supports innovation without sacrificing reliability, privacy, or scalability. Strengthening these digital foundations ultimately benefits individuals, businesses, and governments by fostering safer, more trustworthy, and sustainable digital environments in an increasingly complex technological landscape. Resilient Privacy-Preserving Mechanisms for Digital Identity Management provides a comprehensive platform that integrates theoretical frameworks, innovative strategies, and practical applications aimed at enhancing the resilience and security of modern digital infrastructures. By drawing upon multidisciplinary perspectives, it addresses critical challenges in ensuring reliability, adaptability, and protection in digital systems. Covering topics such as identity management, network security, and privacy, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, technology developers, IT professionals, policymakers, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. With the growing security challenges at the intersection of distributed machine learning and malicious interference, there are growing challenges that federated learning can address. Federated learning enables collaborative model training across devices while preserving data privacy. However, this decentralized nature also opens new vulnerabilities, particularly to adversarial attacks and data poisoning, where malicious actors can inject corrupted data or manipulate updates to degrade models or extract sensitive information. As the adoption of federated learning accelerates, understanding and these threats are essential to ensure model integrity and resilience in real-world situations. Adversarial AI and Data Poisoning in Federated Learning provides a comprehensive examination of emerging threats, attack vectors, and defense mechanisms within federal learning systems. This book highlights vulnerabilities of federated learning architectures, explores strategies for detection and mitigation of adversarial threats, and presents real-world case studies. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. As artificial intelligence (AI) systems confront the limits of classic computing, emerging models that integrate neuromorphic AI with quantum computing create a compelling path forward. Neuromorphic tools inspire the dynamics of the human brain, while quantum computing promises speedups for certain classes of complex optimization and probabilistic problems. By uniting these models, hybrid neuromorphic-quantum models aim to overcome efficiency and learning constraints that challenge each approach in isolation. Emerging Hybrid Models for Neuromorphic AI and Quantum Computing explores two technological revolutions, AI, powered by intricate networks of artificial neurons, and quantum computing, which harnesses the counterintuitive principles of quantum mechanics. It examines the future of computation within sophisticated hybrid models. Covering topics such as AI, quantum computing, and hybrid models, this book is an excellent resource for software developers, technology developers, business organizations, academicians, researchers, and graduate students. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. With the growing security challenges at the intersection of distributed machine learning and malicious interference, there are growing challenges that federated learning can address. Federated learning enables collaborative model training across devices while preserving data privacy. However, this decentralized nature also opens new vulnerabilities, particularly to adversarial attacks and data poisoning, where malicious actors can inject corrupted data or manipulate updates to degrade models or extract sensitive information. As the adoption of federated learning accelerates, understanding and these threats are essential to ensure model integrity and resilience in real-world situations. Adversarial AI and Data Poisoning in Federated Learning provides a comprehensive examination of emerging threats, attack vectors, and defense mechanisms within federal learning systems. This book highlights vulnerabilities of federated learning architectures, explores strategies for detection and mitigation of adversarial threats, and presents real-world case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. As societies become increasingly dependent on interconnected digital systems, ensuring their resilience and security is essential for economic stability, public trust, and continuity of critical services. Addressing evolving cyber threats, system failures, and data vulnerabilities helps protect sensitive information while enabling organizations to adapt and recover during disruptions. A multidisciplinary approach to secure digital ecosystems supports innovation without sacrificing reliability, privacy, or scalability. Strengthening these digital foundations ultimately benefits individuals, businesses, and governments by fostering safer, more trustworthy, and sustainable digital environments in an increasingly complex technological landscape. Resilient Privacy-Preserving Mechanisms for Digital Identity Management provides a comprehensive platform that integrates theoretical frameworks, innovative strategies, and practical applications aimed at enhancing the resilience and security of modern digital infrastructures. By drawing upon multidisciplinary perspectives, it addresses critical challenges in ensuring reliability, adaptability, and protection in digital systems. Covering topics such as identity management, network security, and privacy, this book is an excellent resource for researchers, academicians, graduate and postgraduate students, technology developers, IT professionals, policymakers, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. Digital ecosystems form the backbone of modern societies, enabling innovation, connectivity, and efficiency across sectors such as cybersecurity, governance, education, healthcare, and supply chain management. At the same time, increasing digital dependence has intensified systemic vulnerabilities related to cyber threats, privacy erosion, governance failures, and operational fragility. Traditional approaches are no longer sufficient in environments characterized by complexity, interdependence, and rapid change. A resilience-oriented perspective integrates technology, governance, and human decision making into adaptive and accountable digital systems. Building Resilient Digital Ecosystems: Security, Governance, and Transformation explores resilience as a core design principle, emphasizing closed-loop architectures, human governance, and ethical accountability. Emerging technologies are examined as components of coordinated ecosystems capable of continuous learning and adaptation. This book covers topics such as machine learning, risk management, and cyber defense, and is a useful resource for business owners, security professionals, engineers, government officials, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.