Synopsis
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.
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About the Author
Dr. Shikha Khullar is an accomplished academic and researcher with a Ph.D. in Data Mining and Artificial Intelligence. She currently serves as an Associate Professor at Poornima University, where she is recognized for her dedication to academic excellence and her dynamic involvement in radical research. With a deep-rooted passion for innovation and discovery, Dr. Khullar actively engages in interdisciplinary research that bridges technology and real-world applications, particularly in areas such as intelligent systems, smart data analytics, and sustainable digital solutions. Her work is characterized by a forward-thinking approach, where she not only explores theoretical models but also contributes to practical implementations that address pressing societal and industrial challenges. At Poornima University, she has been instrumental in mentoring young minds, guiding Ph.D. scholars, and contributing to curriculum development in emerging areas such as Artificial Intelligence, Blockchain, and Smart Agriculture Technologies. She is the member of IAENG and ERDA.
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Hardcover. Condition: 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. Seller Inventory # 9798260000083
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Hardcover. Condition: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798260000083
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Buch. Condition: Neu. Hybrid AI Architectures for Intelligent Systems | Surbhi Bhatia Khan (u. a.) | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260000083 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134998609