Mobin Akhtar (24 results)

Condition: New. pp. 84.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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

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

Condition: New.

Paperback. Condition: New.

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

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

Condition: New.

Hardback. Condition: New.

Paperback. Condition: New.

Hardback. Condition: New.

Condition: New. Print on Demand pp. 84 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 84.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zamani Abu SarwarAbu Sarwar Zamani was born in Bihar, India. He is working as a Lecturer in Dept. of Computer Sc. ( Al Quwaiyyah) in Shaqra University, KSA. He worked in Dept. of Computer Sc. in King Saud University, KSA. He did Mast.…

Paperback. Condition: new. Paperback. In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, 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.…

Hardcover. Condition: new. Hardcover. In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, 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. Condition: new. Paperback. In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, 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.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Here, in this proposed scheme the significant advantage is that the decoding operation is all-optical in nature. The architecture can be used for generalized decoding scheme. This scheme can easily and successfully be extended and implemented for any higher number of input digits by proper incorporation of NOLM based optical switches, vertical and horizontal extension of the tree and by suitable branch selection. It is also very interesting that the reverse tree architecture can be used for all-optical encoding scheme. It is important to note that the above discussions are based on simple model. In order to experimentally achieve result from the proposed scheme, some design issues have to be considered. For example, predetermined values of the intensities / wavelength of laser light for control and incoming signals, introduction of filter , polarization properties of fiber, intensity losses due to beam splitters/fiber couplers etc. CW beam of wave length 1535 nm and pulsed signal of wave length 1500 nm can be used as incoming and control signal respectively. Intensity losses due to beam splitters/couplers may not create much trouble in producing the desired optical bits.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 84 pp. Englisch.…

Hardcover. Condition: new. Hardcover. In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, 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.…

Taschenbuch. Condition: Neu. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows | Abu Sarwar Zamani (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337370477 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, policymakers, and more.…

Buch. Condition: Neu. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows | Abu Sarwar Zamani (u. a.) | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337370460 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In a world where data never stops moving, the ability to act on information in real time has become a defining advantage. Financial systems must detect fraud the instant it happens, healthcare platforms must monitor patients continuously, and supply chains must adapt to disruptions as they unfold. Success no longer depends only on collecting data, it depends on transforming it into actionable intelligence in milliseconds. Artificial intelligence (AI) bridges this gap. By embedding AI inference into real-time data pipelines, organizations can move from reacting to events to anticipating them, making decisions that are faster, smarter, and more reliable. Harnessing AI Inference for Intelligent Decision-Making in Real-Time Dataflows examines how AI inference can be seamlessly embedded into real-time dataflows to enable faster, smarter, and more responsible decision-making. This book bridges theoretical advancements, emerging technologies, and real-world use cases, providing a roadmap for designing and managing AI-driven systems that operate at the edge of speed, scale, and responsibility. Covering topics such as smart cities, federated learning, and the internet of things (IoT), this book is an excellent resource for graduate and doctoral students, researchers, engineers, developers, system architects, business leaders, policymakers, and more.…