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.
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Dr. Abu Sarwar Zamani is currently working as an Asst. Professor at Prince Sattam bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia and as an Adjunct Senior Fellow in Kulliyyah of Engineering at International Islamic University Malaysia, Gombak, Malaysia. Previously he worked as a Senior Lecturer in Shaqra University and King Saud University, Kingdom of Saudi Arabia. He received his Ph.D. in Computer Science from Pacific Academy of Higher Education and Research University, India and Master in Computer Science from Hamdard University, New Delhi, India in 2007. His research interests are, AI, ML, IoT, Health Informatics, Big Data, Cloud Computing and Data Mining. He has more than 15 years of experience in teaching, research, and industries. He has published 100+ research papers in many reputed journals in high indexed outlets. He has around three international patents from Australia and India. He has completed numerous research projects that were financed by Saudi Arabia's Ministry of Education's Deanship of Scientific Research. He is currently working for numerous reputable journals, such as Springer, Elsevier, and MDPI, as an Academic Editor, Associate Editor, and Guest Editor.
Professor Aisha Hassan Abdalla Hashim received her Ph.D in Computer Engineering (2007), M.Sc. in Computer Science (1996) and B.Sc. in Electronics Engineering (1990). She won the Best Graduating Ph.D Student Award during the IIUM Convocation ceremony in 2007. She joined IIUM in 1997 and is currently a Professor at the Department of Electrical and Computer Engineering. Professor Aisha has taught several courses related to Communication and Computer Engineering and is actively involved in curriculum development and programme accreditation. She has been a member of the Department Board of Studies for several years. She received the Best Teacher Award during IIUM Quality Day in 2007. Prof. Aisha has been appointed as external examiner/visiting professor/adjunct professor at different universities. Professor Aisha who is actively involved in research and postgraduate programmes, has published more than 200 journal/conference papers, and supervised/co-supervised more than 40 Ph.D/Master's students. She received the Promising Researcher Award in 2009 during IIUM Quality Day. She has also received many medals/awards in different national/international research exhibitions. One of her research exhibitions won the Promising Commercial Value Award (Second Runner Up) in IRIIE 2014. As a researcher, she has secured research grants from IIUM, Ministry of Higher Education (MOHE) and Ministry of Science, Technology and Innovation (MOSTI). She has actively contributed as a reviewer /technical committee member in many journals/conferences. Professor Aisha has established several teaching/ research networks between IIUM and overseas universities. She has been appointed as IIUMÂ Internationalisation Ambassador to Sudan (October 2014) and has participated in initiating several MoUs as well as encouraging the PhD Student Mobility programme between IIUM and Sudanese Universities. Professor Aisha also participates in community services. She was appointed as a Board of Studies member at the International Islamic School, Malaysia. She also served as Parent/Teacher Committee member at the school for more than 10 years.
Dr. Md. Mobin Akhtar is Assistant professor of Computer Science at Riyadh Elm University (REU) Saudi Arabia with over 10 years of experience in Teaching. Before joining REU in Sep. 2018, he served Shaqra University as a Lecturer of Computer Science from August. 2009 to Sep. 2016, Md. Mobin Akhtar received his Master degree, M.Sc.Tech (IMCA), from JAMIA MILLIA ISLAMIA, NEW DELHI in 2008, and PHD degree in Computer Science from pacific university Udaipur, Rajasthan in JAN. 2019. His research interests span over the areas of Online Information System Development, Big Data, Internet of thing(IOT), IOMT and Cloud Computing. His current interests are in Artificial intelligence, and Big data. Besides teaching, he enjoys giving tech talks, reading biographies, reading world history and reading about new technology articles on the internet. His teaching abilities include innovative skills and extensive use of technology in teaching.
Dr. Hazra Imran is an Associate Professor of Khoury College of Computer Sciences, Northeastern University, Vancouver Canada. She has instructed over fifty courses in computer science at University of British Columbia (Canada), Langara College (Canada), Douglas College (Canada) and Hamdard University (India), with a class size ranges from 35 to 160. As an Assistant Professor of Computer Science at Hamdard University, she has supervised several graduate and under graduate students. She completed her Postdoctoral fellowship at Athabasca University in Canada, funded by MITACS under the guidance of Prof. Sabine Graf and Prof. Kinshuk at Athabasca University. She received PhD from School of Computer and Systems Sciences Jawaharlal Nehru University (JNU), New Delhi, India in 2012.
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Paperback. Condición: 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. Nº de ref. del artículo: 9798337370477
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Paperback. Condición: 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. Nº de ref. del artículo: 9798337370477
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Paperback. Condición: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9798337370477
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 134912163
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9798337370477
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