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portada Applying AI-Based IoT Systems to Simulation-Based Information Retrieval
Type
Physical Book
Publisher
Author
Language
English
Pages
256
Format
Hardcover
Dimensions
25.4 x 17.8 x 1.6 cm
Weight
0.66 kg.
ISBN13
9781668452554
Categories

Applying AI-Based IoT Systems to Simulation-Based Information Retrieval

Madhulika, Bhatia ; Surabhi, Bhatia ; Tanwar, Poonam (Author) · IGI Global · Hardcover

Applying AI-Based IoT Systems to Simulation-Based Information Retrieval - Madhulika, Bhatia ; Surabhi, Bhatia ; Tanwar, Poonam

New Book Imported to Austria
Delivery: 07 Aug - 11 Aug Shipping: 4 to 5 business days.
339,49 €
Import costs and 10% VAT included in the price ✅
339,49 €

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Synopsis "Applying AI-Based IoT Systems to Simulation-Based Information Retrieval"

Communication based on the internet of things (IoT) generates huge amounts of data from sensors over time, which opens a wide range of applications and areas for researchers. The application of analytics, machine learning, and deep learning techniques over such a large volume of data is a very challenging task. Therefore, it is essential to find patterns, retrieve novel insights, and predict future behavior using this large amount of sensory data. Artificial intelligence (AI) has an important role in facilitating analytics and learning in the IoT devices. Applying AI-Based IoT Systems to Simulation-Based Information Retrieval provides relevant frameworks and the latest empirical research findings in the area. It is ideal for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society and trust at the levels of the global economy, networks and organizations, teams and work groups, information systems, and individuals as actors in the networked environments. Covering topics such as blockchain visualization, computer-aided drug discovery, and health monitoring, this premier reference source is an excellent resource for business leaders and executives, IT managers, security professionals, data scientists, students and faculty of higher education, librarians, hospital administrators, researchers, and academicians.

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