Advances Applications Data Driven Computing (20 results)

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Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Advances in Applications of Data-Driven Computing | Jagdish Chand Bansal (u. a.) | Taschenbuch | xii | Englisch | 2021 | Springer | EAN 9789813369184 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Paperback. Condition: Brand New. 196 pages. 9.25x6.10x0.46 inches. In Stock.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today's software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.

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Condition: As New. Unread book in perfect condition.

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Hardcover. Condition: Brand New. 464 pages. 9.18x6.12x9.45 inches. In Stock.

Buch. Condition: Neu. Neuware - This book reflects the comprehensive exploration of the intersection between fuzzy decision-making and soft computing. It delves into the theoretical foundations of fuzzy decision-making and soft computing, exploring the underlying principles, algorithms, and mathematical frameworks. The text showcases the latest methodological advances in combining fuzzy decision-making with various soft computing techniques, such as neural networks, and evolutionary computation.This book: - Provides a comprehensive coverage of data-driven decision-making techniques and soft computing methods - Aim at bridging the gap between theoretical concepts and practical implementation by offering real-world case studies, examples, and applications - Highlight emerging trends, recent advancements, and cutting-edge research in the field of data-driven decision-making and soft computing - Emphasize the real-world relevance of Fuzzy Decision Making and Soft Computing by showcasing practical applications across various domains - Showcases applications in domains such as control systems, pattern recognition, optimization, and decision support systems It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and applied mathematics.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today's software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book. 196 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discusses machine and deep learning approaches to data-driven applicationsProvides original works presented at GUCON 2020Serves as a reference for researchers and practitioners in academia and industryDr. Jagdish Chand Bansa.

Condition: New. PRINT ON DEMAND.

Condition: New. Print on Demand.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today's software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 196 pp. Englisch.