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This book presents a semantic-based explainable framework based on knowledge graph and semantic web technology focusing on designing XAI system that... Læs mere
The book discusses converter design, control techniques, and other functionality augmented in... Læs mere
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package... Læs mere
This book explains how the Z Garbage Collector was designed, why it was designed this way, the common pitfalls to avoid when using ZGC in JDK 25 and how to get useful... Læs mere
This book covers deep learning-based communication frameworks for multiuser detection and sparse channel estimation and elaborates discussion on deep learning-based ultra-dense cell communication and sensor networks and ad-hoc communication.
This book takes readers on an exploratory journey into the transformative potential of Deep Generative Models (DGMs), hybrid architectures, and Foundation Models (FMs) to redefine the future urban landscape.
Research Advances in Intelligent Computing: Volume 3 explores this dynamic field, where artificial intelligence (AI) and computational models converge to create systems capable of learning, reasoning, and problem-solving.
ICADAC 2025 has received a remarkable number of high-quality submissions from across the globe, reflecting the growing interest and ongoing innovation in this dynamic domain.
This book explores how Pega Cloud 3 leverages agentic AI, Google Cloud Platform and Amazon Web Services to... Læs mere
The Streamlit framework empowers Python developers to build web applications quickly without writing a single line of HTML, CSS, or JavaScript. There’s no need to switch stacks just... Læs mere
Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles.
The text presents various methods for applying machine learning and deep learning techniques to heterogeneous agricultural data and incorporating advanced Internet of Things-enabled technologies for web and mobile based applications for crop lifecycle tracking.