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Gesamtkatalog

Catalogue complet

Securing the Connected World

Exploring Emerging Threats and Innovative Solutions

Quantum Computing, Machine Learning, and Bio-inspired Optimization

Advanced Techniques for Optimal Sizing of Analog Integrated Circuits

Medical and Technical Analysis and Signal Processing Perspectives

Exploring Infant Cry

Information Security and Cryptography

[TO BE FINALISED] Lattice-based cryptography is a promising post-quantum cryptographic approach that can withstand potential attacks from future quantum computers. This book will introduce the theoretical mathematics that underpins lattice-based cryptography, such as the hard problems based on lattices upon which the security of lattice-based cryptosystems rely. It will provide examples of how these hard problems can be used to build basic lattice-based cryptographic primitives, namely encryption, digital signatures and key exchange, in addition to more advanced primitives such as identity-based encryption (IBE), attribute-based encryption (ABE) and hierarchical identity-based encryption (HIBE). Subsequently, the practical aspects of designing efficient and robust lattice-based cryptosystems in both hardware and software will be detailed. The resilience of lattice-based implementations to physical attacks will be discussed in addition to appropriate countermeasures that can be integrated into implementations to defend against such attacks. Finally, three case studies will be described to illustrate how lattice-based cryptography can be successfully embedded into real-world scenarios. <ul><li>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Considers both the theory and practical aspects of lattice-based cryptography, which is a very promising post-quantum cryptographic approach</li><li>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Provides a comprehensive theoretical overview of lattice-based cryptography detailing how to build both basic and advanced cryptographic primitives from lattice-based hard problems</li><li>&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Discusses practical implementation approaches for lattice-based cryptography for both hardware and software platforms</li><li>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Describes physical-attack resistant methodologies for lattice-based cryptography</li><li>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Describes how lattice-based cryptography can be utilized in practice and integrated into real-word scenarios, including applications requires real-time speeds (satellite communications) and resource constrained applications (IoT)</li></ul>







Assuming only&nbsp;basic knowledge of cryptography and the mathematics typically taught in undegraduate Computer Science, the book will appeal to scientists and researchers, lecturers and tutors, as well as to students studying the field and to practitioners and professionals looking to learn more about it.

From Theory to Practice

Lattice-Based Cryptography

Machine Learning For Dummies

Key Technologies on New Energy Vehicles

This book focuses on the current advanced technology field of battery operation big data and comprehensively describes the frontier technology of big data analysis and intelligent control of battery safe operation. The content systematically introduces the coupling mechanism of battery system safety and consistency, battery SOH estimation and prediction, battery safety state prediction, battery fault diagnosis, battery system risk pre-warning, and safety control strategy research.This book is suitable for teachers and students of universities and colleges in the automotive and related industries, engineers and technicians in the R&amp;D departments of enterprises or automotive enthusiasts to read and refer.The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.

Big Data Analysis and Application of Power Battery Operation Safety

Retrieval-Augmented Generation (RAG) represents the cutting edge of AI innovation, bridging the gap between large language models (LLMs) and real-world knowledge. This book provides the definitive roadmap for building, optimizing, and deploying enterprise-grade RAG systems that deliver measurable business value.This comprehensive guide takes you beyond basic concepts to advanced implementation strategies, covering everything from architectural patterns to production deployment. You'll explore proven techniques for document processing, vector optimization, retrieval enhancement, and system scaling, supported by real-world case studies from leading organizations.Key Learning Objectives<ul><li>Design and implement production-ready RAG architectures for diverse enterprise use cases</li><li>Master advanced retrieval strategies including graph-based approaches and agentic systems</li><li>Optimize performance through sophisticated chunking, embedding, and vector database techniques</li><li>Navigate the integration of RAG with modern LLMs and generative AI frameworks</li><li>Implement robust evaluation frameworks and quality assurance processes</li><li>Deploy scalable solutions with proper security, privacy, and governance controls</li></ul>Real-World Applications<ul><li>Intelligent document analysis and knowledge extraction</li><li>Code generation and technical documentation systems</li><li>Customer support automation and decision support tools</li><li>Regulatory compliance and risk management solutions</li></ul>Whether you're an AI engineer scaling existing systems or a technical leader planning next-generation capabilities, this book provides the expertise needed to succeed in the rapidly evolving landscape of enterprise AI.What You Will Learn<ul><li>Architecture Mastery: Design scalable RAG systems from prototype to enterprise production</li><li>Advanced Retrieval: Implement sophisticated strategies, including graph-based and multi-modal approaches</li><li>Performance Optimization: Fine-tune embedding models, vector databases, and retrieval algorithms for maximum efficiency</li><li>LLM Integration: Seamlessly combine RAG with state-of-the-art language models and generative AI frameworks</li><li>Production Excellence: Deploy robust systems with monitoring, evaluation, and continuous improvement processes</li><li>Industry Applications: Apply RAG solutions across diverse sectors, including finance, healthcare, legal, and technology</li></ul>

Advanced Techniques and Production-Ready Solutions for Enterprise AI

Mastering Retrieval-Augmented Generation

Applications in R and Python

Essentials of Big Data Analytics

Machine Learning und Künstliche Intelligenz sind mehr als nur Schlagworte – sie sind Schlüsseltechnologien, die unsere Wirtschaft und Gesellschaft nachhaltig verändern. Dennoch zögern viele Unternehmen, diese Potenziale zu nutzen. Woran liegt das? Häufig fehlt es an Know-how, Vertrauen und einem klaren Verständnis der Methoden und Einsatzmöglichkeiten.Dieses Buch vermittelt praxisnahes Know-how für Entscheider und Interessierte, die verstehen wollen, wie Machine Learning funktioniert und welchen Nutzen es für Unternehmen stiften kann. Mit der kostenlosen Programmiersprache Python als Werkzeug werden reale Business Cases wie Hauspreisprognosen (Regression), Kreditrisikobewertung (Klassifikation), Kundensegmentierung (Clustering), Cross-Selling (Empfehlungsmaschinen) und Dynamic Pricing (Bestärkendes Lernen) nachvollziehbar erklärt.Darüber hinaus werden Best Practices zur Datenvorverarbeitung sowie die grundlegende Methodik des Machine Learnings vorgestellt. So entsteht ein fundiertes Verständnis, das hilft, Vertrauen in die Technologie aufzubauen. Ein Buch für alle, die verstehen wollen, wie aus Daten echte Entscheidungen werden.Der Inhalt<ul><li>Nutzen von Machine Learning und KI</li><li>Best Practices</li><li>Python-Grundlagen</li><li>Erstellung von KI-Anwendungen</li><li>Anwendung von Machine Learning</li></ul>Der Autor Bernd Heesen ist Professor an der wirtschaftswissenschaftlichen Fakultät der Hochschule Ansbach in Bayern. Vor seiner Tätigkeit an der Hochschule war er mehr als 10 Jahre als Unternehmensberater im In- und Ausland tätig. Er berät Unternehmen auch weiterhin bezüglich der Nutzung von IT-Innovationen.

Erstellung eigener Anwendungen mit Python

Künstliche Intelligenz im Business

An authoritative deep dive into the most recent machine learning approaches to hyperspectral remote sensing image processing In Machine Learning-Based Hyperspectral Image Processing, distinguished researcher Dr. Bing Zhang delivers an up-to-date discussion of machine learning-based approaches to hyperspectral image analysis. The author comprehensively reviews machine learning approaches to hyperspectral image denoising and super-resolution tasks, offering coverage of a variety of perspectives. Dr. Zhang also explores the most recent research on machine learning hyperspectral unmixing methods and hyperspectral image classification. He explains the algorithms used for hyperspectral image target and change detection, as well. Readers will also find: <ul> <li>A thorough introduction to the novel concept of applying advanced machine learning techniques to the analysis of hyperspectral imagery</li> <li>Comprehensive explorations of the most recent developments in this technology and its applications</li> <li>Practical discussions of how to effectively process and extract valuable insights from hyperspectral data</li> <li>Complete treatments of a variety of hyperspectral remote sensing image processing tasks, including classification, target detection, and change detection.</li> </ul> Perfect for postgraduate students and research scientists with an interest in the subject, Machine Learning-Based Hyperspectral Image Processing will also benefit researchers, academicians, and students engaged in machine learning-based approaches to image analysis.

Machine Learning-Based Hyperspectral Image Processing

Unlock the power of Claude, an advanced conversational AI assistant, and integrate it into your daily professional workflows and decision-making. This book offers a clear, relatable path, regardless of your technical background, from first prompt to advanced user, through a guided, real-life learning approach that explains the AI learning curve.You’ll start with an overview of Claude’s capabilities. In Part I, Claude Fundamentals introduces you to Claude AI in a clear, beginner-friendly way, using relatable comparisons and real examples. It guides you through initial setup, early conversations, and key lessons learned from trial and error. The section also explores prompt design, showing how skills evolve from basic to advanced, with practical before-and-after insights. &nbsp;Part II dives into hands-on applications—writing, research, coding, creativity, and data analysis—demonstrating how Claude can support a wide range of professional and personal use cases.&nbsp;Part III then introduces advanced strategies like prompt chaining and feature optimization, while Part IV explores professional domains including business, education, and artistic collaboration. In Part V, you’ll gain insights into troubleshooting, responsible AI use, and keeping up with rapid AI advancements. Finally, Part VI synthesizes the full journey, offering guidance on becoming a true power user and shaping the future of human-AI collaboration.&nbsp;Through transparent storytelling, tested frameworks, and actionable strategies, this practical guide will empower you to turn Claude AI from a tool into a transformative partner.&nbsp;What You Will Learn&nbsp;<ul><li>Use Claude AI effectively, even with no technical background or coding experience </li><li>Master prompting techniques that evolve from simple queries to advanced, optimized conversations </li><li>Apply Claude in writing, research, coding, creativity, and data analysis with real-life examples </li><li>Explore Claude’s advanced features and integrate them into daily professional workflows and decision-making </li></ul>

Practical Journey from First Prompts to Pro with Claude AI

Mastering Claude AI

Concepts, Models, and Applications

Learning-Driven Game Theory for AI

Learn how to manage the risks and opportunities of agentic AI with this practical guide to ensuring the safe and beneficial developments of advanced AI systems.

Principles and Responsible Practices

Safer Agentic AI

Securing the Connected World

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