Search results for "Generative AI System Design"
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R 672.00
Generative AI for Film and VFX
This book provides a rigorous, practice-led introduction to generative AI for screen-based media, combining foundational theory with applied workflows grounded in professional production environments.Written by an experienced visual effects supervisor and educator, the book offers an authoritative framework for understanding and responsibly adopting AI technologies within contemporary visual effects pipelines. It is structured around six core domains: computer science fundamentals of Artificial Intelligence and machine learning, hardware and performance considerations, ethics and legal responsibilities, practical use of the open-source ComfyUI framework, professional integration with NUKE, and the broader ecosystem of community-driven tools and resources. Key concepts, including neural networks, diffusion models, latent space, ControlNets, and LoRAs, are explained from first principles and contextualized for creative practice. Particular attention is given to authorship, copyright, data provenance, and ethical use, ensuring readers can critically assess both the capabilities and limitations of generative systems.Intended for academic libraries, educators, researchers, and industry professionals, this book functions as both a teaching resource and a long-term reference. It supports curricula in visual effects, film production, digital media, and creative technologies, while also serving practitioners seeking a structured, open-source-oriented approach to integrating generative AI into professional workflows with technical clarity and ethical awareness.
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Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises
This book explores a pioneering exploration of how deep generative models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), renovating early neurological disorder detection. This book is a bridge between computational neuroscience and clinical neurology gaps, providing novel AI-driven methodologies for diagnosing conditions such as Alzheimer’s, Parkinson’s, epilepsy, and neurodevelopmental disorders. With a strong focus on neuroimaging, genomic data analysis, and biomedical informatics, the book equips researchers and practitioners with the tools to improve diagnostic accuracy and decision-making. It includes practical case studies, visual illustrations, and structured methodologies for training and validating deep learning models. Designed for neurologists, radiologists, data scientists, and AI researchers, this book is an essential resource for advancing precision medicine and next-generation healthcare innovation.
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Intelligent and Sustainable Systems
The International Conference in Pathway of Electrical, Automation, Communication, and Electronics (PEACE-2025) s a premier platform dedicated to addressing the latest advancements, trends, and challenges in these rapidly evolving fields. As industries move towards intelligent automation, sustainable energy solutions, advanced communication networks, and AI-driven electronic systems, this conference aims to bridge the gap between academic research and industrial applications. The scope of this conference extends across diverse areas, including but not limited to: Electronics & Embedded Systems: Next-generation semiconductor technologies, VLSI, embedded AI, and sensor networks. Electrical Engineering & Power Systems: Smart grids, renewable energy integration, power electronics, and energy-efficient systems. Automation & Control Systems: Industrial automation, robotics, IoT in automation, and AI-driven process optimization. PEACE-2025 fosters interdisciplinary research and stands out due to: A Holistic Approach: Encouraging cross-domain discussions that bridge gaps between theory, practical applications, and future technological disruptions. Focus on Emerging Trends: Addressing cutting-edge topics such as AI-driven automation, sustainable power solutions, next-gen wireless communication, and Industry 4.0 transformations.
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Organizing for Generative AI and the Productivity Revolution
As leaders plan to make significant investments to harness the power of foundational models such as ChaptGPT, they need to understand the changes in organizational behaviors required for the successful implementation of such systems. The size, complexity and nature of this new wave of technologies requires a refresh in roles and responsibilities in conventional IT organizations. This handbook reveals practical and non-sense guidance on what and how to leverage generative AI to transform their business processes and organizational structures to achieve breakthroughs in efficiency, effectiveness and competitive advantage. Written in a lively, engaging and often humorous style, this collaboration by a leading corporate executive/practitioner and published academic researcher and provides practical insights and timely survival skills for leaders with anonymous but real-world experiences and case studies.What You Will Learn• Key changes in current state roles and responsibilities – as well as organizational reporting structures and associated incentives – required to successfully deploy generative AI systems• The financial, regulatory and operational risks created by organizational behavioral issues that arise when organization take on building and deploying large language models • Strategic differences in emerging vs. traditional organizational behaviors, incentives, roles and responsibilitiesWho This Book is ForExecutives and team leaders at enterprises large and small are under siege with new wave of buzzwords that the proponents of foundation models have created. This book is designed to help the layperson understand how large language foundation models such as ChatGPT are reshaping managerial roles and organizational structures, and how they can leverage this knowledge to survive and thrive in this Brave New World.
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Challenges and Applications of Generative Large Language Models
Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.
R 5,297.00
AI for Decision Intelligence in Critical Systems
This book is a multi-disciplinary reference on how domain-aware AI models can outperform generic approaches by addressing sector-specific complexities. It offers comparative frameworks, reproducible case studies, and real-world applications of emerging AI methods.Collectively, the book emphasizes a unifying theme: the effective deployment of AI to strengthen decision-making, enhance system reliability, and mitigate risks in domains where precision, trust, and efficiency are critical. This edited volume brings together twenty-one chapters of original research, each exploring how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are shaping innovation across critical domains. The book highlights the application of advanced architectures—including Convolutional Neural Networks (CNNs), Quaternion Neural Networks (QCNNs), Large Language Models (LLMs), and Gradient-Boosted Decision Trees (GBDTs)—to solve complex, domain-specific challenges.In computer vision and infrastructure safety, chapters discuss the use of CNNs and QCNNs for automated road crack detection, offering scalable approaches to improving transportation safety while reducing dependence on manual inspections. In software engineering, contributions focus on leveraging ML, DL, and LLMs to enhance software quality assurance, minimize defects, and improve resilience in high-stakes industries. Additional chapters examine ML-driven methods, particularly GBDT, to uncover non-linear drivers of equity valuation across sectors, supporting more accurate forecasts and risk-sensitive decision-making.Academics and researchers in computer science, AI, and data science, industry professionals in transportation, software engineering, finance, and policymakers seeking to apply AI systems effectively will find this book useful.
R 5,532.00
Power Electronics for Next-Generation Drives and Energy Systems
Power electronics converters are devices that change parameters of electric power, such as voltage and frequency, as well as between AC and DC. They are essential parts of both advanced drives, for machines and vehicles, and energy systems to meet required flexibility and efficiency criteria. In energy systems both stationary and mobile, control and converters help ensure reliability and quality of electric power supplies. This reference in two volumes is useful reading for scientists and researchers working with power electronics, drives and energy systems; manufacturers developing power electronics for advanced applications; professionals working in the utilities sector; and for advanced students of subjects related to power electronics. Volume 1 covers converters and control for drives, while Volume 2 addresses clean generation and power grids. The chapters enable the reader to directly apply the knowledge gained to their research and designs. Topics include reliability, WBG power semiconductor devices, converter topology and their fast response, matrix and multilevel converters, nonlinear dynamics, AI and machine learning. Robust modern control is covered as well. A coherent chapter structure and step-by-step explanation provide the reader with the understanding to pursue their research.
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Generative AI: Current Trends and Applications
This comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.
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Return on Intelligence
A call to lead with empathy, this book redefines what transformation means in the age of artificial intelligence. Where most organizations still chase automation and efficiency, Kristin Milchanowski, PhD, reveals a deeper truth: intelligence is not a tool. It is infrastructure. Cutting through the hype and ambiguity that surrounds AI, she bridges the gap between technological deployment and meaningful transformation.Through 43 governing principles, Milchanowski demonstrates how AI agents can reshape decisions, orchestrate resources, and embed permanence into complex enterprises. She introduces two transformative metrics that quantify how well institutions integrate intelligent systems into their workflows and governance. Drawing from global leadership experience, Milchanowski argues that innovation without empathy is merely efficiency without trust. True intelligence must be earned through ethical design, transparent implementation, and human-centered leadership. With precision and moral clarity, she shows how organizations can move from pilots to permanence, from experiments to ecosystems. The real return, Milchanowski concludes, is measured not in algorithms, but in the trust we build, the empathy we embed, and the promises we keep.This book is written for senior executives, transformation leaders, policymakers, and technologists who are navigating the frontier of AI adoption, as well as MBA, AI strategy, and leadership students.
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Application of Computational Techniques in Power Systems for Power Quality Improvement
This book explores the role of computational intelligence techniques in addressing power quality challenges in modern power systems. It examines the integration of multiple energy sources, renewable energy systems, and energy storage units while analyzing nonlinearities in power plants. The book introduces optimization methods, including machine learning, genetic algorithms, and neural networks, to enhance power system stability, reliability, and efficiency. It bridges theoretical insights with real-world applications in modern power networks.• Explores the use of AI techniques, including genetic algorithms and fuzzy logic, for power system analysis and optimization.• Examines power quality issues such as frequency deviations, tie-line power flow variations, and area control errors• Discusses the tuning of secondary controllers, including PID controllers, using advanced optimization algorithms• Addresses PQ challenges posed by renewable energy integration and energy storage units• Covers real-time system monitoring and control for maintaining power quality under nonlinear operating conditions• Provides a structured approach to analysing system behaviour using mathematical modeling and time-domain simulationsThis book serves as a valuable resource for researchers, professionals, and students in power system engineering, computational intelligence, and smart grid technologies.
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Generative AI and Stochastic Thermodynamics
Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.
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Advances in Deep Generative Models for Healthcare and Medical Applications
This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.
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Generative AI in Teaching and Learning
Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes. The book covers a wide range of topics, including generative AI applications in artwork, coding, writing, research, collaboration, and more. It examines the implications of public generative AI interfaces and explores how generative AI can be effectively utilized in formal education settings, such as accredited institutions. The roles of teachers, content creation, instructional design, research, community service, sports, policies, assignments, grading, student work, and programming are all examined in relation to generative AI. Furthermore, the book explores the use of generative AI in nonformal and informal teaching and learning, emphasizing the opportunities for self-learning, group learning, and social change that arise from its implementation. It also delves into the professional ethics surrounding the application of generative AI in education. While examining the potential benefits of generative AI, the book also addresses the risks associated with demotivation for skill acquisition, deskilling, and downskilling. It highlights the opportunities for upskilling and encourages educators and learners to engage in learning practices that leverage the power of generative AI.
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R 458.00
Building Design Systems
Learn how to build a design system with your specific business needs in mind. This book guides you through the process of defining a shared design language, establishing design tokens, introducing scalable governance models, and understanding the growing role of AI in design system workflows. You'll learn how to surface inconsistencies through an interface inventory, document patterns and components, and create self-documenting styles that streamline collaboration across teams. From the beginning, this edition focuses on organizational alignment and long-term scalability, ensuring your system grows with your product and your company. With a defined set of components, tokens, and guidelines, designers can focus their efforts on solving user needs rather than recreating elements or reinventing solutions. You'll explore methods for structuring your product's visual language, aligning design and engineering, and establishing contribution workflows that scale across teams. This new edition of Building Design Systems provides critical insights into how to set up a design system within your organization, measure its effectiveness, and maintain it over time. You will develop the skills needed to approach your design process systematically, ensuring that your design system supports the goals of your organization, your product, and your team. You Will: Develop communication strategies that create organizational buy-in and drive system adoptionEstablish principles and foundations based on your specific needsDesign, build, implement, and maintain a design system from the ground upMeasure the effectiveness and impact of your system over timeUnderstand governance approaches and contribution models that scaleExplore how AI can augment workflows, inventories, and component management New for this Edition: Brand new interviews and case studies from practitioners across companies of different sizes and maturitiesExpanded content on scalability, governance models, and long-term maintenanceNew material on the role of AI in design and design systems, including automation and intelligent toolingGreater insight into the evolving role of design and how to maintain creativity and joy in a changing landscape This Book Is For All teams, large and small, seeking to unify their design language through a cohesive design system and create buy-in for design thinking within their organization. UX, Visual, and Interaction Designers, Product Managers, Front-end Developers, and DesignOps practitioners will all benefit from a systematic approach to design.
R 798.00
Engineering Interactive Computer Systems. EICS 2025 International Workshops
This book constitutes revised selected papers from the workshops held in conjunction with EICS 2025 and it contains This book presents a series of revised papers selected from the Workshops and Doctoral Consortium organized in conjunction with the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2025) which was held in Trier, Germany, during June 2025.The 17 full papers together with 16 short papers included in this book were carefully reviewed and selected from 71 submissions. They were organized in topical sections as follows: Engineering Interactive Computing Systems Doctoral Consortium (EICS DC 2025)Engineering Interactive Systems Embedding AI Technologies (EISEAIT 2025)Experience 2.0 and Beyond – Engineering Cross Devices and Multiple Realities (EXDMR 2025)Interactive AI for Preventive Health: Personalization, Gamification, and Ethics (IAI4PH 2025)
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Practical Guide to Integrating Generative AI into HR Management
This book is a practical guide to the integration of generative AI into human resources, covering use cases across recruitment, onboarding, training, and retention. It offers strategic guidance, ethical frameworks, and technical insights to help organisations deploy generative AI effectively while safeguarding fairness, privacy, and compliance. It covers AI-driven applications in talent acquisition, onboarding, employee engagement, career development, and decision-making, while addressing key concerns such as algorithmic bias, data privacy, and regulatory compliance. With practical case studies and deployment templates, the book provides a structured approach to evaluating and integrating generative AI tools within HR systems. From resume screening to personalised learning paths and predictive analytics, this book bridges the gap between AI technologies and HR operations to help organisations build agile, ethical, and employee-centric workplaces.• Examines generative AI’s applications across recruitment, onboarding, L&D, engagement, and retention• Provides strategic frameworks for integrating AI into HR ecosystems• Includes case studies and practical deployment examples from real organizations• Addresses key issues of AI bias, explainability, data privacy, and compliance• Offers guidance on selecting and evaluating HR-specific AI tools• Explains core AI concepts for both technical and non-technical HR professionals• Includes future-focused insights into AI-driven workforce planning and employee experience designThis book is for HR professionals, business leaders, AI developers, and organizational strategists seeking to understand, implement, and manage generative AI solutions in the HR domain.
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Practical LLM Evaluation for Production Systems
Build reliable Build reliable AI evaluation frameworks that measure quality, safety, grounding, and production readiness across modern LLM and SLM applicationsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesDesign evaluation frameworks for LLMs, SLMs, multimodal, reasoning, and agentic AI systemsMeasure quality, safety, grounding, robustness, and production readiness with practical metricsApply unified evaluation methods to text, multimodal, and agentic AI systemsBook DescriptionModern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks.Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives.Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production.By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence.*Email sign-up and proof of purchase requiredWhat you will learnDesign repeatable evaluation pipelines for LLM systemsAssess inference quality, latency, and operational costEvaluate multimodal, agentic, and reasoning AI systemsBuild regression gates and deployment evaluation workflowsDetect hallucinations and grounding failures in VLMsAssess routing stability in mixture-of-experts modelsEvaluate Text2SQL, OCR, and retrieval-based systemsTranslate evaluation signals into production decisionsWho this book is forML engineers, GenAI engineers, AI architects, data scientists, platform engineers, and engineering managers responsible for deploying LLM-powered systems in production will benefit from this book. Applied AI researchers and technical decision-makers looking to measure reliability, safety, and operational readiness across modern AI systems will also find it valuable. Readers should have a working understanding of machine learning, Python, and modern LLM concepts.
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Dynamic Data Driven Applications Systems
This book constitutes the refereed proceedings of the 5th International Conference on Dynamic Data Driven Applications Systems, DDDAS/Infosymbiotics for Reliable AI 2024, held in New Brunswick, NJ, USA, during November 6-8, 2024. The 43 full papers included in this book were carefully reviewed and selected from 52 submissions. By combining DDDAS and typical AI approaches, the papers address state-of-the-art efforts to create frameworks for enabling new and advanced Science and Technology capabilities to address challenges and create opportunities in important areas, spanning a wide set of areas, such as: materials and aerospace systems; communications networks; energy infrastructures; cyber-security; adverse environmental situations; societal dynamics; computer vision; robotics; laboratory automation; bio-informatics and pharmaceuticals design; and more.
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R 269.00
AI-Based Forecasting of Solar Photovoltaics Power Generation
The widespread deployment of photovoltaics (PV) systems has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy systems. Driven by a combination of supportive regulatory frameworks, government incentive programs, technical developments, and increasing environmental awareness, the adoption of PV technologies has witnessed remarkable growth in recent years. However, the rapid integration of distributed PV systems into existing electricity grid infrastructure introduces new challenges, particularly concerning voltage regulation, reverse power flow, and congestion within the electricity grid. These issues are intensified when PV systems are integrated without proper strategy. In this context, solar PV power forecasting has become an essential tool for ensuring the reliable and efficient integration of solar PV systems into power systems. Artificial intelligence (AI) and machine learning (ML) offer means to forecast PV power and energy generation based on historical data of PV generation, meteorological data, and/or weather forecasts. AI-Based Forecasting of Solar Photovoltaics Power Generation blends theoretical knowledge with practical case studies, which serves as a comprehensive and timely contribution to the rapidly evolving field of solar PV forecasting. It covers topics such as data collection and processing, solar forecasting based on statistical time-series, machine and deep learning, hybrid and probabilistic approaches, model optimization, hyperparameter tuning, and solar PV forecasting for energy system integration and control. As solar PV systems become increasingly integrated into energy systems, a dedicated book on PV generation forecasting is incredibly useful, making this book an important resource for energy system operators, policymakers, researchers, and students seeking to improve the reliability, resiliency, and efficiency of solar PV systems and the broader systems into which they are integrated.
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Generative Artificial Intelligence in Higher Education
AsGenerative AI accelerates its impact on the educational landscape, Generative Artificial Intelligence inHigher Education provides a visionary roadmap for universityprofessionals to navigate this transformative era.Throughinspiring case studies, expert analysis, and well-grounded learning theories,this book reveals how Generative AI can empower educators to personaliselearning, promote inclusivity, and elevate pedagogical innovation. At the sametime, it tackles the ethical dilemmas and practical challenges posed byadopting AI, offering actionable strategies for institutions committed toprogress and integrity.Keyhighlights include: Practical insights into integrating AI tools to enhancestudent engagement and learning outcomes – Theoretical frameworks to guide AI’sresponsible and effective use in education – Strategies for institutions toscale AI, improve efficiency, and remain globally competitive. Withcontributions from leading scholars worldwide, this book is a vital resourcefor academics, administrators, and policymakers who aspire to harness AI’spotential while upholding the principles of ethics, equity, and excellence ineducation.Whyshould you read this book? Because AI will transform the future of highereducation. As AI reshapes the academic landscape, this book equips you with theknowledge and tools to lead your institution and students into an innovativeand equitable future. Prepareto engage, inspire, and lead in the AI-enabled academic era.
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