Search results for "Deep Generative Modeling"
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Machine Learning for Neurodegenerative Disorders
This book explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders. This comprehensive resource is intended for neuroscientists, students, researchers, and neurologists to understand the emerging scope of machine learning in neurodegenerative disorders.
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Supply Chain Transformation Through Generative AI and Machine Learning
The transformative role of Generative Artificial Intelligence (AI) and Machine Learning (ML) in supply chain management is increasingly being recognized as a game-changer in the industry. Recent statistics underscore this trend, highlighting the rapid adoption and significant impact of these technologies. However, the path to digital transformation is not without its challenges. Despite improved success rates, about 60% of digital transformation initiatives in supply chains still struggle to fully meet their objectives. This shortfall is often attributed to several key factors: the complexity and scale of integrating new technologies into existing systems; organizational resistance to change and inadequate stakeholder buy-in; lack of skilled professionals adept in these new technologies; insufficient data governance and quality; and underestimation of the need for a robust change management strategy. These challenges highlight the critical need for a comprehensive approach that addresses both the technical and human aspects of digital transformation. Supply Chain Transformation Through Generative AI and Machine Learning is a comprehensive resource to the best practices in digital enablement, change management, and process optimization, with a specific focus on Generative AI and ML. It equips readers with the knowledge and strategies necessary for successful integration of these technologies, drawing on the latest industry insights and expert recommendations, to enhance supply chain efficiency and effectiveness, reduce costs, and drive revenue growth. Covering topics such as AI-powered visual models, demand planning, and product clustering, this book is an excellent resource for executives, business leaders, program managers, data scientists, AI and ML developers, industry analysts, consultants, professionals, scholars, researchers, academicians, and more.
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Generative AI for Web Engineering Models
Web engineering faces a pressing challenge in keeping pace with the rapidly evolving digital landscape. Developing, designing, testing, and maintaining web-based systems and applications require innovative approaches to meet the growing demands of users and businesses. Generative Artificial Intelligence (AI) emerges as a transformative solution, offering advanced capabilities to enhance web engineering models and methodologies. This book presents a timely exploration of how Generative AI can revolutionize the web engineering discipline, providing insights into future challenges and societal impacts. Generative AI for Web Engineering Models offers a comprehensive examination of integrating AI-driven generative approaches into web engineering practices. It delves into methodologies, models, and the transformative impact of Generative AI on web-based systems and applications. By addressing topics such as web browser technologies, website scalability, security, and the integration of Machine Learning, this book provides a roadmap for researchers, scientists, postgraduate students, and AI enthusiasts interested in the intersection of AI and web engineering. Through insightful analysis and real-world examples, this book equips readers with the knowledge and tools to navigate the complexities of web engineering in the AI era. It offers practical strategies for leveraging Generative AI to develop more efficient, scalable, and secure web-based solutions. As the digital landscape continues to evolve, Generative AI for Web Engineering Models is a valuable resource for those seeking to stay at the forefront of web technology.
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Generative Theory of Relevance
A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way.Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events.Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.
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GENERATIVE AI AS THE SECOND MIND: REWRITING THE RULES OF BUSINESS AND ORGANIZATION
This book focuses on redefining organizational strategy and management in the era of Generative AI (GenAI). It challenges the foundational assumptions of the Industrial Revolution, arguing that GenAI dissolves the boundaries between labor, materials, and production by introducing a "Second Mind" — a non human entity with human level capabilities and infinite memory. Positioned as workforce, tool, and raw material simultaneously, this paradigm compels leaders to rethink management principles that have guided enterprises for two centuries.The text provides both corporate and academic communities with a rigorous cognitive framework and a strategic roadmap for navigating the transition from legacy hierarchies to symbiotic intelligence. It distinguishes the logic of Classical AI from Generative AI, mapping how each reshapes organizational structures, employee roles, and decision making authority. Central to this framework is the concept of Symbiotic Agency Modes (SAM) — a hierarchy of Coordination, Cooperation, and Collaboration — which specifies how human and digital minds operate as a coupled system.Moving beyond vague "human in the loop" notions, the book introduces actionable governance mechanisms and tailored performance indicators, such as learning rate and reversibility, to evaluate hybrid workforces. By offering a systematic roadmap for implementation, it ensures measurable ROI and sustainable competitive advantage.Serving as an "Origin of Species" for 21st century organizational evolution, this book equips leaders, strategists, and institutions to harness symbiotic intelligence and lead the next era of business transformation.
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Improving the Marriage of Modeling and Theory for Accurate Forecasts of Outcomes
This volume in the series has big objectives: describe the bad science practices now in use in most studies in business-to-business marketing strategy and describe a true paradigm shift to good science practices by replacing the variable-based linear-symmetric null hypothesis testing (NHST) approach in theory construction and testing—with case-based asymmetric models with somewhat precise outcome testing (SPOT). Whether the question refers to success or failure, wise executives ask, how did we get here? What’s in store for the next decade? Unfortunately, the majority of scholarly articles examining the causes of success and failure offers scant useful information that is accurate in forecasting success or failure strategy outcomes. The majority of studies on strategy performance outcomes focus on variable relationships and testing for the directionality (positive or negative relationships) and effect size of relationships—using multiple regression analysis and structural equation modeling (MRA/SEM) using null hypothesis statistical testing (NHST). Research on the value of NHST indicates that such studies are worse than useless: such research does not focus on case-based outcomes and achieving a statistically significant relationship greatly depends on the sample size of firms in the studies. Researchers using NHST are answering the wrong questions in examining the net effects of independent variables on dependent variable of interest (e.g., net earnings per revenue). Here are the right questions to ask. What configurations of antecedent conditions combine to generate positive outcomes for our firm and similar firms? What configurations of antecedent conditions combine to generate negative outcomes for firms in our industry? Sound reasoning and empirical evidence supports the wisdom of business executives ignoring the scholarly empirical literature on forecasting successful and unsuccessful management strategies using the NHST of the size and directionality of relationships. Good science practice relies on the complexity theory tenets covered in the chapters in this volume. Good science practice includes matching case-focused theory with case-focused data analytic tools and using somewhat precise outcome tests (SPOT) of asymmetric models. Good science practice achieves requisite variety necessary for deep explanation, description, and accurate prediction. The fear of submission rejection is another reason for rejecting case-based asymmetric modeling and SPOT. Overcome such fear by learning to apply complexity theory tenets, constructing separate case-based, mid-range, models of successful versus unsuccessful outcomes, and testing for accuracy via SPOT. This volume provides tools necessary for you to accomplish this task.
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Apache Spark for Machine Learning
Develop your data science skills with Apache Spark to solve real-world problems for Fortune 500 companies using scalable algorithms on large cloud computing clustersKey FeaturesApply techniques to analyze big data and uncover valuable insights for machine learningLearn to use cloud computing clusters for training machine learning models on large datasetsDiscover practical strategies to overcome challenges in model training, deployment, and optimizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark’s capabilities for efficient data processing, model building, and optimization. With Deepak’s expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes.This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You’ll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks.By the end of this book, you’ll be ready to handle big data and create advanced machine learning models with Apache Spark.What you will learnMaster Apache Spark for efficient, large-scale data processing and analysisUnderstand core machine learning concepts and their applications with SparkImplement data preprocessing techniques for feature extraction and transformationExplore supervised learning methods – regression and classification algorithmsApply unsupervised learning for clustering tasks and recommendation systemsDiscover frequent pattern mining techniques to uncover data trendsWho this book is forThis book is ideal for data scientists, ML engineers, data engineers, students, and researchers who want to deepen their knowledge of Apache Spark’s tools and algorithms. It’s a must-have for those struggling to scale models for real-world problems and a valuable resource for preparing for interviews at Fortune 500 companies, focusing on large dataset analysis, model training, and deployment.
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Understanding the Ethics of Generative AI
The book discusses the ethical complexities that software developers face as they build AI systems capable of autonomous creation. It explores the ethical decisions developers must make when building generative AI systems, from mitigating bias in training data to protecting user privacy and navigating regulatory compliance. Through real-world case studies and actionable frameworks, it equips technical professionals with both the understanding and tools to build AI systems that are fair, transparent, and worthy of public trust.Identifies and corrects algorithmic bias in training datasets, ensuring AI systems produce equitable outputs that don't systematize discriminationDesigns privacy-first AI architectures and implements transparency practices that comply with data protection regulations while building user trustNavigates evolving legal and regulatory landscapes (GDPR, AI Act, sector-specific rules), helping teams stay ahead of compliance requirementsApplies ethical frameworks to real-world decisions: what to do when fairness and accuracy conflict, how to audit AI systems for hidden harms, when to say no to a projectProvides a governance model for embedding ethics into development workflows, not as an afterthought but as core design practice
R 5,165.00
Leveraging Artificial Intelligence (AI) Competencies for Next-Generation Cybersecurity Solutions
Modern enterprises are facing growing cybersecurity issues due to the massive volume of security-related data they generate over time. AI systems can be developed to resolve a range of these issues with comparative ease. This new book describes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help eliminate them.With chapters from industry and security experts, this volume discribes the various types of cybersecurity problems faced by businesses and how advanced AI algorithms and models can help elimintate them. With chapters from industry and security experts, this volume discusses the many new and emerging AI technologies and approaches that can be harnessed to combat cyberattacks, including big data analytics techniques, deep neural networks, cloud computer networks, convolutional neural networks, IoT edge devices, machine learning approaches, deep learning, blockchain technology, convolutional neural networks, and more.Some unique features of this book include:Detailed overview of various security analytics techniques and toolsComprehensive descriptions of the emerging and evolving aspects of artificial intelligence (AI) technologiesIndustry case studies for practical comprehension and applicationThis book, Leveraging the Artificial Intelligence Competencies for Next-Generation Cybersecurity Solutions, illustrates how AI is a futuristic and flexible technology that can be effectively used for tackling the growing menace of cybercriminals. It clearly demystifies the unique contributions of AI algorithms, models, frameworks, and libraries in nullifying the cyberattacks. The volume will be a valuable resource for research students, scholars, academic professors, business executives, security architects, and consultants in the IT industry.
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Modeling Groundwater Flow and Pollution
Groundwater constitutes an important component of many water resource systems, supplying water for domestic use, for industry, and for agriculture. Management of a groundwater system, an aquifer, or a system of aquifers, means making such decisions as to the total quantity of water to be withdrawn annually, the location of wells for pumping and for artificial recharge and their rates, and control conditions at aquifer boundaries. Not less important are decisions related to groundwater qUality. In fact, the quantity and quality problems cannot be separated. In many parts of the world, with the increased withdrawal of ground water, often beyond permissible limits, the quality of groundwater has been continuously deteriorating, causing much concern to both suppliers and users. In recent years, in addition to general groundwater quality aspects, public attention has been focused on groundwater contamination by hazardous industrial wastes, by leachate from landfills, by oil spills, and by agricultural activities such as the use of fertilizers, pesticides, and herbicides, and by radioactive waste in repositories located in deep geological formations, to mention some of the most acute contamination sources. In all these cases, management means making decisions to achieve goals without violating specified constraints. In order to enable the planner, or the decision maker, to compare alternative modes of action and to ensure that the constraints are not violated, a tool is needed that will provide information about the response of the system (the aquifer) to various alternatives.
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Systems Science: Theory, Analysis, Modeling, and Design
This book describes a comprehensive approach to applying systems science formally to the deep analysis of a wide variety of complex systems. Detailed ‘how-to’ examples of the three phases (analysis-modeling-design) of systems science are applied to systems of various types (machines, organic (e.g. ecosystem), and supra-organic (e.g. business organizations and government). The complexity of the global system has reached proportions that seriously challenge our abilities to understand the consequences of our use of technology, modification of natural ecosystems, or even how to govern ourselves. For this reason, complex mathematics is eschewed when simpler structures will suffice, allowing the widest possible audience to apply and benefit from the available tools and concepts of systems science in their own work. The book shows, in detail, how to functionally and structurally deconstruct complex systems using a fundamental language of systems. It shows how to capture the discovered details in a structured knowledge base from which abstract models can be derived for simulation. The knowledge base is also shown to be a basis for generating system design specifications for human-built artifacts, or policy recommendations/policy mechanisms for socio-economic-ecological systems management. The book builds on principles and methods found in the authors’ textbook Principles of Systems Science (co-authored with Michael Kalton), but without prerequisites. It will appeal to a broad audience that deals with complex systems every day, from design engineers to economic and ecological systems managers and policymakers.
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Modeling, Analysis, and Applications in Metaheuristic Computing
Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing. Providing researchers, practitioners, and academicians with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization, this book compile the latest findings, analysis, improvements, and applications of technologies within metaheuristic computing.
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Modeling, Mesh Generation, and Adaptive Numerical Methods for Partial Differential Equations
This IMA Volume in Mathematics and its Applications MODELING, MESH GENERATION, AND ADAPTIVE NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS is based on the proceedings of the 1993 IMA Summer Program "Modeling, Mesh Generation, and Adaptive Numerical Methods for Partial Differential Equations." We thank Ivo Babuska, Joseph E. Flaherty, William D. Hen- shaw, John E. Hopcroft, Joseph E. Oliger, and Tayfun Tezduyar for orga- nizing the workshop and editing the proceedings. We also take this oppor- tunity to thank those agencies whose financial support made the summer program possible: the National Science Foundation (NSF), the Army Re- search Office (ARO) the Department of Energy (DOE), the Minnesota Su- percomputer Institute (MSI), and the Army High Performance Computing Research Center (AHPCRC). A vner Friedman Willard Miller, Jr. xiii PREFACE Mesh generation is one of the most time consuming aspects of com- putational solutions of problems involving partial differential equations. It is, furthermore, no longer acceptable to compute solutions without proper verification that specified accuracy criteria are being satisfied.Mesh gen- eration must be related to the solution through computable estimates of discretization errors. Thus, an iterative process of alternate mesh and so- lution generation evolves in an adaptive manner with the end result that the solution is computed to prescribed specifications in an optimal, or at least efficient, manner. While mesh generation and adaptive strategies are becoming available, major computational challenges remain. One, in particular, involves moving boundaries and interfaces, such as free-surface flows and fluid-structure interactions.
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Traditional vs Generative AI Pentesting
Traditional vs Generative AI Pentesting: A Hands-On Approach to Hacking explores the evolving landscape of penetration testing, comparing traditional methodologies with the revolutionary impact of Generative AI. This book provides a deep dive into modern hacking techniques, demonstrating how AI-driven tools can enhance reconnaissance, exploitation, and reporting in cybersecurity assessments.Bridging the gap between manual pentesting and AI automation, this book equips readers with the skills and knowledge to leverage Generative AI for more efficient, adaptive, and intelligent security testing. By blending practical case studies, hands-on exercises, and theoretical insights, it guides cybersecurity professionals, researchers, and students through the next generation of offensive security strategies.The book offers comprehensive coverage of key topics, including:Traditional vs AI-Driven Pentesting: Understanding the evolution of security testing methodologiesBuilding an AI-Powered Pentesting Lab: Leveraging Generative AI tools for reconnaissance and exploitationGenAI in Social Engineering and Attack Automation: Exploring AI-assisted phishing, deepfake attacks, and deception tacticsPost-Exploitation and Privilege Escalation with AI: Enhancing persistence and lateral movement techniquesAutomating Penetration Testing Reports: Utilizing AI for streamlined documentation and risk analysisThis book is an essential resource for ethical hackers, cybersecurity professionals, and academics seeking to explore the transformative role of Generative AI in penetration testing. It provides practical guidance, in-depth analysis, and cutting-edge techniques for mastering AI-driven offensive security.
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Modeling and Control of Antennas and Telescopes
Mechanical engineering, and engineering discipline born of the needs of the industrial revolution, is once again asked to do its substantial share in the call for industrial renewal. The general call is urgent as we face profound issues of productivity and competitiveness that require engineering solutions, among others. The Mechanical Engineering Series is a series featuring graduate texts and research monographs intended to address the need for information in contemporary areas of mechanical engineering. The series is conceived as a comprehensive one that covers a broad range of c- centrations important to mechanical engineering graduate education and research. We are fortunate to have a distinguished roster of series editors, each an expert in one of the areas of concentration. The names of the series editors are listed on page vi of this volume. The areas of concentration are applied mechanics, biomechanics, computational mechanics, dynamic systems and control, energetics, mechanics of materials, processing, thermal science, and tribology. Preface This book is based on my experience with the control systems of antennas and radiotelescopes. Overwhelmingly, it is based on experience with the NASA Deep Space Network (DSN) antennas. It includes modeling the antennas, developing control algorithms, eld testing, system identi cation, performance evaluation, and 1 troubleshooting. My previous book emphasized the theoretical aspects of antenna control engineering, while this one describes the application part of the antenna control engineering.
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Methods and Applications for Modeling and Simulation of Complex Systems
This volume constitutes the proceedings of the 19th Asia Simulation Conference, AsiaSim 2019, held in Singapore, Singapore, in October 2019.The 19 revised full papers and 5 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on simulation and modeling methodology; numerical and Monte Carlo simulation; simulation applications: blockchain, deep learning and cloud; simulation and visualization; simulation applications; short papers.
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Dynamic Modeling, Simulation and Control of Energy Generation
This book addresses the core issues involved in the dynamic modeling, simulation and control of a selection of energy systems such as gas turbines, wind turbines, fuel cells and batteries. The principles of modeling and control could be applied to other non-convention methods of energy generation such as solar energy and wave energy.A central feature of Dynamic Modeling, Simulation and Control of Energy Generation is that it brings together diverse topics in thermodynamics, fluid mechanics, heat transfer, electro-chemistry, electrical networks and electrical machines and focuses on their applications in the field of energy generation, its control and regulation. This book will help the reader understand the methods of modelling energy systems for controller design application as well as gain a basic understanding of the processes involved in the design of control systems and regulators. It will also be a useful guide to simulation of the dynamics of energy systems and for implementing monitoring systems based on the estimation of internal system variables from measurements of observable system variables.Dynamic Modeling, Simulation and Control of Energy Generation will serve as a useful aid to designers of hybrid power generating systems involving advanced technology systems such as floating or offshore wind turbines and fuel cells. The book introduces case studies of the practical control laws for a variety of energy generation systems based on nonlinear dynamic models without relying on linearization. Also the book introduces the reader to the use nonlinear model based estimation techniques and their application to energy systems.
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Illustrated Guide to AI Agents
Artificial intelligence is entering a new phase. No longer limited to answering prompts or completing simple writing tasks, AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand—and effectively work with—this emerging technology.Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. With its visual storytelling and accessible explanations, An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.Explore the core architecture of AI agents: tools, memory, and planningUnderstand reasoning LLMs, multimodal models, and multi-agent collaborationLearn advanced methods, including distillation, quantization, and reinforcement learningEvaluate real-world applications, strengths, and limitations of AI agents
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Generative Engine Optimization: Sichtbar in KI-Systemen
Dieses Essential erklärt, wie Unternehmen in der neuen Ära der KI-Systeme online sichtbar bleiben. Denn: ChatGPT, AI Mode oder Microsoft Copilot verändern die Art und Weise, wie Menschen nach Informationen suchen - und stellen Unternehmen vor einen grundlegenden Wandel. Wer nicht in KI-Antworten auftaucht, verliert Reichweite und Relevanz.Die Autoren führen in die neue Disziplin Generative Engine Optimization (GEO) ein - die die klassische Suchmaschinenoptimierung (SEO) ersetzen wird. Sie zeigen, wie Marken in KI-Antworten auftauchen, welche Faktoren die Sichtbarkeit beeinflussen und wie sich Erfolge messen lassen.Ein kompaktes Fachbuch für Marketingverantwortliche und Entscheider*innen, die verstehen wollen, wie ihr Unternehmen in KI-Systemen präsent bleibt.
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