Information Processing in Medical Imaging
This two-volume set LNCS 15829-15830 constitutes the proceedings of the 29th International Conference on Information Processing in Medical Imaging, IPMI 2025, held on Kos, Greece, during May 25-30, 2025. The 51 full papers presented in this volume were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: Part I: Classification/Detection; Registration; Reconstruction; Image synthesis; Image enhancement; and Segmentation.Part II: Computer-aided diagnosis/surgery; Brain; Diffusion models; Self-supervised learning; Vision-language models; Shape analysis; and Time-series image analysis.
New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence
AI Technologies for Information Systems and Management Science
This book brings together leading experts, academics, and industry professionals to explore how AI is transforming decision-making, data analytics, operations, and strategic management across diverse sectors. Dive into the cutting-edge world of artificial intelligence with AI Technologies for Information Systems and Management Science, a comprehensive book is featuring peer-reviewed research from the 7th International Conference on Information Systems and Management Science (ISMS 2024). Covering topics such as intelligent systems, machine learning integration, AI-driven process optimization, and ethical considerations, this proceedings book offers a rich blend of theoretical insights and practical applications. Whether you're a researcher, practitioner, or student, you'll find valuable perspectives on how emerging AI technologies are reshaping the foundations of modern information systems and management practices. Gain inspiration from real-world case studies, stay ahead with the latest innovations, and deepen your understanding of AI's role in driving smarter, more adaptive organizations. With contributions from global thought leaders, this book is an essential resource for anyone interested in the future of intelligent business and digital transformation. Embrace the power of AI--empowering tomorrow's systems, today.
Explainable AI with Python
This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expansion of the "Intrinsic Explainable Models" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models. Further details in "Model-Agnostic Methods for XAI" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively. New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface. Revision in "Adversarial Machine Learning and Explainability" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices. New chapter on "Generative Models and Large Language Models (LLM)" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models. New "Artificial General Intelligence and XAI" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI. Enhancements in "Explaining Deep Learning Models" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.
Lecture Notes in Deep Learning
The compendium provides an introduction to the theory of deep learning, from basic principles of neural network modeling and optimization to more advanced topics of neural networks as Gaussian processes, neural tangent and information theory.This unique reference text complements a largely missing theoretical introduction to neural networks without being overwhelmingly technical in a level accessible to upper-level undergraduate engineering students.Advanced chapters were designed to offer an additional intuition into the field by explaining deep learning from statistical and information theory perspectives. The book further provides additional intuition to the field by relating it to other statistical and information modeling approaches.
In Search of the Master Prompt
What if the right AI prompt could unlock your best story yet? In Search of the Master Prompt is a practical and inspiring guide for writers who want to use AI without losing their voice. Author R.A. Wilson shows how prompts can do more than generate content-they can reveal structure, deepen characters, and ignite fresh ideas.Blending clear instruction with creative insight, this book reframes AI not as a replacement for imagination but as a powerful partner in the writing process. Each chapter offers tools, examples, and exercises designed to help you think like a storyteller and prompt like a pro.Whether you're drafting your tenth novel or just beginning, this book will help you collaborate with AI in a way that keeps your creativity-and your integrity-at the center of the work.
Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work, and Life
This book is a practical, non-technical guide for business leaders, professionals, and curious minds navigating the next frontier of AI.In a world where ChatGPT took us by storm, a far more powerful revolution is now unfolding: AI Agents. Like Jarvis in Iron Man or Samantha in Her, these intelligent systems can execute actions, learn from experience, and orchestrate digital interactions with minimal human supervision. They promise to redefine business and society.However, behind the excitement lies a crucial reality: a significant gap between promise and reality.This comprehensive guide on agentic AI cuts through the hype and offers a clear, jargon-free strategic roadmap to understanding and applying this technology. The authors bring a rare perspective, having implemented agentic AI across diverse organizations - from global enterprises to agile startups - witnessing both remarkable successes and sobering failures.The question isn't whether AI agents will transform your industry - it's how you will lead that change. Every revolution demands foresight and responsibility. This book challenges you to not just adopt agentic AI, but to shape it with purpose and integrity. Through illuminating case studies and hands-on experiments, the authors reveal: A step-by-step method for identifying high-value agentic opportunities and building impactful agents in your business, work, and personal lifeThe secrets behind today's most successful agentic transformations at scale: cutting costs by over 25% while boosting customer satisfaction by over 40%Approaches to seize the new opportunities of the Agent Economy - new business models, Agentic-driven startups, rapid scaling, and game-changing revenue opportunitiesHands-on guidance to navigate common pitfalls such as workflow integration, error handling, data quality, agent control, and user adoptionThe new mindset and skills required to lead effectively in a world where humans and AI agents need to work seamlessly togetherThe profound impact of agentic AI on society, employment, education, and our personal livesAI agents create what the authors call "compounding intelligence advantages" - the more they're used, the smarter and more valuable they become, creating an accelerating gap between early adopters and laggards. Hence, those who understand and leverage AI agents today will define the next era of business.
Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work, and Life
This book is a practical, non-technical guide for business leaders, professionals, and curious minds navigating the next frontier of AI.In a world where ChatGPT took us by storm, a far more powerful revolution is now unfolding: AI Agents. Like Jarvis in Iron Man or Samantha in Her, these intelligent systems can execute actions, learn from experience, and orchestrate digital interactions with minimal human supervision. They promise to redefine business and society.However, behind the excitement lies a crucial reality: a significant gap between promise and reality.This comprehensive guide on agentic AI cuts through the hype and offers a clear, jargon-free strategic roadmap to understanding and applying this technology. The authors bring a rare perspective, having implemented agentic AI across diverse organizations - from global enterprises to agile startups - witnessing both remarkable successes and sobering failures.The question isn't whether AI agents will transform your industry - it's how you will lead that change. Every revolution demands foresight and responsibility. This book challenges you to not just adopt agentic AI, but to shape it with purpose and integrity. Through illuminating case studies and hands-on experiments, the authors reveal: A step-by-step method for identifying high-value agentic opportunities and building impactful agents in your business, work, and personal lifeThe secrets behind today's most successful agentic transformations at scale: cutting costs by over 25% while boosting customer satisfaction by over 40%Approaches to seize the new opportunities of the Agent Economy - new business models, Agentic-driven startups, rapid scaling, and game-changing revenue opportunitiesHands-on guidance to navigate common pitfalls such as workflow integration, error handling, data quality, agent control, and user adoptionThe new mindset and skills required to lead effectively in a world where humans and AI agents need to work seamlessly togetherThe profound impact of agentic AI on society, employment, education, and our personal livesAI agents create what the authors call "compounding intelligence advantages" - the more they're used, the smarter and more valuable they become, creating an accelerating gap between early adopters and laggards. Hence, those who understand and leverage AI agents today will define the next era of business.
Lecture Notes in Deep Learning
The compendium provides an introduction to the theory of deep learning, from basic principles of neural network modeling and optimization to more advanced topics of neural networks as Gaussian processes, neural tangent and information theory.This unique reference text complements a largely missing theoretical introduction to neural networks without being overwhelmingly technical in a level accessible to upper-level undergraduate engineering students.Advanced chapters were designed to offer an additional intuition into the field by explaining deep learning from statistical and information theory perspectives. The book further provides additional intuition to the field by relating it to other statistical and information modeling approaches.
Pattern Recognition and Computer Vision in the New AI Era
While traditional approaches in pattern recognition and computer vision have continued to evolve, along with the advances of artificial intelligence (AI), this unique compendium presents recent research activities in deep learning, graph-based and semantic-based approaches and applications.The book covers the most recent advances as well as traditional topics in pattern recognition and computer vision in this new AI area in the first part. The second part presents emerging applications of deep learning and AI.This useful reference text benefits academics, professionals, researchers and graduate students in pattern recognition, computer vision, image segmentation and artificial intelligence.
Computer Vision and Image Processing
The Six-volume proceedings set CCIS 2473 and 2478 constitutes the refereed proceedings of the 9th International Conference on Computer Vision and Image Processing, CVIP 2024, held in Chennai, India, during December 19-21, 2024. The 178 full papers presented were carefully reviewed and selected from 647 submissions.The papers focus on various important and emerging topics in image processing, computer vision applications, deep learning, and machine learning techniques in the domain.
Visual Quality Assessment and Enhancement for Natural Images
Digital images play a significant role in representing useful information; however, they may get distorted while passing through several operational stages, such as image acquisition, compression, transmission, processing, and reconstruction. Similarly, during the transmission of images, some data may be lost and the quality of the image may degrade due to bandwidth limitation. The use of distorted images in any application compromises its performance; and therefore, the identification and quantification of degradation become key issues to address in restoring the distorted images.This book covers different quality assessment techniques for natural images. It further discusses image enhancement techniques that are based on estimated quality. This book presents image quality assessment techniques for different distortions, such as poor contrast, poor illumination, noise and artifacts in deblocked images. Also presented are quality-aware techniques for image enhancement for different distortions.Review and objective questions (with answers) for each chapter will be available online as part of the supplementary material.
Computational Science and Computational Intelligence
This CCIS book constitutes selected papers accepted in the Research Track on Cyber Warfare, Cyber Defense and Cyber Security and the Research Track on Mobile Computing, Wireless Networks and Security held as part of the 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024, which took place in Las Vegas, NV, USA, during December 11-13, 2024. The Research Track on Cyber Warfare, Cyber Defense and Cyber Security, CSCI-RTCW, received 98 submissions of which 18 papers were accepted. For the Research Track on Mobile Computing, Wireless Networks and Security, CSCI-RTMC, 7 papers were accepted from 31 submissions. They were organized in topical sections on Cyber Warfare, Cyber Defense and Cyber Security; and Mobile Computing, Wireless Networks and Security.
Optimization and Data Science in Industrial Engineering
This volume constitutes the refereed proceedings of the Second International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2024, held in Istanbul, Turkey, during November 7-8, 2024. The 25 full papers and 1 short paper included in this book were carefully reviewed and selected from 92 submissions. They were organized into topics such as: Artificial Intelligence and Expert Systems in Engineering; Digital Technologies in Healthcare Systems; Smart Supply Chain and Manufacturing Systems; Smart Transportation and Logistics Systems; Data Analytics and Advanced Optimization; and Internet and Mobile Technologies.
Human Resources Development in a Digital Age
This book leads to a transformative journey through the world of human resources development (HRD) in the digital age, where technology and innovation converge to redefine the workplace. It unveils the dynamic shifts driven by artificial intelligence (AI), data analytics, and immersive technologies, offering a roadmap to harness these advancements for organizational success. This comprehensive exploration examines the critical aspects of HRD, including learning and development, engagement, competencies, organizational culture, and other important topics. Through engaging insights and real-world examples, the book highlights how AI personalizes learning experiences and enhances decision-making processes. It also addresses the ethical considerations of adopting digital tools, emphasizing data privacy, fairness, and the human element in technology-driven strategies. From overcoming the digital divide to fostering a culture of continuous learning, this book equips readers with the knowledge to navigate challenges and seize opportunities in a rapidly evolving landscape. This book is an essential resource for HR professionals, organizational leaders, professors and students, and anyone seeking to future-proof their workforce and drive sustainable growth. It redefines the HR strategy and embraces the limitless potential of digital transformation with this groundbreaking book.
Artificial Intelligence for Neural Health
This book explores the transformative role of artificial intelligence (AI) in diagnosing and treating neurological disorders. It offers a comprehensive overview of how AI can enhance diagnostic accuracy, improve treatment plans, and ultimately lead to better patient outcomes in the realm of neural health. It covers a wide range of topics, including fundamental AI technologies and their relevance in neural health, detailed case studies demonstrating the application of AI in diagnosing and treating neurological conditions, and the ethical, legal, and social implications of using AI in healthcare. It also delves into future trends and innovations in AI-driven neural health solutions. The book is essential for healthcare professionals, AI researchers, and neuroscientists as it bridges the gap between cutting-edge AI technologies and practical medical applications.
Waves and Words
A Journey to Peace in a Noisy WorldIn a world that never quiets-where the ping of notifications, the grind of work, and the relentless pace of AI-driven lives drown out silence-peace can feel elusive. Yet, what if serenity were closer than you think, woven into the rhythm of waves or the flow of a poem? How to Find Serenity through Waves and Words: A Journey Through Sound and Poetry offers a lifeline, blending the soothing power of soundscapes with the healing grace of poetry to guide you to calm amid life's storm.This isn't just another self-help book-it's a companion for the weary, a map for the restless, and a celebration of humanity. Whether you're battling digital overload, seeking meaning in a tech-heavy age, or craving stillness, this book lights a fresh path to tranquility with vivid stories, practical tools, and exercises you can start today.At its core are the Twelve Pillars of Wellness, a holistic framework fusing physical, emotional, and spiritual practices to ground you in the moment. The concept of Flow-total immersion in the present-helps you ride life's waves with ease, blending science and soul into a foundation for well-being.Tools You Can Use TodayPractical exercises fill the pages. Craft a sound map: listen to rain's patter or leaves' rustle, then shape those sounds into a haiku. Backed by science-soundscapes lower stress hormones, poetry turns tension into wonder-these simple, profound tools fit any life, anywhere. Real stories, like Sarah finding solace in ocean waves or Mark reclaiming joy through a verse, show serenity is for everyone, not just the few.Humanity in an AI AgeAs artificial intelligence reshapes our days, this book champions what tech can't touch: creativity, empathy, and the soulful depth of being human. It casts technology as a tool, not a tyrant, inviting you to reclaim peace through the timeless arts of sound and words-a sanctuary for beginners and seekers alike.A Vision of BalanceImagine a future where technology lifts without eclipsing us. This book blends science-waves steady your breath, poetry boosts empathy-with art's transformative beauty to nurture well-being. Picture this: after a draining day, you sit with the book, hear a distant tide, read a gentle verse, and feel tension fade to calm.Your InvitationHow to Find Serenity through Waves and Words beckons you to open its pages and let sound and poetry guide you to peace. Begin today-no expertise needed. Just you, the waves, and the words.
Data Rookies Data Mining Basics
Jumpstart your journey into data mining with this clear, beginner-friendly guide. Whether you're a student, educator, or career-changer, this book makes complex ideas approachable. You'll explore the foundations of clustering, classification, and association using real-world examples, practical explanations, and plain language-no prior experience required.Pair this book with companion books with lab exercises to practice (available in R and Orange), slides, solution guides and more available resources at our website to bring your learning data analytics to life.This book has associated lab books using Orange and R technologies with hands on exercises.
AI Use Cases for Diplomats
Drawing on over 21 years of experience integrating technology into foreign affairs, the author reveals how AI is revolutionizing diplomatic engagement, crisis management, and public diplomacy. Through real-world examples, this book shows the opportunities and ethical complexities at the intersection of tech and international relations.
AI Use Cases for Diplomats
Drawing on over 21 years of experience integrating technology into foreign affairs, the author reveals how AI is revolutionizing diplomatic engagement, crisis management, and public diplomacy. Through real-world examples, this book shows the opportunities and ethical complexities at the intersection of tech and international relations.
Bioinformatics Research and Applications
This two-set volume LNCS 15756 and 15767 constitutes the refereed proceedings of the 21st International Symposium on Bioinformatics Research and Applications, ISBRA 2025, held in Helsinki, Finland, during August 3-5, 2025. The 66 full papers were carefully reviewed and selected from 167 submissions. This year's symposium brought together leading researchers, scientists, and industry professionals from around the world to share cutting-edge advancements, foster collaboration, and explore the future of bioinformatics and computational biology.
Fostering Machine Learning and Iot for Blockchain Technology
This book covers Blockchain Technology and its emerging field by developing socio-economic systems viz. efficiently establishing transparency, and trust, increasing utilization of various resources, and reducing cost. This book is organized into 2 volumes that provide an overview of Blockchain technology foundations of Blockchain, Machine Learning and Distributed Systems, Cryptography, Consensus algorithm, Bitcoin concepts, and its properties, Smart Contracts, developing knowledge of tools like Hyperledger, Multichain, Ethereum, etc. Machine learning algorithms, applications of a smart city using machine learning and blockchain technology. While this book will dwell on the foundations of Blockchain Technology as a part of transparency, scalability, integrity, security, and how the machine learning algorithms integrate with blockchain technology-based smart city applications, it will also focus on contemporary topics for Research and Development in various sectors. With an in-depth knowledge of the technology underlying various platforms such as Bitcoin, Ethereum, and Hyperledger. As a beginner, the reader will be learning the importance of consensus in transactions, how transactions are stored on Blockchain & how to use it in the financial domain, such as virtual currency, cross-border payment and settlement bills and supply chain finance, securities insurance, and transactions viz. Bitcoin. The reader will be able to develop custom smart contracts using solidity and Remix IDE using the Ethereum platform and deploy them on the test Blockchain network using Truffle. The reader will learn how to build a machine learning model and analyze the data which is generated by the practical use cases of Blockchain in various smart cities sectors like Smart Health, Smart food, and agriculture, smart transport, smart water management, smart waste management, smart energy management, etc.
Information Processing in Medical Imaging
This two-volume set LNCS 15829-15830 constitutes the proceedings of the 29th International Conference on Information Processing in Medical Imaging, IPMI 2025, held on Kos, Greece, during May 25-30, 2025. The 51 full papers presented in this volume were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: Part I: Classification/Detection; Registration; Reconstruction; Image synthesis; Image enhancement; and Segmentation.Part II: Computer-aided diagnosis/surgery; Brain; Diffusion models; Self-supervised learning; Vision-language models; Shape analysis; and Time-series image analysis.
Sustainable Blind Quantum Computing
Quantum computing systems are powerful for allowing a client to perform any quantum computations from a remote quantum server while concealing the structure and content of the computation fall under the category of blind quantum computing (BQC). In BQC, the client delegates the quantum processing to one or more powerful quantum servers while retaining privacy over the input, computation and output. This makes it suitable for secure quantum cloud computing. This feature is powerful to ensure that even untrusted servers cannot learn the details of the user's computation. With quantum computing, there is a fast-growing need to transition from general-purpose quantum systems to customized architectures tailored to specific application requirements. This transition is critical while considering sustainability goals and financial limitations. With this advanced computing architecture, a custom system can optimize energy use, hardware complexity, and resource allocation to better serve individual user needs while staying within budgetary boundaries.
Bioinformatics Research and Applications
This two-set volume LNCS 15756 and 15767 constitutes the refereed proceedings of the 21st International Symposium on Bioinformatics Research and Applications, ISBRA 2025, held in Helsinki, Finland, during August 3-5, 2025. The 66 full papers were carefully reviewed and selected from 167 submissions. This year's symposium brought together leading researchers, scientists, and industry professionals from around the world to share cutting-edge advancements, foster collaboration, and explore the future of bioinformatics and computational biology.
Image Processing and Computer Vision Masterclass with Python
Image processing and computer vision technologies, combined with the rapid advancements in generative AI, have become foundational to many modern applications. As visual data continues to grow exponentially, the ability to analyze, interpret, and generate images using advanced algorithms and AI is more critical than ever for driving innovation across industries.This book provides a thorough exploration of advanced techniques and practical implementations in the field of computer vision. This book offers a problem-oriented approach that bridges traditional image processing with modern machine learning and generative AI methods. ​​This new edition significantly expands into specialized domains with medical imaging applications using professional libraries like pydicom, ITK, and nnUNet for clinical diagnosis, including COVID-19 detection and brain tumor segmentation, plus remote sensing analysis with satellite processing.By the end of this book, readers will have developed strong practical skills in both classical and cutting-edge image processing and computer vision techniques, empowered to confidently design, implement, and adapt solutions across a wide range of real-world applications. They will emerge with a deep understanding of theory, hands-on coding experience, and the ability to leverage AI and generative models to push the boundaries of visual computing. WHAT YOU WILL LEARN● Restore and enhance images using classical and deep learning methods.● Segment images with advanced clustering and neural network techniques.● Extract and match features for image alignment and recognition.● Build and train image classifiers with ML and AI.● Learn advanced restoration and inpainting techniques using cutting-edge deep learning models. ● Explore specialized domain expertise in medical imaging applications using professional libraries. WHO THIS BOOK IS FORThis book is ideal for undergraduate and graduate students, researchers, and professionals in computer vision, image processing, and AI. It also serves computer vision engineers, image analysts, data scientists, software engineers, and industry practitioners seeking practical, hands-on expertise using Python.
Why Machines Learn
A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics--the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene. We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both? As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible. In a brand-new afterword exclusively in the paperback edition, Ananthaswamy dives into the Transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.
Legal Challenges of a Transformation from a Product-Centric to a Service-Centric Business Model and Procedures
Recent Advances in Neuromorphic Computing
Artificial Intelligence (AI) is a transformative technology that reshapes our daily lives. Machine Learning (ML), the engine of such a revolution, empowers computers to learn from data, driving innovation in areas such as medicine, robotics, and smart cities through edge applications. These applications bring AI processing closer to the data source, enabling real-time insights and decisions. This evolution is fueled by advancements in hardware and architecture: (1) neuromorphic computing promises unparalleled efficiency; (2) in-memory computing eliminates data access bottlenecks, while emerging memory materials offer denser, faster, and more energy-efficient storage. Looking ahead, AI promises even more profound changes. For instance, explainable AI will make decision-making more transparent, and truly autonomous systems will adapt to unforeseen circumstances. Last but not least, the convergence of AI with quantum computing could unlock entirely new possibilities. This journey showcases a deep understanding of both the theoretical foundations and practical applications of AI. It also demands careful consideration of ethical implications and a commitment to responsible development, ensuring that AI benefits all of humanity.
Robot Localizationusing Visualimage Mapping
One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to determine its location. The calculation of the robot's pose is an optimization problem making use of the robot's internal navigation sensors and data fusion of range sensor readings in calculating the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use for obstacle avoidance, communication with other agents in the same environment, and target location. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera's field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extracting distance information to each obstacle. This range data is then used in conjunction with mapping and localization software to accurately navigate the AIBO.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery
Hyperspectral imagery (HSI) analysis is frequently employed by the Department of Defense for the purpose of classifying objects within an image as a form of target detection. In this research a robust Two-Phase Filtering Independent Component Analysis (ICA) Target Detection Method is proposed and validated. This new method resolves two main challenges encountered when implementing target detection methods using ICA, a high order statistics feature extraction (FE) method. The first challenge is the high computational demand imposed by the large volume of data associated with HSI during the FE process. To alleviate the effort required for ICA data processing, principal component analysis (PCA), a classical second order statistics method, is used for data reduction. Furthermore, the performance of using PCA under classification is compared against recently developed supervised FE techniques. The second challenge arises during the feature selection (FS) phase after the statistically independent components have been extracted. Current ICA target FS techniques have shown to be either unreliable or require significant user-intervention. A reliable FS process is essential in automating the target detection process. This proposed method uses ICA to extract independent features from the retained principal components, and is followed by an unsupervised target FS with a two-phase filtering process using kurtosis and mean silhouette values. This method achieved promising results when tested against a wide range of benchmark images.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Multi-Robot Fastslam for Large Domains
For a robot to build a map of its surrounding area it must have accurate position information within the area and to obtain the accurate position information within the area, the robot need to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these entire maps pose a serious limitation to the algorithm's capability when the problem space is large. In addition this problem will only get worse if the algorithm is adapted to a multirobot mapping. This thesis presents an alternate mapping algorithm that extends this single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations to represent the world.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Coalition Formation Under Uncertainty
Many multiagent systems require allocation of agents to tasks in order to ensure successful task execution. Most systems that perform this allocation assume that the quantity of agents needed for a task is known beforehand. Coalition formation approaches relax this assumption, allowing multiple agents to be dynamically assigned. Unfortunately, many current approaches to coalition formation lack provisions for uncertainty. This prevents application of coalition formation techniques to complex domains, such as real-world robotic systems and agent domains where full state knowledge is not available. Those that do handle uncertainty have no ability to handle dynamic addition or removal of agents from the collective and they constrain the environment to limit the sources of uncertainty.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Realtime Color Stereovision Processing
Recent developments in aviation have made micro air vehicles (MAVs) a reality. These featherweight palm-sized radio-controlled flying saucers embody the future of air-to-ground combat. No one has ever successfully implemented an autonomous control system for MAVs. Because MAVs are physically small with limited energy supplies, video signals offer superiority over radar for navigational applications. This research takes a step forward in realtime machine vision processing. It investigates techniques for implementing a realtime stereovision processing system using two miniature color cameras. The effects of poor-quality optics are overcome by a robust algorithm, which operates in realtime and achieves frame rates up to 10 fps in ideal conditions. The vision system implements innovative work in the following five areas of vision processing: fast image registration preprocessing, object detection, feature correspondence, distortion-compensated ranging, and multiscale nominal frequency-based object recognition. Results indicate that the system can provide adequate obstacle avoidance feedback for autonomous vehicle control. However, typical relative position errors are about 10%--to high for surveillance applications. The range of operation is also limited to between 6 - 30m. The root of this limitation is imprecise feature correspondence: with perfect feature correspondence the range would extend to between 0.5 - 30m. Stereo camera separation limits the near range, while optical resolution limits the far range. Image frame sizes are 160x120 pixels. Increasing this size will improve far range characteristics but will also decrease frame rate.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Scaling Ant Colony Optimization With Hierarchical Reinforcement Learning Partitioning
This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This research tests two clustering algorithms, k-means and G-means.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Artificial Media
A groundbreaking exploration of the evolving relationship between the fields of artificial intelligence and creativity studies, Artificial Media charts the course of a transformative path toward hybrid methodologies involving computing and human-centric approaches. Scholars and practitioners from leading research centers in South America, Asia and Europe delve into theoretical and philosophical frameworks, practical deployments and data-based critical analyses of artificial-media initiatives that reconfigure authorship and collaboration. Co-creation, collective memory, and situated-knowledge practices are featured in multiple hands-on examples of technological design, music, visual-arts, journalistic and educational projects that address the ethical and social implications of generative techniques. Through an interdisciplinary lens, this collection, projects a nuanced panorama of both the remarkable results and the complex challenges of emerging artificial-media methods, offering practical insights for anyone seeking to engage with the future of creativity in the age of autonomous machines.
Artificial Intelligence Security and Safety
This book proposes the architecture of artificial intelligence (AI) security and safety, discusses the topics about AI for security, AI security and AI safety, and makes an in-depth study on the ethical code of AI security and safety. Meanwhile, this book makes a detailed analysis of "artificial intelligence actant" (AIA) concept and its possible security problems, proposes the solutions for the AIA safely hoop, and provides the assessment and detection methods for AIA. Finally, this book discusses the AI cutting-edge technologies, as well as the future development trend of AI security and safety. This book is suitable for researchers, practitioners, regulators and enthusiasts in the field of AI, cyberspace security, etc.
Tactile Robotics
Tactile Robotics structures and unifies the information processing of tactile data--not only for extracting object property but also for controller computation. This book systematically introduces tactile sensors, perception, and control, providing readers with no prior background with a better sense and knowledge of robotics and machine learning and helping users understand the concept of tactile robots and their various applications for use in real-world scenarios.
Pattern Recognition and Image Analysis
The two volume set LNCS 15937 + 15938 constitutes the proceedings of the 12th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2025, which took place in Coimbra, Portugal, during June 30-July 3, 2025. The 67 full papers included in the proceedings were carefully reviewed and selected from 115 submissions. They were organized in topical sections as follows: Part I: Computer vision; faces, body, fingerprints and biometrics; machine and deep learning; explainability, bias and fairness in DL; Part II: Natural language processing; biomedical applications; and other applications.
Convergence of Artificial Intelligence, Machine Learning, and the Internet of Things in Industry 4.0 Applications
The book offers valuable insights into research related to Industry 4.0 applications that utilize artificial intelligence (AI), machine learning (ML), and the Industrial Internet of Things (IIoT). Industry 4.0, also known as the Fourth Industrial Revolution, includes disruptive technologies such as the Internet of Things (IoT), robotics, virtual reality (VR), VLSI architecture, and AI, all of which are transforming modern society and manufacturing practices. This book addresses various aspects of smart industrial application design strategies and their effects on next-generation systems, including quantum computing, edge computing, IoT, cybersecurity, nano-communications, and robotic automation. The application of AI, machine learning techniques, and IoT is anticipated to improve the performance of automated and controlled systems. Intended as a resource for academics, researchers, and professionals in the fields of AI and ML, the content also explores their applications within the industrial revolution and the influence of VLSI on the global market. Additionally, the book serves as a reference for developing sustainable engineering solutions to address various global industrial challenges.
The Generalizability of Private Sector Research on Software Project Management in Two USAF Organizations
Project managers typically set three success criteria for their projects: meet specifications, be on time, and be on budget. However, software projects frequently fail to meet these criteria. Software engineers, acquisition officers, and project managers have all studied this issue and made recommendations for achieving success. But most of this research in peer reviewed journals has focused on the private sector. Researchers have also identified software acquisitions as one of the major differences between the private sector and public sector MIS. This indicates that the elements for a successful software project in the public sector may be different from the private sector. Private sector project success depends on many elements. Three of them are user interaction with the project's development, critical success factors, and how the project manager prioritizes the traditional success criteria. High user interaction causes high customer satisfaction, even when the traditional success criteria are not completely met. Critical success factors are those factors a project manager must properly handle to avoid failure. And priorities influence which success criteria the project manager will most likely succeed in meeting.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Social Networking Website Users and Privacy Concerns
Social networking websites are the fastest growing entity on the Internet. Users of social networking websites post personal information and pictures on these websites. Privacy and social networking websites has been previously studied, however, since those studies were conducted the rules for those websites have changed dramatically. A mixed methods approach was used in this study to examine what privacy concerns users of social networking websites have, whether it's regarding information on their accounts or the pictures they have posted. This study also considered if there were common personality traits present in people with those concerns. A comparison of user preferences between MySpace and Facebook was also conducted. Quantitative data in the form of survey information was used in addition to qualitative data gathered from semistructured interviews. This study supports that Social Desirability Bias was correlated with a user being selective of what pictures were displayed on social networking website accounts. Few users expressed a preference for one social networking website over the other. Over half of the participants did express concern for their privacy on social networking website accounts, but there were no personality factors that showed to be predictive of that concern.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Using Upper Layer Weights to Efficiently Construct and Train Feedforward Neural Networks Executing Backpropagation
Feed-forward neural networks executing back propagation are a common tool for regression and pattern recognition problems. These types of neural networks can adjust themselves to data without any prior knowledge of the input data. Feed-forward neural networks with a hidden layer can approximate any function with arbitrary accuracy. In this research, the upper layer weights of the neural network structure are used to determine an effective middle layer structure and when to terminate training. By combining these two techniques with signal-to-noise ratio feature selection, a process is created to construct an efficient neural network structure. The results of this research show that for data sets tested thus far, these methods yield efficient neural network structure in minimal training time. Data sets used include an XOR data set, Fisher's Iris problem, a financial industry data set, among others.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Taking the High Ground
Cloud computing offers tremendous opportunities for private industry, governments, and even individuals to access massive amounts of computational resources on-demand at very low cost. Recent advancements in bandwidth availability, virtualization technologies, distributed programming paradigms, security services and general public awareness havecontributed to this new business model for employing information technology (IT) resources. IT managers face tough decisions as they attempt to balance the pros and cons of integrating commercial cloud computing into their existing IT architectures. On one hand, cloud computing provides on-demand scalability, reduces capital and operational expenses, decreases barriers to entry, and enables organizations to refocus on core competencies rather than on IT expertise.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Multi-Objective Mission Route Planning Using Particle Swarm Optimization
The Mission Routing Problem (MRP) is the selection of a vehicle path starting at a point, going through enemy terrain defended by radar sites to get to the target(s) and returning to a safe destination (usually the starting point). The MRP is a three-dimensional, multi-objective path search with constraints such as fuel expenditure, time limits, multi-targets, and radar sites with different levels of risks. It can severely task all the resources (people, hardware, software) of the system trying to compute the possible routes. The nature of the problem can cause operational planning systems to take longer to generate a solution than the time available. Since time is critical in MRP, it is important that a solution is reached within a relatively short time. It is not worth generating the solution if it takes days to calculate since the information may become invalid during that time. Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) technique that tries to find optimal solutions to complex problems using particles that interact with each other. Both Particle Swarm Optimization (PSO) and the Ant System (AS) have been shown to provide good solutions to Traveling Salesman Problem (TSP). PSO_AS is a synthesis of PSO and Ant System (AS). PSO_AS is a new approach for solving the MRP, and it produces good solutions. This thesis presents a new algorithm (PSO_AS) that functions to find the optimal solution by exploring the MRP search space stochastically.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
