Informatik

This book gathers selected papers presented at the 19th ¿Transport Systems. Theory and Practice¿ Scientific and Technical Conference organised by the Department of Transport Systems, Traffic Engineering and Logistics at the Faculty of Transport and Aviation Engineering of the Silesian University of Technology. How to ensure effectiveness and reduce risk in urban public transport? What factors have an influence on activity-travel participation? What is the future of electromobility in the context of decisions to buy electric vehicles? How to support tourists travelling by people with special needs? This publication provides you with answers to these and many other questions. It also contains an abundance of numerical analyses based on significant data sets, illustrating the close affiliation between mobility and quality of life. With regard to the research results discussed and the selected solutions applied, the book primarily addresses the needs of three target groups:¿ Scientists and researchers (ITS field).¿ Local authorities (responsible for the transport systems at the urban and regional level).¿ Representatives of business (traffic strategy management) and industry (manufacturers of ITS components).The conference was held on 18¿19 September 2023 in Katowice (Poland).

This book constitutes the refereed post-conference proceedings of the 13th International Conference on Big Data Technologies and Applications, BDTA 2023, held in Edinburgh, United Kingdom, in August 2023. The 8 full papers and 3 short papers of BDTA 2023 were selected from 23 submissions and present new advances and research results in the fields of big data technologies, collection and storage, big data management and retrieval, big data mining and approaches.

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most relevant findings discussed at the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2023), which took place as a hybrid event on October 19 ¿ 21, 2023, in/from Riga, Latvia. It spans a broad spectrum of advanced theories and methods, giving a special emphasis to the digitalization of transport systems, as well as smart, artificial intelligence, and digital twins applications.

This book constitutes revised selected papers from the 4th International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2023, which took place in Bizerte, Tunisia, on May 27, 2023.The 6 full papers included in this book were carefully reviewed and selected from 13 submissions. The book also contains one invited talk. The contributions focus on vehicular networks and emerging technologies and cyber safety and security of intelligent transportation systems.

IoTHIC-2023 is a multidisciplinary, peer-reviewed international conference on Internet of Things (IoT) and healthcare systems with Artificial Intelligence (AI) techniques such as data mining, machine learning, image processing, and meta-heuristic algorithms. The AI-based techniques are applied on many fields of healthcare systems, including predicting and detecting diseases in hospitals, clinics, smart health monitoring systems, surgery, medical services, and etc.

This book constitutes the 5th International Workshop, IoTBDH 2023, held in Birmingham, UK, during October 21¿25, 2023.The 7 full papers and 4 short papers included in this volume were carefully reviewed and selected from 33 submissions. They focus on the state-of-the-art research and applications in utilizing IoT and big data technology for healthcare by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new technologies, and providing visions for future research and development.

This book of the SPAR series contains 39 scientific articles presented in the Distributed Autonomous Robotic Systems conference organized in November 28¿30, 2022, in Montbéliard, France.The contributions are covering a broad scope of topics within distributed robotics including mobile sensor networks, unmanned aerial vehicles, multi-agent systems, algorithms for multi-robot systems, modular robots, swarm robotics, and reinforcement learning or deep learning applied to multi-robot systems.

This book presents the first volume of the proceedings of the 12th World Conference "Intelligent systems for industrial automation", WCIS-2022 held in Tashkent, Uzbekistan, on November 25-26, 2022. It includes contributions from diverse areas of intelligent industrial systems design, intelligent information systems, decision making under imperfect information and others. The topics of the papers include hybrid control systems, pattern recognition, industry 4.0, information security, neural computing, fuzzy computation, decision making and support systems, and others.

This monograph explores the synergy of Artificial Intelligence (AI), Augmented Reality (AR) and cognitive processes to enhance spatial abilities ¿ an integral aspect of cognitive development. The ability to comprehend and manipulate spatial information is not only fundamental to our understanding of the physical world but also plays a pivotal role in numerous academic and professional fields. Recognizing the profound impact of spatial ability on scientific disciplines and educational achievement, this monograph takes on the challenge of enhancing spatial skills among users.The authors present the design and development of a mobile training system that incorporates AR features to enhance learners¿ spatial ability. Involving mental generation, transformation, and rotation of visual images for understanding spatial relationships, spatial ability is closely linked to success in various scientific disciplines and educational pursuits. While spatial visualization skills testsare available, this monograph takes a unique approach by focusing on developing targeted interventions to improve spatial ability. It aims to unlock new avenues for cognitive growth and dive into the untapped potential within the realm of spatial intelligence. Beyond its emphasis on spatial ability enhancement, this monograph goes above and beyond traditional approaches by integrating AI techniques into the training system. As such, it aims to provide personalized and adaptive learning experiences for learners. The training system, through intelligent techniques, dynamically analyzes individual learners¿ strengths, weaknesses, and progress, tailoring content and challenges to their specific needs. This effort establishes a new frontier in educational technology, offering a groundbreaking solution that not only augments spatial ability development but also showcases the transformative potential of AI in reshaping the learning experience.The book is a valuable resource for researchers, educators, developers and technology enthusiasts, as it exemplifies the profound impact of AI and AR in shaping the future of online learning experiences.

Preparing for your iSAQB CPSA--Foundation Level exam? Make the grade with this study guide! First, tackle essential topics and fundamentals, beginning with development approaches and design techniques for scalable software architectures. Then, see how views, templates, and documents are used to communicate design decisions, and understand how to evaluate software architectures. Level up your exam prep with this handy resource!Highlights include:iSAQB CPSA-Foundation Level exam Architecture designDevelopment approachesArchitectural patternsTemplatesDocumentationPrototypesQuality managementDeploymentModeling toolsStatic code analysis toolsCode managementHighlights:iSAQB CPSA-Foundation Level exam Architecture designDevelopment approachesArchitectural patternsTemplatesDocumentationPrototypesQuality managementDeploymentModeling toolsStatic code analysis toolsCode management

Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is forThis book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LearningLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data AugmentationLabeling Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools

Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software productsKey FeaturesLearn how to scale-up your machine learning software to a professional levelSecure the quality of your machine learning pipeline at runtimeApply your knowledge to natural languages, programming languages, and imagesBook DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products.The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you'll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality.Towards the end, you'll address the most challenging aspect of large-scale machine learning systems - ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began - large-scale machine learning software.What you will learnIdentify what the machine learning software best suits your needsWork with scalable machine learning pipelinesScale up pipelines from prototypes to fully fledged softwareChoose suitable data sources and processing methods for your productDifferentiate raw data from complex processing, noting their advantagesTrack and mitigate important ethical risks in machine learning softwareWork with testing and validation for machine learning systemsWho this book is forIf you're a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.Table of ContentsMachine Learning Compared to Traditional SoftwareElements of a Machine Learning Software SystemData in Software Systems - Text, Images, Code, FeaturesData Acquisition, Data Quality and NoiseQuantifying and Improving Data PropertiesTypes of Data in ML SystemsFeature Engineering for Numerical and Image DataFeature Engineering for Natural Language DataTypes of Machine Learning Systems - Feature-Based and Raw Data Based (Deep Learning)Training and evaluation of classical ML systems and neural networksTraining and evaluation of advanced algorithms - deep learning, autoencoders, GPT-3Designing machine learning pipelines (MLOps) and their testingDesigning and implementation of large scale, robust ML software - a comprehensive exampleEthics in data acquisition and management(N.B. Please use the Look Inside option to see further chapters)

Level up your career by learning best practices for managing the data quality and integrity of your financial dataKey Features:Accelerate data integrity management using artificial intelligence-powered solutionsLearn how business intelligence tools, ledger databases, and database locks solve data integrity issuesFind out how to detect fraudulent transactions affecting financial report integrityBook Description:Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What You Will Learn:Develop a customized financial data quality scorecardUtilize business intelligence tools to detect, manage, and resolve data integrity issuesFind out how to use managed cloud-based ledger databases for financial data integrityApply database locking techniques to prevent transaction integrity issues involving finance dataDiscover the methods to detect fraudulent transactions affecting financial report integrityUse artificial intelligence-powered solutions to resolve various data integrity issues and challengesWho this book is for:This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it'll help you grasp the more advanced topics covered in this book.

Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflowsKey FeaturesGrasp MLOps and machine learning project lifecycle through concept introductionsGet hands on with provisioning and configuring Red Hat OpenShift Data ScienceExplore model training, deployment, and MLOps pipeline building with step-by-step instructionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you'll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more.With the groundwork in place, you'll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform.As you advance through the chapters, you'll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models.Armed with this comprehensive knowledge, you'll be able to implement MLOps workflows on the OpenShift platform proficiently.What you will learnBuild a solid foundation in key MLOps concepts and best practicesExplore MLOps workflows, covering model development and trainingImplement complete MLOps workflows on the Red Hat OpenShift platformBuild MLOps pipelines for automating model training and deploymentsDiscover model serving approaches using Seldon and Intel OpenVinoGet to grips with operating data science and machine learning workloads in OpenShiftWho this book is forThis book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you're a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.Table of ContentsIntroduction to MLOps and OpenShiftProvisioning an MLOps platform in the CloudBuilding Machine Learning ModelsEmbedding ML Models into the ApplicationsDeploying ML Models as a ServiceOperating ML workloadsBuilding a face detector using the Red Hat ML Platform

In today's world, where artificial intelligence (AI) is everywhere, shaping every facet of our lives and businesses, "AI Native Enterprise" emerges as an indispensable guide for visionary leaders seeking to navigate the journey towards integrating AI at the heart of their business strategy. This book advocates for AI-Native thinking, a transformative approach that embeds AI into the very DNA of business, marking a shift towards next-generation enterprises. With insights drawn from analyzing over 500 business cases across most industries and organizational functions, it offers a rich, data-backed exploration of opportunities and challenges of becoming an AI-native leader.Harnessing the Transformative Power of Generative AI"AI Native Enterprise" delves into the revolutionary impact of Generative AI (GenAI), showcasing its role in redefining competitive landscapes, reshaping business models, and establishing new paradigms for growth and innovation. With AI everywhere, the book underscores the importance of embracing AI-native thinking to tackle digital age challenges, encompassing ethical considerations, governance complexities, and the imperative for a profound cultural shift towards AI integration.Strategic Frameworks and Practical Insights from 500+ Real-World Business CasesThrough strategic frameworks like the AI Business Value Octagon, the book empowers leaders to assess and leverage AI's strategic impact effectively. It navigates through the Total Cost of Ownership (TCO), Return on Investment (ROI), Return on Experience (ROE), and introduces a comprehensive AI Risk Mitigation Framework, equipping leaders with the necessary tools for informed decision-making and responsible AI deployment. Engaging case studies provide actionable insights for spearheading AI-powered business transformation in an era where AI is everywhere.Who Will Benefit from This Book?"AI Native Enterprise" is tailored for a diverse audience eager to spearhead the transformation into AI-native enterprises. Executives, board members, and business leaders across industries will find invaluable guidance on leveraging AI to craft the new business DNA. Investors will discover insights into capitalizing on AI-driven opportunities. Technical leaders, including CTOs and CIOs, will gain a deep understanding of AI integration challenges and solutions. Business strategists and transformation leaders will learn about cutting-edge strategies for next-generation businesses. AI professionals, academic luminaries, and scholars-in-the-making will explore advanced concepts and the latest in AI research. In essence, this book is a must-read for anyone passionate about pioneering AI-powered business transformation.Embark on Your AI Transformation JourneyThe book lays out a visionary roadmap for organizations ready to embrace the AI-everywhere paradigm. It calls for a strategic shift to AI-Native thinking, guiding readers through intricacies of AI integration for achieving unparalleled success. Start your journey towards transforming your organization into an AI-native enterprise today and lead your business into a new era of pervasive innovation and growth. This book is your gateway to becoming a frontrunner in the AI-powered future.

Discover the power of storytelling with Unreal Engine 5's real-time technology, exploring storyboarding, character creation, world building, animating using sequencers, and much more with the help of an Unreal Authorized InstructorKey Features- Learn in detail about filmmaking techniques and understand how to tie that into your Unreal Engine workflow- Generate and animate near-photorealistic actors using the innovative MetaHuman technology- Build expansive worlds with Nanite and Lumen Global Illumination and Reflections technology- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionVirtual Filmmaking with Unreal Engine 5 is the first Unreal Engine book to guide you through the complete process of virtual film production. Encompassing the full spectrum of filmmaking, this book demonstrates the use of an industry-standard tool used by studios such as Disney, ILM, DNEG, and Framestore.Walking through the process systematically, you'll collect references and create a simple storyboard to plan your shots. You'll create virtual environments, import 3D models and add materials and textures to build photorealistic, dynamic worlds. You'll also create actors using highly customizable MetaHumans, understand how to import, re-target, and animate them. You'll bring it all together with cinematic lighting and camera animation before exporting your film.By the end of this book, you'll have discovered new tools for your toolkit to work on your virtual film projects in Unreal Engine 5, leveraging Quixel Megascans, Lumen, Nanite, and MetaHuman technology.What you will learn- Grasp the principles of photography and the art of storytelling- Collect references and create storyboards- Discover Unreal Engine's hidden features and useful keyboard shortcuts- Explore the capabilities of Quixel Megascans, Lumen, Nanite, and MetaHumans- Adopt cinematic techniques to achieve professional-looking shots- Creatively apply Lumen Global Illumination and Reflections techniques- Use multiple virtual cameras and apply post-production techniques- Implement best practices for optimization, post processing, and renderingWho this book is forWhether you're a beginner or intermediate filmmaker, 3D artist, animator, visual effects artist, or virtual production professional with just basic knowledge of the Unreal Engine, this book is designed to help you enter the world of virtual films and animations.While prior experience with the Unreal Engine would be beneficial, a deep working knowledge is not essential, as the book introduces the relevant tools and features needed throughout the activities.Table of Contents- Getting Started with Unreal Engine- Understanding the Principles of Photography, Film Cameras, and Lenses- Understanding the Art of Storytelling and Creating Your Storyboard- Importing 3D Objects and Creating Levels- Creating Environments with Quixel Megascans- Creating and Applying Materials to 3D Meshes- Creating Actors with Unreal Engine MetaHumans- Retargeting the MetaHumans for Unreal Engine 5- Adding Animations and Facial Expressions to Your MetaHuman Characters- Adding and Animating Virtual Cameras Using the Level Sequencer- Enhancing Set Dressing, Retiming Shots, and Adding Niagara Particles- Setting the Mood with Lighting and Adding Post-Processing Effects- Exploring Color Management, Additional Camera Settings and Rendering Your Shots- Adding Sound and Finalizing Your Virtual Film- Appendix: Creating Material IDs Using Blender

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applicationsKey FeaturesWork with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithmsEvaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoringUncover effective approaches to deep learning for computer vision, time series analysis, and forecastingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions.This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks.By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learnDiscover different ways to transform data into valuable insightsExplore the different types of regression techniquesGrasp the basics of classification through Naive Bayes and decision treesUse clustering to group data based on similarity measuresPerform data fitting, pattern recognition, and cluster analysisImplement feature selection and extraction for dimensionality reductionHarness MATLAB tools for deep learning explorationWho this book is forThis book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.Table of ContentsExploring MATLAB for Machine LearningWorking with Data in MATLABPrediction Using Classification and RegressionClustering Analysis and Dimensionality ReductionIntroducing Artificial Neural Networks ModelingDeep Learning and Convolutional Neural NetworksNatural Language Processing Using MATLABMATLAB for Image Processing and Computer VisionTime Series Analysis and Forecasting with MATLABMATLAB Tools for Recommender SystemsAnomaly Detection in MATLAB

Die Digitalisierung eröffnet neue Chancen für Bildung: Doch wie entstehen digitale Lernangebote, die Lernerfahrungen eröffnen und zu Lernerfolgen beitragen? Das Thema E-Learning war lange Zeit ein Nischenthema für spezielle Anliegen. Spätestens mit der Corona-Pandemie sind Bildungstechnologien zu einem zentralen Thema avanciert, das alle Lehrenden und Bildungsverantwortlichen beschäftigt. Digitales Lernen ist ein selbstverständlicher Bestandteil von Lernarrangements, die verschiedene mediale und methodische Elemente integrieren, um Lernen flexibel anzulegen und der Vielfalt der individuellen Voraussetzungen entgegenzukommen. In jedem Bildungskontext und in allen Altersgruppen ist zu überlegen, wie und mit welchen digitalen Elementen ein Lernangebot profitieren kann. Die Chancen des digitalen Lernens lassen sich dabei nur mit einem didaktischen Design einlösen, das auf einer Analyse der didaktischen Parameter aufsetzt und erkennbar werden lässt, wie es zur Lösung eines identifizierten Bildungsproblems beiträgt. Das Buch führt ein in die Grundlagen des Lehrens und Lernens mit digitalen Medien und beschreibt die Vorgehensweise des didaktischen Designs bei der Konzeption digitaler Lernangebote.

"Dive into the future of filmmaking with 'AI and ML Technological Solutions for the Film Industry.' This groundbreaking book explores the fusion of artificial intelligence (AI) and machine learning (ML) with the cinematic world, revealing a revolution that transcends traditional boundaries. From AI-optimized scriptwriting to facial recognition technology shaping actor expressions, the book unravels the transformative impact on storytelling, production, and audience engagement. Explore the realms of intelligent film preservation, joint AI-ML applications, linguistic AI processing, and machine learning in distribution, witnessing the seamless integration of creativity and technology. As the pages unfold, discover a cinematic landscape where algorithms enhance every aspect of the filmmaking process, ushering in a new era of immersive storytelling and unprecedented possibilities. 'AI and ML Technological Solutions for the Film Industry' is a captivating exploration for film enthusiasts, industry professionals, and tech aficionados alike."

In an era marked by rapid technological progress, women's safety remains a pressing concern despite strides toward gender equality. Women continue to grapple with safety challenges in both public and private spaces, enduring harassment, violence, and discrimination driven by entrenched societal norms and modern complexities. Amidst these challenges, harnessing the potential of artificial intelligence (AI) emerges as a promising avenue to reshape the landscape of women's safety. The groundbreaking book, AI Tools and Applications for Women's Safety , curated by experts Sivaram Ponnusamy, Vibha Bora, Prema Daigavane, and Sampada Wazalwar, delves into the transformative power of AI to address the daily safety concerns women face. This timely volume explores innovative AI-driven resources and applications that redefine personal security, offering tailored protection through real-time threat assessment and emergency response coordination. With comprehensive insights spanning academia, law enforcement, policymaking, and advocacy, this book covers predictive safety analytics, smart surveillance, ethical considerations, and more. AI Tools and Applications for Women's Safety not only sheds light on the promise of AI but also paves the way for informed discourse and meaningful action, ushering in a future defined by women's empowerment and security.

The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence , edited by Zhihan Lv from Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation problem in AI systems. It explores the latest concepts, technologies, and applications of Adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, real-world case studies, and thorough analysis, the Handbook provides practitioners with practical ideas and solutions, while also serving as a valuable teaching material and reference guide for students and educators in AI-related disciplines. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems, making it an indispensable resource for researchers, professionals, and students seeking to revolutionize their research and applications in the field of Adaptive AI.

This book constitutes the refereed revised selected papers of the 10th Latin American Conference on High Performance Computing, CARLA 2023, held in Cartagena, Colombia, during September 18¿22, 2023.The 14 full papers included in this book were carefully reviewed and selected from 26 submissions. They were organized in topical sections as follows: High Performance Computing (HPC), Artificial Intelligence using HPC Scale and High Performance Computing Applications.

This book provides the latest research and technology advances in building fire safety and smart firefighting. Different experts systemically review the application of new technologies like Artificial Intelligence, Internet of Things, Virtual Reality, Digitalization, and Metaverse in fire safety areas. These multi-disciplinary research and technology fusion will significantly change the fire resilience design and firefighting practices in the next 20 years. Achieving urban fire safety and resilience also plays a key role in developing future smart buildings and cities. This book attracts more young researchers into the latest multi-disciplinary fire safety research and promotes the application of the new technologies in firefighting.

¿As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector.The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints.Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.