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Machine Learning in Computer Vision
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Machine Learning for Multimedia Content Analysis
Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.
Machine learning for cyber-physical systems: selected papers from the international conference ML4CPS 2023
Contains selected papers from the international conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), from 29 to 31 March 2023. Cyber-physical systems are adaptive and learning: they analyze their environment and, based on observations, learn patterns, associations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnostics. Machine learning is the key technology for these developments.
Machine Learning and Robot Perception
Presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine learning and its application to reacting flows: ml and combustion
These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges.
Machine Learning and Data Mining for Computer Security : Methods and Applications
Presents research conducted in academia and industry on methods and applications of machine learning and data mining for problems in computer security and will be of interest to researchers and practitioners, as well students.
Machine Learning and Big Data Analytics Paradigms : Analysis, Applications and Challenges
Intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.
Machine Learning : The Basics
Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Machine Learning : Modeling Data Locally and Globally
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.
Long-Term Preservation of Digital Documents : Principles and Practices
Key to our culture is that we can disseminate information, and then maintain and access it over time. While we are rapidly advancing from vulnerable physical solutions to superior, digital media, preserving and using data over the long term involves complicated research challenges and organization efforts. Uwe Borghoff and his coauthors address the problem of storing, reading, and using digital data for periods longer than 50 years. They briefly describe several markup and document description languages like TIFF, PDF, HTML, and XML, explain the most important techniques such as migration and emulation, and present the OAIS (Open Archival Information System) Reference Model. To complement this background information on the technology issues the authors present the most relevant international preservation projects, such as the Dublin Core Metadata Initiative, and experiences from sample projects run by the Cornell University Library and the National Library of the Netherlands. A rated survey list of available systems and tools completes the book.
Logics of Specification Languages
Dedicated chapters address : the use of ASM (Abstract State Machines) in the classroom; the Event-B modelling method; a methodological guide to CafeOBJ logic; CASL, the Common Algebraic Specification Language; the Duration Calculus; the logic of the RAISE specification language (RSL); the specification language TLA+; the typed logic of partial functions and the Vienna Development Method (VDM); and Z logic and its applications. Each chapter is self-contained, with references, and symbol and concept indexes. Finally, in a unique feature, the book closes with short commentaries on the specification languages written by researchers closely associated with their original development.
Logical Data Modeling : What it is and How to do it
LOGICAL DATA MODELING: What It Is and How To Do IT is directed toward three groups of people: (1) Business subject matter experts, (2) information technology professionals, (3) advanced students in Computer Science, Management Information Systems, and e-Business. Its purpose is to outline the basics of logical data modeling—specifically, data modeling for relational database management systems—in simple, practical terms and in a business context. The focus on relational data modeling is consciously made because it is superior in modeling real business activities.
Location, Transport and Land-Use : Modelling Spatial-Temporal Information
Shows the use of statistical tools for forecasting and analyzing implications of land-use decisions. The idea is that la- use on a map is necessarily a consequence of individual, and often conflicting, siting decisions over time.
LNA-ESD Co-Design for Fully Integrated CMOS Wireless Receivers
LNA-ESD Co-Design for Fully Integrated CMOS Wireless Receivers fits in the quest for complete CMOS integration of wireless receiver front-ends. With a combined discussion of both RF and ESD performance, it tackles one of the final obstacles on the road to CMOS integration.
Linguistics for the age of AI
One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Law and the Semantic Web : Legal Ontologies, Methodologies, Legal Information Retrieval, and Applications
As part of this objective, ICT (information and communication technologies) services should become available for every citizen, and for all schools, homes and businesses. The book you have in front of you is about Semantic Web technology and law. Law is something omnipresent; all citizens — at some points in their lives — have to deal with it. In addition, law involves a large group of professionals, and is a mul- billion business world wide. Information technology is important because it that can improve citizens’ interaction with law, as well as improve legal professionals’ work environment. Legal professionals dedicate a significant amount of their time to finding, reading, analyzing and synthesizing information in order to take decisions, and prepare advice and trials, among other tasks. As part of the “Semantic-Based Knowledge and Content Systems” Strategic Objective, the European Commission is funding projects to construct technology to make the Semantic Web vision come true. 1 The articles in this book are related to two current foci of the Strategic Objective : • Knowledge acquisition and modelling, capturing knowledge from raw information and multimedia content in webs and other distributed repositories to turn poorly structured information into machi- processable knowledge.
Laser additive manufacturing: design, materials, processes and applications
Laser-based additive manufacturing (LAM) is a revolutionary advanced digital manufacturing technology developed in recent decades, which is also a key strategic technology for technological innovation and industrial sustainability. This technology unlocks the design and constraints of traditional manufacturing and meets the needs of complex geometry fabrication and high-performance part fabrication. A deeper understanding of the design, materials, processes, structures, properties and applications is desired to produce novel functional devices, as well as defect-free structurally sound and reliable LAM parts.The topics in this Special Issue reprint include macro- and micro-scale additive manufacturing with lasers, such as structure/material design, fabrication, modeling and simulation, in situ characterization of additive manufacturing processes and ex situ materials characterization and performance, with an overview that covers various applications in aerospace, biomedicine, optics and energy.
Knowledge Sharing in the Integrated Enterprise : Interoperability Strategies for the Enterprise Architect
The IFIP series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of referred international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP series is to encourage education and the dissemination and exchange of information about all aspects of computing.
