تحسين النتائح
ترتيب حسب
من
الى
الصفحة 1
الصفحة 1
Linked Democracy : Foundations, Tools, and Applications
This book shows the factors linking information flow, social intelligence, rights management and modelling with epistemic democracy, offering licensed linked data along with information about the rights involved. This model of democracy for the web of data brings new challenges for the social organisation of knowledge, collective innovation, and the coordination of actions. Licensed linked data, licensed linguistic linked data, right expression languages, semantic web regulatory models, electronic institutions, artificial socio-cognitive systems are examples of regulatory and institutional design (regulations by design). The web has been massively populated with both data and services, and semantically structured data, the linked data cloud, facilitates and fosters human-machine interaction. Linked data aims to create ecosystems to make it possible to browse, discover, exploit and reuse data sets for applications. Rights Expression Languages semi-automatically regulate the use and reuse of content.
AI and law : How automation is changing the law
Provides insights into how AI is changing legal practice, government processes, and individuals’ access to those processes, encouraging each of us to consider how technological advances are changing the legal system. Particularly, and distinct from current debates on how to regulate AI, this books focuses on how the progressive merger between computational methods and legal rules changes the very structure and application of the law itself.
Machine Learning Refined : Foundations, Algorithms, and Applications
Provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology.
Machine learning methods for reverse engineering of defective structured surfaces
Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.
Machine Learning Approaches in Cyber Security Analytics
Introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
Machine learning and big data : Concepts, algorithms, tools and applications
Showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention
Machine Learning Algorithms Using Python Programming
Presents the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning.
Logic for Computer Scientists
This book introduces the notions and methods of formal logic from a computer science standpoint, covering propositional logic, predicate logic, and foundations of logic programming. It presents applications and themes of computer science research such as resolution, automated deduction, and logic programming in a rigorous but readable way.The style and scope of the work, rounded out by the inclusion of exercises, make this an excellent textbook for an advanced undergraduate course in logic for computer scientists.
Liapunov Functions and Stability in Control Theory
Presents a modern and self-contained treatment of the Liapunov method for stability analysis, in the framework of mathematical nonlinear control theory. A Particular focus is on the problem of the existence of Liapunov functions (converse Liapunov theorems) and their regularity, whose interest is especially motivated by applications to automatic control.
Knowledge and Skill Chains in Engineering and Manufacturing : Information Infrastructure in the Era of Global Communications
Explores knowledge and skill chains in engineering and manufacturing in the age of global communications. Information infrastructure involves a range of activities from product planning, engineering, and manufacturing trough transportation, marketing, and repair/upgrade to returns and recycling/disposal. Distinct from the traditional engineering database, life-cycle support information has its own characteristic requirements, -- flexible extensibility, distributed architecture, multiple viewpoints, long-time archiving, and product usage information. Several authors address the architecture of the information infrastructure, its services and its requirements. Other papers focus on the knowledge and skill chains that develop in a variety of situations: the supply chain, the factory floor, the man-system interaction, etc. For each of these, state-of-the-art and state-of-research scenarios for various industrial sectors address both engineering and operations requirements in the current socio-economic environment.
Computation Engineering : Applied Automata Theory and Logic
This book covers automata in depth, providing good intuitions along the way, and culminating with applications that are used every day in the field. In this respect, it is a departure from the conventional textbooks on complexity and computability, although these 'tradtional' aspects remain well represented.
Computable Models of the Law : Languages, Dialogues, Games, Ontologies
This book originate from a workshop held at the European University Institute of Florence, Italy, in December 2006. The workshop was devoted to the discussion of the different ways of understanding and explaining contemporary law, for the purpose of building computable models of it -- especially models enabling the development of computer applications for the legal domain.
Chaos and fractals : New frontiers of science
Covers the central ideas and concepts of chaos and fractals as well as many related topics including: the Mandelbrot set, Julia sets, cellular automata, L-systems, percolation and strange attractors.
Challenges and Solutions for Sustainable Smart City Development
Discusses advances in smart and sustainable development of smart environments. The authors discuss the challenges faced in developing sustainable smart applications and provide potential solutions. The solutions are aimed at improving reliability and security with the goal of affordability, safety, and durability. Topics include health care applications, sustainable smart transportation systems, intelligent sustainable wearable electronics, and sustainable smart building and alert systems. Authors are from both industry and academia and present research from around the world. Addresses problems and solutions for sustainable development of smart cities; Includes applications such as healthcare, transportation, wearables, security, and more ; Relevant for scientist and researchers working on real time smart city development.
Biomedical data mining for information retrieval : Methodologies, techniques, and applications
Discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Beginning Ubuntu Server Administration : From novice to professional
You love it as the world's most popular desktop Linux distribution, and now Ubuntu is available at a server near you. Embracing the very same features desktop users have grown to love, system administrators are rapidly adopting Ubuntu due to their ability to configure, deploy, and manage network services more effectively than ever. Beginning Ubuntu Server Administration guides you through all of the key configuration and administration tasks you'll need to know. Whether you're interested in adopting Ubuntu within a Fortune 500 environment or just want to use Ubuntu to manage your home network, this book is your go–to guide to using the distribution securely for a wide variety of network services. Topics include file, print, web, and FTP management, command–line tips and tricks, automated installation, configuration and deployment processes, and kernel management.
Beginning Groovy and Grails : From novice to professional
Web frameworks are playing a major role in the creation of today's most compelling web applications, because they automate many of the tedious tasks, allowing developers to instead focus on providing users with creative and powerful features. Java developers have been particularly fortunate in this area, having been able to take advantage of Grails, an open source framework that supercharges productivity when building Java–driven web sites. Grails is based on Groovy, which is a very popular and growing dynamic scripting language for Java developers and was inspired by Python, Ruby, and Smalltalk.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
