تحسين النتائح
ترتيب حسب
من
الى
الصفحة 36
الصفحة 36
Artificial intelligence : A modern approach ; global ed.
Explores the full breadth and depth of the field of artificial intelligence (AI). The 4th edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI
Artificial general intelligence
This book focused on engineering general intelligence – autonomous, self-reflective, self-improving, commonsensical intelligence.Each author explains a specific aspect of AGI in detail in each chapter, while also investigating the common themes in the work of diverse groups, and posing the big, open questions in this vital area.
Architecture of advanced numerical analysis systems: designing a scientific computing system using ocaml
Applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language.
Architecting dependable systems IV
As software systems become ubiquitous, the issues of dependability become more and more crucial. Given that solutions to these issues must be considered from the very beginning of the design process, it is reasonable that dependability is addressed at the architectural level. It also contains sections on architectural description languages, architectural components and patterns, architecting distributed systems, and architectural assurances for dependability.
Arakelov Geometry and Diophantine Applications
Bridging the gap between novice and expert, the aim of this book is to present in a self-contained way a number of striking examples of current diophantine problems to which Arakelov geometry has been or may be applied. Arakelov geometry can be seen as a link between algebraic geometry and diophantine geometry.The first chapters provide some background and introduction to the subject. These are followed by a presentation of different applications to arithmetic geometry. The final part describes the recent application of Arakelov geometry to Shimura varieties and the proof of an averaged version of Colmez's conjecture. This book thus blends initiation to fundamental tools of Arakelov geometry with original material corresponding to current research.
Arabic and Chinese Handwriting Recognition ; SACH 2006 Summit College Park, MD, USA, September 27-28, 2006 Selected Papers
Cheriet provides an overview of the problems of Arabic recognition and how systems can use natural language processing techniques to correct errors in lexicon-based systems.
Applied Parallel Computing ; State of the Art in Scientific Computing
Introduction The PARA workshops in the past were devoted to parallel computing methods in science and technology. There have been seven PARA meetings to date: PARA’94, PARA’95 and PARA’96 in Lyngby, Denmark, PARA’98 in Umea, ? Sweden, PARA 2000 in Bergen, N- way, PARA 2002 in Espoo, Finland, and PARA 2004 again in Lyngby, Denmark. The ?rst six meetings featured lectures in modern numerical algorithms, computer science, en- neering, and industrial applications, all in the context of scienti?c parallel computing. This meeting in the series, the PARA 2004 Workshop with the title “State of the Art in Scienti?c Computing.
Applied mathematics and machine learning
The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering; on the other hand, theoretical discoveries in mathematical algorithms, differential equations, and statistical inferences, to name a few, have provided the foundation for the exploration of new multidisciplinary models for solving practical problems. This Special Issue endeavors to continue the journey that started in our previous Special Issue (Applied Mathematics and Computational Physics) by providing a platform for researchers from both academia and industry, as well as government, to present their new computational methods that have engineering and physics applications.
Applied Deep Learning with TensorFlow 2 : Learn to Implement Advanced Deep Learning Techniques with Python
Focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: Understand the fundamental concepts of how neural networks work / Learn the fundamental ideas behind autoencoders and generative adversarial networks / Be able to try all the examples with complete code examples that you can expand for your own projects / Have available a complete online companion book with examples and tutorials.
Applied and computational mathematics for digital environments
Contains the 11 papers that were accepted and published in the Special Issue “Applied and Computational Mathematics for Digital Environments” of the MDPI Mathematics journal. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: The construction of mathematical and information models of intelligent computer systems for monitoring and controlling the parameters of digital environments; The development of intelligent optimization algorithms that search for optimal parameter values of mathematical and information models in digital environments; Software and mathematical technologies in the implementation of intelligent monitoring and computer control of the parameters of digital environments; The development and application of mathematical and information models, machine learning methods, and artificial intelligence for the analysis and processing of big data in digital environments.
Applications of Graph Transformations with Industrial Relevance ; 3rd International Symposium, AGTIVE 2007, Kassel, Germany, October 10-12, 2007, Revised Selected and Invited Papers
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Symposium on Applications of Graph Transformations, AGTIVE 2007, held in Kassel, Germany, in October 2007.
Applications of computational intelligence
Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems.
Applications of artificial intelligence, big data and internet of things in sustainable development
Focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science.
Applications of Agent Technology in Traffic and Transportation
Building effective and user-friendly transportation systems is one of the big challenges for engineers in the 21st century. There is an increasing need to understand, model, and govern such systems at both the individual (micro) and the society (macro) level. Still, this raises significant technical problems, as transportation systems may contain thousands of autonomous, "intelligent" entities that need to be simulated and/or controlled. Therefore, traffic and transportation scenarios are extraordinarily appealing for Distributed Artificial Intelligence, and (multi-) agent technology in particular. This book gives an overview of recent advances in agent-based transportation systems. It includes both a state-of-the-art survey and reports on cutting-edge research in the field.
Applications and Innovations in Intelligent Systems XV ; Proceedings of AI-2007, the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
The papers in this volume are the refereed application papers presented at AI-2007, the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2007.
Application of power electronics converters in smart grids and renewable energy systems
Focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller.
Anti-Spam Measures : Analysis and Design
The goal of this book is the methodical analysis of the potential, limitations, advantages, and drawbacks of anti-spam measures. These determine to which extent the measures can contribute to the reduction of spam in the long run. The range of considered anti-spam measures includes legislative, organizational, behavioral and technological ones. Furthermore, the conceptual development and analysis of an infrastructural email framework that features such a complementary application, is pointed out. The technological and organizational facets, the framework is analyzed twofold: its theoretical effectiveness is assessed with the aid of the formal model mentioned above, its storage and traffic requirements are analyzed quantitatively.
Anti-fragile ICT Systems
Introduces a novel approach to the design and operation of large ICT systems. It views the technical solutions and their stakeholders as complex adaptive systems and argues that traditional risk analyses cannot predict all future incidents with major impacts. To avoid unacceptable events, it is necessary to establish and operate anti-fragile ICT systems that limit the impact of all incidents, and which learn from small-impact incidents how to function increasingly well in changing environments. The book applies four design principles and one operational principle to achieve anti-fragility for different classes of incidents. It discusses how systems can achieve high availability, prevent malware epidemics, and detect anomalies. Analyses of Netflix’s media streaming solution, Norwegian telecom infrastructures, e-government platforms, and Numenta’s anomaly detection software show that cloud computing is essential to achieving anti-fragility for classes of events with negative impacts.
Anticipatory Behavior in Adaptive Learning Systems : From Brains to Individual and Social Behavior
Anticipatory behavior in adaptive learning systems is steadily gaining the - terest of scientists, although many researchers still do not explicitly consider the actual anticipatory capabilities of their systems.The introductory chapter of this volume therefore does not only provide an overview of the contributions included in this volume but also proposes a taxonomy of how anticipatory mechanisms can improve adaptive behavior and learning in cognitive systems. During the workshop it became clear that ant- ipations are involved in various cognitive processes that range from individual anticipatory mechanisms to social anticipatory behavior.
Anomaly Detection : Techniques and Applications
When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data.
