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Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
Avatars at Work and Play : Collaboration and Interaction in Shared Virtual Environments
examining uses of shared virtual environments in practical settings such as scientific collaboration, distributed meetings, building models together, and others. It also covers online gaming in virtual environments, which has attracted hundreds of thousands of users and presents an opportunity for studying a myriad of social issues. Covering both ‘work’ and ‘play’, the volume brings together issues common to the two areas.
Artificial neural networks : Recent advances, new perspectives and applications
This book explores the potential of ANNs for applications in different fields. Itincludes eight chapters that discuss deep learning, ANN tools, and other cutting-edgetechnologies. It also suggests avenues for further research into ANN techniques formedical imaging to detect breast tumors, classification of COVID-19 surveillancedatasets, health management, estimation of materials processing parameters, solarenergy management, and control of a petrochemical unit.
Artificial neural network-based optimized design of reinforced concrete structures
Introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. Uniquely applies the new powerful tools of AI to concrete structural design and optimization Multi-objective functions of concrete structures optimized either separately or simultaneously Design requirements imposed by codes are automatically satisfied by constraining conditions Heavily illustrated in color with practical design examples
Artificial intelligence in the design process : The impact on creativity and team collaboration
Discusses how to include artificial intelligence (AI) systems in the early stages of the design process. Today designers need new tools capable of supporting them in dealing with the increasing project's complexity and empowering their performances and capabilities. AI systems appear to be powerful means to enhance designers' creativity. This assumption was tested in a workshop where sixteen participants collaborated with three AI systems throughout the creative phases of research, sketching, and color selection. Results show that designers can access a broader level of variance and inspiration while reducing the risk of fossilization by triggering lateral thinking through AI-generated data. Therefore, AI could significantly impact the creative phases of the design process if applied consciously. Being AI systems intelligent agents, the book treats the Human-AI collaboration as a collaboration between human agents, proposing a set of guidelines helpful to achieving an efficient partnership with the machine.
Artificial intelligence in medicine
Provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting.
Artificial Intelligence Applications for Health Care
Covers topics on health care and artificial intelligence. Data sets related to biomedical signals (ECG, EEG, EMG) and images (X-rays, MRI, CT) are explored, analyzed, and processed through different computation intelligence methods. Applications of computational intelligence techniques like artificial and deep neural networks, swarm optimization, expert systems, decision support systems, clustering, and classification techniques on medial datasets are explained. Survey of medical signals, medial images, and computation intelligence methods are also provided.
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 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 Membrane Computing
Membrane computing is a branch of natural computing which investigates computing models abstracted from the structure and functioning of living cells and from their interactions in tissues or higher-order biological structures. The models considered, called membrane systems (P systems), are parallel, distributed computing models, processing multisets of symbols in cell-like compartmental architectures. In many applications membrane systems have considerable advantages – among these are their inherently discrete nature, parallelism, transparency, scalability and nondeterminism.
Analyzing computer system performance with Perl::PDQ
Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means to this end is the open-source performance analyzer Pretty Damn Quick (PDQ) written in Perl As the epigraph in this book points out, Common sense is the pitfall of performance analysis. The performance analysis framework that replaces common sense is revealed in the first few chapters of Part I. The important queueing concepts embedded in PDQ are explained in a very simple style that does not require any knowledge of formal probability theory. Part II begins with a full specification of how to set up and use PDQ replete with examples written in Perl. Subsequent chapters present applications of PDQ to the performance analysis of multicomputer architectures, benchmark results, client/server scalability, and Web-based applications.
Analysis and Design of Information Systems : Third ed.
This third edition of the successful Analysis and Design of Information Systems provides a comprehensive introduction and user-friendly survey to all aspects of business transformation and analysis, and aims to provide the complex set of tools covering all types of systems, including legacy, transactional, database, and web/e-commerce topics.
An Undergraduate Primer in Algebraic Geometry
This book consists of two parts. The first is devoted to an introduction to basic concepts in algebraic geometry: affine and projective varieties, some of their main attributes and examples. The second part is devoted to the theory of curves: local properties, affine and projective plane curves, resolution of singularities, linear equivalence of divisors and linear series, Riemann–Roch and Riemann–Hurwitz Theorems.The approach in this book is purely algebraic. The main tool is commutative algebra, from which the needed results are recalled, in most cases with proofs. The prerequisites consist of the knowledge of basics in affine and projective geometry, basic algebraic concepts regarding rings, modules, fields, linear algebra, basic notions in the theory of categories, and some elementary point–set topology.
An Introduction to Kolmogorov Complexity and Its Applications
Written by two experts in the field, this book is ideal for advanced undergraduate students, graduate students, and researchers in all fields of science. It is self-contained: it contains the basic requirements from mathematics, probability theory, statistics, information theory, and computer science. Included are history, theory, new developments, a wide range of applications, numerous (new) problem sets, comments, source references, and hints to solutions of problems. This is the only comprehensive treatment of the central ideas of Kolmogorov complexity and their applications.
Ambient intelligence for scientific discovery : Foundations, theories, and systems
Many difficult scientific discovery tasks can only be solved in interactive ways, by combining intelligent computing techniques with intuitive and adaptive user interfaces. It is inevitable to use human intelligence in scientific discovery systems: human eyes can capture complex patterns and relationships, along with detecting the exceptional cases in a data set; the human brain can easily manipulate perceptions to make decisions. Ambient intelligence is about this kind of ubiquitous and autonomous human interaction with information. Scientific discovery is a process of creative perception and communication, dealing with questions like: how do we significantly reduce information while maintaining meaning, or how do we extract patterns from massive data and growing data resources. Originating from the SIGCHI Workshop on Ambient Intelligence for Scientific Discovery, this state-of-the-art survey is organized in three parts: new paradigms in scientific discovery, ambient cognition, and ambient intelligence systems. Many chapters share common features such as interaction, vision, language, and biomedicine.
Algorithms and Programming : Problems and Solutions
This book containing classical and well-known problems supplemented by clear and in-depth explanations. The material covered includes such topics as combinatorics, sorting, searching, queues, grammar and parsing, selected well-known algorithms and much more.
Algorithms and data structures for massive datasets
Learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You'll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects--and there's no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you'll find the sweet spot of saving space without sacrificing your data's accuracy. About the Technology Standard algorithms and data structures may become slow--or fail altogether--when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost.
AI in learning : Designing the future
AI (artificial intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning.
Agile Development with the ICONIX Process : People, Process, and Pragmatism
Describes how to apply ICONIX Process (a minimal, use case-driven modeling process) in an agile software project. It's full of practical advice for avoiding common agile pitfalls. Further, the book defines a core agile subset so those of you who want to get agile need not spend years learning to do it. Instead, you can simply read this book and apply the core subset of techniques. The book follows a real-life .NET/C# project from inception and UML modeling, to working code through several iterations. You can then go on-line to compare the finished product with the initial set of use cases. The book also introduces several extensions to the core ICONIX Process, including combining test-driven development (TDD) with up-front design to maximize both approaches (with examples using Java and JUnit). And the book incorporates persona analysis to drive the projects goals and reduce requirements churn.
Advancing Computational Intelligence Techniques for Security Systems Design
Security systems have become an integral part of the building and large complex setups, and intervention of the computational intelligence (CI) paradigm plays an important role in security system architecture. This book covers both theoretical contributions and practical applications in security system design by applying the Internet of Things (IoT) and CI. It further explains the application of IoT in the design of modern security systems and how IoT blended with computational intel- ligence can make any security system improved and realizable. Focuses on the computational intelligence techniques of security system design / Covers applications and algorithms of discussed computational intelligence techniques / Includes convergence-based and enterprise integrated security systems with their applications / Explains emerging laws, policies, and tools affecting the landscape of cyber security / Discusses application of sensors toward the design of security systems
