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Books > Computing & IT > General theory of computing
This book provides a first-of-its-kind approach for using blockchain to enhance resilience in disaster supply chain and logistics management, especially when dealing with dynamic communication, relief operations, prioritization, coordination, and distribution of scarce resources - these are elements of volatility, uncertainty, complexity, and ambiguity (VUCA) describing a dynamic environment that now form the "new norm" for many leaders. Blockchain-Enabled Resilience: An Integrated Approach for Disaster Supply Chain and Logistics Management analyzes the application of blockchain technology used to enable resilience in a disaster supply chain network. It discusses IoT and DVFS algorithms for developing a network-based simulation and presents advancements in disaster supply chain strategies using smart contacts for collaborations. The book covers how success is based on collaboration, coordination, sovereignty, and equality in distributing resources and offers a theoretical analysis that reveals that enhancing resilience can improve collaboration and communication and can result in more time-efficient processing for disaster supply management. This book provides a first-of-its-kind approach for managers and policy-makers as well as researchers interested in using blockchain to enhance resilience in disaster supply chains, especially when dealing with dynamic communication, relief operations, prioritization, coordination, and distribution of scarce resources. Practical guidance is provided for managers interested in implementation. A robust research agenda is also provided for those interested in expanding present research.
This book describes a cross-domain architecture and design tools for networked complex systems where application subsystems of different criticality coexist and interact on networked multi-core chips. The architecture leverages multi-core platforms for a hierarchical system perspective of mixed-criticality applications. This system perspective is realized by virtualization to establish security, safety and real-time performance. The impact further includes a reduction of time-to-market, decreased development, deployment and maintenance cost, and the exploitation of the economies of scale through cross-domain components and tools. Describes an end-to-end architecture for hypervisor-level, chip-level, and cluster level. Offers a solution for different types of resources including processors, on-chip communication, off-chip communication, and I/O. Provides a cross-domain approach with examples for wind-power, health-care, and avionics. Introduces hierarchical adaptation strategies for mixed-criticality systems Provides modular verification and certification methods for the seamless integration of mixed-criticality systems. Covers platform technologies, along with a methodology for the development process. Presents an experimental evaluation of technological results in cooperation with industrial partners. The information in this book will be extremely useful to industry leaders who design and manufacture products with distributed embedded systems in mixed-criticality use-cases. It will also benefit suppliers of embedded components or development tools used in this area. As an educational tool, this material can be used to teach students and working professionals in areas including embedded systems, computer networks, system architecture, dependability, real-time systems, and avionics, wind-power and health-care systems.
Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions - the key decisions that influence 90% of business outcomes - starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners' handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.
A more accessible approach than most competitor texts, which move into advanced, research-level topics too quickly for today's students. Part I is comprehensive in providing all necessary mathematical underpinning, particularly for those who need more opportunity to develop their mathematical competence. More confident students may move directly to Part II and dip back into Part I as a reference. Ideal for use as an introductory text for courses in quantum computing. Fully worked examples illustrate the application of mathematical techniques. Exercises throughout develop concepts and enhance understanding. End-of-chapter exercises offer more practice in developing a secure foundation.
Systems Analysis and Design, Eighth Edition offers a practical, visually appealing approach to information systems development.
Evaluates innovation policy and actions and considers real-world examples. Looks to the future of innovation and the role of future technologies. Provides an overview of recent policy trends in innovation and how they contribute to the creation of technology hotspots. Identifies how governments, industry, the research community and local communities can work together to craft individualised approaches to increasing innovation at a local level and building new industries.
Unique selling point: * Industry standard book for merchants, banks, and consulting firms looking to learn more about PCI DSS compliance. Core audience: * Retailers (both physical and electronic), firms who handle credit or debit cards (such as merchant banks and processors), and firms who deliver PCI DSS products and services. Place in the market: * Currently there are no PCI DSS 4.0 books
The book investigates various MPPT algorithms, and the optimization of solar energy using machine learning and deep learning. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental. This book: Discusses data acquisition by the internet of things for real-time monitoring of solar cells. Covers artificial neural network techniques, solar collector optimization, and artificial neural network applications in solar heaters, and solar stills. Details solar analytics, smart centralized control centers, integration of microgrids, and data mining on solar data. Highlights the concept of asset performance improvement, effective forecasting for energy production, and Low-power wide-area network applications. Elaborates solar cell design principles, the equivalent circuits of single and two diode models, measuring idealist factors, and importance of series and shunt resistances. The text elaborates solar cell design principles, the equivalent circuit of single diode model, the equivalent circuit of two diode model, measuring idealist factor, and importance of series and shunt resistances. It further discusses perturb and observe technique, modified P&O method, incremental conductance method, sliding control method, genetic algorithms, and neuro-fuzzy methodologies. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental.
This new volume provides the information needed to understand the simplex method, the revised simplex method, dual simplex method, and more for solving linear programming problems. Following a logical order, the book first gives a mathematical model of the linear problem programming and describes the usual assumptions under which the problem is solved. It gives a brief description of classic algorithms for solving linear programming problems as well as some theoretical results. It goes on to explain the definitions and solutions of linear programming problems, outlining the simplest geometric methods and showing how they can be implemented. Practical examples are included along the way. The book concludes with a discussion of multi-criteria decision-making methods. Advances in Optimization and Linear Programming is a highly useful guide to linear programming for professors and students in optimization and linear programming.
It explores a variety of modern applications in soft computing, including bioinspired computing, reconfigurable computing, fuzzy logic, fusion-based learning, intelligent healthcare systems, bioinformatics, data mining, functional approximation, genetic and evolutionary algorithms, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI developers, researchers, and academicians as it addresses the recent technological developments in the field of soft computing.
This book deals with how to measure innovation in crisis management, drawing on data, case studies, and lessons learnt from different European countries. The aim of this book is to tackle innovation in crisis management through lessons learnt and experiences gained from the implementation of mixed methods through a practitioner-driven approach in a large-scale demonstration project (DRIVER+). It explores innovation from the perspective of the end-users by focusing on the needs and problems they are trying to address through a tool (be it an app, a drone, or a training program) and takes a deep dive into what is needed to understand if and to what extent the tool they have in mind can really bring innovation. This book is a toolkit for readers interested in understanding what needs to be in place to measure innovation: it provides the know-how through examples and best practices. The book will be a valuable source of knowledge for scientists, practitioners, researchers, and postgraduate students studying safety, crisis management, and innovation.
Discusses concepts such as Basic Programming Principles, OOP Principles, Database Programming, GUI Programming, Network Programming, Data Analytics and Visualization, Statistical Analysis, Virtual Reality, Web Development, Machine Learning, Deep Learning Provides the code and the output for all the concepts discussed Includes a case study at the end of each chapter
The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations. Companies employ big data technology in the manufacturing field to acquire data about the products. Manufacturing companies could gain a deep business insight by tracking customer details, monitoring fuel consumption, detecting product defects, and supply chain management. Moreover, the convergence of smart manufacturing and big data analytics currently suffers due to data privacy concern, short of qualified personnel, inadequate investment, long-term storage management of high-quality data. The technological advancement makes the data storage more accessible, cheaper and the convergence of these technologies seems to be more promising in the recent era. This book identified the innovative challenges in the industrial domains by integrating heterogeneous data sources such as structured data, semi-structures data, geo-spatial data, textual information, multimedia data, social networking data, etc. It promotes data-driven business modelling processes by adopting big data technologies in the manufacturing industry. Big data analytics is emerging as a promising discipline in the manufacturing industry to build the rigid industrial data platforms. Moreover, big data facilitates process automation in the complete lifecycle of product design and tracking. This book is an essential guide and reference since it synthesizes interdisciplinary theoretical concepts, definitions, and models, involved in smart manufacturing domain. It also provides real-world scenarios and applications, making it accessible to a wider interdisciplinary audience. Features The readers will get an overview about the smart manufacturing system which enables optimized manufacturing processes and benefits the users by increasing overall profit The researchers will get insight about how the big data technology leverages in finding new associations, factors and patterns through data stream observations in real time smart manufacturing systems The industrialist can get an overview about the detection of defects in design, rapid response to market, innovative products to meet the customer requirement which can benefit their per capita income in better way Discusses technical viewpoints, concepts, theories, and underlying assumptions that are used in smart manufacturing Information delivered in a user-friendly manner for students, researchers, industrial experts, and business innovators, as well as for professionals and practitioners
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms - When to use them & Why - for Application developers and Researchers Machine Learning from an Application Perspective - General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.
This book is designed as a reference book and presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. Beginning with an introduction to optimization methods and algorithms and various enzymes, the book then moves on to provide a unified framework of process optimization for enzymes with various algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems. The book compiles the different machine learning models for optimization of process parameters for production of industrially important enzymes. The production and optimization of various enzymes produced by different microorganisms are elaborated in the book It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making Covers the best-performing methods and approaches for optimization sustainable enzymes production with AI integration in a real-time environment Featuring valuable insights, the book helps readers explore new avenues leading towards multidisciplinary research discussions The book is aimed primarily at advanced undergraduates and graduates studying machine learning, data science and industrial biotechnology. Researchers and professionals will also find this book useful.
Technology Innovation discusses the fundamental aspects of processes and structures of technology innovation. It offers a new perspective concerning fundamentals aspects not directly involved in the complex relations existing between technology and the socio-economic system. By considering technology and its innovation from a scientific point of view, the book presents a novel definition of technology as a set of physical, chemical, and biological phenomena, producing an effect exploitable for human purposes. Expanding on the general model of technology innovation by linking the model of technology, based on a structure of technological operations, with the models of the structures for technology innovation, based on organization of fluxes of knowledge and capitals, the book considers various technological processes and the stages of the innovation process. Offers a perspective on the evolution of technology in the frame of an industrial platform network Explains a novel definition of technology as a set of physical, chemical, and biological phenomena producing an effect exploitable for human purposes Discusses technology innovation as result of structures organizing fluxes of knowledge and capitals Provides a technology model simulating the functioning of technology with its optimization Presents a technology innovation model explaining the territorial technology innovation process The book is intended for academics, graduate students, technology developers who are involved in operations management and research, innovation, and technology development.
Influence action through data! This is not a book. It is a one-of-a-kind immersive learning experience through which you can become--or teach others to be--a powerful data storyteller. Let's practice! helps you build confidence and credibility to create graphs and visualizations that make sense and weave them into action-inspiring stories. Expanding upon best seller storytelling with data's foundational lessons, Let's practice! delivers fresh content, a plethora of new examples, and over 100 hands-on exercises. Author and data storytelling maven Cole Nussbaumer Knaflic guides you along the path to hone core skills and become a well-practiced data communicator. Each chapter includes: Practice with Cole: exercises based on real-world examples first posed for you to consider and solve, followed by detailed step-by-step illustration and explanation Practice on your own: thought-provoking questions and even more exercises to be assigned or worked through individually, without prescribed solutions Practice at work: practical guidance and hands-on exercises for applying storytelling with data lessons on the job, including instruction on when and how to solicit useful feedback and refine for greater impact The lessons and exercises found within this comprehensive guide will empower you to master--or develop in others--data storytelling skills and transition your work from acceptable to exceptional. By investing in these skills for ourselves and our teams, we can all tell inspiring and influential data stories!
Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. * Foundational reinforcement learning concepts and methods * The most popular deep reinforcement learning agents solving high-dimensional environments * Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.
ITIL is the leading best-practice framework for ITSM (IT service management) and is globally adopted in both the public and private sectors. The latest evolution of the framework - ITIL 4 - has been significantly updated and addresses new ITSM challenges, includes new technologies and incorporates new ways of working. ITIL 4 has evolved to a value system-focused approach that can be integrated with other management practices and ways of working, such as Agile and DevOps. Its end-to-end digital operation model has been designed to help IT teams create, deliver and operate technical products and services that fit their organisation's wider business strategy. ITIL(R) 4 Essentials contains everything you need to know to pass the ITIL 4 Foundation Certificate, plus more. It covers practices and concepts that are not addressed as part of the Foundation syllabus, making it ideal for newly qualified practitioners. The book offers practical tips - based on the author's extensive experience - for applying service management in the real world, with symbols used throughout to highlight which content is related to the ITIL 4 Foundation syllabus and which is not. Ideal for self-study candidates and training participants, ITIL 4 Essentials will prove a helpful companion to their studies and a practical aid for their professional development. Project managers, contractors or consultants with limited study time will also find it essential to their part-time education. This second edition has been updated to align with amendments to the ITIL(R) 4 Foundation syllabus, including: Replacing 'change control' with 'change enablement' throughout; The removal of 'IT' from the definition of a change; and Updating definitions for customer, sponsor and user. A perfect companion before, during and after your ITIL exam - buy your copy today. ITIL(R) is a registered trademark of AXELOS Limited. All rights reserved. This book is an official AXELOS licensed product.
More frequent and complex cyber threats require robust, automated and rapid responses from cyber security specialists. This book offers a complete study in the area of graph learning in cyber, emphasising graph neural networks (GNNs) and their cyber security applications. Three parts examine the basics; methods and practices; and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber security applications. Part two explains three different categories of graph learning including deterministic, generative and reinforcement learning and how they can be used for developing cyber defence models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.
Pierri clearly links modern psychoanalytic practice with Freud's interests in the occult using primary sources, some of which have never before been published in English. Assesses the origins of key psychoanalytic ideas.
Focusing on the critical role IT plays in organizational development, the book shows how to employ action learning to improve the competitiveness of an organization. Defining the current IT problem from an operational and strategic perspective, it presents a collection of case studies that illustrate key learning issues. It details a dynamic model for effective IT management through adaptive learning techniques-supplying proven educational theories and practices to foster the required changes in your staff. It examines existing organizational learning theories and the historical problems that occurred with companies that have used them, as well as those that have failed to use them.
Preparing for your SAP S/4HANA business process integration exam? Make the grade with this certification study guide to C_TS410! From financial accounting to warehouse management, this guide will review the key technical and functional knowledge you need to exceed the cut score. Explore test methodology, key concepts for each topic area, and practice questions and answers. Your path to C_TS410 certification begins here! In this book, you'll learn about:a. The TestGet ready for test day! This guide follows the exact structure of the exam, so align your study of SAP S/4HANA business process integration with the test objectives and walk through the topics covered in C_TS410_2020. b. Core ContentReview major subject areas like financial accounting, source-to-pay processing, and human experience management. Then master important terminology and key takeaways for each subject. c. Q&AAfter reviewing each chapter, solidify your knowledge with questions and answers for each section and improve your test-taking skills.Highlights Include: 1) Exams C_TS410_2020 2) Financial and management accounting 3) Source-to-pay processing 4) Lead-to-cash processing 5) Design-to-operate processing 6) Procurement7) Supply chain 8) Production planning 9) Enterprise asset management 10) Warehouse management 11) Project systems 12) Human experience management |
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