Allgemein
SAP S/4 HANA-Systeme in Hyperscaler Clouds
Dieses Buch hilft Ihnen bei der Architektur, dem Setup, der Installation und dem Betrieb von SAP S/4HANA-Systemen in der Public Cloud von Amazon, Microsoft und Google. Blue-Prints, Beispielarchitekturen und konkrete Handlungsanweisungen helfen bei der Erreichung Ihres Ziels.ANDRÉ BÖGELSACK arbeitet als Principal Director bei der Firma Accenture in der Schweiz und berät Kunden aller Industrien bei der Nutzung von Hyperscaler Services für den Betrieb von SAP-Systemen. Er wurde in Informatik über das Thema SAP promoviert und ist weithin in der SAP Community und bei den Hyperscalern bekannt.ELENA WOLZ studierte Wirtschaftsinformatik an der Technischen Universität München. Als Produktverantwortliche für SAP S/4HANA am SAP University Competence Center München beschäftigt sie sich zentral mit der Bereitstellung von SAP S/4HANA-Systemen. Weiterhin begleitete sie ganzheitlich ein S/4HANA Deployment-Projekt in Hyperscaler-Cloud-Umgebung.JOHANNES RANK leitet die Basis am SAP University Competence Center in München und verantwortet seit vielen Jahren den Betrieb von SAP S/4HANA-Systemen in Cloud- und On-Premise Umgebungen.JESSICA TISCHBIEREK übernimmt seit Herbst 2021 die Rolle als SAP GTM Lead EMEA bei Google Cloud mit Standort München. Sie hat zuvor seit 2018 bei Google Cloud im Pre-Sales Umfeld als Customer Engineer Specialist for SAP on Google Cloud Kunden bei ihrer Cloud Transformation beraten. Dabei arbeitet sie mit globalen Unternehmen und Partnern zusammen.DHIRAJ KUMAR arbeitet als Manager bei Accenture in Indien und leitet mit seinem Team die Ausführung der Migrationen von sehr großen und komplexen SAP-Landschaften. Sein Fokus sind der Einsatz von neuartigen Methoden für die Bereitstellung von SAP.UTPAL CHAKRABORTY arbeitet als Manager bei der Accenture GmbH in Deutschland und hat mehrjährige Erfahrung beim Betrieb und der Migration von SAP-Systemen in die Cloud. Er arbeitet branchenübergreifend und hat bereits die SAP-Systeme einer Vielzahl von Kunden in die Cloud gehoben.Einleitung und Einführung zu Hyperscaler Clouds - SAP S/4HANA-Systeme in den Hyperscaler Clouds - Konzepte und Architekturen für SAP S/4HANA-Systeme auf Amazon Web Services – AWS - Konzepte und Architekturen für SAP S/4HANA-Systeme auf Microsoft Azure - Konzepte und Architekturen für SAP S/4HANA-Systeme auf Google Cloud - Installation und Provisionierung von SAP S/4HANA-Systemen auf den Hyperscaler Clouds - Zusammenfassung und Ausblick
Microsoft Orleans for Developers
Use a simple programming model and the .NET language of your choice to build large distributed systems. This book teaches you the Microsoft Orleans framework.Even well-versed professional software developers with expertise in C# (or another language) find themselves unequipped to meet the challenges of distributed systems as infrastructure moves to multi-core; multiple computers are being used for scale, redundancy, and cloud computing; and multi-region deployment is taking place.Orleans handles many of the concerns of distributed computing and cloud infrastructure, allowing you to concentrate on writing application logic.WHAT YOU WILL LEARN* Know the key concepts for building distributed systems* Gain a background in the origin and evolution of Orleans, and why it is important for your projects* Dive into each of the features available in Orleans by building an example application* Develop troubleshooting skills for fixing bugs and running diagnostics* Achieve performance optimization and advanced configuration* Use the Orleans Dashboard to discern valuable insight in system performanceWHO THIS BOOK IS FORExperienced C# developers who want to build a new high-scale application (perhaps for an IoT requirement) and are interested in learning the concepts and features available in OrleansRICHARD ASTBURY works at Microsoft UK, helping software teams build software systems to run in the cloud. Richard is a former Microsoft MVP for Windows Azure. He is often found developing open source software in C# and Node.js, navigating the river on his paddle board, and riding his bike. He lives in rural Suffolk, UK with his wife, three children, and golden retriever. 1. Fundamentals2. Grains and Silos3. Hello World4. Debugging an Orleans Application5. Orleans Dashboard6. Adding Persistence7. Adding ASP.NET Core8. Unit Testing9. Streams10. Timer and Reminders11. Transactions12. Event Sourced Grains13. Updating Grains14. Optimizations15. Advanced Features16. Interviews
Model-based Systems Architecting
Model-based Systems Architecting is a key tool for designing complex industrial systems. It is dedicated to the working systems architects, engineers and modelers, in order to help them master the complex integrated systems that they are dealing with in their day-to-day professional lives.It presents the CESAMES Systems Architecting Method (CESAM), a systems architecting and modeling framework which has been developed since 2003 in close interaction with many leading industrial companies, providing rigorous and unambiguous semantics for all classical systems architecture concepts. This approach is practically robust and easy-to-use: during the last decade, it was deployed in more than 2,000 real system development projects within the industry, and distributed to around 10,000 engineers around the globe. DANIEL KROB is one of the leading world experts in systems architecting and engineering. He was Institute Professor at Ecole Polytechnique, Palaiseau, France, and founder and Director of its Industrial Chair dedicated to complex systems engineering for more than 15 years. He is currently President of the Center of Excellence on Systems Architecture, Management, Economy & Strategy (CESAMES) and has been an INCOSE Fellow since 2014.Preface ixAcknowledgments xvIntroduction xviiCHAPTER 1 INTRODUCTION TO CESAM 11.1 CESAM: a mathematically sound system modeling framework 11.2 CESAM: a framework focused on complex integrated systems 81.3 CESAM: a collaboration-oriented architecting framework 121.4 CESAM: a business-oriented framework 16CHAPTER 2 WHY ARCHITECTING SYSTEMS? 192.1 Product and project systems 192.2 The complexity threshold 222.3 Addressing systems architecting becomes key 252.4 The value of systems architecting 312.5 The key role of systems architects 342.6 How to analyze a systems architect profile? 36CHAPTER 3 CESAM FRAMEWORK 393.1 Elements of systemics 393.1.1 Interface 393.1.2 Environment of a system 413.2 The three architectural visions 423.2.1 Architectural visions definition 423.2.2 Architectural visions overview 463.2.3 Relationships between the three architectural visions 523.2.4 Organization of a system model 553.3 CESAM systems architecture pyramid 573.3.1 The three key questions to ask 573.3.2 The last question that shall not be forgotten 593.4 More systems architecture dimensions 603.4.1 Descriptions versus expected properties 603.4.2 Descriptions 623.4.3 Expected properties 733.5 CESAM systems architecture matrix 78CHAPTER 4 IDENTIFYING STAKEHOLDERS: ENVIRONMENT ARCHITECTURE 834.1 Why identify stakeholders? 834.2 The key deliverables of environment architecture 854.2.1 Stakeholder hierarchy diagram 854.2.2 Environment diagram 87CHAPTER 5 UNDERSTANDING INTERACTIONS WITH STAKEHOLDERS: OPERATIONAL ARCHITECTURE 915.1 Why understand interactions with stakeholders? 915.2 The key deliverables of operational architecture 945.2.1 Need architecture diagram 945.2.2 Lifecycle diagram 955.2.3 Use case diagrams 975.2.4 Operational scenario diagrams 995.2.5 Operational flow diagram 101CHAPTER 6 DEFINING WHAT THE SYSTEM SHALL DO: FUNCTIONAL ARCHITECTURE 1036.1 Why understand what the system does? 1036.2 The key deliverables of functional architecture 1056.2.1 Functional requirement architecture diagram 1066.2.2 Functional mode diagram 1086.2.3 Functional breakdown and interaction diagrams 1096.2.4 Functional scenario diagrams 1116.2.5 Functional flow diagram 112CHAPTER 7 DECIDING HOW THE SYSTEM SHALL BE FORMED: CONSTRUCTIONAL ARCHITECTURE 1157.1 Understanding how the system is formed? 1157.2 The key deliverables of constructional architecture 1177.2.1 Constructional requirement architecture diagram 1187.2.2 Configuration diagram 1207.2.3 Constructional breakdown and interaction diagram 1217.2.4 Constructional scenario diagram 1237.2.5 Constructional flow diagram 124CHAPTER 8 TAKING INTO ACCOUNT FAILURES: DYSFUNCTIONAL ANALYSIS 1278.1 Systems do not always behave as they should 1278.2 The key deliverables of dysfunctional analysis 1348.2.1 Dysfunctional analysis from an operational perspective 1358.2.2 Dysfunctional analysis from a functional perspective 1368.2.3 Dysfunctional analysis from a constructional perspective 138CHAPTER 9 CHOOSING THE BEST ARCHITECTURE: TRADE-OFF TECHNIQUES 1419.1 Systems architecting does not usually lead to a unique solution 1419.2 Trade-off techniques 1439.2.1 General structure of a trade-off process 1439.2.2 Managing trade-offs in practice 145Conclusion 149APPENDICES 157Appendix 1 System Temporal Logic 159Appendix 2 Classical Engineering Issues 163Appendix 3 Example of System Model Managed with CESAM 177Appendix 4 Implementing CESAM through a SysML Modeling Tool 199Appendix 5 Some Good Practices in Systems Modeling 209References 211Index 219
Erzeugende Funktionen verständlich erklärt
Erzeugende Funktionen sind ein wichtiges Werkzeug in der Kombinatorik und der Theoretischen Informatik. Das Buch zeigt an vielen Beispielen, wie man dieses Werkzeug verwendet, mit dem eine Folge reeller Zahlen durch eine einzige Funktion repräsentiert wird. Es wird eine Einführung in die Technik der Gewinnung und der Manipulation erzeugender Funktionen gegeben; wichtige Folgen und ihre korrespondierenden Funktionen werden behandelt.
From Logistic Networks to Social Networks
As a result of its widespread implementation in economic and social structures, the network concept appears to be a paradigm of the contemporary world. The need for various services – transport, energy, consumption of manufacturing goods, provision of care, information and communication, etc. – draws users into interwoven networks which are meshes of material and immaterial flows. In this context, the user is a consumer of goods and services from industries and administrations, or they themselves are part of the organization (digital social networks).This book examines the invariants that unify networks in their diversity, as well as the specificities that differentiate them. It provides a reading grid that distinguishes a generic level where these systems find a common interpretation, and a specific level where appropriate analytical methods are used. Three case studies from different fields are presented to illustrate the purpose of the book in detail. Jean-Paul Bourrières is Emeritus Professor at the University of Bordeaux, France.Nathalie Pinède is Associate Professor at Bordeaux Montaigne University, France.Mamadou Kaba Traoré is Professor at the University of Bordeaux, France.Gregory Zacharewicz is Professor at IMT Mines Alès, France.Foreword ixIntroduction xiPART 1. NETWORK VARIETY AND MODELING 1CHAPTER 1. NETWORK TYPOLOGY 31.1. Introduction 31.1.1. Network description levels 31.1.2. Network, graph and flow 41.1.3. Shared or dedicated infrastructure 51.1.4. User inclusion 61.2. The principal networks 61.2.1. (Human) transport networks 61.2.2. (Goods) distribution and collection networks 71.2.3. Dedicated distribution and collection networks (of fluids and energy) 81.2.4. IT networks 91.2.5. Communication networks 91.2.6. Social and digital social networks 101.3. Characterization and typology of networks 111.3.1. Key characteristics 111.3.2. Network integration 121.3.3. Typology 131.4. Engineering issues 161.5. Performance indicators, evaluation, optimization 181.5.1. Performance indicators 181.5.2. Evaluation and optimization 201.6. Conclusion 23CHAPTER 2. MODELING DISCRETE FLOW NETWORKS 252.1. Introduction 252.2. Structure 282.3. Characterization of a discrete flow 302.3.1. Statistical description 302.3.2. Probabilistic description 322.4. Activities 322.5. Control system 372.6. Resources 402.7. Fluid kinematics 412.7.1. Flow/resource/decision synchronization 422.7.2. Congestion phenomenon 482.7.3. Dissemination of information in social networks 512.8. Formalisms for modeling flows in a network 522.8.1. BPM tools 532.8.2. Timed Petri nets 532.8.3. Flow networks 542.8.4. Queuing networks 552.9. Multi-modeling 572.9.1. Multi-formalism versus mono-formalism 572.9.2. The DEVS hierarchical model 602.9.3. Multi-layer networks 622.10. Conclusion 64PART 2. NETWORK ANALYSIS METHODS AND APPLICATIONS 67CHAPTER 3. EXACT METHODS APPLIED TO THE FLOW ANALYSIS OF TOPOLOGICAL NETWORKS 693.1. Introduction 693.2. Additive flow networks – deterministic modelling by flow networks 713.2.1. Two-terminal series–parallel graph 723.2.2. General case – max-flow/min-cut 743.3. Additive flow networks – stochastic modelling by queuing networks 763.4. Synchronized flow networks – modeling by timed event graphs 813.4.1. Steady-state analysis of timed event graphs 813.4.2. Example of application: sizing a flow-shop 833.5. Conclusion 88CHAPTER 4. SIMULATION TECHNIQUES APPLIED TO THE ANALYSIS OF SOCIOLOGICAL NETWORKS 914.1. Introduction 914.2. Simulation techniques 924.2.1. Discrete event simulation (worldviews) 944.2.2. DEVS formalism 964.2.3. Coupling simulation/resolutive methods 1004.2.4. Distributed simulation 1024.2.5. Architectural solutions 1034.2.6. Time management and synchronization 1044.2.7. Pessimistic approach 1044.2.8. Optimistic approach 1054.2.9. HLA 1064.2.10. Cosimulation 1074.2.11. FMI/FMU 1084.2.12. FMI/FMU and HLA coupling 1094.3. Simulation of flows in sociological networks 1104.3.1. Behavioral simulation based on DEVS formalism 1114.3.2. Application study 1134.4. Conclusion 116PART 3. CASE STUDIES 119CHAPTER 5. SMART GRID 1215.1. Summary of the study 1225.2. Demand profile 1225.3. Solar power station, fuel station and regional import 1235.4. Hydroelectric power station and PHES 1235.5. Operational issues 1245.6. Model 1255.6.1. Decision variables 1255.6.2. Constraints 1265.6.3. Objective function 1275.7. Optimization results 128CHAPTER 6. FORESTRY LOGISTICS 1316.1. Summary of the study 1326.2. Forest timber supply problem 1326.3. Tactical planning model 1346.4. Logistics benchmarking 1366.4.1. AS IS scenario (non-collaborative logistics) 1366.4.2. TO BE scenario (collaborative logistics) 1376.4.3. Results 1386.5. Conclusion 139CHAPTER 7. MULTI-LAYERED DIGITAL SOCIAL NETWORKS 1437.1. Summary of the study 1447.2. Digital social networks 1447.3. Studying digital social networks via an interview broadcast 1457.3.1. Pre-interview social network scenario 1467.3.2. Social network audience 1487.4. Modeling and simulation 1487.4.1. Modeling the interview production and broadcast processes 1487.4.2. MSN/HLA simulation architecture 1497.5. Simulation results 1527.6. Conclusion and perspectives 154References 157Index 167
Pro Angular
Welcome to this one-stop shop for learning Angular. Pro Angular is the most concise and comprehensive guide available, giving you the knowledge you need to take full advantage of this popular framework for building your own dynamic JavaScript applications.Angular is an open-source JavaScript library maintained by Google. It has many excellent options when it comes to server-side development and is used in some of the largest and most complex web applications in the world to enhance HTML in the browser. Its cornerstone is the ability to create applications that are extendable, maintainable, testable, and standardized. Knowing Angular’s foundations and understanding its applications is an asset in any developer toolbox.The fifth edition of this popular guide explains how to get the most from Angular, presenting the range of benefits it can offer. You will begin learning how to use Angular in your projects, starting with the nuts-and-bolts concepts, and progressing to more advanced and sophisticated features. Each topic in this full-color book provides you with precisely enough learning and detail to be effective. In true Adam Freeman style, the most important features are given full-court press treatment, while also addressing common problems and how to avoid them.WHAT YOU WILL LEARN* Access accompanying online files for Angular 13 and 14 (when it is released)* Create rich and dynamic web app clients using Angular* Tap into some of the best aspects of server-side development* Know when to use Angular and when to seek an alternative* Use the ng tools to create and build an Angular project* Extend and customize Angular* Take advantage of popular component libraries* Utilize source code located at github.com/Apress/pro-angular-5edWHO THIS BOOK IS FORThis book is for web developers who want to create rich client-side applications. Foundational knowledge of HTML and JavaScript is recommended."Adam's books provide a finely tuned blend of architectural overview, technical depth, and experience-born wisdom. His clear, concise writing style, coupled with project-driven, real-world examples make me comfortable recommending his books to a broad audience, ranging from developers working with a technology for the first time to seasoned professionals who need to learn a new skill quickly."KEITH DUBLIN, STAFF ARCHITECT, Upfront Health Care“Adam’s books are the print version of a chat bot. His investment in learning how developers learn pays off in dividends, making this one of the most comprehensive resources available. Novices and experienced professionals alike will gain knowledge from the accessible and insightful material.”MARK DONILE, SOFTWARE ENGINEER, MS CSADAM FREEMAN is an experienced IT professional who has held senior positions in a range of companies, most recently serving as chief technology officer and chief operating officer of a global bank. Now retired, he spends his time writing and long-distance running.PART I – GETTING READYChapter 1. Getting ReadyChapter 2. Jumping Right InChapter 3. Primer, Part IChapter 4. Primer, Part IIChapter 5. SportsStore: A Real ApplicationChapter 6. SportsStore: Orders and CheckoutChapter 7. SportsStore:AdministrationChapter 8. SportsStore: Progressive Features and DeploymentPART II - WORKING WITH ANGULARChapter 9. Understanding Angular Projects and ToolsChapter 10. Using Data Bindings Chapter 11. Using the Built-In DirectivesChapter 12. Using Events and FormsChapter 13. Creating Attribute DirectivesChapter 14. Creating Structural DirectivesChapter 15. Understanding ComponentsChapter 16. Using and Creating PipesChapter 17. Using ServicesChapter 18. Using Service ProvidersChapter 19. Using and Creating ModulesPART III - ADVANCED ANGULAR FEATURESChapter 20. Creating the Example ProjectChapter 21. Using the Forms API, Part IChapter 22. Using the Forms API, Part IIChapter 23. Making HTTP RequestsChapter 24. Routing and Navigation, Part IChapter 25. Routing and Navigation, Part IIChapter 26. Routing and Navigation, Part IIIChapter 27. Using AnimationsChapter 28. Working with Component LibrariesChapter 29. Angular Unit Testing
Just ›A Machine for Doing Business‹?
An analysis of the informal practices and strategies surrounding the technology implementation process. How is a new intranet involved in an ongoing merger integration process? Katja Schönian analyses internal communication and branding strategies in connection with the implementation of a new company intranet. Based on qualitative data, the study contrasts managerial expectations and everyday usage of the intranet in distinct work settings. Relying on social practice theories and research in Science & Technology Studies, Katja Schönian unpacks the different logics the intranet brings together and, furthermore, interrogates the characteristics that make an (un)workable technology. The book sheds light on the informal practices and politics surrounding the technology implementation process. It provides readers with new insights into the dynamics of a merger integration process, the production of worker subjectivity, and the increasing involvement of technologies in contemporary knowledge work.
Anisotropic hp-Mesh Adaptation Methods
Mesh adaptation methods can have a profound impact on the numerical solution of partial differential equations. If devised and implemented properly, adaptation significantly reduces the size of the algebraic systems resulting from the discretization, while ensuring that applicable error tolerances are met. In this monograph, drawing from many years of experience, the authors give a comprehensive presentation of metric-based anisotropic hp-mesh adaptation methods.A large part of this monograph is devoted to the derivation of computable interpolation error estimates on simplicial meshes, which take into account the geometry of mesh elements as well as the anisotropic features of the interpolated function. These estimates are then used for the optimization of corresponding finite element spaces in a variety of settings. Both steady and time dependent problems are treated, as well as goal-oriented adaptation. Practical aspects of implementation are also explored, including several algorithms. Many numerical experiments using the discontinuous Galerkin method are presented to illustrate the performance of the adaptive techniques.This monograph is intended for scientists and researchers, including doctoral and master-level students. Portions of the text can also be used as study material for advanced university lectures concerning a posteriori error analysis and mesh adaptation. Introduction.- Metric Based Mesh Representation.- Interpolation Error Estimates for Two Dimensions.- Interpolation Error Estimates for Three Dimensions.- Anisotropic Mesh Adaptation, h-Variant.- Anisotropic Mesh Adaptation Method, hp-Variant.- Framework of the Goal-Oriented Error Estimates.- Goal-Oriented Anisotropic Mesh Adaptation.- Implementation Aspects.- Applications.
Digitalization and Control of Industrial Cyber-Physical Systems
Industrial cyber-physical systems operate simultaneously in the physical and digital worlds of business and are now a cornerstone of the fourth industrial revolution. Increasingly, these systems are becoming the way forward for academics and industrialists alike. The very essence of these systems, however, is often misunderstood or misinterpreted. This book thus sheds light on the problem areas surrounding cyber-physical systems and provides the reader with the key principles for understanding and illustrating them.Presented using a pedagogical approach, with numerous examples of applications, this book is the culmination of more than ten years of study by the Intelligent Manufacturing and Services Systems (IMS2) French research group, part of the MACS (Modeling, Analysis and Control of Dynamic Systems) research group at the CNRS. It is intended both for engineers who are interested in emerging industrial developments and for master’s level students wishing to learn about the industrial systems of the future. OLIVIER CARDIN is a lecturer in Industrial Engineering at the IUT de Nantes, Nantes University, France.WILLIAM DERIGENT is a Professor in Industrial Engineering at the University of Lorraine, France.DAMIEN TRENTESAUX is a Professor in Industrial Engineering at the Université Polytechnique Hauts-de-France, France.Foreword xiiiAndré THOMASIntroduction xviiOlivier CARDIN, William DERIGENT and Damien TRENTESAUXPART 1 CONCEPTUALIZING INDUSTRIAL CYBER-PHYSICAL SYSTEMS 1CHAPTER 1 GENERAL CONCEPTS 3Olivier CARDIN and Damien TRENTESAUX1.1 Industry at the heart of society 31.2 Industrial world in search of a new model 41.3 Cyber-physical systems 61.4 From cyber-physical systems to industrial cyber-physical systems 81.5 Perspectives on the study of industrial cyber-physical systems 111.6 References 15CHAPTER 2 MOVING TOWARDS A SUSTAINABLE MODEL: SOCIETAL, ECONOMIC AND ENVIRONMENTAL 17Patrick MARTIN, Maroua NOUIRI and Ali SIADAT2.1 Industry of the future and sustainable development 172.2 Contribution of ICPS to the social dimension 182.2.1 Background 182.2.2 Cognitive aspects 212.2.3 Health and safety aspects at work 222.3 Contribution of ICPS to the environmental dimension 282.3.1 Objectives and expectations 282.3.2 Example of application 292.4 Contribution of ICPS to the economic dimension 302.5 Conclusion 322.6 References 32PART 2 SENSING AND DISTRIBUTING INFORMATION WITHIN INDUSTRIAL CYBER-PHYSICAL SYSTEMS 37CHAPTER 3 INFORMATION FLOW IN INDUSTRIAL CYBER-PHYSICAL SYSTEMS 39Thierry BERGER and Yves SALLEZ3.1 Introduction 393.2 Information and decision loops when using an ICPS 393.3 Decision-making processes within the loops of an ICPS 413.3.1 Nature of decision-making processes 413.3.2 Nature of information 423.3.3 Approach to studying the informational loops of the cyber part of an ICPS 433.4 Elements for the implementation of loops 453.4.1 Generic architecture 453.4.2 Link to decision-making processes and the nature of the information 483.5 Illustrative examples 483.5.1 Example from rail transport 493.5.2 Example from the manufacturing sector 503.6 Conclusion 523.7 References 52CHAPTER 4 THE INTELLIGENT PRODUCT CONCEPT 55William DERIGENT4.1 The intelligent product, a leading-edge concept in industrial cyber-physical systems 554.2 Definitions of the intelligent product concept 564.3 Developments in the concept of intelligent products 594.3.1 Group 1: product-driven systems (PDS) 614.3.2 Group 2: product lifecycle information management (PLIM) 634.4 Conclusions and perspectives on the intelligent product 664.5 References 67PART 3 DIGITALIZING AT THE SERVICE OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS 71CHAPTER 5 VIRTUALIZING RESOURCES, PRODUCTS AND THE INFORMATION SYSTEM 73Theodor BORANGIU, Silviu RĂILEANU and Octavian MORARIU5.1 Virtualization – the technology for industrial cyber-physical systems 735.2 Virtualization in the industrial environment 745.3 Shop floor virtualization of resource and product workloads 785.3.1 Resource and product virtualization through shop floor profiles 785.3.2 Virtualization of collaborative product and resource workloads 835.4 MES virtualization in the cloud (vMES) 895.5 Perspectives offered by virtualization to industry of the future 945.6 References 95CHAPTER 6 CYBERSECURITY OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS 97Antoine GALLAIS and Youcef IMINE6.1 What are the risks involved? 986.1.1 Unavailability of systems 986.1.2 Loss of confidentiality or integrity 1016.1.3 Bypassing access and authentication controls 1046.2 What means of protection? 1056.2.1 Ensuring availability 1056.2.2 Ensuring confidentiality 1076.2.3 Implementing authentication mechanisms 1086.2.4 Controlling access, permissions and logging 1096.3 Conclusion 1126.4 References 114PART 4 CONTROLLING INDUSTRIAL CYBER-PHYSICAL SYSTEMS117CHAPTER 7 INDUSTRIAL AGENTS: FROM THE HOLONIC PARADIGM TO INDUSTRIAL CYBER-PHYSICAL SYSTEMS 119Paulo LEITÃO, Stamatis KARNOUSKOS and Armando Walter COLOMBO7.1 Overview of multi-agent systems and holonics 1207.1.1 Multi-agent systems 1207.1.2 Holonic paradigm 1227.2 Industrial agents 1247.2.1 Definition and characteristics 1247.2.2 Interfacing with physical assets 1267.3 Industrial agents for realizing industrial cyber-physical systems 1277.3.1 Supporting the development of intelligent products, machines and systems within cyber-physical systems 1277.3.2 Implementing an industrial multi-agent system as ICPS 1297.4 Discussion and future directions 1307.5 References 131CHAPTER 8 HOLONIC CONTROL ARCHITECTURES 135Olivier CARDIN, William DERIGENT and Damien TRENTESAUX8.1 Introduction 1358.2 HCA fundamentals 1368.3 HCAs in the physical part of ICPS 1378.4 Dynamic architectures, towards a reconfiguration of the physical part from the cyber part of ICPS 1408.5 HCAs and Big Data 1438.6 HCAs and digital twin: towards the digitization of architectures 1448.7 References 145PART 5 LEARNING AND INTERACTING WITH INDUSTRIAL CYBER-PHYSICAL SYSTEMS 149CHAPTER 9 BIG DATA ANALYTICS AND MACHINE LEARNING FOR INDUSTRIAL CYBER-PHYSICAL SYSTEMS 151Yasamin ESLAMI, Mario LEZOCHE and Philippe THOMAS9.1 Introduction 1519.2 Data massification in industrial cyber-physical systems 1539.3 Big Data and multi-relational data mining (MRDM) 1549.3.1 Formal concept analysis (FCA) 1549.3.2 Relational concept analysis (RCA) 1579.4 Machine learning 1609.4.1 Basics of machine learning 1609.4.2 Multilayer perceptron (MLP) 1609.5 Illustrative example 1659.6 Conclusion 1679.7 References 167CHAPTER 10 HUMAN–INDUSTRIAL CYBER-PHYSICAL SYSTEM INTEGRATION: DESIGN AND EVALUATION METHODS 171Marie-Pierre PACAUX-LEMOINE and Frank FLEMISCH10.1 Introduction 17110.2 Design methods 17510.3 Method of integrating HICPS 17610.3.1 Descending phase 17710.3.2 Ascending phase 18010.4 Summary and conclusion 18510.5 References 186PART 6 TRANSFORMING INDUSTRIES WITH INDUSTRIAL CYBER-PHYSICAL SYSTEMS 189CHAPTER 11 IMPACT OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS ON RECONFIGURABLE MANUFACTURING SYSTEMS 191Catherine DA CUNHA and Nathalie KLEMENT11.1 Context 19111.1.1 Developments 19211.1.2 Issues 19311.1.3 Resources 19311.2 Reconfiguration 19411.2.1 Implementation and decision levels 19411.2.2 Information systems 19511.2.3 Adaptation in the context of CPPS/RMS 19611.2.4 Where and when to reconfigure? 19711.3 Modeling 19711.3.1 Data collection 19811.3.2 Simulation platforms 19911.4 Ergonomics/cognitive aspects 20011.5 Operation of the information system 20111.5.1 Operational level: procurement 20111.5.2 Responding to disruptions 20211.5.3 Decision support 20311.6 Illustrative example 20311.7 References 205CHAPTER 12 IMPACT OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS ON GLOBAL AND INTERCONNECTED LOGISTICS 207Shenle PAN, Mariam LAFKIHI and Eric BALLOT12.1 Logistics and its challenges 20712.2 Contemporary logistics systems and organizations 20812.2.1 Intra-site logistics 20912.2.2 Intra-urban logistics 21012.2.3 Inter-site inter-city logistics 21112.3 The Physical Internet as a modern and promising logistics organization 21212.3.1 Concept and definition 21212.3.2 Topologies of networks of networks 21312.4 Perspectives of ICPS applications in interconnected logistics: the example of the Physical Internet 21512.4.1 Modeling the Physical Internet by ICPS: the example of routing 21612.4.2 Exploiting ICPS: the data-driven approach and the digital twin-driven approach 21912.5 Conclusion 22112.6 References 222CHAPTER 13 IMPACT OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS ON TRANSPORTATION 225John MBULI and Damien TRENTESAUX13.1 Introduction 22513.1.1 Pull forces 22613.1.2 Complexity factors of the transportation sector 22713.1.3 Push forces 22813.2 The impact of ICPS on transportation 22913.3 Rail transportation service: an illustrative example 23113.3.1 The physical space of SUPERFLO 23313.3.2 The human fleet supervisor 23513.3.3 The cyber space of SUPERFLO 23613.3.4 Evaluation of the proposed model and industrial expectations 23613.4 Concluding remarks 23813.5 Acknowledgments 23913.6 References 239CHAPTER 14 IMPACTS OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS ON THE BUILDING TRADES 243William DERIGENT and Laurent JOBLOT14.1 General introduction 24314.2 The place of BIM in Construction 4.0 24514.3 Examples of transformations in the construction sector 24714.3.1 Control: real-time site management 24814.3.2 Learning and interacting: virtual reality and machine learning 24914.3.3 Capturing and distributing: use of wireless technologies (RFID and WSN) 25114.3.4 Digitalizing: digitalizing technologies for BIM 25214.4 Example of ICPS in construction 25414.5 Achieving the digital transformation of businesses 25514.6 References 257CHAPTER 15 IMPACT OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS ON THE HEALTH SYSTEM 261Franck FONTANILI and Maria DI MASCOLO15.1 Introduction 26115.1.1 The health system and its specificities 26115.1.2 The digital evolution of healthcare production and health 26315.2 HCPS in the literature 26315.2.1 HCPS for medical monitoring 26615.2.2 HCPS for well-being and prevention 26615.2.3 HCPS for organizational monitoring of patient pathways 26715.2.4 Sensors for monitoring patients and resources 26815.3 The contribution of a digital twin in an HCPS 27015.3.1 General principle of digital twins in health 27015.3.2 A proposal for an HCPS based on a digital twin of patient pathways in the hospital 27115.4 Conclusion 27415.5 References 275PART 7 ENVISIONING THE INDUSTRIAL CYBER-PHYSICAL SYSTEMS OF THE FUTURE 279CHAPTER 16 ETHICS AND RESPONSIBILITY OF INDUSTRIAL CYBER-PHYSICAL SYSTEMS 281Sylvie JONAS and Françoise LAMNABHI-LAGARRIGUE16.1 Introduction 28116.2 Ethics and ICPS 28316.2.1 Data management and protection 28416.2.2 Control in the design of algorithms 28516.3 Liability and ICPS 28816.3.1 Existing liability regimes applied to ICPS 28916.3.2 Proposals for changes in liability regimes 29116.4 References 294CHAPTER 17 TEACHING AND LEARNING ICPS: LESSONS LEARNED AND BEST PRACTICES 297Bilal AHMAD, Freeha AZMAT, Armando Walter COLOMBO and Gerrit JAN VELTINK17.1 Introduction 29717.2 University of Warwick – Bachelor-level curriculum 29917.2.1 ICPS education: Fusion of computer science and engineering 30017.2.2 Key enabling technologies in the ICPS curriculum 30117.2.3 Pedagogical principles: teaching ICPS modules 30117.3 University of Applied Sciences Emden/Leer – master’s-level curriculum 30217.3.1 ICPS education: fusion of computer science, electrical and mechatronics engineering 30317.3.2 Key enabling technologies in the ICPS curriculum 30517.3.3 Pedagogical principles: teaching ICPS modules 30717.4 Conclusion 30817.5 References 309Conclusion 313William DERIGENT, Olivier CARDIN and Damien TRENTESAUXList of Authors 317Index 321
Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution.The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today's NISQ hardware, the algorithm is evaluated on IBM's quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning.About the authorLeonhard Kunczik obtained his Dr. rer. nat. in 2021 in Quantum Reinforcement Learning from the Universität der Bundeswehr München as a member of the COMTESSA research group. Now, he continues his research as a project leader at the forefront of Quantum Machine Learning and Optimization in the context of Operations Research and Cyber Security.Motivation: Complex Attacker-Defender Scenarios - The eternal conflict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman’s Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing.- Approximation in Quantum Computing.- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning.- Applying Quantum REINFORCE to the Information Game.- Evaluating quantum REINFORCE on IBM’s Quantum Hardware.- Future Steps in Quantum Reinforcement Learning for Complex Scenarios.- Conclusion.
Infobody Theory and Infobody Model
This book presents a new concept: infobody.It contains four chapters:Chapter 1: Infobody OverviewThis chapter talks about the new concept—infobody and all related concepts and definitions. It also gives a summary for each following chapter and potential applications and research topics.Chapter 2: Infobody ChartingThis chapter talks about infobody charting with detailed steps and illustrations. Infobody charting is actually a visualization of the infobody model described in Chapter 3.Chapter 3: Infobody Model in Terms of Graph TheoryThis chapter talks about the infobody model in terms of graph theory. It gives detailed mathematical definitions and descriptions.Chapter 4: Chaos and Entropy in Infobody StructuresThis chapter talks about the chaos and entropy in infobody structures and gives detailed definitions and mathematical formulas.The author would like to discuss the infobody topics with anyone who is interested in it, such as university professors and students, managers and employees in any business industries and government agencies. His dedicated email address for this topic is yuhuinfobody@gmail.com.
Machines Behaving Badly
Can we build moral machines?Artificial intelligence is an essential part of our lives – for better or worse. It can be used to influence what we buy, who gets shortlisted for a job and even how we vote. Without AI, medical technology wouldn't have come so far, we'd still be getting lost in our GPS-free cars, and smartphones wouldn't be so, well, smart. But as we continue to build more intelligent and autonomous machines, what impact will this have on humanity and the planet?Professor Toby Walsh, a world-leading researcher in the field of artificial intelligence, explores the ethical considerations and unexpected consequences AI poses. Can AI be racist? Can robots have rights? What happens if a self-driving car kills someone? What limitations should we put on the use of facial recognition? Machines Behaving Badly is a thought-provoking look at the increasing human reliance on robotics and the decisions that need to be made now to ensure the future of AI is a force for good, not evil.
Energy Resilience and Climate Protection
The increasingly interconnected, fast-moving, unmanageable and unpredictable world brings with it an unprecedented variety of known and as yet unknown challenges and risks. Some of the global risks have a direct impact on critical infrastructures as well as those of energy supply in particular. A high level of functionality of critical infrastructures (CRITIS), which include the sectors of energy, information technology and telecommunications, transportation and traffic, health, water, food, finance and insurance, government and administration, as well as media and culture, is indispensable for a modern industrial society. In the context of the Corona crisis of 2020/2021, the worldwide inadequate preparation for pandemics became obvious, although the probability of epidemic outbreaks and their global spread has increased significantly in recent decades and was thus predictable to a certain extent. Moreover, it has been shown that in a globally interconnected world, complex crisis phenomena can mutually amplify and thus escalate within a short period of time. In particular, the deficits in preparedness for major risks that became apparent in the course of the Corona pandemic cannot be managed by nation states alone, especially since the probability of such events has risen continuously in recent decades and will continue to increase with growing globalization and urbanization and, in particular, as a result of climate change and its consequences. This publication addresses the challenges of energy resilience and climate protection, which will require immense attention in the future.
Medical Imaging and Health Informatics
MEDICAL IMAGING AND HEALTH INFORMATICSPROVIDES A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL IMAGING AS WELL AS PRACTICAL RECOMMENDATIONS FOR THE USAGE OF MACHINE LEARNING (ML) AND DEEP LEARNING (DL) TECHNIQUES FOR CLINICAL APPLICATIONS.Medical imaging and health informatics is a subfield of science and engineering which applies informatics to medicine and includes the study of design, development, and application of computational innovations to improve healthcare. The health domain has a wide range of challenges that can be addressed using computational approaches; therefore, the use of AI and associated technologies is becoming more common in society and healthcare. Currently, deep learning algorithms are a promising option for automated disease detection with high accuracy. Clinical data analysis employing these deep learning algorithms allows physicians to detect diseases earlier and treat patients more efficiently. Since these technologies have the potential to transform many aspects of patient care, disease detection, disease progression and pharmaceutical organization, approaches such as deep learning algorithms, convolutional neural networks, and image processing techniques are explored in this book.This book also delves into a wide range of image segmentation, classification, registration, computer-aided analysis applications, methodologies, algorithms, platforms, and tools; and gives a holistic approach to the application of AI in healthcare through case studies and innovative applications. It also shows how image processing, machine learning and deep learning techniques can be applied for medical diagnostics in several specific health scenarios such as COVID-19, lung cancer, cardiovascular diseases, breast cancer, liver tumor, bone fractures, etc. Also highlighted are the significant issues and concerns regarding the use of AI in healthcare together with other allied areas, such as the Internet of Things (IoT) and medical informatics, to construct a global multidisciplinary forum.AUDIENCEThe core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.TUSHAR H. JAWARE, PHD, received his degree in Medical Image Processing and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published more than 50 research articles in refereed journals and IEEE conferences, and has three international patents granted and two Indian patents published.K. SARAT KUMAR, PHD, received his degree in Electronics Engineering and is now a professor in the Department of Electronics & Communication Engineering, K L University, Andhra Pradesh, India. RAVINDRA D. BADGUJAR, PHD, received his degree in Electronics Engineering and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published many research articles in refereed journals and IEEE conferences as well as one international patent granted and two Indian patents published. SVETLIN ANTONOV, PHD, received his degree in Telecommunications and is now a lecturer in the Faculty of Telecommunications, TU-Sofia, Bulgaria. He is the author of several books and more than 60 peer-reviewed articles. PREFACE XVII1 MACHINE LEARNING APPROACH FOR MEDICAL DIAGNOSIS BASED ON PREDICTION MODEL 1Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale1.1 Introduction 21.1.1 Heart System and Major Cardiac Diseases 21.1.2 ECG for Heart Rate Variability Analysis 21.1.3 HRV for Cardiac Analysis 31.2 Machine Learning Approach and Prediction 31.3 Material and Experimentation 41.3.1 Data and HRV 41.3.1.1 HRV Data Analysis via ECG Data Acquisition System 51.3.2 Methodology and Techniques 61.3.2.1 Classifiers and Performance Evaluation 71.3.3 Proposed Model With Layer Representation 81.3.4 The Model Using Fixed Set of Features and Standard Dataset 111.3.4.1 Performance of Classifiers With Feature Selection 111.4 Performance Metrics and Evaluation of Classifiers 131.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model 131.4.2 HRV Model With Flexi Set of Features 141.4.3 Performance of the Proposed Modified With ISM-24 151.5 Discussion and Conclusion 181.5.1 Conclusion and Future Scope 19References 202 APPLICATIONS OF MACHINE LEARNING TECHNIQUES IN DISEASE DETECTION 23M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen2.1 Introduction 242.1.1 Overview of Machine Learning Types 242.1.2 Motivation 252.1.3 Organization the Chapter 252.2 Types of Machine Learning Techniques 252.2.1 Supervised Learning 252.2.2 Classification Algorithm 252.2.3 Regression Analysis 262.2.4 Linear Regression 272.2.4.1 Applications of Linear Regression 272.2.5 KNN Algorithm 282.2.5.1 Working of KNN 282.2.5.2 Drawbacks of KNN Algorithm 292.2.6 Decision Tree Classification Algorithm 292.2.6.1 Attribute Selection Measures 292.2.6.2 Information Gain 292.2.6.3 Gain Ratio 292.2.7 Random Forest Algorithm 292.2.7.1 How the Random Forest Algorithm Works 292.2.7.2 Advantage of Using Random Forest 302.2.7.3 Disadvantage of Using the Random Forest 312.2.8 Naive Bayes Classifier Algorithm 312.2.8.1 For What Reason is it Called Naive Bayes? 312.2.8.2 Disservices of Naive Bayes Classifier 312.2.9 Logistic Regression 312.2.9.1 Logistic Regression for Machine Learning 312.2.10 Support Vector Machine 322.2.11 Unsupervised Learning 322.2.11.1 Clustering 332.2.11.2 PCA in Machine Learning 352.2.12 Semi-Supervised Learning 382.2.12.1 What is Semi-Supervised Clustering? 382.2.12.2 How Semi-Supervised Learning Functions? 382.2.13 Reinforcement Learning 392.2.13.1 Artificial Intelligence 392.2.13.2 Deep Learning 402.2.13.3 Points of Interest of Machine Learning 412.2.13.4 Why Machine Learning is Popular 412.2.13.5 Test Utilizations of ML 422.3 Future Research Directions 432.3.1 Privacy 432.3.2 Accuracy 43References 433 DENGUE INCIDENCE RATE PREDICTION USING NONLINEAR AUTOREGRESSIVE NEURAL NETWORK TIME SERIES MODEL 47S. Dhamodharavadhani and R. Rathipriya3.1 Introduction 473.2 Related Literature Study 483.2.1 Limitations of Existing Works 503.2.2 Contributions of Proposed Methodology 503.3 Methods and Materials 503.3.1 NAR-NNTS 503.3.2 Fit/Train the Model 513.3.3 Training Algorithms 543.3.3.1 Levenberg-Marquardt (LM) Algorithm 543.3.3.2 Bayesian Regularization (BR) Algorithm 553.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm 553.3.4 DIR Prediction 553.4 Result Discussions 563.4.1 Dataset Description 563.4.2 Evaluation Measure for NAR-NNTS Models 573.4.3 Analysis of Results 573.5 Conclusion and Future Work 65Acknowledgment 66References 664 EARLY DETECTION OF BREAST CANCER USING MACHINE LEARNING 69G. Lavanya and G. Thilagavathi4.1 Introduction 704.1.1 Objective 704.1.2 Anatomy of Breast 704.1.3 Breast Imaging Modalities 714.2 Methodology 714.2.1 Database 714.2.2 Image Pre-Processing 714.3 Segmentation 724.4 Feature Extraction 724.5 Classification 724.5.1 Naive Bayes Neural Network Classifier 724.5.2 Radial Basis Function Neural Network 734.5.2.1 Input 734.5.2.2 Hidden Layer 734.5.2.3 Output Nodes 744.6 Performance Evaluation Methods 744.7 Output 754.7.1 Dataset 754.7.2 Pre-Processing 754.7.3 Segmentation 754.7.4 Geometric Feature Extraction 774.8 Results and Discussion 784.8.1 Database 784.9 Conclusion and Future Scope 81References 815 MACHINE LEARNING APPROACH FOR PREDICTION OF LUNG CANCER 83Hemant Kasturiwale, Swati Bhisikar and Sandhya Save5.1 Introduction 845.1.1 Disorders in Lungs 845.1.2 Background 845.1.3 Material, Datasets, and Techniques 855.2 Feature Extraction and Lung Cancer Analysis 865.3 Methodology 875.3.1 Proposed Algorithm Steps 875.3.2 Classifiers in Concurrence With Datasets 885.4 Proposed System and Implementation 895.4.1 Interpretation via Artificial Intelligence 895.4.2 Training of Model 905.4.3 Implementation and Results 905.5 Conclusion 995.5.1 Future Scope 99References 1006 SEGMENTATION OF LIVER TUMOR USING ANN 103Hema L. K. and R. Indumathi6.1 Introduction 1036.2 Liver Tumor 1046.2.1 Overview of Liver Tumor 1046.2.2 Classification 1056.2.2.1 Benign 1056.2.2.2 Malignant 1076.3 Benefits of CT to Diagnose Liver Cancer 1086.4 Literature Review 1086.5 Interactive Liver Tumor Segmentation by Deep Learning 1096.6 Existing System 1096.7 Proposed System 1106.7.1 Pre-Processing 1106.7.2 Segmentation 1116.7.3 Feature Extraction 1126.7.4 GLCM 1126.7.5 Backpropagation Network 1136.8 Result and Discussion 1136.8.1 Processed Images 1146.8.2 Segmentation 1166.9 Future Enhancements 1176.10 Conclusion 118References 1187 DMSAN: DEEP MULTI-SCALE ATTENTION NETWORK FOR AUTOMATIC LIVER SEGMENTATION FROM ABDOMEN CT IMAGES 121Devidas T. Kushnure and Sanjay N. Talbar7.1 Introduction 1217.2 Related Work 1227.3 Methodology 1237.3.1 Proposed Architecture 1237.3.2 Multi-Scale Feature Characterization Using Res2Net Module 1257.4 Experimental Analysis 1267.4.1 Dataset Description 1267.4.2 Pre-Processing Dataset 1277.4.3 Training Strategy 1287.4.4 Loss Function 1287.4.5 Implementation Platform 1297.4.6 Data Augmentation 1297.4.7 Performance Metrics 1297.5 Results 1317.6 Result Comparison With Other Methods 1357.7 Discussion 1367.8 Conclusion 137Acknowledgement 138References 1388 AI-BASED IDENTIFICATION AND PREDICTION OF CARDIAC DISORDERS 141Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale8.1 Introduction 1428.1.1 Cardiac Electrophysiology and Electrocardiogram 1438.1.2 Heart Arrhythmia 1448.1.2.1 Types of Arrhythmias 1458.1.3 ECG Database 1478.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard 1478.1.4 An Overview of ECG Signal Analysis 1488.2 Related Work 1498.3 Classifiers and Methodology 1518.3.1 Databases for Cardiac Arrhythmia Detection 1528.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database 1528.3.3 Arrhythmia Detection and Classification 1538.3.4 Methodology 1538.3.4.1 Database Gathering and Pre-Processing 1538.3.4.2 QRST Wave Detection 1538.3.4.3 Features Extraction 1548.3.4.4 Neural Network 1558.3.4.5 Performance Evaluation 1568.4 Result Analysis 1568.4.1 Arrhythmia Detection and Classification 1568.4.2 Dataset 1568.4.3 Evaluations and Results 1568.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection) 1578.5 Conclusions and Future Scope 1598.5.1 Arrhythmia Detection and Classification 1598.5.2 Future Scope 161References 1619 AN IMPLEMENTATION OF IMAGE PROCESSING TECHNIQUE FOR BONE FRACTURE DETECTION INCLUDING CLASSIFICATION 165Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala9.1 Introduction 1659.2 Existing Technology 1669.2.1 Pre-Processing 1669.2.2 Denoise Image 1679.2.3 Histogram 1689.3 Image Processing 1699.3.1 Canny Edge 1699.4 Overview of System and Steps 1709.4.1 Workflow 1709.4.2 Classifiers 1719.4.2.1 Extra Tree Ensemble Method 1719.4.2.2 SVM 1729.4.2.3 Trained Algorithm 1739.4.3 Feature Extraction 1739.5 Results 1749.5.1 Result Analysis 1759.6 Conclusion 176References 17610 IMPROVED OTSU ALGORITHM FOR SEGMENTATION OF MALARIA PARASITE IMAGES 179Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil10.1 Introduction 17910.2 Literature Review 18010.3 Related Works 18210.4 Proposed Algorithm 18310.5 Experimental Results 18410.6 Conclusion 193References 19311 A RELIABLE AND FULLY AUTOMATED DIAGNOSIS OF COVID-19 BASED ON COMPUTED TOMOGRAPHY 195Bramah Hazela, Saad Bin Khalid and Pallavi Asthana11.1 Introduction 19611.2 Background 19611.3 Methodology 19911.3.1 Models Used 19911.3.2 Architecture of the Image Source Classification Model 19911.3.3 Architecture of the CT Scan Classification Model 20011.3.4 Architecture of the Ultrasound Image Classification Model 20111.3.5 Architecture of the X-Ray Classification Model 20111.3.6 Dataset 20211.3.6.1 Training 20211.4 Results 20411.5 Conclusion 206References 20712 MULTIMODALITY MEDICAL IMAGES FOR HEALTHCARE DISEASE ANALYSIS 209B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai12.1 Introduction 21012.1.1 Background 21012.2 Brief Survey of Earlier Works 21212.3 Medical Imaging Modalities 21312.3.1 Computed Tomography (CT) 21412.3.2 Magnetic Resonance Imaging (MRI) 21412.3.3 Positron Emission Tomography (PET) 21412.3.4 Single-Photon Emission Computed Tomography (SPECT) 21512.4 Image Fusion 21612.4.1 Different Levels of Image Fusion 21612.4.1.1 Pixel Level Fusion 21612.4.1.2 Feature Level Fusion 21712.4.1.3 Decision Level Fusion 21712.5 Clinical Relevance for Medical Image Fusion 21812.5.1 Clinical Relevance for Neurocyticercosis (NCC) 21812.5.2 Clinical Relevance for Neoplastic Disease 21812.5.2.1 Clinical Relevance for Astrocytoma 21812.5.2.2 Clinical Relevance for Anaplastic Astrocytoma 21912.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma 22012.5.3 Clinical Relevance for Alzheimer’s Disease 22112.6 Data Sets and Softwares Used 22112.7 Generalized Image Fusion Scheme 22112.7.1 Input Image Modalities 22212.7.2 Image Registration 22212.7.3 Fusion Process 22312.7.4 Fusion Rule 22312.7.5 Evaluation 22412.7.5.1 Subjective Evaluation 22412.7.5.2 Objective Evaluation 22412.8 Medical Image Fusion Methods 22412.8.1 Traditional Image Fusion Techniques 22412.8.1.1 Spatial Domain Image Fusion Approach 22512.8.1.2 Transform Domain Image Fusion Approach 22512.8.1.3 Fuzzy Logic–Based Image Fusion Approach 22712.8.1.4 Filtering Technique–Based Image Fusion Approach 22712.8.1.5 Neural Network–Based Image Fusion Approach 22712.8.2 Hybrid Image Fusion Techniques 22812.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion 22812.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 22912.8.2.3 Transforms With Neural Network–Based Image Fusion 22912.9 Conclusions 23312.9.1 Future Work 234References 23413 HEALTH DETECTION SYSTEM FOR COVID-19 PATIENTS USING IOT 237Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede13.1 Introduction 23713.1.1 Overview 23713.1.2 Preventions 23813.1.3 Symptoms 23813.1.4 Present Situation 23813.2 Related Works 23913.3 System Design 23913.3.1 Hardware Implementation 23913.3.1.1 NodeMCU 24013.3.1.2 DHT 11 Sensor 24013.3.1.3 MAX30100 Oxygen Sensor 24113.3.1.4 ThingSpeak Server 24213.3.1.5 Arduino IDE 24313.4 Proposed System for Detection of Corona Patients 24513.4.1 Introduction 24513.4.2 Arduino IDE 24613.4.3 Hardware Implementation 24613.5 Results and Performance Analysis 24713.5.1 Hardware Implementation 24713.5.1.1 Implementation of NodeMCU With Temperature Sensor 24713.5.2 Software Implementation 24813.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software 24813.5.2.2 Interfacing of LCD With Arduino 25013.6 Conclusion 250References 25014 INTELLIGENT SYSTEMS IN HEALTHCARE 253Rajiv Dey and Pankaj Sahu14.1 Introduction 25314.2 Brain Computer Interface 25514.2.1 Types of Signals Used in BCI 25614.2.2 Components of BCI 25714.2.3 Applications of BCI in Health Monitoring 25814.3 Robotic Systems 25814.3.1 Advantages of Surgical Robots 25814.3.2 Centralization of the Important Information to the Surgeon 25914.3.3 Remote-Surgery, Software Development, and High SpeedConnectivity Such as 5G 26014.4 Voice Recognition Systems 26014.5 Remote Health Monitoring Systems 26014.5.1 Tele-Medicine Health Concerns 26214.6 Internet of Things–Based Intelligent Systems 26214.6.1 Ubiquitous Computing Technologies in Healthcare 26414.6.2 Patient Bio-Signals and Acquisition Methods 26514.6.3 Communication Technologies Used in Healthcare Application 26714.6.4 Communication Technologies Based on Location/Position 26914.7 Intelligent Electronic Healthcare Systems 27014.7.1 The Background of Electronic Healthcare Systems 27014.7.2 Intelligent Agents in Electronic Healthcare System 27014.7.3 Patient Data Classification Techniques 27114.8 Conclusion 271References 27215 DESIGN OF ANTENNAS FOR MICROWAVE IMAGING TECHNIQUES 275Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana15.1 Introduction 27515.1.1 Overview 27615.2 Literature 27715.2.1 Microstrip Patch Antenna 27815.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application 27915.2.3 UWB for Microwave Imaging 27915.3 Design and Development of Wideband Antenna 28015.3.1 Overview 28015.3.2 Design of Rectangular Microstrip Patch Antenna 28115.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna 28315.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 28515.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground 28615.4 Results and Inferences 29015.4.1 Overview 29015.4.2 Rectangular Microstrip Patch Antenna 29015.4.2.1 Reflection and VSWR Bandwidth 29015.4.2.2 Surface Current Distribution 29115.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 29215.4.3.1 Reflection and VSWR Bandwidth 29215.4.3.2 Surface Current Distribution 29215.4.3.3 Inference 29315.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground 29415.4.4.1 Reflection and VSWR Bandwidth 29415.4.4.2 Surface Current Distribution 29415.4.4.3 Results of the Fabricated Antenna 29515.4.4.4 Inference 29615.5 Conclusion 297References 29816 COVID-19: A GLOBAL CRISIS 303Savita Mandan and Durgeshwari Kalal16.1 Introduction 30316.1.1 Structure 30416.1.2 Classification of Corona Virus 30416.1.3 Types of Human Coronavirus 30416.1.4 Genome Organization of Corona Virus 30516.1.5 Coronavirus Replication 30516.1.6 Host Defenses 30616.2 Clinical Manifestation and Pathogenesis 30616.2.1 Symptoms 30716.2.2 Epidemiology 30716.3 Diagnosis and Control 30816.3.1 Molecular Test 30816.3.2 Serology 30816.3.3 Concerning Lab Assessments 30916.3.4 Significantly Improved D-Dimer 30916.3.5 Imaging 30916.3.6 HRCT 30916.3.7 Lung Ultrasound 31016.4 Control Measures 31016.4.1 Prevention and Patient Education 31116.5 Immunization 31216.5.1 Medications 31216.6 Conclusion 313References 31317 SMART HEALTHCARE FOR PREGNANT WOMEN IN RURAL AREAS 317D. Shanthi17.1 Introduction 31717.2 National/International Surveys Reviews 31917.2.1 National Family Health Survey Review-11 31917.2.2 National Family Health Survey Review-2.2 31917.2.3 National Family Health Survey Reviews-3 32017.3 Architecture 32017.4 Anganwadi’s Collaborative Work 32117.5 Schemes Offered by Central/State Governments 32117.5.1 AAH (Anna Amrutha Hastham) 32117.5.2 Programme Arogya Laxmi 32317.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years 32317.5.4 Nutri TASC (Tracking of Group Responsibility for Services) 32317.5.5 Akshyapatra Foundation (ISKCON) 32417.5.6 Mahila Sishu Chaitanyam 32417.5.7 Community Management of Acute Malnutrition 32517.5.8 Child Health Nutrition Committee 32517.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna 32517.6 Smart Healthcare System 32617.7 Data Collection 32817.8 Hardware and Software Features of HCS 32817.9 Implementation 32917.9.1 Modules 32917.9.2 Modules Description 32917.9.2.1 Data Preprocessing 32917.9.2.2 Component Features Extraction 32917.9.2.3 User Sentimental Measurement 33017.9.2.4 Sentiment Evaluation 33017.10 Results and Analysis 33117.11 Conclusion 333References 33318 COMPUTER-AIDED INTERPRETATION OF ECG SIGNAL—A CHALLENGE 335Shalini Sahay and A.K. Wadhwani18.1 Introduction 33618.1.1 Electrical Activity of the Heart 33618.2 The Cardiovascular System 33818.3 Electrocardiogram Leads 34018.4 Artifacts/Noises Affecting the ECG 34218.4.1 Baseline Wander 34318.4.2 Power Line Interference 34318.4.3 Motion Artifacts 34418.4.4 Muscle Noise 34418.4.5 Instrumentation Noise 34418.4.6 Other Interferences 34518.5 The ECG Waveform 34618.5.1 Normal Sinus Rhythm 34718.6 Cardiac Arrhythmias 34718.6.1 Sinus Bradycardia 34718.6.2 Sinus Tachycardia 34818.6.3 Atrial Flutter 34818.6.4 Atrial Fibrillation 34918.6.5 Ventric ular Tachycardia 34918.6.6 AV Block 2 First Degree 35018.6.7 Asystole 35018.7 Electrocardiogram Databases 35118.8 Computer-Aided Interpretation (CAD) 35118.9 Computational Techniques 35418.10 Conclusion 356References 357Index 359
Up and Running with DAX for Power BI
Take a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX.While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it’s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Knowledge of everything else in DAX is built on top of these mandatory aspects.Author Alison Box has been teaching and working with DAX for over eight years, starting with DAX for PowerPivot, the Excel add-in, before moving into the Power BI platform. The guide you hold in your hands is an outcome of these years of experience explaining difficult concepts in a way that people can understand. Over the years she has refined her approach, distilling down the truth of DAX which is “you can take people through as many functions as you like, but it’s to no avail if they don’t truly understand how it all works.”You will learn to use DAX to gain powerful insights into your data by generating complex and challenging business intelligence calculations including, but not limited to:* Calculations to control the filtering of information to gain better insight into the data that matters to you* Calculations across dates such as comparing data for the same period last year or the previous period* Finding rolling averages and rolling totals* Comparing data against targets and KPIs or against average and maximum values* Using basket analysis, such as “of customers who bought product X who also bought product Y”* Using “what if” analysis and scenarios* Finding “like for like” sales* Dynamically showing TopN/BottomN percent of customers or products by sales* Finding new and returning customers or sales regions in each month or each yearWHO THIS BOOK IS FORExcel users and non-technical users of varying levels of ability or anyone who wants to learn DAX for Power BI but lacks the confidence to do soALISON BOX is Director of Burningsuit Ltd, and an IT trainer and consultant with over 30 years of experience delivering computer applications training to all skill levels, from basic users to advanced technical experts. Currently, she specializes in delivering training in Microsoft Power BI Service and Desktop, Data Modeling, DAX (Data Analysis Expressions), and Excel. Alison also works with organizations as a DAX and Data Analysis consultant. She was one of the first Excel trainers to move into delivering courses in Power Pivot and DAX, from where Power BI was born. Part of her job entails promoting Burningsuit as a knowledge base for Power BI and includes writing regular blog posts on all aspects of Power BI that are published on her website.Chapter 1: Show Me the DataChapter 2: DAX Objects, Syntax & FormattingChapter 3: Calculated Columns & MeasuresChapter 4: Evaluation ContextChapter 5: IteratorsChapter 6: The CALCULATE FunctionChapter 7: DAX Table FunctionsChapter 8: The ALL Function and All its VariationsChapter 9: Calculations on Dates: Using DAX Time IntelligenceChapter 10: Empty Values Versus ZeroChapter 11: Using Variables: Making Our Code More ReadableChapter 12: Returning Values in the Current FilterChapter 13: Controlling the Direction of Filter PropagationChapter 14: Working with Multiple Relationships Between TablesChapter 15: Understanding Context TransitionChapter 16: Leveraging Context TransitionChapter 17: Virtual Relationships: the LOOKUPVALUE and TREATAS FunctionsChapter 18: Table ExpansionChapter 19: The CALCULATETABLE Function
Electronic Governance
Noch nie sind die technologischen Entwicklungen und die Veränderungen der Märkte so rasant verlaufen wie heutzutage. Die Digitalisierung von Wirtschaft und Gesellschaft stehen dabei erst am Anfang. Viele Menschen beobachten die Entwicklungen misstrauisch. Sie können mit den in Verbindung stehenden Methoden und Begriffen kaum etwas anfangen. Dieses Buch schafft Abhilfe, in dem es umfassend und verständlich aufklärt und erklärt. Beispiele aus Theorie und Praxis veranschaulichen die Inhalte. Die Beherrschung der zugehörigen Komplexitäten ist noch nicht gelungen, wie z.B. die lange Liste gescheiterter Digitalisierungsvorhaben anschaulich belegt. Es geht darum, die Unternehmen zukunftsfest zu machen und die Beschäftigten zu befähigen. Hierfür braucht es einer Art digitaler bzw. technisierter „Leitplanken“, die mit einer „Electronic Governance“ entwickelt und spezifiziert werden. Es handelt sich um ein Steuerungs- und Regelungssystem, welches Organisationen und ihre Beschäftigten in Zeiten der Digitalisierung erfolgreich in die Zukunft führt. PROF. DR. ANDREAS SCHMID lehrt und forscht an der Hochschule Hannover. Er hat zahlreiche (IT-) Projekte und Organisationen evaluiert. Electronic Governance.- Digitalisierung.-Scheitern von Digitalisierungsprojekten.- Disruption.-(Digitale) Strategie.- (Digitales) Geschäftsmodell.-Industrie 4.0.- Robotic Process Automation.- Agilität.-Elektronische Akte.- Design Thinking.-Customer Journey.-Blockchain.-Kryptowährungen.- Künstliche Intelligenz.- Big Data inklusive Praxisbeispiel.
Echtzeit 2021
Mit seiner Tagung 2021 zum Thema „Echtzeitkommunikation“ greift der GI/GMA/ITG-Fachausschuss Echtzeitsysteme ein immer wichtiger werdendes Thema in einer mehr und mehr vernetzten Welt auf. Die präsentierten Lösungen reichen dabei von Hardware über Systementwurf bis hin zu einzelnen Applikationen.Das Buch stellt die auf der Tagung Echtzeit präsentierten Ergebnisse der Forscher auf dem diesjährigen Themengebiet der Echtzeitkommunikation dar. Diese Tagung des Fachausschusses Echtzeitsysteme der Gesellschaft für Informatik ist einzigartig im deutschem Sprachraum und fand 2021 zum 42. Mal statt.HERWIG UNGER und MARCEL SCHAIBLE sind Sprecher bzw. stellv. Sprecher des GI/GMA/ITG-Fachausschuss Echtzeitsysteme, der sich dem immer wichtiger werdenden Thema zeitkritischer Systeme von Hardware bis hin zu einzelnen Applikationen widmet.Real-Time Systems Through the Ages - Dynamische Migrationsentscheidungen in Multicore-Systemen - Ausführungszeit und Stromverbrauch von Inferenzen künstlicher neuronaler Netze auf einem Tensorprozessor - Dynamic Vision-Sensoren zur Texturklassifikation in der automatischen Sichtprüfung - Sind Bitcoin-Transaktionen sicher, echtzeitfähig und ressourcenadäquat? - Analysemethodiken zur Berechnung der WCET mit asynchroner Ein-/Ausgabeverarbeitung - Ein auf Bluetooth 5.1 und Ultrabreitband basierendes Innenraum- Positionssystem - Hardware-Beschleuniger für automobile Multicore-Mikrocontroller mit einer harten Echtzeitanforderung - Fault Tolerance in Heterogeneous Automotive Real-time Systems - Echtzeitfähige Ethernet-Kommunikation in automobilen Multicore-Systemen mit hierarchischem Speicherlayout - Zeitgesteuerte Kommunikationsschnittstellen in unterschiedlichen Anwendungskontexten - Ein Konferenzsystem mit biometrisch basierter Gesichtsvisualisierung für sehr große Teilnehmerzahlen - Machine Learning für die Temperaturermittlung eines Permanentmagnet-Synchronmotors.- Zeitoptimierungsuntersuchungen für Algorithmen des maschinellen Lernens
Introducing Microsoft Orleans
Welcome to Orleans, a virtual actor framework from Microsoft that allows a single developer to create immensely scalable, available applications while maintaining a high throughput. This guide is designed to give you a foundational understanding of Orleans, an overview of its implementations, and plenty of hands-on coding experience. Side-by-side monolithic and microservice patterns alongside Orleans' framework features are also discussed, to help readers without an actor model background understand how they can enhance applications.Author Nelson’s approach is to introduce patterns as needed for business requirements, including monolithic microservices and to convert monolithic to microservices, in order to keep a microservice from growing into a monolithic application. Orleans is a good choice for either of these scenarios as the next step to build your backend services and reduce unnecessary orchestration, overhead, and tooling.The Orleans framework was designed to handle tedious overhead, allowing the developer to focus on the solution. You will learn how Orleans can support billions of virtually parallel transactions while sustaining low latency and high availability. In addition, you will glimpse under the hood at Orleans to discover its useful attributes. All key learning points include hands-on coding examples to reinforce understanding.This book goes beyond what Orleans is to explain where it fits within the realm of development. You will gain an in-depth understanding to build a foundation for future growth.WHAT YOU WILL LEARN* Understand how Orleans can benefit your monolithic and/or microservice applications* Gain a brief overview of actor models and how they relate to Orleans* Observe the design patterns and how Orleans can simplify or reduce tooling requirements* Know the pros and cons of microservices and Orleans to determine the best course of action based on the needs of an application* Discover Orleans' design patterns and practices, including life cycle, messaging guarantees, cluster management, streams, load balancing, and more* Build your first Orleans' application; build base knowledge of application structure, unit testing, dashboard, scheduled eventsWHO THIS BOOK IS FORThis book is for developers. A basic understanding of .NET development and an understanding of service concepts is helpful. Readers will need a connection to download Nuget packages and a code editor (Community Edition Visual Studio or VS Code).THOMAS NELSON, a Lead Cloud Architect and a Microsoft Certified Azure Solutions Architect Expert, has worked in several technical fields spanning from the graphic design of websites to development and architecture. During his 10 years of backend development, his interest has gravitated towards DevSecOps and automation. He enjoys teaching others and is often found at local meetups presenting various technologies, patterns, and software examples. He is thrilled to be using Orleans and considers it one of those wonderful and valuable frameworks that should be in the tool kit of every architect and backend developer. Also, he is pleased to have graduated from monolithic and microservice systems to build cloud-native solutions, including actor model backends. He has an associate's degree in Graphic Design, bachelor's degree in Computer Information Systems, and is currently attending Harvard Extension pursuing his master's degree in Information Management Systems.Chapter 1: A Primer on Microsoft Orleans and the Actor ModChapter 2: Introducing Microsoft OrleansChapter 3: LifecyclesChapter 4: Enhancing Current DesignsChapter 5: Starting DevelopmentChapter 6: Timers and RemindersChapter 7: Unit TestsChapter 8: Orleans' DashboardChapter 9: DeploymentChapter 10: Conclusion
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICSPROVIDES COVERAGE OF DEVELOPMENTS AND STATE-OF-THE-ART METHODS IN THE BROAD AND DIVERSIFIED DATA ANALYTICS FIELD AND APPLICABLE AREAS SUCH AS BIG DATA ANALYTICS, DATA MINING, AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS.The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. AUDIENCEResearchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning. SUNIL KUMAR DHAL, PHD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.SUBHENDU KUMAR PANI, PHD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents. SRINIVAS PRASAD, PHD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters. SUDHIR KUMAR MOHAPATRA, PHD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains. Preface xiii1 AN INTRODUCTION TO BIG DATA ANALYTICS TECHNIQUES IN HEALTHCARE 1Anil Audumbar Pise1.1 Introduction 11.2 Big Data in Healthcare 31.3 Areas of Big Data Analytics in Medicine 51.4 Healthcare as a Big Data Repository 91.5 Applications of Healthcare Big Data 101.6 Challenges in Big Data Analytics 161.7 Big Data Privacy and Security 171.8 Conclusion 181.9 Future Work 182 IDENTIFY DETERMINANTS OF INFANT AND CHILD MORTALITY BASED USING MACHINE LEARNING: CASE STUDY ON ETHIOPIA 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique2.1 Introduction 222.2 Literature Review 232.3 Methodology and Data Source 252.4 Implementation and Results 282.5 Conclusion 443 PRE-TRAINED CNN MODELS IN EARLY ALZHEIMER'S PREDICTION USING POST-PROCESSED MRI 47Kalyani Gunda and Pradeepini Gera3.1 Introduction 483.2 Experimental Study 513.3 Data Exploration 553.4 OASIS Dataset Pre-Processing 613.5 Alzheimer's 4-Class-MRI Features Extraction 693.6 Alzheimer 4-Class MRI Image Dataset 693.7 RMSProp (Root Mean Square Propagation) 803.8 Activation Function 813.9 Batch Normalization 813.10 Dropout 813.11 Result--I 823.12 Conclusion and Future Work 894 ROBUST SEGMENTATION ALGORITHMS FOR RETINAL BLOOD VESSELS, OPTIC DISC, AND OPTIC CUP OF RETINAL IMAGES IN MEDICAL IMAGING 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal4.1 Introduction 984.2 Basics of Proposed Methods 1004.3 Experimental Results and Discussion 1074.4 Conclusion 1155 ANALYSIS OF HEALTHCARE SYSTEMS USING COMPUTATIONAL APPROACHES 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani5.1 Introduction 1205.2 AI & ML Analysis in Health Systems 1245.3 Healthcare Intellectual Approaches 1275.4 Precision Approaches to Medicine 1335.5 Methodology of AI, ML With Healthcare Examples 1345.6 Big Analytic Data Tools 1365.7 Discussion 1415.8 Conclusion 1426 EXPERT SYSTEMS IN BEHAVIORAL AND MENTAL HEALTHCARE: APPLICATIONS OF AI IN DECISION-MAKING AND CONSULTANCY 147Shrikaant Kulkarni6.1 Introduction 1486.2 AI Methods 1496.3 Turing Test 1566.4 Barriers to Technologies 1576.5 Advantages of AI for Behavioral & Mental Healthcare 1576.6 Enhanced Self-Care & Access to Care 1586.7 Other Considerations 1606.8 Expert Systems in Mental & Behavioral Healthcare 1616.9 Dynamical Approaches to Clinical AI and Expert Systems 1656.10 Conclusion 1736.11 Future Prospects 1757 A MATHEMATICAL-BASED EPIDEMIC MODEL TO PREVENT AND CONTROL OUTBREAK OF CORONA VIRUS 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra7.1 Introduction 1887.2 Related Work 1897.3 Proposed Frameworks 1907.4 Results and Discussion 1947.5 Conclusion 2018 AN ACCESS AUTHORIZATION MECHANISM FOR ELECTRONIC HEALTH RECORDS OF BLOCKCHAIN TO SHEATHE FRAGILE INFORMATION 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy8.1 Introduction 2068.2 Related Work 2128.3 Need for Blockchain in Healthcare 2168.4 Proposed Frameworks 2198.5 Use Cases 2238.6 Discussions 2298.7 Challenges and Limitations 2318.8 Future Work 2318.9 Conclusion 2329 AN EPIDEMIC GRAPH'S MODELING APPLICATION TO THE COVID-19 OUTBREAK 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani9.1 Introduction 2379.2 Related Work 2399.3 Theoretical Approaches 2409.4 Frameworks 2439.5 Evaluation of COVID-19 Outbreak 2469.6 Conclusions and Future Works 25010 BIG DATA AND DATA MINING IN E-HEALTH: LEGAL ISSUES AND CHALLENGES 257Amita Verma and Arpit BansalObject of Study 25710.1 Introduction 25810.2 Big Data and Data Mining in e-Health 26010.3 Big Data and e-Health in India 26210.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 26310.5 Big Data and Issues of Privacy in e-Health 27110.6 Conclusion and Suggestions 27211 BASIC SCIENTIFIC AND CLINICAL APPLICATIONS 275Manna Sheela Rani Chetty and Kiran Babu C. V.11.1 Introduction 27511.2 Case Study-1: Continual Learning Using ML for Clinical pplications 28311.3 Case Study-2 28611.4 Case Study-3: ML Will Improve the Radiology Patient Experience 28911.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 29211.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 29511.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 29811.8 Conclusion 30012 HEALTHCARE BRANDING THROUGH SERVICE QUALITY 305Saraju Prasad and Sunil Dhal12.1 Introduction to Healthcare 30512.2 Quality in Healthcare 30712.3 Service Quality 31112.4 Conclusion and Road Ahead 315References 316Index 321
Human-Computer Interaction in Game Development with Python
Deepen your understanding of human-computer interaction (HCI) in game development and learn how to develop video games that grab players and don't let them go. This book explores HCI design in computer games to maximize collaborative and interactive functions.You'll first gain a basic introduction to fundamental concepts and practices of HCI before diving into the fundamental concepts of game interface design and technology. You'll learn how to design a gaming interface through practical examples using Python. This is followed by a brief look at how HCI can offer immersive gaming experiences for players and a review of key elements such as interface, usability, user-centered design, and user interface in terms of efficacy. You will also learn how to implement usability aspects in gaming interfaces with examples using Python.Additionally, the book discusses major challenges that game publishers and developers face, and how they can be resolved using HCI techniques. The question of playability is reviewed throughout the game production process. After working through this book's practical examples, you'll have the knowledge required to begin developing compelling, can't-put-the-controller down games of your own.WHAT YOU'LL LEARN* Master HCI tools and methodologies * Understand the concept of HCI strategies in the game development cycle* Develop a game in Python using the HCI approach* Utilize gamification techniques in Human-Computer Interaction* Grasp concepts of usability, user experience and user-centered design processes and their applicationWHO THIS BOOK IS FORProgrammers, engineers, and students interested in creating and implementing computer games using HCI technologies. Prior experience with game development is recommended.JOSEPH THACHIL GEORGE is an IT Security Engineer based in Germany. He also worked as a technical consultant for International Game Technology (IGT) in Italy. Joseph is currently pursuing his doctorate (PhD) in computer science and engineering at the University of Lisbon, Portugal. He has an M.S. in cybersecurity from the Università degli Studi di Firenze, Italy. He is also part of the DISIA research group at the University of Florence, Italy, and the research group (INESC-ID Lisbon) at the University of Lisbon, Portugal. His research interests cover automatic exploit generation; exploitation of vulnerabilities; chaining of vulnerabilities; security of web applications; and JavaScript code exploits. At IGT, he has been a part of various projects related to game configuration and integration in various platforms, specializing in Java and Spring Boot-based projects. He has also worked for various companies in India, Angola, Portugal, and UK and has seven years of experience with various IT companies.MEGHNA JOSEPH GEORGE is a Cloud Engineer based in Germany. She is an AWS-certified solutions architect. She received a B.S. in System Management and M.S. in economics.Chapter 1: Human–Computer Interaction Tools and MethodologiesSub-Topics• Fundamentals of HCI• Tools and techniques• Eye tracking technique and usability• Use of effective interface• Advantage of HCI toolsChapter 2: Human–Computer Interaction and Game Design and DevelopmentSub-Topics• Games and game world• Concept of game design and development• Connection between HCI and game design and development• Interactive design of the game interface• Window and Icon design• Impact of eye tracking and usability• Effect of Thumbnail• Communication, dynamic Interface, and better user experience• Gamification in HCI• Project overviewChapter 3: Game Interface DevelopmentSub-Topics• What is game interface?• What need to be addressed for effective game interface?• Project - Explaining game interface using Python• Best practice for developing game interfaces• Different standard that companies use for game interface developmentChapter 4: Applying Usability in Computer Game InterfaceSub-Topics• Connection between usability and game interface• Sample project for explaining usability• Project description• Design phase• Applying usability tools• Evaluation of interface based on tools outcome• Conclusion of usability projectChapter 5: Project - Gamification in Human-Computer InteractionSub-Topics:• Relationship between the various processes of game development• Game programming• FAN translation• Game design document and production• Development of game using Python• Discussing HCI technique• Expected problems and solutions• Effectiveness of HCI techniqueChapter 6: Human-Computer Interaction New Trends: Research, DevelopmentSub-Topics:• Research, development• HCI new trends• Research project description of HCI in game development• Examples of HCI in game development - From various game developing companies• European policy and standard for game developmentChapter 7: Tips for Developers and StudentsSub-Topics:• Overview• Tips for developers and students for creating HCI-based games• Advantage in terms of cost and effectiveness of game development• Various industry standards in game development• Impact of games in the global economyChapter 8: ConclusionSub-Topics:• Overview• Recommendation and concluding comments
Artificial Intelligence in Medical Sciences and Psychology
Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques.The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification.This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers.WHAT YOU WILL LEARN* Apply artificial neural networks when modelling medical data* Know the standard method for Markov decision making and medical data simulation* Understand survival analysis methods for investigating data from a clinical trial* Understand medical record categorization* Measure personality differences using psychological modelsWHO THIS BOOK IS FORMachine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work settingTSHEPO CHRIS NOKERI harnesses advanced analytics and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, medical sciences, and manufacturing industries. He initially completed a bachelor’s degree in information management. Afterward, he graduated with an Honours degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. Chapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:● Context of the book.● The book’s central point.● Artificial Intelligence subsets covered in this book.● Structure of the book.● Tools that this book implements.○ Python distribution package.○ Anaconda distribution package.○ Jupyter Notebook.○ Python libraries.● Encapsulating Artificial Intelligence.● Debunking algorithms.● Debunking supervised algorithms.● Debunking unsupervised algorithms.● Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:● Classifying patients’ Cardiovascular disease diagnosis outcome data by executing a deepbelief network.● Preprocessing the Cardiovascular disease diagnosis outcome data.● Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.● Training the deep belief network.● Outlining the deep belief networks predictions.● Considering the deep belief network’s performance.● Classifying patients’ diabetes diagnosis outcome data by executing a deep belief network.● Outlining the deep belief networks predictions .● Considering the deep belief network’s performance.● Conclusion.Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation ResultsChapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the patternChapter content:● Debunking the Hidden Markov Model● Descriptive analysis● Carrying Out the Gaussian Hidden Markov Modelo Considering the Hidden States in US COVID-19 Confirmed Cases with the GaussianHidden Markov Model● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Methodo US COVID-19 confirmed cases simulation results● ConclusionChapter 4: Cancer Segmentation with Neural NetworksChapter goal: This chapter typically exhibits the practical application of computer vision andconvolutional neural networks for breast and skin Cancer realization and segmentation. Equally, it shows an approach to filter medical scans by applying canny, luplican, and sobel filters. It concludes by ascertaining the extent to which the networks accurately differentiate scans of patients with and without Cancer.Chapter content:● Debunking Cancer.● Debunking Skin Cancer● Depicting scans of a patient with Skin Cancer.● Classifying Patients’ Skin Cancer Diagnosis Image Data by Executing a Convolutional Neural Network.o Preprocessing the training Skin Cancer Image Data.o Preprocessing the Validation Skin Cancer Image Data.o Generating the Training Skin Cancer Diagnosis Image Data.o Tuning the Training Skin Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Skin CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.o Debunking Breast Cancer.● Classifying Ultrasound Scans of Breast Cancer Patients by Executing a Convolutional Neural Network.o Preprocessing the Validation Breast Cancer Image Data .o Preprocessing the Validation Breast Cancer Image Data .o Generating the Training Breast Cancer Diagnosis Image Data.o Tuning the Training Breast Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Breast CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 5: Modelling Magnetic Resonance Imaging and X-Rays by Carrying out Artificial Neural NetworksChapter goal: This chapter intimately acquaints you with the practical application of computer vision and artificial neural networks in neurology and radiology. It promptly carries out convolutional neural networks for image classification. The initial network models MRI scans to set apart patients with and without a brain tumor, and the second network models X-ray scans to set apart patients with and without pneumonia. Besides that, it unveils an effective technique for appraising networks in medical image classification.Sub-topics:● Debunking Brain Tumors.● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Executing aConvolutional Neural Network.o Depicting MRI Scan of Patients with a Brain Tumor.o Depicting Brain Scans without a Brain Tumor.o Preprocessing the Training MRI Image Data.o Preprocessing the Validation MRI Image Data.o Generating the Training MRI Image Data.o Tuning the Training MRI Image Data.o Executing the Convolutional Neural Network to Classify Patients’ MRI Image Data.o Considering the Convolutional Neural Network’s Performance.● Debunking Pneumonia.o Classifying Patients’ CT scan Data by Executing a Convolutional Neural Network.o Depicting an X-Ray scan of a Patient with Pneumonia.o Depicting an X-Ray scan of a Patient without Pneumonia.o Processing the X-Ray Image Data.o Generating the Training Chest X-Ray Image Data.o Preprocessing the Validation Chest X-Ray Image Data.o Generating the Validation Chest X-Ray Image Data.o Tuning the Training Chest X-Ray Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Chest X-Ray ImageData.▪ Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 6: A Case for COVID-19 CT Scan SegmentationChapter goal: This chapter presents an approach for carrying out convolutional neural networks to model chest CT scan images and differentiate between patients with and without COVID-19.Sub-topics:● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Carrying out aConvolutional Neural Network.o Depicting a Chest CT scan of a COVID-19 Negative Patient.o Depicting a CT scan of COVID-19 Negative Patient.o Preprocessing the Training COVID-19 Data.o Preprocessing the Validation COVID-19 CT Scan Data.o Generating the Training COVID-19 CT Scan Data.o Tuning the Training COVID-19 CT Scan Data.● Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 7 Modelling Clinical Trial DataChapter goal: This chapter familiarizes you with the prime essentials of the most widespread method for adequately investigating data from a clinical trial, recognized as a survival method. It debunks the Nelson-Aalen additive model. To begin with, it encapsulates the method. Subsequently, it promptly presents exploratory analysis, then correlation analysis by carrying out the Pearson correlation method. Following that, it outlines the survival table, then fits the model. It concludes by carefully outlining the profile table, confidence interval, and reproducing the cumulative and baseline hazard.sub-topics:● Debunking Clinical Trials.● An Overview of Survival Analysis.● Context of the Chapter.● Exploring the Nelson-Aalen Additive Model.● Descriptive Analysis.● Realizing a Correlation Relationship.● Outlining the Survival Table.● Carrying out the Nelson-Aalen Additive Model.o Outlining the Nelson-Aalen additive Model’s Confidence Intervalo Discerning the Survival Hazard.o Discerning the Cumulative Survival Hazard.o Baseline Survival Hazard.● Conclusion.● References.Chapter 8: Medical Record CategorizationChapter goal: This chapter sufficiently apprises a wholesome approach for realizing patterns in medical records by carrying out a linear discriminant analysis model. To begin with, it summarizes medical recording. Subsequently, it exhibits a technique of cleansing textual data by carrying out fundamental methods like regularization and TfidfVectorizer. Afterward, it executes the method to classify the medical specialty, then it assesses the extent to which it segregates classes.Sub-topics:● Medical Records.● Context of Chapter.● Debunking Categorization with Linear Discriminant Analysis.o Descriptive Statistics.o Preprocessing the Medical Records Data.o Carrying out Regular Expression.o Carrying Out Word Vectorization.o Carrying out the Linear Discriminant Analysis Model to Classify Patients’ MedicalRecords.o Considering the Linear Discriminant Analysis Model’s Performance.● Conclusion.Chapter 9: A Case for Psychology: Factoring and Clustering Personality DimensionsChapter goal: This chapter introduces you to analyzing the underlying patterns in human behavior by promptly carrying out exploratory factor analysis and cluster analysis. To begin with, it covers the big five personality dimensions. Following that, it presents an approach for typically collecting data by retaining a Likert scale and measuring the reliability of the scale with Cronbach’s reliability testing strategy. Subsequently, it performs factor analysis; beginning with estimating Bartlett Sphericity statistics, then the Kaiser-Meyer-Olkin statistic. Following that, it rotates the eigenvalues by carrying out the varimax rotation method and estimates the proportional variances and cumulative variances. In addition, it executes the K-Means method to observe clusters in the data; beginning with standardizing the data and carrying out principal component analysis.Sub-topics:● Debunking Personality Dimensions.● Questionnaires.● Likert Scale.● Reliability.o Spearman-Brown Reliability Testing Strategy.o Carrying out Cronbach's Reliability Testing Strategy.● Carrying out Factor Model.o Carrying out the Bartlett Sphericity Test.o Carrying out the Kaiser-Meyer-Olkin Test.o Discerning K with a Scree Plot.o Carrying out Eigenvalue Rotation.▪ Varimax Rotation.● Carrying out Cluster Analysis.o Carrying out Principal Component Analysis.O Returning K-Means label.
Festschrift zum 90. Geburtstag von Prof. Dr. Dr. h.c. mult. Günter Hotz
Die vorliegende Festschrift zum 90. Geburtstag von Prof. Dr. Dr. h.c. mult. Günter Hotz zeigt insbesondere über Kurzberichte der Doktorkinder die Nachwirkung von Günter Hotz’ Schaffen auf. Sie gibt damit auch einen schönen Überblick über die Informatik in Deutschland.Dr. Jan Messerschmidt, Jg. 1954, war nach seiner Zeit als wissenschaftlicher Mitarbeiter am Lehrstuhl von Prof. Hotz viele Jahre als geschäftsführender Gesellschafter der DIaLOGIKa GmbH tätig. Seit der 2018 erfolgten Übergabe der Geschäftsleitung der DIaLOGIKa in jüngere Hände engagiert er sich im Rahmen der eigens zu diesem Zweck neu gegründeten LibroDuct GmbH & Co. KG für Projekte aus dem Bereich der Elektromobilität im öffentlichen Verkehr.Prof. Dr. Paul Molitor, Jg. 1959, war von 1982 bis 1993 als wissenschaftlicher Mitarbeiter am Lehrstuhl von Prof. Hotz im Sonderforschungsbereich 124 „VLSI-Entwurfsmethoden und Parallelität“ tätig. In 1993 wurde er auf eine Professur für Schaltungstechnik an die HU Berlin (1993) berufen. Seit 1994 ist er Professor für Technische Informatik an der Martin-Luther-Universität Halle-Wittenberg. Unter anderem war er von 1995 bis 2015 Vorsitzender der Hochschul-DV-Kommission des Landes Sachsen-Anhalt, von 2003 bis 2019 Hauptherausgeber der Zeitschrift it – Information Technology des Oldenbourg Verlages, jetzt de Gruyter, und von 2012 bis 2019 Vorsitzender des Wissenschaftlich-Technischen Beirates der GISA GmbH Halle.Prof. Dr. Jürgen Steimle ist seit 2016 Professor für Mensch-Computer-Interaktion an der Fachrichtung Informatik der Universität des Saarlandes. Derzeit ist er auch Dekan der Fakultät für Mathematik und Informatik. Er hat an den Universitäten Freiburg und Lyon studiert und unter der Betreuung von Prof. Dr. Max Mühlhäuser an der TU Darmstadt promoviert. Seine Dissertation wurde mit dem Dissertationspreis der Gesellschaft für Informatik ausgezeichnet. Vor seiner Berufung an die Universität des Saarlandes war Jürgen Steimle als Visiting Assistant Professor am Massachusetts Institute of Technology und als Senior Researcher am Max-Planck-Institut für Informatik tätig.
Functional Aesthetics for Data Visualization
What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https://www.functionalaestheticsbook.com VIDYA SETLUR, PHD, is the head of Tableau Research. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Her expertise is in natural language processing and computer graphics, and she seeks to develop new algorithms and user interfaces that enhance communication and understanding.BRIDGET COGLEY is the Chief Visualization Officer at Versalytix and is a Tableau Hall of Fame Visionary. As an American Sign Language interpreter turned analyst, her practice incorporates semantics to draw meaning in her designs. She focuses on innovative use cases in data visualization. Acknowledgments ixAbout the Authors xiAbout the Technical Editor xiiForeword by Pat Hanrahan xiiiIntroduction xvPART A: PERCEPTION 1CHAPTER 1: THE SCIENCE BEHIND PERCEPTION 3Seeing and Understanding Imagery 3Color Cognition 6Saccade and Directed Attention 10The Notion of Space and Spatial Cognition 11Diagramming the World 13Summary 20CHAPTER 2: PERCEPTION IN CHARTS 21Visualization and Task 23Chart as an Informational Unit 24Unboxing Functional Aesthetics in the Physical World 27Recursive Proportions 28The Digitized Space: Creating Experiences on the Screen 31Summary 34CHAPTER 3: CHARTS IN USE 35The First Charts 36Standardizing Visualization 40The Shifting Role of Data Visualization 43Maturity within the Profession 49Summary 56PART B: SEMANTICS 57CHAPTER 4: COMING TO TERMS 59Statistical Graphics Are Inherently Abstract 60Flattening the Curve 63Toward Meaningful Depictions 65Situating with Semiotics 68Summary 69CHAPTER 5: VAGUENESS AND AMBIGUITY 71How Tall Is Tall? 71Spicy or Hot—What’sthe Difference? 76Clarification, Repair, and Refinement 78Iconicity of Representation 80The Art of Chart 82Summary 85CHAPTER 6: DATA LITERACY 87Navigating Data Literacy 89The Impact of Writing 90Data Orality 92Changing Exposition Styles 96Data Literacy Democratization 97Summary 99CHAPTER 7: DATA PREPARATION 101Hairy Dates 102Common Transformations 103Clarity in Conversation 107Shaping for Intent 109Prepping for the Future 110Data Enrichment 113Summary 115CHAPTER 8: SCALING IT DOWN 117Generalization 118Natural Sizes 119Fat Fingers and Small Screens 120Color as a Function of Size 123Thumbnails and Visual Summaries 124Summary 128CHAPTER 9: COHESIVE DATA MESSAGES 129Cohesion in Designing Visualizations 131Analytical Conversation 144Summary 152CHAPTER 10: TEXT AND CHARTS 153Medium Being the Message 154Types of Text 155Balancing Text with Charts 161Chart and Text Agreement 163Text in Analytical Conversation 166Making Data More Accessible 168Text for Supporting Reading Fluency 170Summary 171PART C: INTENT 173CHAPTER 11: DEFINING AND FRAMING 175Analytical Intent 176Register 178Repair and Refinement 179Pragmatics 181Practicing Intent 182Summary 185CHAPTER 12: VISUAL COMMUNICATION 187Do What I Mean, Not What I Say 189Register in Charts 192Registers in Composition 194Mood and Metaphor 197Beyond Language Communication 197Expansion and Contraction 200Summary 201CHAPTER 13: SCAFFOLDS 203Visualization Scaffolding 206Scaffolding Data Discovery 210Scaffolding Natural Language Recommendations 213Analytical Conversation to Repair and Refine 217Summary 221CHAPTER 14: BALANCING EMPHASIS 223Individual Choices 224Collective Choices 225Correcting Common Problems 228View Snapping 232Summary 238CHAPTER 15: MODE 239Navigate Like a Local 241Revisiting Analytical Chatbots 247Video Killed the Radio Star 249Beyond the Desktop 251Future Forward 255Summary 257PART D: PUTTING IT ALL TOGETHER 259CHAPTER 16: BRINGING EVERYTHING TOGETHER 261Addressing the Paper Towel Problem 263Crafting Recipes for Functional Aesthetics 267Summary 286CHAPTER 17: CLOSE 287Data in Everything and Everywhere 288New Tools and New Experiences 295Sign-off 297Technical Glossary 299Index 305
Planung und Reporting im BI-gestützten Controlling
Planungs- und Reportinglösungen leiden in vielen Unternehmen immer noch unter mangelnder Datenqualität, sind unzureichend integriert und häufig zeit- und kostenintensiv. Dieses praxisorientierte Buch zeigt Schritt für Schritt, wie es anders geht. Es wird systematisch gezeigt, wie moderne Planungs- und Reportingsysteme im BI-gestützten Controlling mit dem Einsatz von Data-Warehouse- und Big-Data-Technologie aufgebaut und sinnvoll um KI-gestützte Features ergänzt werden können. Für die 4. Auflage wurde das Buch umfassend aktualisiert. Hierbei wurde das umfangreiche Controlling-Cockpit-Beispiel erweitert. Es enthält nun Vorschläge für die Bereiche Unternehmensleitung (operatives und strategisches Controlling), Vertrieb, Produktion, Einkauf und Projektsteuerung. Zudem werden die neusten Entwicklungen im BI-gestützten Controlling mit Unterstützung der traditionellen und explorativen BI aufgezeigt, u.a. Data Mining, Predictive Analytics, Künstliche Intelligenz, RPA, Chatbots, Data Discovery, Data Visualization, App-Technologie, Self Service BI sowie Cloud Computing. Weitere Neuerungen betreffen die Themen Datenqualität und Datenmodellierung. Den Abschluss bildet weiterhin das Kapitel „Mobile BI“, bei dem es um den Ausbau von leistungsfähigen mobilen Analyse- und Planungslösungen mit Hilfe von Tablets, Handys und anderen mobilen Endgeräten geht. Einführung.- Grundlagen.- Fachliche inhaltliche Ausgestaltung.- Organisation und Prozesse.- IT-Unterstützung.- Zusammenfassung und Ausblick.