Computer und IT
Security Compliance in Model-driven Development of Software Systems in Presence of Long-Term Evolution and Variants
For ensuring a software system's security, it is vital to keep up with changing security precautions, attacks, and mitigations. Although model-based development enables addressing security already at design-time, design models are often inconsistent with the implementation or among themselves. An additional burden are variants of software systems. To ensure security in this context, we present an approach based on continuous automated change propagation, allowing security experts to specify security requirements on the most suitable system representation. We automatically check all system representations against these requirements and provide security-preserving refactorings for preserving security compliance. For both, we show the application to variant-rich software systems. To support legacy systems, we allow to reverse-engineer variability-aware UML models and semi-automatically map existing design models to the implementation. Besides evaluations of the individual contributions, we demonstrate the approach in two open-source case studies, the iTrust electronics health records system and the Eclipse Secure Storage.Since 2016, Sven Matthias Peldszus has been working as a research associate at the University of Koblenz-Landau and joined the Ruhr University Bochum after defending this thesis. His research interests include continuous tracing of non-functional requirements over the entire software life cycle and software quality analysis in variant-rich software systems.Introduction.- Running Example: iTrust.- State of the Art in Secure Software Systems Development.- A Walkthrough of the Proposed Development Approach.- Program Model for Object-oriented Languages.- Model-Synchronization and Tracing.- Application to Legacy Projects using Reverse-Engineering.- Static Security Compliance Checks.- Verification and Enforcement of Security at Run-time.- Specification of Variability throughout Variant-rich Software Systems.- Security in UML Product Lines.- Security Compliance and Restructuring in Variant-rich Software Systems.- The GRaViTY Framework.- Case Studies.- Related Work.- Conclusion.
CI/CD Pipeline Using Jenkins Unleashed
Understand continuous integration (CI), continuous delivery, and continuous deployment (CD) with Jenkins. These processes allow users as well as administrators to catch problems as soon as they get injected into software systems.This book starts with an introduction to Jenkins and covers its architecture and role in CI/CD. The basics are covered, including installing and configuring Jenkins. Tool configuration and plugins are discussed as well as available security measures such as credentials. You will learn what is meant by Job in Jenkins, its types, sections, and much more. You will look at Java API: projects, jobs, configuration. The concluding chapters take you through creating pipelines, their role in managing web apps, and distributed pipelines. The book also covers unit testing using TestNG as well as end-to-end testing using Selenium Python as a part of building a life cycle and setting up Jenkins on different physical and Docker environments as well as Jenkins integration with cloud environments such as AWS. And you will learn how to create reusable libraries for use in Jenkins Pipeline and control Jenkins servers using Jenkins CLI and REST APIs. The new Jenkins Blue Ocean also is covered.The book helps you understand CI/CD implementation using Jenkins from scratch in your projects and prepare for end-to-end DevOps practices.WHAT YOU WILL LEARN* Apply Jenkins to create end-to-end pipelines* Integrate Jenkins with AWS, Docker, Git, and many more tools* Use Selenium automation for end-to-end testing* Create distributed pipelinesWHO IS THIS BOOK FORDevelopers and test automation professionals who are involved in creating CI/CD pipelines as well as prospective DevOps aspirants who want to make their way ahead as professionalsPRANODAY PRAMOD DINGARE is a certified software testing professional with more than 15 years of experience in software testing, including more than 10 years in automation testing. Pranoday has been leading test automation of mobile applications for the last eight years and has been involved in test automation tools evangelism, R&D, proof of concept, and pilot projects. He has worked as a freelance test automation consultant for various startups and mid-sized IT companies from India and abroad. Pranoday's open-source test automation tools have successfully replaced licensed automation tools, leading to major savings. He is responsible for incorporating DevOps practices into test automation processes of organizations by implementing DevOps tools such as Jenkins, Gitlab, Nexus, Docker, etc. He has recently shifted into full-time DevOps profile and has been working as a Lead DevOps professional since last 1 year. He has implemented various DevOps tools like Dockers, Maven, Kubernetese, Git, Nexus, Azure DevOps, AWS, SonarQube, Jenkins etc. and has been instrumental in automating various applications’ build and deployment processes. Pranoday is a tutor who has been involved in software testing and DevOps training for more than nine years, including conducting more than 200 retail and corporate trainings on the latest test automation and DevOps tools. He is a blogger on the latest test automation tools and technologies. Pranoday is passionate about working as a test automation architect, teaching and sharing knowledge about the latest tools and technologies, and helping professionals achieve their dreams.Chapter 1: Understanding CI/CDSub-Topics:• History• What is continuous integration, continuous delivery and deployment• Need of CI/CDChapter 2: Introduction to JenkinsSub-Topics• History of Jenkins• Understanding concept of Jenkins• Understanding architecture of Jenkins• Understanding role of Jenkins in CI/CDChapter 3: Installation of JenkinsSub - Topics:• Hardware/software requirements of Jenkins• Problems and troubleshooting• Installing Jenkins on Windows• Installing Jenkins using .war file• Installing Jenkins using .msi file• Installing Jenkins as a service• Installing Jenkins on Linux environment• Using Jenkins as a Docker Image• Understanding directory structure and different configuration filesChapter 4: Configuring JenkinsSub - Topics:• Maven project configurationo What is Maveno Configuring Maven with Jenkins• Jenkins location• Gitlabsection• Githubsection• Global pipeline libraries• Email notification• Extended email notificationChapter 5:Understanding Global Tool ConfigurationSub - Topics:• Maven configuration• JDK configuration• Gitconfiguration• Ant configuration• Gradle configurationChapter 6: Manage PluginsSub - Topics:• What is plugin• List of widely used Jenkins plugin• How to install new plugin• Commonly faced problems and troubleshootingChapter 7: Managing Security with JenkinsSub - Topics:• Configure global securityo Configure LDAP- What is LDAP- Understanding need of LDAP configuration with Jenkins - How to configure LDAP with Jenkins• Setting up authorizationo Creating different groupso Assigning rights• API Token• What is API token• How to generate API token• SSHserverChapter 8: Manage CredentialsSub - Topics:• What is credentials• Need of creating credentials• How to create different types of credentials:o Basic authentiicationo SSHauthenticationo API tokenChapter 9: Manage UsersSub - Topics:• Creating userso Assigning different rights to the usersChapter 10: Understanding Jobs in JenkinsSub - Topics:• What is job• Understanding Jenkins dashboard• Different types of Jenkins job• Different sections of jobo Trigger o Build stepo Post job• Creating first freestyle job• Checking result of jobChapter 11: Preparing Java API ProjectSub - Topics:• Implementing a JAVA library project• Understanding unit testing• Integrating TestNG unit testing framework• Understanding different build lifecycle phases of API project• Implementing build lifecycle using MavenChapter 12: Creating Freestyle Job to Manage Java API ProjectSub - Topics:• Introduction to Git• Creating Java API code repository on Gitlab• Pushing JAVA API project on Git• Understanding Nexuso What is Nexuso Configuring Nexuso Creating artifact repository on Nexus• Integrating Git and Nexus with Jenkins• Creating self-executed freestyle job to manage releases of Java APIo Configuring SCMo Create a build Stepo Configure post build phaseChapter 13: Creating an Auto-trigger Free Style Job To Manage JAVA API ReleasesSub - Topics:• Configuring SCM• Setting Jenkins to poll SCM• Configuring build step• Configuring JAVA API release notificationsChapter 14: Creating a Pipeline JobSub - Topics:• Understanding pipeline• Understanding declarative pipeline• Understanding scripted pipeline• Understanding basics of Groovy• String interpolation in GroovyChapter 15: Creating Pipeline Job to Manage Web Application ProjectSub - Topics:• Creating sample calculator web application using html, CSS• Introducing UI automation using Selenium Wbdriver• Creating Selenium script to test addition, subtraction operations of calculator web app• Creating regression, smoke test suites• Creating a scripted pipeline to automate build lifecycle of calculator web app• Configuring email notifications to send Selenium script email at the end of job• Creating parameterized pipeline job to run specific Selenium script suites (regression, smoke, etc.)Chapter 16: Triggering Pipeline as Code from GitlabSub - Topics:• Installing Gitlab Jenkins plugin• Creating API token in Gitlab• Configuring Gitlab API token in Jenkins• Creating pipeline as a code (Jenkins file)• Configuring Gitlab project to integrate with Jenkins• Understanding web hook• Creating Jenkins web hook in Gitlab project• Pushing created Jenkins file in Gitlab project• Triggering pipeline on pushing changes in Gitlab projectChapter 17: Understanding Distributed PipelineSub - Topics:• Introduction of distributed pipeline• Understanding architecture of distributed pipeline (master/slave)• Configuring master/slave for distributed pipeline• Creating web app build using distributed pipelineChapter 18: Integrating Jenkins with AWSSub - Topics:• Creating EC2 instance• Pushing web application code in EC2 Code Commit• Writing a pipeline to deploy calculator web app in EC2 instance• Triggering Selenium E2E tests on deployed calculator web appChapter 19: Miscellaneous TopicsSub - Topics:• Jenkins Blue Ocean• Jenkins API
Theoretical Cybersecurity
There is a distinct lack of theoretical innovation in the cybersecurity industry. This is not to say that innovation is lacking, as new technologies, services, and solutions (as well as buzzwords) are emerging every day. This book will be the first cybersecurity text aimed at encouraging abstract and intellectual exploration of cybersecurity from the philosophical and speculative perspective. Technological innovation is certainly necessary, as it furthers the purveying of goods and services for cybersecurity producers in addition to securing the attack surface of cybersecurity consumers where able.The issue is that the industry, sector, and even academia are largely technologically focused. There is not enough work done to further the trade—the craft of cybersecurity. This book frames the cause of this and other issues, and what can be done about them. Potential methods and directions are outlined regarding how the industry can evolve to embrace theoretical cybersecurity innovation as it pertains to the art, as much as to the science.To do this, a taxonomy of the cybersecurity body of work is laid out to identify how the influences of the industry’s past and present constrain future innovation. Then, cost-benefit analysis and right-sizing of cybersecurity roles and responsibilities—as well as defensible experimentation concepts—are presented as the foundation for moving beyond some of those constraining factors that limit theoretical cybersecurity innovation. Lastly, examples and case studies demonstrate future-oriented topics for cybersecurity theorization such as game theory, infinite-minded methodologies, and strategic cybersecurity implementations.WHAT YOU’LL LEARN* The current state of the cybersecurity sector and how it constrains theoretical innovationHow to understand attacker and defender cost benefit * The detect, prevent, and accept paradigm* How to build your own cybersecurity box* Supporting cybersecurity innovation through defensible experimentation* How to implement strategic cybersecurity* Infinite vs finite game play in cybersecurityWHO THIS BOOK IS FORThis book is for both practitioners of cybersecurity and those who are required to, or choose to, employ such services, technology, or capabilities.DR. JACOB G. OAKLEY is a cybersecurity author and subject matter expert with 16 years of experience focusing on strategic enterprise-level cybersecurity architectures as well as offensive cybersecurity operations within government and commercial sectors. His previous technical books, Professional Red Teaming, Waging Cyber War, and Cybersecurity for Space, are also published by Apress.MICHAEL BUTLER is a cybersecurity subject matter expert with 12 years of experience focusing on building, developing, and leading teams of ethical hackers. He is a primary instructor and developer of an offensive cloud security course taught both privately and at Blackhat conferences in the United States, Europe, and Asia. He has previously collaborated with Dr. Oakley as the technical reviewer for Professional Red Teaming.WAYNE YORK is a cybersecurity technical editor and subject matter expert with 18 years of experience focusing on offensive cybersecurity operations and program protection within government and commercial sectors. His previous technical edited book is Waging Cyber War by Dr. Oakley, published by Apress.DR. MATTHEW PUCKETT is a mathematics professor and former software engineer. His areas of interest include theology, cognitive science, and artificial intelligence. His hobbies include chess, where he is currently one of the top 300 players in the United States (according to FIDE).DR. J. LOUIS SEWELL is a mathematician trained in Graph Theory. As Technical Fellow of a Huntsville, AL, technology company, he develops enduring solutions to critical infrastructure challenges in government and civilian sectors. Professionally and personally, he has a special interest in artificial intelligence ethics, infinite game dynamics, and the role of personal experience in the philosophy of science.CHAPTER 1. INTRODUCTION TO STRATEGIC CYBERSECURITYDiscuss what strategic cybersecurity isDiscussion on how it is not cybersecurity strategyCHAPTER 2. NO ONE CARES ABOUT CYBERSECURITYReal motivationsConsequencesHow can cybersecurity help them in spite of themselves?CHAPTER 3. COST-BENEFIT & CYBERSECURITYCost-Benefit to the defenderCost-Benefit to the attackerCHAPTER 4. WHAT IS THE BOX THAT IS CYBERSECURITY?Where do we draw the line?What are areas that should be abandonedWhat are areas that should be exploredCHAPTER 5. THE DETECT, PREVENT, ACCEPT PARADIGMOutline the paradigmCase studiesCHAPTER 6. BUILDING YOUR OWN CYBERSECURITY BOXWhat is out of the cyber domain: sim swapping, go daddyWhat is likely to be your attackerWhat is likely to be your lossCHAPTER 7. CYBERSECURITY AS A MATERIALCapability AnalysisSourcingTest and evaluationUtilizationDeprecationFailure analysisCHAPTER 8. CYBERSECURITY AS INFRASTRUCTUREA comparison to electricityHow do we get there and what will it mean?CHAPTER 9. STRATEGIC DEFENSIVE SECURITYDetectIdentifyMonitorHuntCHAPTER 10. STRATEGIC OFFENSIVE SECURITYPenetration testingRed TeamingReverse Red TeamingCHAPTER 11. STRATEGIC ASSURANCEFrameworksAuditingCase studyCHAPTER 12. STRATEGIC CYBERSECURITY IN COMMERCIAL SECTORSThe approachCase studyCHAPTER 13. STRATEGIC CYBERSECURITY IN ACCOUNTABLE SECTORSHealthcare approachCase studyFinancial approachCase studyCHAPTER 14. STRATEGIC MAGICAIMLBlock ChainCHAPTER 15. LOOKING FORWARDWhere is the theory-craft?We need new ideas and paradigms as much as we need new technologyWhy is it difficult for academia to evolve cybersecurity?Especially offensive cybersecurityWhat is wrong with cybersecurity currently in academicsDegree programsUnrealistic expectationsPotential solutionsWhy does industry often lack an academic approach?
Blockchain-Implementierung in eine Automotive Supply Chain
In der Automotive-Branche ist eine schnell agierende und perfekt abgestimmte Supply Chain ein entscheidender Vorteil gegenüber dem Wettbewerb. Durch die Implementierung der Blockchain-Technologie lässt sich die Geschwindigkeits- und Transparenzerhöhung gewährleisten. Dieses essentiell simuliert eine Supply Chain an verschiedenen Instanzen, in der die Blockchain exemplarisch genutzt wird und dadurch die Supply-Chain-Abläufe automatisiert werden.Als Studierende und Absolventen der Universität Duisburg-Essen am Lehrstuhl der Transportsysteme und -logistik im Studiengang der Technischen Logistik haben die Autoren durch das natürliche Interesse an aktuellen Forschungsfeldern wie die Blockchain Technologie die Weichen für darauf aufbauende Abschlussarbeiten in diesem Themenfeld legen können. Unter der Betreuung von DR.-ING. ALEXANDER GOUDZ forsch(t)en sowohl YILMAZ als auch SANCHEZ-GONZALEZ im Rahmen ihrer Masterarbeit zum Thema der Blockchain Technologie weiter, während sich MEYHÖFER mit der Umsetzung der Logistik 4.0 im ÖPNV befasste.
Nachhaltige MITO-Businessmodell-Transformation
Entscheidungen in Unternehmen haben Auswirkungen auf die Gesellschaft und die Umwelt. Unternehmen müssen solche Auswirkungen ernst nehmen und Nachhaltigkeitsthemen in der Organisation und den Geschäftstätigkeiten berücksichtigen sowie geeignete Maßnahmen treffen (Corporate Social Responsibility – CSR). Entsprechend geht dies mit Transformationen einher, die vielfältige Herausforderungen hinsichtlich Bewertung und Entscheidungsfindung mit sich bringen. Dieses Buch beschreibt einen ganzheitlichen Ansatz zur Businessmodell-Nachhaltigkeitsgestaltung mit dem Ziel einer Integration aller Nachhaltigkeitsthemen. Eingesetzt wird das MITO-Methoden-Tool, das sich streng an den Vorgaben der DIN ISO 26000, den dort genannten Kernthemen der Nachhaltigkeit, den Handlungsempfehlungen und auch weiterführenden Nachhaltigkeitsregelwerken orientiert.
Mathematik für Informatiker
Dieses Lehrbuch vermittelt auf anschauliche und anwendungsorientierte Weise die für ein Informatikstudium notwendigen mathematischen Grundlagen. Dabei wird großer Wert auf den Praxisbezug der mathematischen Inhalte gelegt. Es wird jeweils anhand einer konkreten Aufgabenstellung der Informatik das mathematische Handwerkszeug entwickelt, das zur Lösung dieser Aufgabe erforderlich ist. So werden Themen der linearen Algebra im Hinblick auf Anwendungen in der Computergrafik erläutert. Aufgabenstellungen der Zeit- und Kalenderrechnung sowie der Kryptografie dienen zur Veranschaulichung der modularen Arithmetik.Die folgenden mathematischen Gebiete werden abgedeckt: Mengenlehre, Logik, Relationen und Funktionen, Kombinatorik, Graphentheorie, Wahrscheinlichkeitsrechnung, modulare Arithmetik, Grundstrukturen der Algebra, lineare Algebra und analytische Geometrie.Eine große Menge an erprobten Beispielen, Übungsaufgaben und Programmierprojekten trägt zum vertieften Verständnis des Stoffes bei.Für die Neuauflage wurde das bewährte Lehrbuch um ein Kapitel zur Wahrscheinlichkeitsrechnung erweitert.Auf plus.hanser-fachbuch.de finden Sie zu diesem Titel die Lösungen der Aufgaben. Prof. Dr. Rolf Socher ist Professor im Ruhestand. An der Technischen Hochschule Brandenburg hielt er Vorlesungen zur Mathematik, Theoretischen Informatik und Computergrafik.
Scrum mit User Stories
- Erfahren Sie, wie Sie Anforderungen im Sinne des Kunden mit Hilfe von User Stories beschreiben und im Product Backlog verwalten. - Lesen Sie, wie User Stories den Flow eines Scrum-Projekts steuern und das Team bei der Entwicklung werthaltiger Software leiten. - Lernen Sie, wie Sie die Geschäftsregeln einer User Story als Akzeptanztests beschreiben und so die Basis für akzeptanzgetriebene Entwicklung schaffen. - Erlernen Sie die Anwendung von Story Maps als neue Methode zur ganzheitlichen Anforderungsanalyse. - Lernen Sie, wie Sie Scrum in mobilen Arbeitsumgebungen einführen und dem Team über die ersten Hürden hinweghelfen. - Ihr exklusiver Vorteil: E-Book inside beim Kauf des gedruckten Buches Scrum als Framework für die Agile Softwareentwicklung erfreut sich zunehmender Beliebtheit. Kombiniert mit User Stories wird daraus ein unschlagbares Doppel. Scrum definiert mit Hilfe einfacher Regeln und klarer Verantwortlichkeiten einen Rahmen für agile Softwareprojekte. User Stories beschreiben Anforderungen aus Sicht des Anwendenden und liefern einen greifbaren Mehrwert. Dieses Buch erklärt die Grundlagen beider Konzepte und beschreibt, wie Sie User Stories in die Elemente und Abläufe von Scrum einbinden. Angefangen vom Schreiben und Priorisieren eines User-Story-basierten Product Backlog bis hin zur User-Story-getriebenen Sprint- und Releaseplanung lernen Sie alles, was für den erfolgreichen Einsatz von User Stories in Ihrem Scrum-Projekt wichtig ist. Das neue Kapitel „Mobiles Arbeiten“ beschäftigt sich mit den Anforderungen und Möglichkeiten des agilen Arbeitens in mobilen Kontexten. Es beschreibt unsere Erfahrungen beim Arbeiten mit mobilen Scrum-Teams und liefert Tipps und Ideen für das Führen solcher Teams. „Egal, ob man Scrum und User Stories einsetzt oder nicht: Mit diesem Buch lernt wohl jeder noch etwas dazu.“ Steffen Gemkow, ObjectFab AUS DEM INHALT // - Einführung - Beispiel: Scrumcoaches.com - Die Grundlagen von Scrum - User Stories - Agiles Schätzen - Agiles Planen - User Stories für das Product Backlog - User Story Mapping - Sprint-Planung - Sprint-Durchführung - User Stories Akzeptanztesten - Sprint-Retrospektive - Agile Releaseplanung - Mobiles Arbeiten - Verticals – SCRUM@OTTO - Glossar
Makupedia
The universe is a secret mine of twelve energy assets concealed in planetary dimensions Endless resources in search to discover develop and connect our core innate potentials Big data processed on these mines are derived through science equations and formulas From a multidisciplinary complex of objective algorithms to a simple smart code on MindThe World Encyclopedia on Creative Sciences and Mind Computing can only be Makupedia.
Unlocking Agile's Missed Potential
UNLOCKING AGILE'S MISSED POTENTIALAgile has not delivered on its promises. The business side expected faster time to market, but they still experience the long delays of bloated releases. Engineers thought they would be given time to build the product right the first time, but they are rushed under pressure to deliver new features within impossible schedules. What went wrong?The culprit is feature-based waterfall release planning perpetuated in a vain attempt to achieve business predictability. Agile didn't address the business need for multi-year financial predictability. The Agile community's answer was the naïve response, "The business needs to be more Agile." Waterfall release planning with fixed schedules undercuts a basic tenet of Agile development – the need to adjust content delivered within a timebox to account for evolving requirements and incorporation of feedback. Agile without flexible content is not Agile.This book introduces a novel solution that enables product teams to deliver higher value within shorter cycle times while meeting the predictability needs of the business. Organizations today want product teams that break down walls between product management and engineering to achieve schedule and financial objectives. Until now they haven’t had a way to implement product teams within the rigid constraints of traditional organizational structures.The Investment planning approach described in this book supports small development increments planned and developed by product teams aligned by common schedule and financial goals. It uses Cost of Delay principles to prioritize work with the highest value and shortest cycle times. Investments provide a vehicle for collaboration and innovation and fulfill the promise of highly motivated self-directed Agile development teams.This book is for engineers, product managers and project managers who want to finally do Agile the way it was envisioned. This book is also for leaders who want to build high-performance teams around the inherent motivational environment of Agile when done right.FOREWORD BY STEVE MCCONNELL, AUTHOR OF MORE EFFECTIVE AGILE: A ROADMAP FOR SOFTWARE LEADERS (CONSTRUX PRESS, 2019).ROBERT WEBBER'S executive experience as VPs of engineering and product management and as a CEO, combined with years of consulting with Fortune 500 companies, provide the broad perspective to create a win-win solution for business and product development that finally achieves the promises of Agile development. Organizations can increase R&D ROI by over 25% using existing Agile development capabilities. Break the chains of waterfall planning!Foreword 11Preface 13Introduction 16The Lost Potential of Agile Development 16Missed Business Expectations 18A New Approach to Agile Planning 19Addressing Traditional Software Development Challenges 21Motivation and Innovation 22Your Organization 22CHAPTER 1: THE PERSISTENCE OF WATERFALL PLANNING 23Introduction to AccuWiz 23The New COO 24Product Management 24PMO 25Engineering 25Customer Perspective 26Synopsis 26Summary 27CHAPTER 2 – WHY AGILE HAS STRUGGLED 29Agile Development Fundamentals 30The Agile Revolution 30Scrum 31Kanban 34Barriers to Real Agile 35Schedule Pressure 35The “Motivation” Factor 37The Mythical Product Owner 39Feature Planning 40Agile Scaling Frameworks 41Summary 42CHAPTER 3: EMBRACING SOFTWARE DEVELOPMENT VARIANCE 43The Cone of Uncertainty 43Software Development Estimation Variance Explained 44Making and Meeting Feature Commitments 45How Other Departments Meet Commitments 47Agile Development Implications 48Summary 48CHAPTER 4: COST OF DELAY 49Weighted Shortest Job First (WSJF) 50Cost of Delay Basics 50Example 52WSJF Proof 54CoD and Net Present Value (NPV) Prioritization Methods 56Non-linear Income Profiles 57CoD for Non-Linear Cumulative Income Profiles 58Payback Period CoD Method 58Third-year Income Slope CoD Method 58CoD NPV Method 63CoD Computation Method 64WSJF and Traditional Finance 66ROI 66Investment Rate of Return (IRR) 67WSJF versus ROI Prioritization 67Summary 69CHAPTER 5: INVESTMENT FUNDAMENTALS 70Investments, Initiatives and Programs 70Investment Hierarchy 71AccuWiz Investment Examples 74Portfolio Allocation 75Investment Forecasts 76Development Effort and Cost 76Investment Income Forecasts 78Investment Backlogs 81Investment WIP 82Investment Backlog WIP 82Investment WIP 83Technical Debt Investments 84Summary 86CHAPTER 6: MAXIMIZING INVESTMENT VALUE 87Great Products 87Business Model Value Considerations 89Stakeholder Value Analysis 90Gilb Stakeholder Definition 90Ford’s Big Mistake 92Trucking Fleet Management Example 93Five Whys 95User Scenarios 96Summary 97CHAPTER 7: PLANNING HIGH-VALUE INVESTMENT FEATURES 99Avoiding the Feature Pit 99Feature ROI 100Summary 104CHAPTER 8: RELEASING INVESTMENTS 105Release Opportunity Cost 105Investment Release Bundling 108Investment Pricing 108Lack of Customer Acceptance 110Release Overhead Costs 111Overcoming Modular Release Challenges 113Architecture for Modular Deployment 113Configuration Management 113Release Investment Prioritization 114Reducing Software Inventory Costs 115Summary 118CHAPTER 9: MEETING INVESTMENT TARGETS 120Meeting Commitments 120Investment Teams 120Managing Investment Scope 123Managing Sales Requests 127Summary 129CHAPTER 10: INVESTMENT PLANNING TEMPLATE 130Investment Description 130Proxy Business Case 130Product Stakeholder Analysis 132Customer Product Stakeholders 132Internal Product Stakeholders 132Constraints 132Competition 133Acceptance Criteria 133Go-to-Market Plan 134Pricing Model 134Deployment Model 134Sales Channels 134Investment Targets 134Development Cost 134Cycle Time 134Income Projections 134WSJF 136Assumption Validation 136Summary 138CHAPTER 11: MANAGING THE AGILE ROADMAP 139The Agile Roadmap Management Database 139The Agile Technology Roadmap 141Stages of Technology Acquisition 142Investment Technology Roadmaps 143Summary 143CHAPTER 12: MAXIMIZING INVESTMENT DEVELOPMENT PRODUCTIVITY 145Measuring Software Productivity 145Cost of Quality (CoQ) 146Cost of Quality and Software Productivity 147Sources of Software Rework 149Agile Cost of Quality 150Reducing Agile User Story Rework 152Reducing Agile Defect Rework 153Agile Cost of Quality Example 154Summary 155CHAPTER 13: MOTIVATING AGILE TEAMS 156Background 156Why You’re the Only Smart One in Your Organization 157Consequences and Behavior 158Performance and Organizational Culture 159Behavior and Software Quality 163Intrinsic Motivation 164Agile and Motivation 165Measuring Motivation 167Motivation Advice 169Summary 171CHAPTER 14: INNOVATING WITH INVESTMENTS 173Innovation – A Working Definition 174Investments as an Innovation Vehicle 175Why Your Organization Can’t Innovate 176An Organizational Behavior Model of Innovation 178An Innovation Tale of Two Companies 181Creating a Culture of Innovation 184Summary 188CHAPTER 15: ACCUWIZ GETS IT TOGETHER 189The Founder Meeting 189The Announcement 190Product Stakeholder Analysis 191Creating the Investment Backlog 192Customer Management 195Investment Development 195Project Management 196Managers 197Executive Team 198Innovation is Revived 199Synopsis 199CHAPTER 16: GETTING IT TOGETHER IN YOUR COMPANY: A PRACTICAL GUIDE 200Step 1: Organizational Support 200Influence Strategy 204Step 2: Stakeholder Value Analysis 205Step 3: Stakeholder Research 206Step 4: Stakeholder Interviews 207Step 5: Investments 207User Scenarios 208Feature Definition 209WSJF Screening 209Step 6: Initial Roadmap 210Resource Allocation 211Step 7: Investment Planning 214Agile Roadmap Alignment Meeting 215Program Review 216Step 8: Consequence Alignment 217Summary 220Appendix 1: General Cost of Delay Formula 221Reinertsen WSJF 222Income Curve Approximation 223Summary 225Appendix 2: Investment Income Profile Forecasts 226Appendix 3: Release Cycle Productivity Formula 228Appendix 4: Rework and Productivity 232Appendix 5: Innovation Behavior Survey 233Glossary 238Index 246
Digitization of Healthcare Data using Blockchain
DIGITIZATION OF HEALTHCARE DATA USING BLOCKCHAINTHE BOOK GIVES A DETAILED DESCRIPTION OF THE INTEGRATION OF BLOCKCHAIN TECHNOLOGY FOR ELECTRONIC HEALTH RECORDS AND PROVIDES THE RESEARCH CHALLENGES TO CONSIDER IN VARIOUS DISCIPLINES SUCH AS SUPPLY CHAIN, DRUG DISCOVERY, AND DATA MANAGEMENT. The aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system. AUDIENCEResearchers in information technology, artificial intelligence, electronics engineering, medical informatics, as well as policymakers and healthcare providers and management systems. T. POONGODI, PHD, is an associate professor in the Department of Computer Science and Engineering at Galgotias University, Delhi – NCR, India. She has more than 15 years of experience working in teaching and research.D. SUMATHI, PHD, is an associate professor at VIT-AP University, Andhra Pradesh. She has an overall experience of 21 years out of which six years in industry, 15 years in the teaching field. Her research interests include cloud computing, network security, data mining, natural language processing, and theoretical foundations of computer science. B. BALAMURUGAN, PHD, is a professor in the School of Computing Sciences and Engineering at Galgotias University, Greater Noida, India. His contributions focus on engineering education, blockchain, and data sciences. He has published more than 30 books on various technologies and more than 150 research articles in SCI journals, conferences, and book chapters. K. S. SAVITA, PHD, is on the academic staff in the Department of Computer and Information Sciences (CISD), Universiti Teknologi PETRONAS (UTP), Malaysia since 2006. She is accredited by the Malaysia Board of Technologies as Professional Technologist (Ts.) in Information and Computing Technology. Preface xiii1 EVOLUTION OF BLOCKCHAIN TECHNOLOGIES AND ITS FUNDAMENTAL CHARACTERISTICS 1Aradhna Saini, R. Gopal, S. Suganthi and T. Poongodi1.1 An Overview of Blockchain Technology 21.1.1 Evolution of Blockchain Technology 21.1.2 Significant Characteristics of Blockchain Technology 31.2 Blockchain Architecture and Its Components 51.3 Comparative Analysis of Blockchain Categories 81.3.1 Permissionless or Public Blockchain 91.3.2 Permissioned or Private Blockchain 111.3.3 Consortium Blockchain 131.3.4 Hybrid Blockchain 151.4 Blockchain Uses Cases in Healthcare 151.5 Research Opportunities and Challenges of Blockchain Technology in Healthcare 201.6 Conclusion 21References 212 GEOSPATIAL BLOCKCHAIN: PROMISES, CHALLENGES, AND SCENARIOS IN HEALTHCARE 25Janarthanan S., S. Vijayalakshmi, Savita and T. Ganesh Kumar2.1 Introduction 262.1.1 Basics of Blockchain 262.1.2 Promises and Challenges in Blockchain 272.1.3 Comparative Study 282.2 Geospatial Blockchain Analysis Based on Healthcare Industry 292.2.1 Remote Monitoring and Geospatial Healthcare System 302.3 Smart Internet of Things Devices and Systems 322.3.1 Main Challenges and Importance in Smart Convention 332.3.2 Recent Innovations in Healthcare 332.4 Implementation Strategies and Methodologies 342.4.1 Promises and Challenges in Implementation 352.5 Information Security and Privacy Protection in Geospatial Blockchain Healthcare Systems 372.5.1 Security and Privacy Protection Framework 372.5.2 Data Access Control System 372.6 Challenges in Present and Past and Future Directions 402.6.1 Present Challenges in Healthcare 402.6.2 Past Challenges in Healthcare 412.6.3 Future Challenges in Healthcare 422.7 Conclusion 45References 453 ARCHITECTURAL FRAMEWORK OF BLOCKCHAIN TECHNOLOGY IN HEALTHCARE 49Kiran Singh, Nilanjana Pradhan and Shrddha Sagar3.1 Introduction 503.2 Healthcare 513.2.1 Electronic Healthcare 523.2.2 Smart Healthcare 533.3 Blockchain Technology 543.4 Architecture of Smart Healthcare 553.5 Blockchain in Electronic Healthcare 573.6 Architecture for Blockchain 593.7 Distributed System 603.8 Security and Privacy 613.9 Applications of Healthcare Management in Blockchain 643.9.1 The Use of the Blockchain for EMR Data Storage 643.9.2 Blockchains and Data Security are Related 663.9.3 Blockchain for Personal Health Information 663.9.4 Blockchain is a Strong Technology at the Point of Treatment Genomic Analytics 673.10 Applications of IoT in Blockchain 673.11 Challenges 683.12 Conclusion 68References 694 SMART CONTRACT AND DISTRIBUTED LEDGER FOR HEALTHCARE INFORMATICS 73Yogesh Sharma and B. Balamurugan4.1 Introduction 744.1.1 History of Healthcare Informatics 754.2 Introduction of Blockchain Technology 764.2.1 A Blockchain Process 774.3 Types of Blockchains 784.3.1 Public Blockchain 794.3.2 Private Blockchain 794.3.3 Consortium Blockchain 804.4 Blockchain in Healthcare 804.5 Distributed Ledger Technology 824.6 Evolution of Distributed Ledger Technology 824.7 Smart Contract 834.7.1 Limitations of Smart Contract 854.7.2 Smart Contract in Healthcare Informatics 854.8 Distributed Ledger in Healthcare Informatics as Blockchain 864.9 Distributed Ledger Technology in Healthcare Payments 884.10 Conclusion 89References 905 CONSENSUS ALGORITHM FOR HEALTHCARE USING BLOCKCHAIN 93Faizan Salim, John A., Rajesh E. and A. Suresh Kumar5.1 Introduction 945.2 Types of Blockchain 955.3 Blockchain Database 985.4 Consensus Algorithm 985.5 Healthcare System 1005.5.1 Healthcare and Blockchain 1015.5.2 Benefits of Blockchain in Healthcare 1015.6 Algorithms 1035.6.1 Smart Contract 1045.6.2 Algorithm for Fault Tolerance Using Blockchain 1045.6.3 Practical Byzantine Fault Tolerance Algorithm 1065.6.4 Algorithm for Distributed Healthcare Using Blockchain 1085.7 Security for Healthcare System Using Blockchain 1095.7.1 Framework for Security Using Blockchain 1105.8 Issues and Challenges in Healthcare Using Blockchain 1125.9 Future Scope 1145.10 Conclusion 115References 1156 INDUSTRY 4.0 AND SMART HEALTHCARE: AN APPLICATION PERSPECTIVE 117R. Saminathan, S. Saravanan and P. Anbalagan6.1 Introduction 1186.2 Evolution of Industry 4.0 1196.3 Vision and Challenges of Industry 4.0 1206.4 Technologies Used in Fourth Industrial Revolution 1216.5 Blockchain in Industry 4.0 1276.6 Smart Healthcare Design Using Healthcare 4.0 Processes 1296.7 Blockchain Tele-Surgery Framework for Healthcare 4.0 1316.8 Digital Twin Technology in Healthcare Industry 1336.9 Conclusion 134References 1347 BLOCKCHAIN POWERED EHR IN PHARMACEUTICAL INDUSTRY 137Piyush Sexena, Prashant Singh, John A. and Rajesh E.7.1 Introduction 1387.2 Traditional Healthcare System vs Blockchain EHR 1407.3 Working of Blockchain in EHR 1417.4 System Design and Architecture of EHR 1437.5 Blockchain Methodologies for EHR 1467.6 Benefits of Using Blockchain in EHR 1497.7 Challenges Faced by Blockchain in HER 1517.8 Future Scope 1547.9 Conclusion 155References 1568 CONVERGENCE OF IOT AND BLOCKCHAIN IN HEALTHCARE 159Swaroop S. Sonone, Kapil Parihar, Mahipal Singh Sankhla, Rajeev Kumar and Rohit Kumar Verma8.1 Introduction 1608.2 Overview of Convergence 1618.3 Healthcare 1628.4 IoTs and Blockchain Technology 1638.5 IoT Technologies for Healthcare 1638.6 Blockchain in Healthcare 1658.7 Integration for Next-Generation Healthcare 1678.8 Basic Structure of Convergence 1708.9 Challenges 1728.10 Conclusion 174References 1759 DISEASE PREDICTION USING MACHINE LEARNING FOR HEALTHCARE 181S. Vijayalakshmi and Ashutosh Upadhyay9.1 Introduction to Disease Prediction 1829.1.1 Artificial Intelligence in Healthcare 1829.1.2 Data Collection and Information Processing 1839.1.3 Human Living Standard and Possible Diseases 1859.1.4 Importance of Data in Disease Prediction 1859.2 Data Analytics for Disease Prediction 1869.3 Segmentation and Features of Medical Images 1869.4 Prediction Model for Healthcare 1889.5 Introduction to ML 1919.5.1 K-Nearest Neighbor, Artificial Neural Network, CNN, Decision Tree, and Random Forest 1959.6 Prediction Model Study of Different Disease 1989.7 Decision Support System 1999.8 Preventive Measures Based on Predicted Results 1999.9 Conclusions and Future Scope 200References 20010 MANAGING HEALTHCARE DATA USING MACHINE LEARNING AND BLOCKCHAIN TECHNOLOGY 203BKSP Kumar Raju Alluri10.1 Introduction 20310.2 Current Situation of Healthcare 20410.3 Introduction to Blockchain for Healthcare 20610.4 Introduction to ML for Healthcare 21110.4.1 Open Issues in Machine Learning for Healthcare 21310.5 Using ML and Blockchain for Healthcare Management 21410.5.1 Bucket 1: Theory Centric 21510.5.2 Bucket 2: Result Oriented 21910.5.3 Outcomes of the Study 22210.5.4 Why are Most of the Current Blockchain + Healthcare Papers Theory-Based? 22710.6 Conclusion 228References 22811 ADVANCEMENT OF DEEP LEARNING AND BLOCKCHAIN TECHNOLOGY IN HEALTH INFORMATICS 235Anubhav Singh, Mahipal Singh Sankhla, Kapil Parihar and Rajeev Kumar11.1 Introduction 23611.2 Associated Works 23811.2.1 Preliminaries 24011.3 Internet of Things 24011.4 Big Data 24111.5 Deep Learning 24111.5.1 Common Deep Learners 24211.5.1.1 Convolutional Neural Network 24211.5.1.2 Recurrent Neural Networks 24211.5.1.3 Deep Autoencoders 24311.5.1.4 Deep Boltzmann Machine 24311.6 Restricted Boltzmann Machine 24311.7 Profound Conviction Organization 24411.8 Application and Challenges of Deep Learners 24411.8.1 Predictive Healthcare 24411.8.2 Medical Decision Support 24511.8.3 Personalized Treatments 24511.8.4 Difficulties 24611.8.5 Blockchain Technology 24711.8.6 Types of Blockchain 24711.8.7 Challenges of Blockchain in Healthcare 24811.8.8 Interoperability 24811.8.9 Management, Privacy, and Anonymity of Data 24811.8.10 Quality of Service 24911.8.11 Heterogeneous Gadgets and Traffic 24911.8.12 Inertness 24911.8.13 Asset Imperatives and Energy Proficiency 24911.8.14 Storage Capacity and Scalability 25011.8.15 Security 25011.8.16 Data Mining 25011.8.17 System Model 25111.8.18 Attack Model 25111.9 Open Research Issues 25211.10 Conclusion 252References 25312 RESEARCH CHALLENGES AND FUTURE DIRECTIONS IN APPLYING BLOCKCHAIN TECHNOLOGY IN THE HEALTHCARE DOMAIN 257Sneha Chakraverty and Sakshi Bansal12.1 Introduction 25812.2 Healthcare 25912.2.1 Stakeholders of Indian Healthcare Ecosystem 25912.2.2 Major Data Related Challenges in Indian Healthcare System 26012.3 Need of Blockchain in Healthcare Domain 26112.4 Application of Blockchain in Healthcare Domain 26212.5 Methodology 26312.5.1 Review of Literature 26412.5.2 Interviews 26412.6 Challenges 26512.6.1 How to Overcome This Problem 26712.7 Future Directions 26812.8 Conclusion 269References 269Appendix 272Appendix 12.1 272Interview Form 272Appendix 12.2: Response 1 273Interview Form 273Appendix 12.3: Response 2 276Interview Form 276Appendix 12.4: Response 3 278Interview Form 278Appendix 12.5: Response 4 280Interview Form 280Index 285
Tele-Healthcare
TELE-HEALTHCARETHIS BOOK ELUCIDATES ALL ASPECTS OF TELE-HEALTHCARE WHICH IS THE APPLICATION OF AI, SOFT COMPUTING, DIGITAL INFORMATION, AND COMMUNICATION TECHNOLOGIES, TO PROVIDE SERVICES REMOTELY AND MANAGE ONE’S HEALTHCARE.Throughout the world, there are huge developing crises with respect to healthcare workforce shortages, as well as a growing burden of chronic diseases. As a result, e-health has become one of the fastest-growing service areas in the medical sector. E-health supports and ensures the availability of proper healthcare, public health, and health education services at a distance and in remote places. For the sector to grow and meet the need of the marketplace, e-health applications have become one of the fastest growing areas of research. However, to grow at a larger scale requires the following:* The availability of user cases for the exact identification of problems that need to be visualized.* A well-supported market that can promote and adopt the e-health care concept. * Development of cost-effectiveness applications and technologies for successful implementation of e-health at a larger scale. This book mainly focuses on these three points for the development and implementation of e-health services globally. In this book the reader will find:* Details of the challenges in promoting and implementing the telehealth industry.* How to expand a globalized agenda of personalized telehealth in integrative medical treatment for disease diagnosis and its industrial transformation.* How to design machine learning techniques for improving the tele-healthcare system.AUDIENCEResearchers and post-graduate students in biomedical engineering, artificial intelligence, and information technology; medical doctors and practitioners and industry experts in the healthcare sector; healthcare sector network administrators. R. NIDHYA, PHD, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research articles in SCI journals and her research interests include wireless body area networks, network security, and data mining.MANISH KUMAR, PHD, is an assistant professor in the School of Computer Science & Engineering, VIT Chennai. His research interests include soft computing applications for bioinformatics problems and computational intelligence. S. BALAMURUGAN, PHD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents. Preface xv1 MACHINE LEARNING–ASSISTED REMOTE PATIENT MONITORING WITH DATA ANALYTICS 1Vinutha D. C., Kavyashree and G. T. Raju1.1 Introduction 21.1.1 Traditional Patient Monitoring System 21.1.2 Remote Monitoring System 31.1.3 Challenges in RPM 41.2 Literature Survey 51.2.1 Machine Learning Approaches in Patient Monitoring 71.3 Machine Learning in RPM 81.3.1 Support Vector Machine 91.3.2 Decision Tree 101.3.3 Random Forest 111.3.4 Logistic Regression 111.3.5 Genetic Algorithm 121.3.6 Simple Linear Regression 121.3.7 KNN Algorithm 131.3.8 Naive Bayes Algorithm 141.4 System Architecture 151.4.1 Data Collection 161.4.2 Data Pre-Processing 171.4.3 Apply Machine Learning Algorithm and Prediction 181.5 Results 211.6 Future Enhancement 231.7 Conclusion 24References 242 A SURVEY ON RECENT COMPUTER-AIDED DIAGNOSIS FOR DETECTING DIABETIC RETINOPATHY 27Priyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain2.1 Introduction 282.2 Diabetic Retinopathy 282.2.1 Features of DR 282.2.2 Stages of DR 292.3 Overview of DL Models 312.3.1 Convolution Neural Network 312.3.2 Autoencoders 322.3.3 Boltzmann Machine and Deep Belief Network 322.4 Data Set 332.5 Performance Metrics 342.6 Literature Survey 362.6.1 Segmentation of Blood Vessels 362.6.2 Optic Disc Feature 492.6.3 Lesion Detections 502.6.3.1 Exudate Detection 502.6.3.2 MA and HM 512.6.4 DR Classification 512.7 Discussion and Future Directions 522.8 Conclusion 53References 533 A NEW IMPROVED CRYPTOGRAPHY METHOD-BASED E-HEALTH APPLICATION IN CLOUD COMPUTING ENVIRONMENT 59Dipesh Kumar, Nirupama Mandal and Yugal Kumar3.1 Introduction 603.1.1 Contribution 613.2 Motivation 623.3 Related Works 623.4 Challenges 643.5 Proposed Work 643.6 Proposed Algorithm for Encryption 663.6.1 Demonstration of Encryption Algorithm 663.6.1.1 When the Number of Columns Selected in the Table is Even 663.6.1.2 When the Number of Columns Selected in the Table is Odd 693.6.2 Flowchart for Encryption 723.7 Algorithm for Decryption 733.7.1 Demonstration of Decryption Algorithm 733.7.1.1 When the Number of Columns Selected in the Table is Even 733.7.1.2 When the Number of Columns Selected in the Table is Odd 753.7.2 Flowchart of Decryption Algorithm 783.8 Experiment and Result 783.9 Conclusion 80References 804 CUTANEOUS DISEASE OPTIMIZATION USING TELEDERMATOLOGY UNDERRESOURCED CLINICS 85Supriya M., Murugan K., Shanmugaraja T. and Venkatesh T.4.1 Introduction 864.2 Materials and Methods 874.2.1 Clinical Setting and Teledermatology Workflow 874.2.2 Study Design, Data Collection, and Analysis 874.3 Proposed System 884.3.1 Teledermatology in an Underresourced Clinic 884.3.2 Teledermatology Consultations from Uninsured Patients 894.3.3 Teledermatology for Patients Lacking Access to Dermatologists 904.3.4 Teledermatologist Management from Nonspecialists 924.3.5 Segment Factors of Referring PCPs and Their Patients 934.3.6 Teledermatology Operational Considerations 944.3.7 Instruction of PCPs 944.4 Challenges 954.5 Results and Discussion 954.5.1 Challenges of Referring to Teledermatology Services 96References 985 COGNITIVE ASSESSMENT BASED ON EYE TRACKING USING DEVICE-EMBEDDED CAMERAS VIA TELE-NEUROPSYCHOLOGY 101Shanmugaraja T., Venkatesh T., Supriya M. and Murugan K.5.1 Introduction 1025.2 Materials and Methods 1025.3 Framework Elements 1025.3.1 Eye Tracker Camera 1025.3.2 Test Construction 1035.3.3 Web Camera 1065.3.4 Camera for Eye Tracking 1065.4 Proposed System 1065.4.1 Camera for Tracking Eye 1065.4.2 Web Camera 1085.4.3 Scoring 1085.4.4 Eye Tracking Camera 1085.4.5 Web Camera Human-Coded Scoring 1085.5 Subjects 1095.5.1 Characteristics of Subject 1095.6 Methodology 1105.6.1 Analysis of Data 1105.7 Results 1105.8 Discussion 1125.9 Conclusion 114References 1156 FUZZY-BASED PATIENT HEALTH MONITORING SYSTEM 117Venkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi6.1 Introduction 1186.1.1 General Problem 1196.1.2 Existing Patient Monitoring and Diagnosis Systems 1196.1.3 Fuzzy Logic Systems 1206.2 System Design 1226.2.1 Hardware Requirements 1226.2.1.1 Functional Requirements 1236.2.1.2 Nonfunctional Specifications 1256.3 Software Architecture 1256.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API) 1266.3.2 Flowchart—API 1286.3.3 Foreign Tag IDs 1296.3.4 Database Manager 1306.3.5 Database Designing 1306.3.6 The Fuzzy Logic System 1316.3.6.1 Introduction to Fuzzy Logic 1316.3.6.2 The Modified Prior Alerting Score (MPAS) 1326.3.6.3 Structure of the Fuzzy Logic System 1346.3.7 Designing a System in Fuzzy 1356.3.7.1 Input Variables 1356.3.7.2 The Output Variable 1386.4 Results and Discussion 1406.4.1 Hardware Sensors Validation 1406.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine 1416.4.3 Normal Group (NRM) 1466.4.4 Low Risk Group 1466.4.5 High Risk Group (HRG) 1536.5 Conclusions and Future Work 1556.5.1 Summary and Concluding Remarks 1556.5.2 Future Directions 155References 1557 ARTIFICIAL INTELLIGENCE: A KEY FOR DETECTING COVID-19 USING CHEST RADIOGRAPHY 159C. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S.7.1 Introduction 1607.2 Related Work 1627.2.1 Traditional Approach 1627.2.2 Deep Learning–Based Approach 1637.3 Materials and Methods 1637.3.1 Data Set and Data Pre-Processing 1637.3.2 Proposed Model 1657.4 Experiment and Result 1717.4.1 Experiment Setup 1717.4.2 Comparison with Other Models 1737.5 Results 1747.6 Conclusion 175References 1768 AN EFFICIENT IOT FRAMEWORK FOR PATIENT MONITORING AND PREDICTING HEART DISEASE BASED ON MACHINE LEARNING ALGORITHMS 179Shanthi S., Nidhya R., Uma Perumal and Manish Kumar8.1 Introduction 1808.2 Literature Survey 1828.3 Machine Learning Algorithms 1838.4 Problem Statement 1848.5 Proposed Work 1858.5.1 Data Set Description 1858.5.2 Collection of Values Through Sensor Nodes 1868.5.3 Storage of Data in Cloud 1878.5.4 Prediction with Machine Learning Algorithms 1888.5.4.1 Data Cleaning and Preparation 1888.5.4.2 Data Splitting 1898.5.4.3 Training and Testing 1898.5.5 Machine Learning Algorithms 1898.5.5.1 Naive Bayes Algorithm 1898.5.5.2 Decision Tree Algorithm 1908.5.5.3 K-Neighbors Classifier 1918.5.5.4 Logistic Regression 1928.6 Performance Analysis and Evaluation 1928.7 Conclusion 197References 1979 BABW: BIOMETRIC-BASED AUTHENTICATION USING DWT AND FFNN 201R. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai9.1 Introduction 2029.2 Literature Survey 2039.3 BABW: Biometric Authentication Using Brain Waves 2089.4 Results and Discussion 2119.5 Conclusion 215References 21610 AUTISM SCREENING TOOLS WITH MACHINE LEARNING AND DEEP LEARNING METHODS: A REVIEW 221Pavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S.10.1 Introduction 22210.2 Autism Screening Methods 22310.2.1 Autism Screening Instrument for Educational Planning—3rd Version 22410.2.2 Quantitative Checklist for Autism in Toddlers 22410.2.3 Autism Behavior Checklist 22410.2.4 Developmental Behavior Checklist-Early Screen 22510.2.5 Childhood Autism Rating Scale Version 2 22510.2.6 Autism Spectrum Screening Questionnaire (ASSQ) 22610.2.7 Early Screening for Autistic Traits 22610.2.8 Autism Spectrum Quotient 22610.2.9 Social Communication Questionnaire 22710.2.10 Child Behavior Check List 22710.2.11 Indian Scale for Assessment of Autism 22710.3 Machine Learning in ASD Screening and Diagnosis 22810.4 DL in ASD Diagnosis 23810.5 Conclusion 242References 24211 DRUG TARGET MODULE MINING USING BIOLOGICAL MULTIFUNCTIONAL SCORE-BASED COCLUSTERING 249R. Gowri and R. Rathipriya11.1 Introduction 24911.2 Literature Study 25011.3 Materials and Methods 25311.3.1 Biological Terminologies 25311.3.2 Functional Coherence 25611.3.3 Biological Significances 25711.3.4 Existing Approach: MR-CoC 25711.4 Proposed Approach: MR-CoCmulti 25811.4.1 Biological Score Measures for DTM 25911.4.2 Multifunctional Score-Based Co-Clustering Approach 25911.5 Experimental Analysis 26411.5.1 Experimental Results 26511.6 Discussion 28011.7 Conclusion 280Acknowledgment 281References 28112 THE ASCENDANT ROLE OF MACHINE LEARNING ALGORITHMS IN THE PREDICTION OF BREAST CANCER AND TREATMENT USING TELEHEALTH 285Jothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar12.1 Introduction 28612.1.1 Objective 28712.1.2 Description and Goals 28712.1.2.1 Data Exploration 28812.1.2.2 Data Pre-Processing 28812.1.2.3 Feature Scaling 28812.1.2.4 Model Selection and Evaluation 28812.2 Literature Review 28912.3 Architecture Design and Implementation 30412.4 Results and Discussion 31012.5 Conclusion 31212.6 Future Work 313References 31413 REMOTE PATIENT MONITORING: DATA SHARING AND PREDICTION USING MACHINE LEARNING 317Mohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi13.1 Introduction 31813.1.1 Patient Monitoring in Healthcare System 31813.2 Literature Survey 32113.3 Problem Statement 32213.4 Machine Learning 32213.4.1 Introduction 32213.4.2 Cloud Computing 32413.4.3 Design and Architecture 32513.5 Proposed System 32613.6 Results and Discussions 33113.7 Privacy and Security Challenges 33313.8 Conclusions and Future Enhancement 334References 33514 INVESTIGATIONS ON MACHINE LEARNING MODELS TO ENVISAGE CORONAVIRUS IN PATIENTS 339R. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik14.1 Introduction 34014.2 Categories of ML Algorithms in Healthcare 34114.3 Why ML to Fight COVID-19? Tools and Techniques 34314.4 Highlights of ML Algorithms Under Consideration 34414.5 Experimentation and Investigation 34914.6 Comparative Analysis of the Algorithms 35314.7 Scope of Enhancement for Better Investigation 354References 35615 HEALTHCARE INFORMATICS: EMERGING TRENDS, CHALLENGES, AND ANALYSIS OF MEDICAL IMAGING 359G. Karthick and N.S. Nithya15.1 Emerging Trends and Challenges in Healthcare Informatics 36015.1.1 Advanced Technologies in Healthcare Informatics 36015.1.2 Intelligent Smart Healthcare Devices Using IoT With DL 36115.1.3 Cyber Security in Healthcare Informatics 36215.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics 36315.2 Performance Analysis of Medical Image Compression Using Wavelet Functions 36415.2.1 Introduction 36415.2.2 Materials and Methods 36615.2.3 Wavelet Basis Functions 36715.2.3.1 Haar Wavelet 36715.2.3.2 db Wavelet 36815.2.3.3 bior Wavelet 36815.2.3.4 rbio Wavelet 36815.2.3.5 Symlets Wavelet 36915.2.3.6 coif Wavelet 36915.2.3.7 dmey Wavelet 36915.2.3.8 fk Wavelet 36915.2.4 Compression Methods 37015.2.4.1 Embedded Zero-Trees of Wavelet Transform 37015.2.4.2 Set Partitioning in Hierarchical Trees 37015.2.4.3 Adaptively Scanned Wavelet Difference Reduction 37015.2.4.4 Coefficient Thresholding 37115.3 Results and Discussion 37115.3.1 Mean Square Error 37115.3.2 Peak Signal to Noise Ratio 37115.4 Conclusion 38015.4.1 Summary 380References 380Index 383
SQL - kurz & gut (3. Auflg.)
Wenn Sie SQL bei Ihrer täglichen Arbeit als Datenanalyst:in, Data Scientist oder Data Engineer verwenden, ist dieses beliebte Taschenbuch das ideale Nachschlagewerk für Sie. Beschrieben werden die wichtigsten Funktionen von SQL und deren Einsatz in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL und SQLite. Zahlreiche Beispiele verdeutlichen zudem die vielfältigen Möglichkeiten der Sprache.In dieser aktualisierten und deutlich erweiterten Ausgabe zeigt Alice Zhao, wie diese fünf Datenbankmanagementsysteme die SQL-Syntax für Abfragen und für Änderungen an einer Datenbank implementieren. Sie finden Näheres zu Datentypen und Konvertierungen, zur Syntax regulärer Ausdrücke, zu Fensterfunktionen, Pivoting und Unpivoting und vieles mehr.- Schlagen Sie schnell nach, wie Sie bestimmte Aufgaben mit SQL ausführen- Nutzen Sie die Syntaxbeispiele des Buchs für Ihre eigenen Abfragen- Passen Sie SQL-Abfragen so an, dass sie auf den fünf verbreitetsten Datenbankmanagementsystemen funktionieren- Neu: Verbinden Sie Python und R mit einer relationalen Datenbank- Neu: Erhalten Sie in dem Kapitel »Wie mache ich …?« Antworten auf häufig gestellte Fragen zu SQLZielgruppe:Data ScientistsDatenanalyst*innenalle, die mit Daten und SQL zu tun habenAutorin:Alice Zhao ist Data Scientist und liebt es, komplexe Dinge leicht verständlich zu erklären. Als Senior Data Scientist bei Metis und als Mitbegründerin von Best Fit Analytics hat sie zahlreiche Kurse zu SQL, Python und R gegeben. Ihre sehr gut bewerteten technischen Tutorials auf YouTube sind dafür bekannt, gleichermaßen praktisch, unterhaltsam und visuell ansprechend zu sein.In ihrem Blog „A Dash of Data“ schreibt sie über Analytics und Popkultur. Ihre Arbeit wurde bereits in der Huffington Post, Thrillist und Working Mother veröffentlicht. Sie hat auf einer Vielzahl von Konferenzen über Themen wie Natural Language Processing und Datenvisualisierung gesprochen und hat einen Master of Science in Analytics und einen Bachelor of Science in Elektrotechnik erworben, beide von der Northwestern University.
Android Tablets & Smartphones
OHNE VORWISSEN ANDROID TABLETS ODER SMARTPHONES SICHER BEDIENEN * Erfolgsautor Günter Born behandelt die typischen Fragen von Einsteigern und Senioren * Alle Bedienungsfragen in verständlichen Schritt-für-Schritt-Anleitungen erklärt * Komplett in Farbe, übersichtlich gestaltet und mit größerer Schrift * Der Bestseller deckt jetzt Android 11 ab und sensibilisiert für Sicherheitsfragen Mit diesem praktischen Ratgeber finden Sie sich schnell zurecht und können Schritt für Schritt nachvollziehen, wie Sie Ihr Android-Handy oder -Tablet einrichten, wie Sie surfen, Fotos machen, Kurznachrichten und E-Mails verschicken, die Einsatzmöglichkeiten Ihres Geräts durch neue Apps erweitern und vieles mehr. Schwerpunkt ist die Bedienung von Smartphones und Tablet-PCs mit den Android-Versionen 9 bis 11. Das Buch kann jedoch auch für Geräte mit älteren Android-Versionen genutzt werden, denn vieles ist hier sehr ähnlich.
Getting Started with Open Source Technologies
Using real life examples, learn how open source projects are consumed and explore the nuances within different industries in adopting open source technologies.After gaining a basic understanding of open source and open standards, understand how licensing helps turn community code into an enterprise worthy component. It also helps you understand future governance of the open source software. Once in effect, continuous security becomes a challenge for open source components so we'll examine its ongoing security aspects.This book will also cover different open source domains and industries and discuss how an enterprise can transform itself by applying key open source principles. In the end Getting Started with Open Source Technologies will provide a 360-degree view of open source and show you how to apply it.WHAT YOU'LL LEARN* Understand current trends in open source and why it is relevant today* Gain entry into the open source world to properly license your source code* Review open source usage within different industries and apply the learnings to your enterprises* Evangelize and create advocates in open source communitiesWHO THIS BOOK IS FOREnterprises (Developers/Operators/Management) and academics who want to get a 360-degree view of open source no matter how early or advanced they are in their adoption of any open source technology.SACHIN RATHEE is a Technologist and Business Executive with experience in multiple facets of the software industry. Sachin has led many transformational projects using open source technologies for various enterprises. He is a strong proponent of open source and presented its value in multiple global conferences. Most recently Sachin has been involved in leading the realization of 5G and edge computing use cases in cloud native environments. He holds a Bachelor’s degree in Engineering as well as Master’s in Business Administration.AMOL CHOBE is managing the Solution Architects organization at world's leading enterprise open source software company. Amol brings over 20+ years of experience across numerous industries such as Telecommunications, Financial etc. Amol has been a big advocate of the open source community and has given several presentations around the world focusing on various open source projects. Lately Amol is focusing on adoption of various kinds of everything-as-a-service (XaaS) in rapidly changing markets and an ever-changing technological landscape. He holds a Master's Degree in Computer Engineering.CHAPTER 1:Open source : How we got here( This chapter will cover history , Todays Software and business challenges, How Open source works )Chapter Goal: Provide basic understanding of the Open source world No of pages 15SUB -TOPICS1. How it all started .2. What is really Open source ?3. Where are we now ?CHAPTER 2: Open source and Open standardsCHAPTER GOAL: There are a number of Open standards that coexist with Open source code. Here we look at the relationship between the two.No of pages 15SUB -TOPICS1. What is Open standard with examples2. Comparison on Open source with standards3. How can both coexistCHAPTER 3 : Licensing the Open sourceCHAPTER GOAL: Understanding the ownership of the Open source softwareNo of pages 15SUB -TOPICS1. Understanding various licenses available today2. Pros cons of various licenses3. How to pick the right license for your useCHAPTER 4 : Securing Open SystemsCHAPTER GOAL: Understanding the security challenges of Open source and how to address themNo of pages 15SUB -TOPICS1. Answering the question -- Can Open source be considered secure ?2. Understanding the security aspects of Open source software that you are considering3. Options for securing Open source softwareCHAPTER 5 :Open Source in InfrastructureCHAPTER GOAL: Here we start breaking down the various categories of Open source software available.No of pages 15SUB -TOPICS1. What are the various domains and why we break it down in to such domains2. Understanding the SaaS model and its Open source components.3. Understanding the PaaS model and its Open source components.4. Understanding the IaaS model and its Open source components.CHAPTER 6: Open Source for Emerging TechnologiesCHAPTER GOAL: This chapter provides details on the Infrastructure software available as Open source.No of pages 15SUB -TOPICS1. How to apply Open source infrastructure components to various models2. Cloud infrastructure and management details3. Networking details4. Storage detailsCHAPTER 7: Open source In IndustriesCHAPTER GOAL: This chapter provides details on the application software available as Open source.NO OF PAGES 15SUB -TOPICS1. How to apply Open source applications components to various models2. Integrating applications with different Open source software projects3. Open source applications tooling5. Industry 4.0 / ManufacturingCHAPTER 8: Open source growth and TrendsChapter Goal: Here we discuss how Open source and its culture has driven growth in various companies. We will get views from various industry leaders.No of pages 15SUB -TOPICS1. What is Open Culture2. How companies are adopting Open Culture3. Open Culture success storyCHAPTER 9: Path forward (Cover aspects relating teaching in schools, evangelizing, growing communities )CHAPTER GOAL: Understanding how Open source can be introduced sooner than later into education systemsNo of pages 15SUB -TOPICS1. Barriers to Open source2. Open source in academia3. Supporting Open source communities
Erfolgreich starten mit YouTube
- Videos produzieren, die begeistern- Kanal managen, Reichweite generieren und Geld verdienenDie Social-Media-Plattform YouTube hat sich zur zweitgrößten Suchmaschine im Internet gemausert. Ob Unterhaltung oder Wissenschaft, Kunst, Kultur oder jedes erdenkliche Hobby: Auf YouTube findet sich das geballte Schwarmwissen und die Begeisterung von Generationen. Pro Tag laden Millionen Nutzer Millionen Videos hoch – und einer davon ist Nick Schreger, der Autor dieses Buchs. Was es braucht, um einen YouTube-Kanal zu starten und mit spannenden Inhalten zu füllen, beschreibt er auf lockere, nicht immer ganz ernste Art. Sie erfahren, wie Sie mit relativ einfachen technischen Mitteln gute Videos produzieren, worauf es bei der Themenplanung ankommt und wie Sie Ihren Kanal erfolgreich präsentieren. Auch ambitioniertere Creators kommen dabei nicht zu kurz – ganz wie auf YouTube selbst: In Nicks kreativen Anregungen wird jeder fündig!Aus dem Inhalt:- Stilfindung- Dein Publikum, das unbekannte Wesen- Script vs. Spontanität- So nicht: No-Gos!- Kamera-Einstellungen- Videolicht- Der gute Ton- Das YouTube-Studio- Live-Streaming- Interaktion mit den Zuschauern- Erfolgreiches Selbstmarketing- Begleitende Medien- YouTube-Monetarisierung- Abonnenten – die geheime Währung- Umgang mit KritikDer AutorNick Schreger, Jahrgang 1972, arbeitete nach seiner Berufsausbildung und dem Studium in Sprachwissenschaften als technischer Übersetzer. Als Ausgleich dazu beschäftigte er sich mit verschiedenen Hobbies, machte Musik, Sound-Design für Computerspiele, fotografierte und drehte Dokumentarfilme. Seit 2004 arbeitet er nebenher als Fotograf und Filmemacher und unterhält seit 2016 einen beliebten deutschsprachigen YouTube-Kanal. Nach diversen Auslandsaufenthalten lebt und arbeitet er in der Schweiz.
Code That Fits in Your Head
Heuristik in der Softwareentwicklung. Komplexität reduzieren | Legacy Code beherrschen | Performance optimieren.Techniken und Konzepte für nachhaltige Softwareentwicklung sowie sauberen und wartbaren Code Reduktion von Komplexität, strukturierte Arbeitsabläufe und effiziente Fehlerbehandlung. Mit Auszügen aus einem vollständigen Beispielprojekt inklusive Code zum Download.»Mark Seemann ist dafür bekannt, komplexe Konzepte anschaulich und präzise zu erläutern. In diesem Buch kondensiert er seine weitreichende Erfahrung in der Softwareentwicklung zu praktischen, pragmatischen Techniken für nachhaltigen und gut lesbaren Code. Dieses Buch ist ein Must Read für jeden Programmierer.«– Scott Wlaschin, Autor von »Domain Modeling Made Functional«Dieses Buch ist ein praktischer Leitfaden für das Schreiben von nachhaltigem Programmcode und die Reduktion von Komplexität. So können Sie verhindern, dass Softwareprojekte langfristig außer Kontrolle geraten.Mark Seemann unterstützt seit Jahrzehnten Softwareentwickler-Teams bei der erfolgreichen Umsetzung ihrer Projekte. In diesem Buch begleitet er Sie von den ersten Codezeilen bis zum Deployment und zeigt Ihnen, wie Sie im Entwicklungsprozess effizient und nachhaltig bleiben, wenn Sie neue Funktionalitäten implementieren. Dabei legt er auch Wert auf Fehlerbehandlung und disziplinübergreifende Themen. Er gibt Ihnen wertvolle Hinweise, Techniken und Arbeitsabläufe für alle wichtigen Kernprobleme an die Hand: von der Verwendung von Checklisten bis zur Teamarbeit, von Kapselung bis zur verteilten Programmierung, von API-Design bis zu Unit Testing.Seemann veranschaulicht seine Konzepte anhand von Codebeispielen aus einem vollständigen Projektbeispiel in C#. Der Code ist so geschrieben, dass er gut verständlich für jeden ist, der eine objektorientierte Programmiersprache verwendet, einschließlich Java, C++ und Python. Der gesamte Code steht zur weiteren Erkundung zum Download bereit.Wenn Sie jemals negative Erfahrungen bei der Umsetzung von Softwareprojekten oder mit schlecht wartbarem Legacy Code gemacht haben, wird dieses Praxisbuch Ihnen helfen, solchen Schwierigkeiten ab sofort aus dem Weg zu gehen.Über den Autor:Mark Seemann ist in der Softwareentwicklung tätig und beschäftigt sich mit funktionaler Programmierung, objektorientierter Entwicklung und Softwareentwicklung im Allgemeinen. Er hat bereits zwei Bücher und zahlreiche Artikel und Blogbeiträge zu verwandten Themen veröffentlicht. Obwohl er hauptsächlich als .NET-Entwickler tätig ist, nutzt er eine große Bandbreite von Technologien als Ressource, einschließlich Haskell und verschiedene Design-Pattern-Bücher.
Link Technology to Your Long-Term Business Goals
Link the use of technology with long-term business goals to optimize the core elements in your organization: people, strategy, and operations. This book will show you how effective planning of processes and execution of strategies with the help of technology can bring about an organization-wide increase in productivity and performance. Business environments have grown increasingly competitive. Before an organization realizes what has happened, it can lose or gain market share. Being agile is the key to success. This book covers the processes that can help your enterprise be agile and follow best practices when executing your business strategy. You'll review case studies from real-world experiences that dive deep into the problems a business encounters and the ways to solve those challenges. They deal with the different ways in which your organization can achieve dramatic performance improvements by changing your company’s processes. The bookalso explains how objectives and key results can be used to align business teams for increased productivity. With Use Tech to Mobilize Your People, Strategy, and Operations you'll learn how the intensity of core processes can ensure that growth does not wane in your organization. What You'll Learn Knowthe role of three core elements in organizations: people, strategy, operationsUnderstand how technology can enhance these three core elementsBe aware of the importance of scale and security in the information eraEliminate distractions and uncertainty in core processes Who This Book Is For People with experience building businesses (founders, CEOs, COOs, CTOs, project managers, product managers, operation heads, sales heads, finance heads, strategy heads,technology leaders) who are looking for technology solutions to business problems
CompTIA Linux+ Study Guide
THE BEST-SELLING, HANDS-ON ROADMAP TO ACING THE NEW LINUX+ EXAMIn the newly updated Fifth Edition of CompTIA Linux+ Study Guide: Exam XK0-005, IT industry veterans and tech education gurus Richard Blum and Christine Bresnahan deliver a concise and practical blueprint to success on the CompTIA Linux+ exam and in your first role as a Linux network or system administrator. In the book, you’ll find concrete strategies and proven techniques to master Linux system management, security, scripting, containers, automation, and troubleshooting. Every competency tested on the Linux+ exam is discussed here. You’ll also get:* Hands-on Linux advice that ensures you’re job-ready on the first day of your new network or sysadmin role* Test-taking tips and tactics that decrease exam anxiety and get you ready for the challenging Linux+ exam* Complimentary access to the Sybex learning environment, complete with online test bank, bonus practice exams, electronic flashcards, and a searchable glossaryPerfect for practicing network and system admins seeking an in-demand and valuable credential for working with Linux servers and computers, CompTIA Linux+ Study Guide: Exam XK0-005, Fifth Edition, will also earn a place in the libraries of people looking to change careers and start down an exciting new path in tech. RICHARD BLUM has over 35 years of experience working as a system and network administrator. He teaches online courses in Linux and Web programming and is co-author with Christine Bresnahan of several Linux titles, including CompTIA Linux+ Study Guide, Linux Essentials, Mastering Linux System Administration, and the Linux Command Line and Shell Scripting Bible.CHRISTINE BRESNAHAN has over 35 years of experience working in the IT industry. She is an Adjunct Professor at Ivy Tech Community College where she teaches Linux certification and Python programming classes. She is co-author with Richard Blum of CompTIA Linux+ Study Guide, Linux Essentials, Mastering Linux System Administration, and the Linux Command Line and Shell Scripting Bible.Introduction xxxiAssessment Test xlivAnswers to Assessment Test lvPART I GATHERING YOUR TOOLS 1Chapter 1 Preparing Your Environment 3Chapter 2 Introduction to Services 17Chapter 3 Managing Files, Directories, and Text 43Chapter 4 Searching and Analyzing Text 89PART II STARTING UP AND CONFIGURING YOUR SYSTEM 131Chapter 5 Explaining the Boot Process 133Chapter 6 Maintaining System Startup and Services 157Chapter 7 Configuring Network Connections 199Chapter 8 Comparing GUIs 235Chapter 9 Adjusting Localization Options 269PART III MANAGING YOUR SYSTEM 289Chapter 10 Administering Users and Groups 291Chapter 11 Handling Storage 329Chapter 12 Protecting Files 363Chapter 13 Governing Software 393Chapter 14 Tending Kernel Modules 423PART IV SECURING YOUR SYSTEM 437Chapter 15 Applying Ownership and Permissions 439Chapter 16 Looking at File and Directory Permissions 440Chapter 17 Implementing Logging Services 503Chapter 18 Overseeing Linux Firewalls 517Chapter 19 Embracing Best Security Practices 547PART V TROUBLESHOOTING YOUR SYSTEM 571Chapter 20 Analyzing System Properties and Remediation 573Chapter 21 Optimizing Performance 607Chapter 22 Investigating User Issues 623Chapter 23 Dealing with Linux Devices 643Chapter 24 Troubleshooting Application and Hardware Issues 667PART VI AUTOMATING YOUR SYSTEM 697Chapter 25 Deploying Bash Scripts 699Chapter 26 Automating Jobs 727Chapter 27 Controlling Versions with Git 749PART VII REALIZING VIRTUAL AND CLOUD ENVIRONMENTS 771Chapter 28 Understanding Cloud and Virtualization Concepts 773Chapter 29 Inspecting Cloud and Virtualization Services 791Chapter 30 Orchestrating the Environment 813Index 897
Productive and Efficient Data Science with Python
This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering.You’ll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You’ll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem.The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You’ll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks.In the end, you’ll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity.WHAT YOU’LL LEARN* Write fast and efficient code for data science and machine learning* Build robust and expressive data science pipelines* Measure memory and CPU profile for machine learning methods* Utilize the full potential of GPU for data science tasks * Handle large and complex data sets efficientlyWHO THIS BOOK IS FORData scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.Dr. Tirthajyoti Sarkar lives in the San Francisco Bay area works as a Data Science and Solutions Engineering Manager at Adapdix Corp., where he architects Artificial intelligence and Machine learning solutions for edge-computing based systems powering the Industry 4.0 and Smart manufacturing revolution across a wide range of industries. Before that, he spent more than a decade developing best-in-class semiconductor technologies for power electronics.He has published data science books, and regularly contributes highly cited AI/ML-related articles on top platforms such as KDNuggets and Towards Data Science. Tirthajyoti has developed multiple open-source software packages in the field of statistical modeling and data analytics. He has 5 US patents and more than thirty technical publications in international journals and conferences.He conducts regular workshops and participates in expert panels on various AI/ML topics and contributes to the broader data science community in numerous ways. Tirthajyoti holds a Ph.D. from the University of Illinois and a B.Tech degree from the Indian Institute of Technology, Kharagpur.Chapter 1: What is Productive and Efficient Data Science?Chapter Goal: To introduce the readers with the concept of doing data science tasks efficiently and more productively and illustrating potential pitfalls in their everyday work.No of pages – 10Subtopics• Typical data science pipeline• Short examples of inefficient programming in data science• Some pitfalls to avoid• Efficiency and productivity go hand in hand• Overview of tools and techniques for a productive data science pipeline• Skills and attitude for productive data scienceChapter 2: Better Programming Principles for Efficient Data ScienceChapter Goal: Help readers grasp the idea of efficient programming techniques and how they can be applied to a typical data science task flow.No of pages – 15Subtopics• The concept of time and space complexity, Big-O notation• Why complexity matters for data science• Examples of inefficient programming in data science tasks• What you can do instead• Measuring code execution timingChapter 3: How to Use Python Data Science Packages more ProductivelyChapter Goal: Illustrate handful of tricks and techniques to use the most well-known Python data science packages – Numpy, Pandas, Matplotlib, Seaborn, Scipy – more productively.No of pages – 20Subtopics• Why Numpy is faster than regular Python code and how much• Using Numpy efficiently• Using Pandas productively• Matplotlib and Seaborn code for and productive EDA• Using SciPy for common data science tasksChapter 4: Writing Machine Learning Code More ProductivelyChapter Goal: Teach the reader about writing efficient and modular machine learning code for productive data science pipeline with hands-on examples using Scikit-learn.No of pages – 15Subtopics• Why modular code for machine learning and deep learning• Scikit-learn tools and techniques• Systematic evaluation of Scikit-learn ML algorithms in automated fashion• Decision boundary visualization with custom function• Hyperparameter search in Scikit-learnChapter 5: Modular and Productive Deep Learning CodeChapter Goal: Teach the reader about mixing modular programming style in deep learning code with hands-on examples using Keras/TensorFlow.No of pages – 25Subtopics• Why modular code and object-oriented style for deep learning• Wrapper functions with Keras for faster deep learning experimentations• A single function to streamline image classification task flow• Visualize activation functions of neural networks• Custom callback functions in Keras and their utilities• Using Scikit-learn wrapper for hyperparameter search in KerasChapter 6: Build Your Own Machine Learning Estimator/PackageChapter Goal: Illustrate how to build a new Python machine learning module/package from scratch.No of pages – 15Subtopics• Why write your own ML package/module?• A simple example vs. a data scientist’s example• A good, old Linear Regression estimator — with a twist• How do you start building?• Add utility functions• Do more with object-oriented approachChapter 7: Some Cool Utility PackagesChapter Goal: Introduce the readers to the idea of executing data science tasks efficiently by going beyond traditional stack and utilizing exciting, new libraries.No of pages – 20Subtopics• The great Python data science ecosystem• Build pipeline using “pdpipe”• Check data integrity and expectations with “great_expectations”• Speed up Numpy and Pandas using Numexpr• Discover best fitted distributions using “distfit”Chapter 8: Testing the Machine Learning CodeChapter Goal: Teach the readers some basic principles of testing Python code and how to apply them to a specific case of machine learning module.No of pages – 20Subtopics• Why testing boosts productivity• Basic principles and variations of testing• Data science or machine learning testing is somewhat different• A PyTest module for a ML moduleChapter 9: Memory and Timing ProfilingChapter Goal: Illustrate how to measure and profile typical data science and machine learning code/ module.No of pages – 15Subtopics• Why profiling is important• Well-known profilers out there• cProfile• Memory_profile• ScaleneChapter 10: Scalable Data ScienceChapter Goal: Demonstrate the importance of scalability in data science tasks with hands-on examples.No of pages – 15Subtopics• Data science pipeline needs to be easily scalable• Common problems - out-of-memory and single-threading• What options are out there?• Hands-on example with Vaex• Hands-on example with ModinChapter 11: Parallelized Data ScienceChapter Goal: Demonstrate the importance of parallel processing in data science tasks with hands-on examples.No of pages – 15Subtopics• Data science pipeline should take advantage of parallel computing• Two great options – Ray and Dask• Hands-on example with Dask cluster• Hands-on example with “Ray serve” and actorsChapter 12: GPU-Based Data Science for High ProductivityChapter Goal: Illustrate how to harness the power of GPU-based hardware for common data science tasks and classical machine learning.No of pages – 20Subtopics• GPU-powered data science (not deep learning)• The RAPIDS ecosystem• CuPy vs. NumPy• CuDF vs. Pandas• CuML vs. Scikit-learnChapter 13: Other Useful Skills to MasterChapter Goal: Give an overview of other related skills to master for executing data science tasks more efficiently.No of pages – 25Subtopics• Key things to learn• Understanding the basics of web technologies• Going from local to cloud• Simple web app to showcase a data science project• GUI programming for a quick demo• Being comfortable with container technologies• Putting it all togetherChapter 14: Wrapping It UpChapter Goal: Show a summary of all the things discussed and some future projections.No of pages – 10Subtopics• Chapter-wise summary• What were not discussed in this book• Future projections• General advice for upcoming data scientists
CompTIA Linux+ Practice Tests
THE BEST TEST PREPARATION RESOURCE FOR THE COMPTIA LINUX+ CERTIFICATION EXAMIn the newly updated Third Edition of CompTIA Linux+ Practice Tests: Exam XK0-005, veteran Linux expert, Steve Suehring, delivers an instructive set of practice questions written to get you ready to ace the new XK0-005 test. Providing hundreds of domain-by-domain questions covering system management, security, scripting, containers, automation, and troubleshooting, the book helps you prepare for the exam with confidence and efficiency. You’ll be able to pinpoint those areas you’ve mastered and those which require more study, as well as get a feel for the structure of the test itself. The book also offers:* Hundreds of practice questions that reinforce your skills and knowledge* A great way for practicing and aspiring Linux network and system administrators to improve their on-the-job skills* One year of complimentary access after activation to the online Sybex test bank, where you can study and work through hundreds of questionsAn indispensable resource for anyone preparing for the CompTIA Linux+ exam, CompTIA Linux+ Practice Tests: Exam XK0-005, Third Edition, is also a must-have for new and experienced sysadmins and network administrators seeking to identify areas of strength and weakness and improve their grasp of Linux systems. ABOUT THE AUTHORSTEVE SUEHRING is a technical architect with extensive experience in technology and Linux. He is the author of several technology education books, and has worked as a systems engineer and security specialist, as well as in roles providing architectural direction to several different technology initiatives. He is an expert in JavaScript, Linux security, Windows Server certifications, Perl, and more. Introduction XIChapter 1 System Management (Domain 1.0) 1Chapter 2 System Operations and Maintenance (Domain 2.0) 49Chapter 3 Scripting, Containers, and Automation (Domain 3.0) 83Chapter 4 Troubleshooting (Domain 4.0) 113Chapter 5 Practice Exam 157Appendix Answers to the Review Questions 175Chapter 1: System Management (Domain 1.0) 176Chapter 2: System Operations and Maintenance (Domain 2.0) 197Chapter 3: Scripting, Containers, and Automation (Domain 3.0) 211Chapter 4: Troubleshooting (Domain 4.0) 224Chapter 5: Practice Exam 241Index 249
Artificial Intelligence in Industry 4.0 and 5G Technology
ARTIFICIAL INTELLIGENCE IN INDUSTRY 4.0 AND 5G TECHNOLOGYEXPLORES INNOVATIVE AND VALUE-ADDED SOLUTIONS FOR APPLICATION PROBLEMS IN THE COMMERCIAL, BUSINESS, AND INDUSTRY SECTORSAs the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise.Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT).* Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks.* Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.* Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.* Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.PANDIAN VASANT is Research Associate at MERLIN Research Centre, TDTU, HCMC, Vietnam, and Editor in Chief of International Journal of Energy Optimization and Engineering (IJEOE). He holds PhD in Computational Intelligence (UNEM, Costa Rica), MSc (University Malaysia Sabah, Malaysia, Engineering Mathematics) and BSc (Hons, Second Class Upper) in Mathematics (University of Malaya, Malaysia). He has co-authored research articles in journals, conference proceedings, presentations, special issues Guest Editor, chapters and General Chair of EAI International Conference on Computer Science and Engineering in Penang, Malaysia (2016) and Bangkok, Thailand (2018).ELIAS MUNAPO, PhD, currently heads the Department of Business Statistics and Operations research at North West University-Mafikeng, South Africa. He has published 50+ articles and contributed to five chapters on industrial engineering and management texts.J. JOSHUA THOMAS is an Associate Professor at UOW Malaysia KDU Penang University College. He obtained his PhD (Intelligent Systems Techniques) from University Sains Malaysia, Penang and master’s degree from Madurai Kamaraj University, India. He is working with Deep Learning algorithms, specially targeting on Graph Convolutional Neural Networks (GCNN) and Bi-directional Recurrent Neural Networks (RNN) for drug target interaction and image tagging with embedded natural language processing. His work involves experimental research with software prototypes and mathematical modelling and design.GERHARD-WILLIAM WEBER, PhD, is Professor and Chair of Marketing and Economic Engineering at Poznan University of Technology, Poland. He is also an Adjunct Professor at Department of Industrial and Systems Engineering, College of Engineering at Istinye University, Istanbul, Turkey.List of Contributors xvPreface xixProfile of Editors xxviiAcknowledgments xxx1 DYNAMIC KEY-BASED BIOMETRIC END-USER AUTHENTICATION PROPOSAL FOR IOT IN INDUSTRY 4.0 1Subhash Mondal, Swapnoj Banerjee, Soumodipto Halder, and Diganta Sengupta1.1 Introduction 11.2 Literature Review 21.3 Proposed Framework 51.3.1 Enrolment Phase 51.3.2 Authentication Phase 71.3.2.1 Pre-processing 71.3.2.2 Minutiae Extraction and False Minutiae Removal 121.3.2.3 Key Generation from extracted Minutiae points 131.3.2.4 Encrypting the Biometric Fingerprint Image Using AES 141.4 Comparative Analysis 181.5 Conclusion 19References 192 DECISION SUPPORT METHODOLOGY FOR SCHEDULING ORDERS IN ADDITIVE MANUFACTURING 25Juan Jesús Tello Rodríguez and Lopez-I Fernando2.1 Introduction 252.2 The Additive Manufacturing Process 262.3 Some Background 282.4 Proposed Approach 302.4.1 A Mathematical Model for the Initial Printing Scheduling 322.4.1.1 Considerations 322.4.1.2 Sets 322.4.2 Parameters 332.4.2.1 Orders 332.4.2.2 Parts 332.4.2.3 Printing Machines 332.4.2.4 Process 332.4.3 Decision Variables 332.4.4 Optimization Criteria 332.4.5 Constrains 342.5 Results 352.5.1 Orders 352.6 Conclusions 39References 393 SIGNIFICANCE OF CONSUMING 5G-BUILT ARTIFICIAL INTELLIGENCE IN SMART CITIES 43Y. Bevish Jinila, Cinthia Joy, J. Joshua Thomas, and S. Prayla Shyry3.1 Introduction 433.2 Background and RelatedWork 473.3 Challenges in Smart Cities 493.3.1 Data Acquisition 493.3.2 Data Analysis 503.3.3 Data Security and Privacy 503.3.4 Data Dissemination 503.4 Need for AI and Data Analytics 503.5 Applications of AI in Smart Cities 513.5.1 Road Condition Monitoring 513.5.2 Driver Behavior Monitoring 523.5.3 AI-Enabled Automatic Parking 533.5.4 Waste Management 533.5.5 Smart Governance 533.5.6 Smart Healthcare 543.5.7 Smart Grid 543.5.8 Smart Agriculture 553.6 AI-based Modeling for Smart Cities 553.6.1 Smart Cities Deployment Model 553.6.2 AI-Based Predictive Analytics 573.6.3 Pre-processing 583.6.4 Feature Selection 583.6.5 Artificial Intelligence Model 583.7 Conclusion 60References 604 NEURAL NETWORK APPROACH TO SEGMENTATION OF ECONOMIC INFRASTRUCTURE OBJECTS ON HIGH-RESOLUTION SATELLITE IMAGES 63Vladimir A. Kozub, Alexander B. Murynin, Igor S. Litvinchev, Ivan A. Matveev, and Pandian Vasant4.1 Introduction 634.2 Methodology for Constructing a Digital Terrain Model 644.3 Image Segmentation Problem 654.4 Segmentation Quality Assessment 674.5 Existing Segmentation Methods and Algorithms 684.6 Classical Methods 694.7 Neural Network Methods 724.7.1 Semantic Segmentation of Objects in Satellite Images 744.8 Segmentation with Neural Networks 764.9 Convolutional Neural Networks 794.10 Batch Normalization 834.11 Residual Blocks 844.12 Training of Neural Networks 854.13 Loss Functions 854.14 Optimization 864.15 Numerical Experiments 884.16 Description of the Training Set 884.17 Class Analysis 904.18 Augmentation 904.19 NN Architecture 924.20 Training and Results 934.21 Conclusion 97Acknowledgments 97References 975 THE IMPACT OF DATA SECURITY ON THE INTERNET OF THINGS 101Joshua E. Chukwuere and Boitumelo Molefe5.1 Introduction 1015.2 Background of the Study 1025.3 Problem Statement 1035.4 Research Questions 1035.5 Literature Review 1035.5.1 The Data Security on IoT 1035.5.2 The Security Threats and Awareness of Data Security on IoT 1055.5.3 The DifferentWays to Assist with Keeping Your IoT Device Safer from Security Threats 1055.6 Research Methodology 1065.6.1 Population and Sampling 1065.6.2 Data Collection 1075.6.3 Reliability and Validity 1085.7 Chapter Results and Discussions 1085.7.1 The Demographic Information 1095.7.1.1 Age, Ethnic Group, and Ownership of a Smart Device 1095.7.2 Awareness of Users About Data Security of the Internet of Things 1095.7.3 The Security Threats that are Affecting the Internet of Things Devices 1115.7.3.1 The Architecture of IoT Devices 1125.7.3.2 The botnets Attack 1125.7.4 The Effects of Security Threats on IoT Devices that are Affecting Users 1125.7.4.1 The Slowness or Malfunctioning of the IoT Device 1125.7.4.2 The Trust of Users on IoT 1135.7.4.3 The Safety of Users 1135.7.4.4 The Guaranteed Duration of IoT Devices 1145.7.5 DifferentWays to Assist with Keeping IoT Smart Devices Safer from Security Threats 1145.7.5.1 The Change Default Passwords 1145.7.5.2 The Easy or Common Passwords 1145.7.5.3 On the Importance of Reading Privacy Policies 1145.7.5.4 The Bluetooth and Wi-Fi of IoT Devices 1155.7.5.5 The VPN on IoT 1155.7.5.6 The Physical Restriction 1155.7.5.7 Two-Factor Authentication 1165.7.5.8 The Biometric Authentication 1165.8 Answers to the Chapter Questions 1165.8.1 Objective 1: Awareness on Users About Data Security of Internet of Things (IoT) 1165.8.2 Objective 2: Determine the Security Threats that are Involved in the Internet of Things (IoT) 1175.8.3 Objective 3: The Effects of Security Threats on IoT Devices that are Affecting Users 1175.8.4 Objective 4: DifferentWays to Assist with Keeping IoT Devices Safer from Security Threats 1175.8.5 Other Descriptive Analysis (Mean) 1185.8.5.1 Mean 1 – Awareness on Users About Data Security on IoT 1185.8.5.2 The Effects of Security Threats on IoT Devices that are Affecting Users 1185.8.5.3 DifferentWays to Assist with Keeping an IoT Device Safer 1225.9 Chapter Recommendations 1225.10 Conclusion 122References 1246 SUSTAINABLE RENEWABLE ENERGY AND WASTE MANAGEMENT ON WEATHERING CORPORATE POLLUTION 129Choo K. Chin and Deng H. Xiang6.1 Introduction 1296.2 Literature Review 1316.2.1 Energy Efficiency 1356.2.2 Waste Minimization 1366.2.3 Water Consumption 1376.2.4 Eco-Procurement 1376.2.5 Communication 1386.2.6 Awareness 1386.2.7 Sustainable and Renewable Energy Development 1386.3 Conceptual Framework 1396.4 Conclusion 1396.4.1 Energy Efficiency 1406.4.2 Waste Minimization 1406.4.3 Water Consumption 1406.4.4 Eco-Procurement 1416.4.5 Communication 1416.4.6 Sustainable and Renewable Energy Development 141Acknowledgment 142References 1427 ADAM ADAPTIVE OPTIMIZATION METHOD FOR NEURAL NETWORK MODELS REGRESSION IN IMAGE RECOGNITION TASKS 147Denis Y. Nartsev, Alexander N. Gneushev, and Ivan A. Matveev7.1 Introduction 1477.2 Problem Statement 1497.3 Modifications of the Adam Optimization Method for Training a Regression Model 1517.4 Computational Experiments 1557.4.1 Model for Evaluating the Eye Image Blurring Degree 1557.4.2 Facial Rotation Angle Estimation Model 1587.5 Conclusion 160Acknowledgments 161References 1618 APPLICATION OF INTEGER PROGRAMMING IN ALLOCATING ENERGY RESOURCES IN RURAL AFRICA 165Elias Munapo8.1 Introduction 1658.1.1 Applications of the QAP 1658.2 Quadratic Assignment Problem Formulation 1668.2.1 Koopmans–Beckmann Formulation 1668.3 Current Linearization Technique 1678.3.1 The General Quadratic Binary Problem 1678.3.2 Linearizing the Quadratic Binary Problem 1698.3.2.1 Variable Substitution 1698.3.2.2 Justification 1698.3.3 Number of Variables and Constraints in the Linearized Model 1708.3.4 Linearized Quadratic Binary Problem 1718.3.5 Reducing the Number of Extra Constraints in the Linear Model 1718.3.6 The General Binary Linear (BLP) Model 1718.3.6.1 Convex Quadratic Programming Model 1728.3.6.2 Transforming Binary Linear Programming (BLP) Into a Convex/Concave Quadratic Programming Problem 1728.3.6.3 Equivalence 1738.4 Algorithm 1748.4.1 Making the Model Linear 1758.5 Conclusions 176References 1769 FEASIBILITY OF DRONES AS THE NEXT STEP IN INNOVATIVE SOLUTION FOR EMERGING SOCIETY 179Sadia S. Ali, Rajbir Kaur, and Haidar Abbas9.1 Introduction 1799.1.1 Technology and Business 1819.1.2 Technological Revolution of the Twenty-first Century 1819.2 An Overview of Drone Technology and Its Future Prospects in Indian Market 1829.2.1 Utilities 1839.2.1.1 Delivery 1839.2.1.2 Media/Photography 1839.2.1.3 Agriculture 1849.2.1.4 Contingency and Disaster Management Scenarios 1849.2.1.5 Civil and Military Services: Search and Rescue, Surveillance,Weather, and Traffic Monitoring, Firefighting 1859.2.2 Complexities Involved 1859.2.3 Drones in Indian Business Scenario 1869.3 Literature Review 1879.3.1 Absorption and Diffusion of New Technology 1889.3.2 Leadership for Innovation 1889.3.3 Social and Economic Environment 1899.3.4 Customer Perceptions 1909.3.5 Alliances with Other National and International Organizations 1909.3.6 Other Influencers 1919.4 Methodology 1919.5 Discussion 1939.5.1 Market Module 1959.5.2 Technology Module 1969.5.3 Commercial Module 1989.6 Conclusions 199References 20010 DESIGNING A DISTRIBUTION NETWORK FOR A SODA COMPANY: FORMULATION AND EFFICIENT SOLUTION PROCEDURE 209Isidro Soria-Arguello, Rafael Torres-Esobar, and Pandian Vasant10.1 Introduction 20910.2 New Distribution System 21110.3 The Mathematical Model to Design the Distribution Network 21410.4 Solution Technique 21610.4.1 Lagrangian Relaxation 21610.4.2 Methods for Finding the Value of Lagrange Multipliers 21610.4.3 Selecting the Solution Method 21610.4.4 Used Notation 21710.4.5 Proposed Relaxations of the Distribution Model 21810.4.5.1 Relaxation 1 21810.4.5.2 Relaxation 2 21910.4.6 Selection of the Best Lagrangian Relaxation 21910.5 Heuristic Algorithm to Restore Feasibility 22010.6 Numerical Analysis 22210.6.1 Scenario 2020 22310.6.2 Scenario 2021 22410.6.3 Scenario 2022 22510.6.4 Scenario 2023 22610.7 Conclusions 228References 22811 MACHINE LEARNING AND MCDM APPROACH TO CHARACTERIZE STUDENT ATTRITION IN HIGHER EDUCATION 231Arrieta-M Luisa F and Lopez-I Fernando11.1 Introduction 23111.1.1 Background 23211.2 Proposed Approach 23311.3 Case Study 23411.3.1 Intelligent Phase 23411.3.2 Design Phase 23511.3.3 Choice Phase 23611.4 Results 23811.5 Conclusion 240References 24012 A CONCISE REVIEW ON RECENT OPTIMIZATION AND DEEP LEARNING APPLICATIONS IN BLOCKCHAIN TECHNOLOGY 243Timothy Ganesan, Irraivan Elamvazuthi, Pandian Vasant, and J. Joshua Thomas12.1 Background 24312.2 Computational Optimization Frameworks 24612.3 Internet of Things (IoT) Systems 24812.4 Smart Grids Data Systems 25012.5 Supply Chain Management 25212.6 Healthcare Data Management Systems 25512.7 Outlook 257References 25813 INVENTORY ROUTING PROBLEM WITH FUZZY DEMAND AND DELIVERIES WITH PRIORITY 267Paulina A. Avila-Torres and Nancy M. Arratia-Martinez13.1 Introduction 26713.2 Problem Description 27013.3 Mathematical Formulation 27313.4 Computational Experiments 27513.4.1 Numerical Example 27613.4.1.1 The Inventory Routing Problem Under Certainty 27913.4.1.2 The Inventory Routing Problem Under Uncertainty in the Consumption Rate of Product 27913.5 Conclusions and FutureWork 280References 28114 COMPARISON OF DEFUZZIFICATION METHODS FOR PROJECT SELECTION 283Nancy M. Arratia-Martinez, Paulina A. Avila-Torres, and Lopez-I Fernando14.1 Introduction 28314.2 Problem Description 28614.3 Mathematical Model 28614.3.1 Sets and Parameters 28714.3.2 Decision Variables 28714.3.3 Objective Functions 28714.4 Constraints 28814.5 Methods of Defuzzification and Solution Algorithm 28914.5.1 k-Preference Method 28914.5.2 Integral Value 29114.5.3 SAUGMECON Algorithm 29114.6 Results 29214.6.1 Results of k-Preference Method 29214.6.2 Results of Integral Value Method 29514.7 Conclusions 299References 30015 RE-IDENTIFICATION-BASED MODELS FOR MULTIPLE OBJECT TRACKING 303Alexey D. Grigorev, Alexander N. Gneushev, and Igor S. Litvinchev15.1 Introduction 30315.2 Multiple Object Tracking Problem 30515.3 Decomposition of Tracking into Filtering and Assignment Tasks 30615.4 Cost Matrix Adjustment in Assignment Problem Based on Re-Identification with Pre-Filtering of Descriptors by Quality 31015.5 Computational Experiments 31315.6 Conclusion 315Acknowledgments 315References 316Index 319
Cyber-Physical Systems
CYBER-PHYSICAL SYSTEMSAcknowledgement xix1 A SYSTEMATIC LITERATURE REVIEW ON CYBER SECURITY THREATS OF INDUSTRIAL INTERNET OF THINGS 1Ravi Gedam and Surendra Rahamatkar1.1 Introduction 21.2 Background of Industrial Internet of Things 31.3 Literature Review 61.4 The Proposed Methodology 131.5 Experimental Requirements 141.6 Conclusion 15References 162 INTEGRATION OF BIG DATA ANALYTICS INTO CYBER-PHYSICAL SYSTEMS 19Nandhini R.S. and Ramanathan L.2.1 Introduction 192.2 Big Data Model for Cyber-Physical System 212.2.1 Cyber-Physical System Architecture 222.2.2 Big Data Analytics Model 222.3 Big Data and Cyber-Physical System Integration 232.3.1 Big Data Analytics and Cyber-Physical System 232.3.1.1 Integration of CPS With BDA 242.3.1.2 Control and Management of Cyber-Physical System With Big Data Analytics 242.3.2 Issues and Challenges for Big Data-Enabled Cyber-Physical System 252.4 Storage and Communication of Big Data for Cyber-Physical System 262.4.1 Big Data Storage for Cyber-Physical System 272.4.2 Big Data Communication for Cyber-Physical System 282.5 Big Data Processing in Cyber-Physical System 292.5.1 Data Processing 292.5.1.1 Data Processing in the Cloud and Multi-Cloud Computing 292.5.1.2 Clustering in Big Data 312.5.1.3 Clustering in Cyber-Physical System 322.5.2 Big Data Analytics 322.6 Applications of Big Data for Cyber-Physical System 332.6.1 Manufacturing 332.6.2 Smart Grids and Smart Cities 342.6.3 Healthcare 352.6.4 Smart Transportation 352.7 Security and Privacy 362.8 Conclusion 37References 383 MACHINE LEARNING: A KEY TOWARDS SMART CYBER-PHYSICAL SYSTEMS 43Rashmi Kapoor, Chandragiri Radhacharan and Sung-ho Hur3.1 Introduction 443.2 Different Machine Learning Algorithms 463.2.1 Performance Measures for Machine Learning Algorithms 483.2.2 Steps to Implement ML Algorithms 493.2.3 Various Platforms Available for Implementation 503.2.4 Applications of Machine Learning in Electrical Engineering 503.3 ML Use-Case in MATLAB 513.4 ML Use-Case in Python 563.4.1 ML Model Deployment 593.5 Conclusion 60References 604 PRECISE RISK ASSESSMENT AND MANAGEMENT 63Ambika N.4.1 Introduction 644.2 Need for Security 654.2.1 Confidentiality 654.2.2 Integrity 664.2.3 Availability 664.2.4 Accountability 664.2.5 Auditing 674.3 Different Kinds of Attacks 674.3.1 Malware 674.3.2 Man-in-the Middle Assault 694.3.3 Brute Force Assault 694.3.4 Distributed Denial of Service 694.4 Literature Survey 704.5 Proposed Work 754.5.1 Objective 754.5.2 Notations Used in the Contribution 764.5.3 Methodology 764.5.4 Simulation and Analysis 784.6 Conclusion 80References 805 A DETAILED REVIEW ON SECURITY ISSUES IN LAYERED ARCHITECTURES AND DISTRIBUTED DENIAL SERVICE OF ATTACKS OVER IOT ENVIRONMENT 85Rajarajan Ganesarathinam, Muthukumaran Singaravelu and K.N. Padma Pooja5.1 Introduction 865.2 IoT Components, Layered Architectures, Security Threats 895.2.1 IoT Components 895.2.2 IoT Layered Architectures 905.2.2.1 3-Layer Architecture 915.2.2.2 4-Layer Architecture 915.2.2.3 5-Layer Architecture 935.2.3 Associated Threats in the Layers 935.2.3.1 Node Capture 935.2.3.2 Playback Attack 935.2.3.3 Fake Node Augmentation 935.2.3.4 Timing Attack 945.2.3.5 Bootstrap Attack 945.2.3.6 Jamming Attack 945.2.3.7 Kill Command Attack 945.2.3.8 Denial-of-Service (DoS) Attack 945.2.3.9 Storage Attack 945.2.3.10 Exploit Attack 955.2.3.11 Man-In-The-Middle (MITM) Attack 955.2.3.12 XSS Attack 955.2.3.13 Malicious Insider Attack 955.2.3.14 Malwares 955.2.3.15 Zero-Day Attack 955.3 Taxonomy of DDoS Attacks and Its Working Mechanism in IoT 975.3.1 Taxonomy of DDoS Attacks 995.3.1.1 Architectural Model 995.3.1.2 Exploited Vulnerability 1005.3.1.3 Protocol Level 1015.3.1.4 Degree of Automation 1015.3.1.5 Scanning Techniques 1015.3.1.6 Propagation Mechanism 1025.3.1.7 Impact Over the Victim 1025.3.1.8 Rate of Attack 1035.3.1.9 Persistence of Agents 1035.3.1.10 Validity of Source Address 1035.3.1.11 Type of Victim 1035.3.1.12 Attack Traffic Distribution 1035.3.2 Working Mechanism of DDoS Attack 1045.4 Existing Solution Mechanisms Against DDoS Over IoT 1055.4.1 Detection Techniques 1055.4.2 Prevention Mechanisms 1085.5 Challenges and Research Directions 1135.6 Conclusion 115References 1156 MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR PHISHING THREATS AND CHALLENGES 123Bhimavarapu Usharani6.1 Introduction 1246.2 Phishing Threats 1246.2.1 Internet Fraud 1246.2.1.1 Electronic-Mail Fraud 1256.2.1.2 Phishing Extortion 1266.2.1.3 Extortion Fraud 1276.2.1.4 Social Media Fraud 1276.2.1.5 Tourism Fraud 1286.2.1.6 Excise Fraud 1296.2.2 Phishing 1296.3 Deep Learning Architectures 1316.3.1 Convolution Neural Network (CNN) Models 1316.3.1.1 Recurrent Neural Network 1316.3.1.2 Long Short-Term Memory (LSTM) 1346.4 Related Work 1356.4.1 Machine Learning Approach 1356.4.2 Neural Network Approach 1366.4.3 Deep Learning Approach 1386.5 Analysis Report 1396.6 Current Challenges 1406.6.1 File-Less Malware 1406.6.2 Crypto Mining 1406.7 Conclusions 140References 1417 NOVEL DEFENDING AND PREVENTION TECHNIQUE FOR MAN-IN-THE-MIDDLE ATTACKS IN CYBER-PHYSICAL NETWORKS 147Gaurav Narula, Preeti Nagrath, Drishti Hans and Anand Nayyar7.1 Introduction 1487.2 Literature Review 1507.3 Classification of Attacks 1527.3.1 The Perception Layer Network Attacks 1527.3.2 Network Attacks on the Application Control Layer 1537.3.3 Data Transmission Layer Network Attacks 1537.3.3.1 Rogue Access Point 1547.3.3.2 ARP Spoofing 1557.3.3.3 DNS Spoofing 1577.3.3.4 mDNS Spoofing 1607.3.3.5 SSL Stripping 1617.4 Proposed Algorithm of Detection and Prevention 1627.4.1 ARP Spoofing 1627.4.2 Rogue Access Point and SSL Stripping 1687.4.3 DNS Spoofing 1697.5 Results and Discussion 1737.6 Conclusion and Future Scope 173References 1748 FOURTH ORDER INTERLEAVED BOOST CONVERTER WITH PID, TYPE II AND TYPE III CONTROLLERS FOR SMART GRID APPLICATIONS 179Saurav S. and Arnab Ghosh8.1 Introduction 1798.2 Modeling of Fourth Order Interleaved Boost Converter 1818.2.1 Introduction to the Topology 1818.2.2 Modeling of FIBC 1828.2.2.1 Mode 1 Operation (0 to d1 Ts) 1828.2.2.2 Mode 2 Operation (d1 Ts to d2 Ts) 1848.2.2.3 Mode 3 Operation (d2 Ts to d3 Ts) 1868.2.2.4 Mode 4 Operation (d3 Ts to Ts) 1888.2.3 Averaging of the Model 1908.2.4 Small Signal Analysis 1908.3 Controller Design for FIBC 1938.3.1 PID Controller 1938.3.2 Type II Controller 1948.3.3 Type III Controller 1958.4 Computational Results 1978.5 Conclusion 204References 2059 INDUSTRY 4.0 IN HEALTHCARE IOT FOR INVENTORY AND SUPPLY CHAIN MANAGEMENT 209Somya Goyal9.1 Introduction 2109.1.1 RFID and IoT for Smart Inventory Management 2109.2 Benefits and Barriers in Implementation of RFID 2129.2.1 Benefits 2139.2.1.1 Routine Automation 2139.2.1.2 Improvement in the Visibility of Assets and Quick Availability 2159.2.1.3 SCM-Business Benefits 2159.2.1.4 Automated Lost and Found 2169.2.1.5 Smart Investment on Inventory 2179.2.1.6 Automated Patient Tracking 2179.2.2 Barriers 2189.2.2.1 RFID May Interfere With Medical Activities 2189.2.2.2 Extra Maintenance for RFID Tags 2189.2.2.3 Expense Overhead 2189.2.2.4 Interoperability Issues 2189.2.2.5 Security Issues 2189.3 IoT-Based Inventory Management—Case Studies 2189.4 Proposed Model for RFID-Based Hospital Management 2209.5 Conclusion and Future Scope 225References 22610 A SYSTEMATIC STUDY OF SECURITY OF INDUSTRIAL IOT 229Ravi Gedam and Surendra Rahamatkar10.1 Introduction 23010.2 Overview of Industrial Internet of Things (Smart Manufacturing) 23110.2.1 Key Enablers in Industry 4.0 23310.2.2 OPC Unified Architecture (OPC UA) 23410.3 Industrial Reference Architecture 23610.3.1 Arrowgead 23710.3.2 FIWARE 23710.3.3 Industrial Internet Reference Architecture (IIRA) 23810.3.4 Kaa IoT Platform 23810.3.5 Open Connectivity Foundation (OCF) 23910.3.6 Reference Architecture Model Industrie 4.0 (RAMI 4.0) 23910.3.7 ThingsBoard 24010.3.8 ThingSpeak 24010.3.9 ThingWorx 24010.4 FIWARE Generic Enabler (FIWARE GE) 24110.4.1 Core Context Management GE 24110.4.2 NGSI Context Data Model 24210.4.3 IDAS IoT Agents 24410.4.3.1 IoT Agent-JSON 24610.4.3.2 IoT Agent-OPC UA 24710.4.3.3 Context Provider 24710.4.4 FIWARE for Smart Industry 24810.5 Discussion 24910.5.1 Solutions Adopting FIWARE 25010.5.2 IoT Interoperability Testing 25110.6 Conclusion 252References 25311 INVESTIGATION OF HOLISTIC APPROACHES FOR PRIVACY AWARE DESIGN OF CYBER-PHYSICAL SYSTEMS 257Manas Kumar Yogi, A.S.N. Chakravarthy and Jyotir Moy Chatterjee11.1 Introduction 25811.2 Popular Privacy Design Recommendations 25811.2.1 Dynamic Authorization 25811.2.2 End to End Security 25911.2.3 Enrollment and Authentication APIs 25911.2.4 Distributed Authorization 26011.2.5 Decentralization Authentication 26111.2.6 Interoperable Privacy Profiles 26111.3 Current Privacy Challenges in CPS 26211.4 Privacy Aware Design for CPS 26311.5 Limitations 26511.6 Converting Risks of Applying AI Into Advantages 26611.6.1 Proof of Recognition and De-Anonymization 26711.6.2 Segregation, Shamefulness, Mistakes 26711.6.3 Haziness and Bias of Profiling 26711.6.4 Abuse Arising From Information 26711.6.5 Tips for CPS Designers Including AI in the CPS Ecosystem 26811.7 Conclusion and Future Scope 269References 27012 EXPOSING SECURITY AND PRIVACY ISSUES ON CYBER-PHYSICAL SYSTEMS 273Keshav Kaushik12.1 Introduction to Cyber-Physical Systems (CPS) 27312.2 Cyber-Attacks and Security in CPS 27712.3 Privacy in CPS 28112.4 Conclusion & Future Trends in CPS Security 284References 28513 APPLICATIONS OF CYBER-PHYSICAL SYSTEMS 289Amandeep Kaur and Jyotir Moy Chatterjee13.1 Introduction 28913.2 Applications of Cyber-Physical Systems 29113.2.1 Healthcare 29113.2.1.1 Related Work 29313.2.2 Education 29513.2.2.1 Related Works 29513.2.3 Agriculture 29613.2.3.1 Related Work 29713.2.4 Energy Management 29813.2.4.1 Related Work 29913.2.5 Smart Transportation 30013.2.5.1 Related Work 30113.2.6 Smart Manufacturing 30113.2.6.1 Related Work 30313.2.7 Smart Buildings: Smart Cities and Smart Houses 30313.2.7.1 Related Work 30413.3 Conclusion 304References 305Index 311
SQL – kurz & gut (3. Auflage)
Wenn Sie SQL bei Ihrer täglichen Arbeit als Datenanalyst:in, Data Scientist oder Data Engineer verwenden, ist dieses beliebte Taschenbuch das ideale Nachschlagewerk für Sie. Beschrieben werden die wichtigsten Funktionen von SQL und deren Einsatz in Microsoft SQL Server, MySQL, Oracle Database, PostgreSQL und SQLite. Zahlreiche Beispiele verdeutlichen zudem die vielfältigen Möglichkeiten der Sprache.In dieser aktualisierten und deutlich erweiterten Ausgabe zeigt Alice Zhao, wie diese fünf Datenbankmanagementsysteme die SQL-Syntax für Abfragen und für Änderungen an einer Datenbank implementieren. Sie finden Näheres zu Datentypen und Konvertierungen, zur Syntax regulärer Ausdrücke, zu Fensterfunktionen, Pivoting und Unpivoting und vieles mehr.Schlagen Sie schnell nach, wie Sie bestimmte Aufgaben mit SQL ausführenNutzen Sie die Syntaxbeispiele des Buchs für Ihre eigenen AbfragenPassen Sie SQL-Abfragen so an, dass sie auf den fünf verbreitetsten Datenbankmanagementsystemen funktionierenNeu: Verbinden Sie Python und R mit einer relationalen DatenbankNeu: Erhalten Sie in dem Kapitel »Wie mache ich …?« Antworten auf häufig gestellte Fragen zu SQLZielgruppe:Data ScientistsDatenanalyst*innenalle, die mit Daten und SQL zu tun habenAutorin:Alice Zhao ist Data Scientist und liebt es, komplexe Dinge leicht verständlich zu erklären. Als Senior Data Scientist bei Metis und als Mitbegründerin von Best Fit Analytics hat sie zahlreiche Kurse zu SQL, Python und R gegeben. Ihre sehr gut bewerteten technischen Tutorials auf YouTube sind dafür bekannt, gleichermaßen praktisch, unterhaltsam und visuell ansprechend zu sein.In ihrem Blog „A Dash of Data“ schreibt sie über Analytics und Popkultur. Ihre Arbeit wurde bereits in der Huffington Post, Thrillist und Working Mother veröffentlicht. Sie hat auf einer Vielzahl von Konferenzen über Themen wie Natural Language Processing und Datenvisualisierung gesprochen und hat einen Master of Science in Analytics und einen Bachelor of Science in Elektrotechnik erworben, beide von der Northwestern University.
Datenanonymisierung im Kontext von Künstlicher Intelligenz und Big Data
Die fortschreitende Digitalisierung, die immer höhere Verfügbarkeit des Internets in Echtzeit sowie die progressive Entwicklung der IT ermöglichen es Unternehmen und Organisationen, Daten in einem nie zuvor dagewesenen Umfang zu erzeugen und zu verarbeiten, wodurch sie einen enormen Stellen- und Marktwert erhalten haben. Zudem kann mithilfe der künstlichen Intelligenz (KI) das in den Daten enthaltene Wissen extrahiert werden. Oft handelt es sich dabei um gesammelte Daten von Personen, mit denen Vorhersagen über verschiedene Aspekte der Personen getroffen werden können.Das Buch befasst sich mit der Anonymisierung im Kontext der KI und Big Data. Dazu werden die wesentlichen Grundlagen dargestellt sowie pseudonymisierte und anonymisierte Daten mit Personenbezug im Rahmen der Datenschutz-Grundverordnung (DSGVO) und des Bundesdatenschutzgesetzes (BDSG) beleuchtet. Es werden Möglichkeiten zur Pseudonymisierung, zu den jeweiligen Techniken und Verfahren der Anonymisierung sowie entsprechende Risikobetrachtungen behandelt. Abschließend wird die Vorgehensweise der Anonymisierung aus rechtlicher und technischer Sicht unter Anwendung entsprechender Software behandelt.DR. HEINZ-ADALBERT KREBS ist geschäftsführender Gesellschafter der Green Excellence GmbH, welche Unternehmen der Energiewirtschaft bei Softwareimplementierungen, Geschäftsprozessoptimierungen, der Informationssicherheit und des Datenschutzes berät. Daneben lehrt er am Fachbereich Wirtschaftsinformatik der Universität Kassel die Einführung von ERP-Systemen (SAP) und ist zertifizierter Datenschutzbeauftragter sowie ISO 27001 Lead Auditor.DR. PATRICIA HAGENWEILER ist langjährige Mitarbeiterin der Green Excellence GmbH und zertifizierte Datenschutzbeauftragte.Einleitung.- Künstliche Intelligenz.- Big Data und Analysemethoden.- Personenbezogene, pseudonymisierte und anonymisierte Daten.- Techniken der Pseudonymisierung.- Anonymisierung strukturierter Daten.- Anonymisierung unstrukturierter Daten.- Risiken der Nutzung anonymisierter Daten.- Verfahren zur Durchführung der Anonymisierung.- Software zur Unterstützung der Anonymisierung.- Fazit und Ausblick.- Literatur.