Allgemein
Practical Haskell
Get a practical, hands-on introduction to the Haskell language, its libraries and environment, and to the functional programming paradigm that is fast growing in importance in the software industry. This updated edition includes more modern treatment of Haskell's web framework and APIs.This book contains excellent coverage of the Haskell ecosystem and supporting tools, including Cabal and Stack for managing projects, HUnit and QuickCheck for software testing, WAI and Elm to develop the back end and front end of web applications, Persistent and Esqueleto for database access, and parallel and distributed programming libraries.You’ll see how functional programming is gathering momentum, allowing you to express yourself in a more concise way, reducing boilerplate, and increasing the safety of your code. Haskell is an elegant and noise-free pure functional language with a long history, having a huge number of library contributors and an active community.This makes Haskell the best tool for both learning and applying functional programming, and Practical Haskell, Third Edition takes advantage of this to show off the language and what it can do. Free source code available on the Apress GitHub page for this book.WHAT YOU WILL LEARN* Get started programming with Haskell* Examine the different parts of the language* Gain an overview of the most important libraries and tools in the Haskell ecosystem* Apply functional patterns in real-world scenarios* Understand monads and monad transformers* Proficiently use laziness and resource managementWHO THIS BOOK IS FORExperienced programmers who may be new to the Haskell programming language. However, some prior exposure to Haskell is recommended.ALEJANDRO SERRANO MENA has more than a decade of experience as a developer, trainer, and researcher in functional programming, with an emphasis on Haskell and related languages. He holds a Ph.D. from Utrecht University on the topic of error message customization in compilers. He's an active member of the community, maintaining a few open-source projects, writing books about Haskell, and collaborating on podcasts and conferences.PART I: FIRST STEPS1. Going Functional2. Declaring the Data Model3. Increasing Code Reuse4. Using Containers and Type Classes5. Laziness and Infinite StructuresPART II: DATA MINING6. Knowing Your Clients Using Monads7. More Monads: Now for Recommendations8. Working in Several CoresPART III: RESOURCE HANDLING9. Dealing with Files: IO and Conduit10. Building and Parsing Text11. Safe Database Access12. Web ApplicationsPART IV: DOMAIN SPECIFIC LANGUAGES13. Strong Types14. Interpreting Offers with AttributesPART V: ENGINEERING THE STORE15. Documenting, Testing, and Verifying16. Architecting Your Application17. Looking Further
Programming 101
Programming permeates almost all aspects of our lives. This includes being active on social media, shopping online, and participating in virtual courses. It also includes driving a car and using many devices. This book will teach you the basics of programming using the Processing programming language and provide practice with logical, algorithmic thinking. It can provide insight into what is involved in producing the technical infrastructure of our world. While reading this book, you can build programs based on your own ideas, using images you create or acquire and making connections to activities you enjoy.The chapters in the book will demonstrate the process of programming, starting with formulating an idea, planning, building on past projects, and refining the work, similar to writing an essay or composing a song. This approach will guide you to make use of logic and mathematics to produce beautiful effects. The text contains an Appendix with an introduction to p5.js, a way to produce JavaScript programs with Processing functionality for drawing, images, and interactions to publish on the Web.The term for program in Processing is sketch. The Processing language has been constructed by visual artists using the Java programming language as a base. However, the sketches featured in this book and typical in Processing are far more than static drawings; they incorporate interaction, animation, video, audio, and accessing files on the local computer and on the Web. Technical features are introduced and explained in the context of complete examples: games (Snake, Hangman, jigsaw, slingshot), making a collage of family images and video clips, preparing directions for folding an origami model, rotating objects in 3D, and others.Programming is a fun, creative, expressive pursuit. It does require attention to details and can be frustrating. Still, there is very little that compares to the satisfaction of building a program out of nothing and making it work (or taking an existing program and fixing a problem, or adding a feature and making it better). Programming 101 is your gateway to making this happen.WHAT YOU'LL LEARN* Gain basic programming skills* Build fun and creative programs* Use files for making a holiday card or a sequence of instructions* Combine videos, images, and graphics in a Processing sketch* Connections to other programming languages, most especially Java and JavaScriptWHO THIS BOOK IS FORAnyone who has been thinking about trying programming, or has tried, but needs assistance. Anyone who wants to use their own creativity and artistic talents. Anyone who wants to know what is involved in the technology that is present in our lives. Anyone who wants to know what is involved in the technology that is present in our lives.JEANINE MEYER is Professor Emerita at Purchase College/SUNY. Before Purchase, she taught at Pace University and worked at IBM Research and other parts of IBM and at other companies. She is the author of 5 books and co-author of 5 more on topics ranging from educational uses of multimedia, programming (two published by Apress, which have been updated for 2nd Editions), databases, number theory and origami. She earned a PhD in computer science at the Courant Institute at New York University, an MA in mathematics at Columbia, and a SB (the college used the Latin form) in mathematics from the University of Chicago. Recently, she has given lectures, in-person and remotely, connecting origami, mathematics, and computer science. She is a member of Phi Beta Kappa, Sigma Xi, Association of Women in Science, Association of Computing Machinery, and a featured reviewer for ACM Computing Reviews. Jeanine is trying but remains a beginner at Spanish and piano.1. Basics2. Interactions3. Animation Using Arrays and Parallel Structures4. Classes5. More Interactions6. Images, Graphics, and Building on Prior Work7. Using Files for Making a Holiday Card8. Combining Videos, Images, and Graphics9. Hangman10. 3DAppendix: Processing and JavaScript: p5.js
Agile Product and Project Management
Use this comprehensive Agile product and project management guide with real-world case studies and examples for self-learning or as a student textbook. Whether you are a CEO or a student, this book will take you from Agile delivery to team topology and product-market fit.Agile delivery is becoming a mainstream project management framework, increasing demand for an understanding of modern related concepts. Agile Product and Project Management covers IT delivery and project management basics while approaching IT as a customer-centric product delivery ecosystem.The book covers two major topics: building the RIGHT product and building the product RIGHT. Each chapter builds on the materials in the previous chapter. Terminology and exercises are introduced sequentially. The book takes you on a journey from identifying a product using Agile principles to delivering and iterating on this process, step-by-step. The final chapter provides practical advice on role-based interviews, career progression, professional certifications and affiliations, and communities of practice.YOU’LL LEARN* The Objectives and Key Results (OKR) framework, which explains why every project has to align with organizational objectives and how these objectives are used to measure project success* Agile (Scrum, Kanban, XP), Waterfall, and hybrid product and project management practices, and how to apply the "working backwards" framework from the customer to IT projects* The Lean Startup framework of product design, based on the "build-measure-learn" feedback loop, and compared with Waterfall requirements gathering and project scope management* Design Thinking and customer research practices* The product backlog taxonomy (epic, user story, subtask, bug, etc.), prioritization techniques, ongoing backlog maintenance, and stakeholder communication* Major aspects of IT delivery, including Agile teams, roles, frameworks, and success criteria* Waterfall planning and Scrum, in detail, including its Sprint structure, artifacts, roles, and ceremonies (meetings) as well as a comparison of Agile scaling frameworks* Case studies of modern technology leaders, from startups to FAANG* Examples of release plans and delivery reports based on actual projects in a wide range of companies, ways to minimize technical debt, implement DevOps, and establish quality management practices for software products* Effective ways of managing dependencies and delivering products that delight customers and made the Silicon Valley giants successful and allowed for rapid business growthWHO THIS BOOK IS FORGraduate students specializing in computer science, information systems, project management, and related management areas; practitioners seeking professional development; and project management professionals looking to grow their careers into Agile product and project managementDR. MARIYA BREYTER is an educator and a practitioner who brings 20 years of leadership experience to the Agile and Lean community. Her passion for managing complex business initiatives and delivering superior products to clients through efficient, Agile and Lean processes has produced success after success in companies ranging from Big 4 consulting and Fortune 100 insurance and financial services firms to mid-sized educational businesses and startups.Dr. Breyter has a PhD in Computational Linguistics from Moscow State University followed by a Post-Doctorate scholarship at Stanford University. She has built her career optimizing and improving software delivery and instilling Agile and Lean values at multitudes of companies while keeping the primary focus on the people within those processes. The list of her certifications includes CSP, SPC, CSM, PMP, PMI-ACP, ITIL 3.0, Agile Facilitation, and Agile Coaching from ACI.Dr. Breyter is an Agile project management thought leader and a frequent presenter at Agile conferences, from the Lean IT conference in Paris to the Agile Conference in San Diego, CA, and a popular blogger. Dr. Breyter’s free educational and coaching websites were nominated for multiple Agile and Lean professional awards.Introduction: The Role of Project and Product Management in ITThis chapter covers the history of project management as a profession and uses IT industry examples to show the need for incremental and iterative delivery, a collaborative work environment, innovation, and a customer-centric approach to product delivery. It incorporates an interactive review of the primary Agile delivery frameworks.CLASS ACTIVITY: COMPARE TRADITIONAL AND AGILE FRAMEWORKSCASE STUDY: FROM BLOCKBUSTER TO NETFLIXSIMULATION PROJECT: A BUSINESS IDEAQUESTIONS AND ANSWERS. TOPIC: IT DELIVERY VALUES AND PRINCIPLES.HOMEWORK ASSIGNMENT: PROJECT MANAGEMENT IN ITADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): AGILE MANIFESTO, PMBOK INTRODUCTION, AND SOFTWARE GUIDEPart I: Building the RIGHT IT ProductChapter 1. Starting your IT Project with WhyThis chapter covers the OKR (Objectives and Key Results) framework as a part of the project management. It outlines why every project has to align with organizational objectives and shows how these objectives are used to measure project success. In addition, it explains the difference between a project and a product and introduces the distinction between project and product management in all primary project management frameworks.CLASS ACTIVITY: REVIEW MISSION STATEMENTS AND OKRS FOR APPLE, AIRBNB, DISNEY, FACEBOOK, ALZHEIMER ASSOCIATIONCASE STUDY: MEASURE WHAT MATTERS (BONO CASE STUDY)SIMULATION PROJECT: CREATE YOUR OKRSTEMPLATES: OKR TEMPLATEQUESTIONS AND ANSWERS. TOPIC: GOOD AND NOT-SO-GOOD OKRS. OKRS FOR ITHOMEWORK ASSIGNMENT: OKR CRITIQUEADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): MEASURE WHAT MATTERS BY JOHN DOERRChapter 2. Getting to Know Your CustomerThis chapter covers Agile, Waterfall, and hybrid product management practices, applying the "working backwards" framework from the customer to IT projects. It builds on the knowledge of business objectives to focus on customer needs. It explains the concept of a persona type and provides tools and templates to identify the customer, empathize with the customer's problem, and define the product as "working backwards" from customer needs.CLASS ACTIVITY: CREATE A CUSTOMER PERSONA TYPECASE STUDY: KNOW YOUR CUSTOMERSIMULATION PROJECT: EMPATHY MAPTEMPLATES: PERSONA DEFINITIONQUESTIONS AND ANSWERS: PERSONA REVIEW AND TARGET MARKET ANALYSIS1 There may be further changes to content outlined in this document, including but not limited to case studies, examples, or subsections. The structure and the number of chapters will remain as stated.HOMEWORK ASSIGNMENT: DESCRIBE PERSONAS FOR FIVE WELL-KNOWN IT COMPANIESADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): LEAN UX BY JEFF GOTHELFChapter 3. Validating the Product Hypothesis in IT ProjectsThis chapter covers the Lean Startup framework of product design, based on the "build-measure-learn" feedback loop, and compares it with Waterfall requirements gathering and project scope management. Once the customer is identified, it is essential to validate whether our understanding of the customer's need is accurate. This chapter introduces the concepts of customer hypothesis, validation, minimum viable product (MVP), and the principles of making a decision to pivot or persevere. It describes the non-linear nature of lean startup validation, which is equally relevant for IT projects.CLASS ACTIVITY: DEFINE AN EXPERIMENT BASED ON AN IT PRODUCTCASE STUDY: AMAZON FIRE AND GOOGLE PLUSSIMULATION PROJECT: VALIDATE YOUR SOFTWARE PRODUCT HYPOTHESISTEMPLATES: VALIDATION CANVAS FOR SOFTWARE DELIVERYQUESTIONS AND ANSWERS: LEAN STARTUP PRINCIPLES AND BUILD-MEASURE-LEARN LOOPHOMEWORK ASSIGNMENT: USE VALIDATION CANVAS TO IDENTIFY MVPADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): LEAN STARTUP BY ERIC RIESChapter 4. Defining the IT ProductThis chapter covers Design Thinking and customer research practices. Once the customer is identified, and the customer need is validated, it is possible to define the product, its features, and sequence of delivery. This chapter introduces value proposition analysis, user story mapping, and other product definition techniques based on industry examples. It includes Google Ventures' Design Sprint concept and related case studies. Customer research practices cover the topics of stating the research goal, identifying research methods, recruiting respondents, conducting interviews, and aggregating results of the research.CLASS ACTIVITY: REVIEW A UNIVERSAL STUDIOS APP CANVASCASE STUDY: IDEO DESIGN THINKING OR GOOGLE VENTURES DESIGN SPRINTSIMULATION PROJECT: CONDUCT FIVE CUSTOMER INTERVIEWSTEMPLATES: PRODUCT CANVAS AND BUSINESS MODEL CANVASQUESTIONS AND ANSWERS: SOFTWARE PRODUCTS, PRODUCT PROPOSITION, VALUE STATEMENT, DESIGN SPRINTADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): BUSINESS MODEL CANVAS BY ALEX OSTERWALDER AND THE PRODUCT CANVAS BY ROMAN PICHLERChapter 5. Creating and Maintaining IT RequirementsThis chapter covers Waterfall scope management and Agile product backlog practices. It addresses high-level software requirements elicitation, process modeling, UML principles and artifacts (use cases, data models, all relevant diagrams), UI wireframing and UX design tools, and covers multiple approaches to managing and defining requirements in IT Once product features have been identified, the next step is to create a prioritized list of features and split them into smaller requirements, referred to as "user stories." This chapter describes the product backlog taxonomy (epic, user story, subtask, bug, etc.), prioritization techniques, ongoing maintenance, and stakeholder communication. Topics such as product backlog health, prioritization techniques, technical vs. functional requirements, and product backlog refinement are covered.CLASS ACTIVITY 1: REVIEW A WATERFALL IT PROGRAM PLAN AND ATLASSIAN'S PRODUCT BACKLOG FOR JIRA, AND IDENTIFY IMPROVEMENT OPPORTUNITIESCLASS ACTIVITY 2: MODEL PROCESSES, DEFINE USE CASES AND USE UML DIAGRAMMING TO CREATE AN AMAZON WEB STORE REQUIREMENTS.CASE STUDY: KINDLESIMULATION PROJECT: BUILD COMMUNICATIONS MANAGEMENT PLANTEMPLATES: USER STORY BACKLOG AND AN INVEST CHECKLIST, REQUIREMENTS MANAGEMENT DOCUMENTQUESTIONS AND ANSWERS: IT REQUIREMENTS QUALITY QUESTHOMEWORK ASSIGNMENT: IDENTIFY FIVE TECHNIQUES FOR REQUIREMENTS PRIORITIZATION BASED ON COST AND RISK MANAGEMENTADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): USER STORY MAPPING BY JEFF PATTONPart II: Building the IT Product RIGHTChapter 6. Waterfall, Agile, and Hybrid Delivery FrameworksThis chapter covers major aspects of IT delivery, including Agile teams, roles, frameworks, and success criteria. Once the IT MVP is created, it is essential to identify the delivery framework, whether it is Waterfall, Scrum, Kanban, Extreme Programming, or any other Agile framework. In this chapter, these frameworks will be described, compared, and discussed based on their fit to a company, its culture, products, and business objectives.CLASS ACTIVITY: COMPARE MAJOR IT DELIVERY FRAMEWORKSCASE STUDY: SPOTIFY DELIVERYSIMULATION PROJECT: SELECT A FRAMEWORK TO FOLLOW FOR YOUR PROJECTTEMPLATES: TEAM STRUCTURE, ROLE DEFINITIONSQUESTIONS AND ANSWERS: FRAMEWORKS COMPARISONHOMEWORK ASSIGNMENT: WHICH TEAM ROLE WOULD YOU PREFER AND WHY?ADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): PMBOKChapter 7. Estimation and Planning in ITThis chapter covers Waterfall planning and Scrum in detail, including its Sprint structure, artifacts, roles, and ceremonies (meetings). It discusses how the Sprint structure is used to estimate effort and plan delivery and compares the approach to Waterfall. It talks about short-term and long-term planning, story point estimation, and Definition of Ready and Definition of Done. The five levels of planning are discussed based on real examples. In addition, it introduces the concepts of integration and IT project management.CLASS ACTIVITY: "FRUIT SALAD" ESTIMATION EXERCISECASE STUDY: PMO TRANSFORMATIONSIMULATION PROJECT: ESTIMATE THE BACKLOG AND CREATE A ROADMAPTOOLS: TOOLS (OVERVIEW OF M.S. PROJECT, TRELLO, JIRA, AND RALLY)QUESTIONS AND ANSWERS: DEFINITION OF DONE AND DEFINITION OF READYHOMEWORK ASSIGNMENT: CREATE A FREE TRELLO ACCOUNT AND ENTER FIVE USER STORIES IN PROPER FORMAT AND WITH STORY POINT ESTIMATION, THEN COMPARE THIS PLAN WITH THE WORK BREAKDOWN STRUCTURE-BASED GANTT CHARTADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): ESTIMATION TECHNIQUES BY MIKE COHNChapter 8. Incremental Delivery and Continuous Improvement in IT Engineering Culture and Communications ManagementThis chapter covers delivery, reporting, and continuous improvement. First, it provides examples of release plans and delivery reports based on actual projects in a wide range of companies and discusses technical debt and quality management concepts for software products. Next, it discusses team empowerment, feedback loops, and retrospective techniques. In addition, it discusses the idea of a product lifecycle and how it affects incremental delivery. Finally, it introduces the topic of engineering culture based on team empowerment and product delivery.CLASS ACTIVITY: IDENTIFY SUCCESS METRICS VS. VANITY METRICSCASE STUDY: FIDELITY'S DEVOPS CASE STUDYSIMULATION PROJECT: IDENTIFY YOUR REPORTING FORMAT BY TARGET PERSONATEMPLATES: RELEASE MANAGEMENT TEMPLATEQUESTIONS AND ANSWERS: RELEASE PLANNING, METRICS, AND REPORTING, PRODUCT LIFECYCLE, CONTINUOUS IMPROVEMENTHOMEWORK ASSIGNMENT: IDENTIFY FIVE RETROSPECTIVE TECHNIQUESADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): AGILE GAMESChapter 9. Agile Implementation Beyond IT Budget management, Risk Management, and Capacity Management in AgileThis chapter covers the topic of delivery beyond software products. It demonstrates that project management covers all work areas, including marketing and finance, human capital management, the service industry, and beyond. In addition, it covers cultural aspects of project management and organizational change management, leadership, and influence without authority. Finally, it also addresses traditional management areas, such as budget management ("beyond budgeting" principles), risk management via impediment resolution, and capacity management.CLASS ACTIVITY: COMPARE THE TRADITIONAL ORGANIZATIONAL STRUCTURE AND AGILE STRUCTURECASE STUDY: SPOTIFY SQUAD MODELSIMULATION PROJECT: IDENTIFY BUDGET, RISK, AND CAPACITY MANAGEMENT MODEL FOR YOUR PROJECTTEMPLATES: BUDGET AND CAPACITY MANAGEMENT IN AGILE AND WATERFALLQUESTIONS AND ANSWERS: WHAT IS THE DIFFERENCE IN BUDGET MANAGEMENT IN MULTIPLE PROJECT MANAGEMENT FRAMEWORKS?HOMEWORK ASSIGNMENT: CREATE BUDGET MONITOR FOR THE SAME DELIVERABLE IN AGILE AND WATERFALL ENVIRONMENTADDITIONAL SOURCES (A LIST OF ONLINE RESOURCES AND RELEVANT MATERIALS TO REVIEW): ORGANIZATIONAL STRUCTURE BY HENRIK KNIBERGChapter 10. Scaling Agile DeliveryThis chapter covers project management at an organizational level, referred to as Scaled Delivery. It compares project and portfolio management with Scaled Agile approaches, including Scaled Agile Framework (SAFe), Large Scale Scrum (LeSS), and Scrum@Scale. Complexities of large-scale project delivery, including enterprise[1]level prioritization, project and product portfolio management, organizational alignment, and related organizational structures, are discussed and corporate culture and mindset. Finally, it covers the concept of organizational Agile transformation and successful patterns (pilots, change management models, communities of practice) and teamwork and innovation at the enterprise level.CLASS ACTIVITY: BUILD YOUR SCALED ORGANIZATION (GAME)CASE STUDY: TRANSFORMERSSIMULATION PROJECT: SCALE YOUR DELIVERY MODELTEMPLATES: P.I. (PRODUCT INCREMENT) PLANNINGQUESTIONS AND ANSWERS: COMPARISON BETWEEN AGILE SCALING MODELSHOMEWORK ASSIGNMENT: REVIEW ONE OF THE SCALED AGILE SUCCESS STORIES (METLIFE, BOSCH, PEPSICO, OR CISCO) AND SHARE THE FINDINGSADDITIONAL SOURCES: PMBOK, SCRUM@SCALE BY SCRUM ALLIANCE, SCALED AGILE FRAMEWORK BY SCALED AGILE ACADEMY, LARGE-SCALE SCRUM BY CRAIG LARMAN AND BAS VODDE, (SLIGER/BRODERICK) THE SOFTWARE PROJECT MANAGER'S BRIDGE TO AGILITYChapter 11 Conclusion: Project and Product Management Interview Techniques, Career Progression, and Continued LearningThe Conclusion covers pragmatic aspects of the project and product management profession. It describes what the possible roles are (Project Manager, Program Manager, Scrum Master, Agile Program Lead, Agile Coach, Product Owner, and Product Manager) and outlines standard career progression for each of those. It provides helpful tips on how to pass a job interview for each of these roles. In addition, it contains a comparison of professional certifications (PMP, PMI-ACP, CSM, CSP, KTP, SCP, and many others), exams, and knowledge areas with advice to undergraduate students and graduate students who already have job experience in adjacent fields. Finally, it provides tips on professional associations to join and communities of practice to follow.APPENDICESA: PRODUCT AND PROJECT CAREER PATHS FOR IT PROJECTSB: INTERVIEW TECHNIQUES (STAR TECHNIQUE AND AGILE STORYTELLING CHECKLIST)C: PROFESSIONAL CERTIFICATIONS IN AGILE PROJECT AND PRODUCT MANAGEMENTD: PROFESSIONAL ASSOCIATIONS AND COMMUNITIES OF PRACTICE GLOSSARYTextbook Features Final Project Presentation: Agile Product and Project Management While each chapter of this textbook can be read independently, the book is structured so that each chapter builds on the previous one, similar to moving from product envisioning to the end of the product lifecycle. In each class, every group of students iterates on their product idea. At the end of the course, student teams present their final project. Evaluation criteria and relevant templates are provided. This course structure makes it highly practical since students go through the whole journey of Agile product and project management in one course - from ideation to delivery. Quizzes The book contains Quizzes with answers, containing over 100 questions to be used for testing. Each quiz has ten multiple-choice questions and a question for a short essay.GlossaryThe book includes a Glossary. There is a lack of accepted definitions around Agile delivery in general, so the Glossary fills this gap by providing precise and non-ambiguous definitions. These definitions cover the Agile terminology used throughout the book. References and Reading Materials The book contains a detailed list of References to a carefully selected set of reputable sources on Agile delivery, both printed and online. In addition, reading materials are grouped by category. Agile Professional Organizations and Certifications The book contains Agile professional organizations, including Agile Alliance, Scrum Alliance, Scaled Agile Academy, and others. In addition, it provides a set of references to Agile certifications described in detail in the Conclusion. Sources for Agile Career Advice The textbook provides practical advice in Agile product- and project management career progression, interviewing techniques, sources for continued learning and contains a list of interview questions for Agile professionals with guidelines for answering them
Practical Ansible
Get ready to go from the basics of using Ansible to becoming proficient at implementing configuration management in your projects. This book begins with the basics of Ansible, providing you with details on how to install and configure your environment while working with different Ansible modules from the command line. Next, it introduces you to working with Ansible tasks and organizing configuration code into playbooks.You’ll then learn how to extend playbooks further, using roles and templates within the configuration code. Author Vincent Sesto then extends your knowledge further by covering custom Ansible modules using Python and Linux shell scripts and demonstrating how you can start to keep your secret values encrypted and secure using Ansible Vault. You’ll also develop Ansible roles with the use of Ansible Galaxy to reuse existing roles that others have created.This updated edition reflects changes added in the latest version of Ansible (2.9). It also includes an expanded chapter on testing Ansible using Molecule and managing large server environments using applications like Ansible Tower.WHAT WILL YOU LEARN* Understand what Ansible is and how to install and run your first basic command line commands* Expand your configuration management using Ansible playbooks, roles and templates* Customize your code further using Ansible Vault and third-party roles in Ansible Galaxy.* Work with Ansible in managing cloud infrastructure, specifically in Amazon Web Services* Troubleshoot your Ansible code and use frameworks like Molecule and Testinfra to help test your code changes* Manage large server environments using real-world examples and extend your configurations using an application like Ansible TowerWHO THIS BOOK IS FORSystems Engineers, Developers, DevOps Engineers and Software Administrators.VINCENT SESTO is a DevOps engineer, endurance athlete and author.As a DevOps engineer Vince specializes in Linux, Amazon Web Services, and open source applications. He is particularly interested in developing his skills in DevOps, continuous integration, security, log aggregation (management, UI, and reporting), and Python development. In his spare time, you’ll find him running or cycling for long periods of time, making the most of his time outdoors.Chapter 1: Configuration Management With AnsibleCHAPTER GOAL: INTRODUCE CONFIGURATION MANAGEMENT AND AUTOMATION, INTRODUCE ANSIBLENO OF PAGES: 20 - 25SUB -TOPICS* Introduce Ansible to readers and installation onto your system.* Start with the basic configurations of Ansible and basic command line options.* Introduce working with Ansible on the command line, and demonstrate the basics of Ansible Playbooks.* Introduce common Ansible Modules used in day to day configuration management.* Introduce the projects we are going to work with during this book. A LAMP Stack for the first part of the book and a Splunk Server towards the end of the book when deploying to AWS.Chapter 2: Ansible PlaybooksCHAPTER GOAL: EXPAND KNOWLEDGE OF ANSIBLE WITH AN IN DEPTH LOOK AT ANSIBLE PLAYBOOKSNO OF PAGES: 20 - 25SUB - TOPICS* Start with a brief discussion on YAML Syntax and how it relates to Ansible Playbooks.Demonstrate how we can join numerous modules together to create a larger project Ansible Playbooks. * Expand Playbook functionality with import, include, loops and variables.Start creating the Ansible Playbook for our first project of the book, the LAMP Stack.Chapter 3: Expanding Playbooks With Roles and TemplatesCHAPTER GOAL: EXPAND THE LAMP STACK PROJECT FURTHER EXPANDING THE READERS KNOWLEDGE ON THE USE OF ANSIBLE PLAYBOOKSNO OF PAGES: 20 - 25SUB - TOPICS:* Introduce Ansible Roles and create roles for the LAMP Stack including Web Server, Database and Django Server.* Provide a discussion on how to use command line options and variables with Ansible Playbooks.3. Configuring Playbooks using conditional tasks and tags to limit what changes are made by the Ansible Playbook.Chapter 4: Custom Ansible Modules, Vaults And Galaxay’sCHAPTER GOAL: PROVIDE AN INTRODUCTION TO USING ANSIBLE VAULT TO MANAGE THEIR SYSTEM SECRETS, AS WELL AS EXPLORING ANSIBLE GALAXY TO HELP THEM FAST TRACK THEIR PROJECT DEPLOYMENTS.NO OF PAGES: 20 - 25SUB - TOPICS:* Provide the reader with a demonstration on how to use Ansible Vault to help manage secrets within your Ansible Projects. * Provide an introduction to Ansible Galaxy getting the reader familiar with searching and using third party roles in Ansible Galaxy.* Demonstrate to the reader how they can start to create their own roles in Ansible Galaxy.Demonstrate to the user how with some basic Python skills, they can create their own Ansible Modules if they need to.Chapter 5: Working with Ansible In The CloudCHAPTER GOAL: THE GOAL OF THIS CHAPTER IS TO PROVIDE A BRIEF INTRODUCTION TO AMAZON WEB SERVICES AND GET THE READER TO IMPLEMENT ANSIBLE PLAYBOOKS THAT DEPLOY TO AWS.NO OF PAGES: 20 - 25SUB - TOPICS:* Introduce Amazon Web Services to the reader and provide a brief discussion on what it is and how you can create your own AWS account.* Discuss how to allow Ansible and your playbooks to interact with AWS and deploy services to the cloud.Introduce the reader to the modules you will be using to create your new services on AWS. * Create our new project playbook for our second project to deploy a Splunk Server to AWS.Chapter 6: AWS Templates and CloudFormation ScriptsCHAPTER GOAL: EXPAND THE READERS KNOWLEDGE FURTHER ON DEPLOYING TO THEIR PROJECTS TO AWS USING TEMPLATES, WORKING WITH ANSIBLE PULL AND USING GITHUB REPOSITORY DEPLOYMENTS.NO OF PAGES: 20 - 25SUB - TOPICS:* Working with Ansible templates in AWS Instances.Using Ansible Pull and GitHub repository deployments. * Provide a demonstration on how to build AWS Images in your projects to improve the speed of your deployments.* Combining CloudFormation and Ansible in our projects.Chapter 7: Ansible Troubleshooting And VariablesCHAPTER GOAL: FURTHER OUR WORK WITH SOME FINER POINTS ON HOW WE CAN REDUCE POTENTIAL ISSUES FROM ARISING IN OUR PLAYBOOKS AND ENSURE THINGS RUN AS SMOOTHLY AS POSSIBLE.NO OF PAGES: 20 - 25SUB - TOPICS:Start with a brief discussion on why modules in AWS work differently than modules in dedicated hardware. * Provide details on how to use the Debug Module in your Playbooks to provide clarity on your running playbooks.* Working with Ansible Facts.Using functions like Ansible Lint to help troubleshoot and ensure you are limiting the chance of your playbooks failing. * Provide a discussion on migrating from one configuration management system like Chef or Puppet to Ansible and how you might want to approach this.Provide details on how you can interact and connect to servers when SSH is not available.Chapter 8: Testing Ansible Playbooks With MoleculeCHAPTER GOAL: THIS CHAPTER WILL INTRODUCE SETTING UP A TEST FRAMEWORK FOR ANSIBLE PLAYBOOKS AND ROLES, AS WELL AS TESTING THE END STATE INFRASTRUCTURE OF WHAT OUR ANSIBLE ROLES ARE TRYING TO ACHIEVE.NO OF PAGES: 20 - 25SUB - TOPICS:1. Introduce Molecule as a way of testing your Ansible code.2. Provide the basic commands on how to set up your roles and playbooks using Molecule and how to begin testing.3. Discuss how you can set up a workflow to automatically test your code and infrastructure when changes are made.Chapter 9: Managing Larger Server EnvironmentsCHAPTER GOAL: INTRODUCE ANSIBLE TOWER TO THE READER AND DISCUSS HOW YOU CAN USE THE APPLICATION AS PART OF MANAGING LAGER SERVER ENVIRONMENTS. DUE TO LICENSING OF ANSIBLE THE CHAPTER WILL DEMONSTRATE HOW TO USE THE OPEN SOURCE VERSION CALLED AWX.NO OF PAGES: 20 - 25SUB - TOPICS:* Discuss how to set up your hosts files for larger server environments.* Introduce Ansible Tower to the reader and show some of the basic usage.* Show how to install Ansible Tower on your system.* Introduce basic usage of Tower and how to start implementing your configuration management using the application.
(ISC)2 CCSP Certified Cloud Security Professional Official Practice Tests
Efficient, effective review for the CCSP® exam (ISC)2®CCSP® Certified Cloud Security Professional Official Practice Tests, Third Edition is the ideal companion to (ISC)2 CCSP Certified Cloud Security Professional Official Study Guide, 3rd Edition. It is your hands-on guide that boosts your confidence and gives you the opportunity to test your level of preparedness for the Certified Cloud Security Professional (CCSP) exam. As the only Practice Questions book reviewed and endorsed by (ISC)2 it provides you with more than 800 practice questions (complete with answers and explanations) covering all the CCSP exam domains with all questions new or updated for the 2022 exam objectives. Organized in a logical sequence by domain, the tests help you review only the areas you need to reconsider without spending time on the topics you clearly understand! Coverage of 100% of all exam objectives in these practice tests means you'll be ready for: Cloud Concepts, Architecture and DesignCloud Data SecurityCloud Platform and Infrastructure SecurityCloud Application SecurityCloud Security OperationsLegal, Risk and Compliance Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/ go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: Interactive test bank Use the interactive online version of the book’s domain by domain questions and 2 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam. Over 800 questions total! ABOUT THE CCSP CERTIFICATION The CCSP is the premier cloud security certification from (ISC)2. This vendor-neutral certification validates IT and information security professionals’ knowledge and competency to apply best practices to cloud security architecture, design, operations and service orchestration. It shows you’re on the forefront of cloud security. (ISC)2 is a global nonprofit organization that maintains the Common Body of Knowledge for information security professionals. Candidates must have experience, adhere to the (ISC)2 Code of Ethics and maintain continuing education requirements or recertify every three years. Visit www.isc2.org to learn more. ABOUT THE AUTHORS The only official CCSP practice test product endorsed by (ISC)2 With over 850 practice questions all new for the 2022-2025 exam objectives, (ISC)2 CCSP Certified Cloud Security Professional Official Practice Tests, 3rd Edition gives you the opportunity to test your level of understanding and gauge your readiness for the Certified Cloud Security Professional (CCSP) exam long before the big day. These questions cover 100% of the CCSP exam domains and include answers with full explanations to help you understand the reasoning and approach for each. Logical organization by domain allows you to practice only the areas you need to bring you up to par, without wasting precious time on topics you’ve already mastered. As the only official practice test product for the CCSP exam endorsed by (ISC)2, this essential resource is your best bet for gaining a thorough understanding of the topic. It also illustrates the relative importance of each domain, helping you plan your remaining study time so you can go into the exam fully confident in your knowledge. When you’re ready, two practice exams allow you to simulate the exam day experience and apply your own test-taking strategies with domains given in proportion to the real thing. The online learning environment and practice exams are the perfect way to prepare and make your progress easy to track. For this new Third Edition, cloud security experts Mike Chapple and David Seidl have delivered an all-new question set for the new CCSP 2022-2025 objectives. These authors are well known for their best-selling (ISC)2 CISSP Certified Information Systems Security Professional Official Practice Tests and now they’ve joined forces again to deliver the same high caliber practice questions for the CCSP exam. Mike Chapple, PhD, CISSP, CCSP, is a bestselling author and Teaching Professor of Information Technology, Analytics, and Operations at Notre Dame's Mendoza College of Business. He is also the Academic Director of the University’s Master of Science in Business Analytics program and holds multiple technical certifications, including the SSCP, CIPP/US, CySA+, CISM, PenTest+, and Security+. David Seidl, CISSP, is Vice President for Information Technology and Chief Information Officer at Miami University. He holds multiple technical certifications including GPEN, GCIH, Pentest+ and CySA+ and has written books on security certification and cyberwarfare. Introduction xv Chapter 1 Domain 1: Cloud Concepts, Architecture, and Design 1 Chapter 2 Domain 2: Architecture and Design 23 Chapter 3 Domain 3: Cloud Platform and Infrastructure Security 45 Chapter 4 Domain 4: Cloud Application Security 65 Chapter 5 Domain 5: Cloud Security Operations 85 Chapter 6 Domain 6: Legal, Risk, and Compliance 105 Chapter 7 Practice Test 1 125 Chapter 8 Practice Test 2 151 Appendix Answers to Review Questions 175 Chapter 1: Domain 1: Cloud Concepts, Architecture, and Design 176 Chapter 2: Domain 2: Architecture and Design 188 Chapter 3: Domain 3: Cloud Platform and Infrastructure Security 198 Chapter 4: Domain 4: Cloud Application Security 213 Chapter 5: Domain 5: Cloud Security Operations 223 Chapter 6: Domain 6: Legal, Risk, and Compliance 232 Chapter 7: Practice Test 1 245 Chapter 8: Practice Test 2 259 Index 273
Deep Learning for Targeted Treatments
DEEP LEARNING FOR TREATMENTSTHE BOOK PROVIDES THE DIRECTION FOR FUTURE RESEARCH IN DEEP LEARNING IN TERMS OF ITS ROLE IN TARGETED TREATMENT, BIOLOGICAL SYSTEMS, SITE-SPECIFIC DRUG DELIVERY, RISK ASSESSMENT IN THERAPY, ETC.Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. AUDIENCEThe book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry. RISHABHA MALVIYA, PHD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. GHEORGHITA GHINEA, PHD, is a professor in Computing, Department of Computer Science Brunel University London. His research activities lie at the confluence of computer science, media, and psychology, and particularly interested in building semantically underpinned human-centered e-systems, particularly integrating human perceptual requirements. Has published more than 30+ articles and received 10+ research grants. RAJESH KUMAR DHANARAJ, PHD, is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed 20+ books on various technologies and 35+ articles and papers in various refereed journals and international conferences and contributed chapters to the books. His research interests include machine learning, cyber-physical systems, and wireless sensor networks. He is an Expert Advisory Panel Member of Texas Instruments Inc USA. BALAMURUGAN BALUSAMY, PHD, is a professor at Galgotias University. He has published 30+ books on various technologies as well as more than 150 journal articles, conferences, and book chapters. SONALI SUNDRAM completed B. Pharm & M. Pharm (pharmacology) from AKTU, Lucknow, and is working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has more than 8 patents to her credit. Preface xviiAcknowledgement xix1 DEEP LEARNING AND SITE-SPECIFIC DRUG DELIVERY: THE FUTURE AND INTELLIGENT DECISION SUPPORT FOR PHARMACEUTICAL MANUFACTURING SCIENCE 1Dhanalekshmi Unnikrishnan Meenakshi, Selvasudha Nandakumar, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan and Shah Alam Khan1.1 Introduction 21.2 Drug Discovery, Screening and Repurposing 51.3 DL and Pharmaceutical Formulation Strategy 111.3.1 DL in Dose and Formulation Prediction 111.3.2 DL in Dissolution and Release Studies 151.3.3 DL in the Manufacturing Process 161.4 Deep Learning Models for Nanoparticle-Based Drug Delivery 191.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory 201.4.2 Artificial Intelligence and Drug Delivery Algorithms 211.4.3 Nanoinformatics 221.5 Model Prediction for Site-Specific Drug Delivery 231.5.1 Prediction of Mode and a Site-Specific Action 231.5.2 Precision Medicine 261.6 Future Scope and Challenges 271.7 Conclusion 29References 302 ROLE OF DEEP LEARNING, BLOCKCHAIN AND INTERNET OF THINGS IN PATIENT CARE 39Akanksha Sharma, Rishabha Malviya and Sonali Sundram2.1 Introduction 402.2 IoT and WBAN in Healthcare Systems 422.2.1 IoT in Healthcare 422.2.2 WBAN 442.2.2.1 Key Features of Medical Networks in the Wireless Body Area 442.2.2.2 Data Transmission & Storage Health 452.2.2.3 Privacy and Security Concerns in Big Data 452.3 Blockchain Technology in Healthcare 462.3.1 Importance of Blockchain 462.3.2 Role of Blockchain in Healthcare 472.3.3 Benefits of Blockchain in Healthcare Applications 482.3.4 Elements of Blockchain 492.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling 512.3.6 Mobile Health and Remote Monitoring 532.3.7 Different Mobile Health Application with Description of Usage in Area of Application 542.3.8 Patient-Centered Blockchain Mode 552.3.9 Electronic Medical Record 572.3.9.1 The Most Significant Barriers to Adoption Are 602.3.9.2 Concern Regarding Negative Unintended Consequences of Technology 602.4 Deep Learning in Healthcare 622.4.1 Deep Learning Models 632.4.1.1 Recurrent Neural Networks (RNN) 632.4.1.2 Convolutional Neural Networks (CNN) 642.4.1.3 Deep Belief Network (DBN) 652.4.1.4 Contrasts Between Models 662.4.1.5 Use of Deep Learning in Healthcare 662.5 Conclusion 702.6 Acknowledgments 70References 703 DEEP LEARNING ON SITE-SPECIFIC DRUG DELIVERY SYSTEM 77Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder3.1 Introduction 783.2 Deep Learning 813.2.1 Types of Algorithms Used in Deep Learning 813.2.1.1 Convolutional Neural Networks (CNNs) 823.2.1.2 Long Short-Term Memory Networks (LSTMs) 833.2.1.3 Recurrent Neural Networks 833.2.1.4 Generative Adversarial Networks (GANs) 843.2.1.5 Radial Basis Function Networks 843.2.1.6 Multilayer Perceptron 853.2.1.7 Self-Organizing Maps 853.2.1.8 Deep Belief Networks 853.3 Machine Learning and Deep Learning Comparison 863.4 Applications of Deep Learning in Drug Delivery System 873.5 Conclusion 90References 904 DEEP LEARNING ADVANCEMENTS IN TARGET DELIVERY 101Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma4.1 Introduction: Deep Learning and Targeted Drug Delivery 1024.2 Different Models/Approaches of Deep Learning and Targeting Drug 1044.3 QSAR Model 1054.3.1 Model of Deep Long-Term Short-Term Memory 1054.3.2 RNN Model 1074.3.3 CNN Model 1084.4 Deep Learning Process Applications in Pharmaceutical 1094.5 Techniques for Predicting Pharmacotherapy 1094.6 Approach to Diagnosis 1104.7 Application 1134.7.1 Deep Learning in Drug Discovery 1144.7.2 Medical Imaging and Deep Learning Process 1154.7.3 Deep Learning in Diagnostic and Screening 1164.7.4 Clinical Trials Using Deep Learning Models 1164.7.5 Learning for Personalized Medicine 1174.8 Conclusion 121Acknowledgment 122References 1225 DEEP LEARNING AND PRECISION MEDICINE: LESSONS TO LEARN FOR THE PREEMINENT TREATMENT FOR MALIGNANT TUMORS 127Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi5.1 Introduction 1285.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis 1325.2.1 Gene Identification and Genome Data 1335.2.2 Image Diagnosis 1355.2.3 Radiomics, Radiogenomics, and Digital Biopsy 1375.2.4 Medical Image Analysis in Mammography 1385.2.5 Magnetic Resonance Imaging 1395.2.6 CT Imaging 1405.3 dl in Next-Generation Sequencing, Biomarkers, and Clinical Validation 1415.3.1 Next-Generation Sequencing 1415.3.2 Biomarkers and Clinical Validation 1425.4 dl and Translational Oncology 1445.4.1 Prediction 1445.4.2 Segmentation 1465.4.3 Knowledge Graphs and Cancer Drug Repurposing 1475.4.4 Automated Treatment Planning 1495.4.5 Clinical Benefits 1505.5 DL in Clinical Trials—A Necessary Paradigm Shift 1525.6 Challenges and Limitations 1555.7 Conclusion 157References 1576 PERSONALIZED THERAPY USING DEEP LEARNING ADVANCES 171Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas6.1 Introduction 1726.2 Deep Learning 1746.2.1 Convolutional Neural Networks 1756.2.2 Autoencoders 1806.2.3 Deep Belief Network (DBN) 1826.2.4 Deep Reinforcement Learning 1846.2.5 Generative Adversarial Network 1866.2.6 Long Short-Term Memory Networks 188References 1917 TELE-HEALTH MONITORING USING ARTIFICIAL INTELLIGENCE DEEP LEARNING FRAMEWORK 199Swati Verma, Rishabha Malviya, Md Aftab Alam and Bhuneshwar Dutta Tripathi7.1 Introduction 2007.2 Artificial Intelligence 2007.2.1 Types of Artificial Intelligence 2017.2.1.1 Machine Intelligence 2017.2.1.2 Types of Machine Intelligence 2037.2.2 Applications of Artificial Intelligence 2047.2.2.1 Role in Healthcare Diagnostics 2057.2.2.2 AI in Telehealth 2057.2.2.3 Role in Structural Health Monitoring 2057.2.2.4 Role in Remote Medicare Management 2067.2.2.5 Predictive Analysis Using Big Data 2077.2.2.6 AI’s Role in Virtual Monitoring of Patients 2087.2.2.7 Functions of Devices 2087.2.2.8 Clinical Outcomes Through Remote Patient Monitoring 2107.2.2.9 Clinical Decision Support 2117.2.3 Utilization of Artificial Intelligence in Telemedicine 2117.2.3.1 Artificial Intelligence–Assisted Telemedicine 2127.2.3.2 Telehealth and New Care Models 2137.2.3.3 Strategy of Telecare Domain 2147.2.3.4 Role of AI-Assisted Telemedicine in Various Domains 2167.3 AI-Enabled Telehealth: Social and Ethical Considerations 2187.4 Conclusion 219References 2208 DEEP LEARNING FRAMEWORK FOR CANCER DIAGNOSIS AND TREATMENT 229Shiv Bahadur and Prashant Kumar8.1 Deep Learning: An Emerging Field for Cancer Management 2308.2 Deep Learning Framework in Diagnosis and Treatment of Cancer 2328.3 Applications of Deep Learning in Cancer Diagnosis 2338.3.1 Medical Imaging Through Artificial Intelligence 2348.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning 2348.3.3 Digital Pathology Through Deep Learning 2358.3.4 Application of Artificial Intelligence in Surgery 2368.3.5 Histopathological Images Using Deep Learning 2378.3.6 MRI and Ultrasound Images Through Deep Learning 2378.4 Clinical Applications of Deep Learning in the Management of Cancer 2388.5 Ethical Considerations in Deep Learning–Based Robotic Therapy 2398.6 Conclusion 240Acknowledgments 240References 2419 APPLICATIONS OF DEEP LEARNING IN RADIATION THERAPY 247Akanksha Sharma, Ashish Verma, Rishabha Malviya and Shalini Yadav9.1 Introduction 2489.2 History of Radiotherapy 2509.3 Principal of Radiotherapy 2519.4 Deep Learning 2519.5 Radiation Therapy Techniques 2549.5.1 External Beam Radiation Therapy 2579.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) 2599.5.3 Intensity Modulated Radiation Therapy (IMRT) 2609.5.4 Image-Guided Radiation Therapy (IGRT) 2619.5.5 Intraoperative Radiation Therapy (IORT) 2639.5.6 Brachytherapy 2659.5.7 Stereotactic Radiosurgery (SRS) 2689.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist 2699.6.1 Deep Learning in Patient Assessment 2699.6.1.1 Radiotherapy Results Prediction 2699.6.1.2 Respiratory Signal Prediction 2719.6.2 Simulation Computed Tomography 2719.6.3 Targets and Organs-at-Risk Segmentation 2739.6.4 Treatment Planning 2749.6.4.1 Beam Angle Optimization 2749.6.4.2 Dose Prediction 2769.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists 2779.7 Conclusion 280References 28110 APPLICATION OF DEEP LEARNING IN RADIATION THERAPY 289Shilpa Rawat, Shilpa Singh, Md. Aftab Alam and Rishabha Malviya10.1 Introduction 29010.2 Radiotherapy 29110.3 Principle of Deep Learning and Machine Learning 29310.3.1 Deep Neural Networks (DNN) 29410.3.2 Convolutional Neural Network 29510.4 Role of AI and Deep Learning in Radiation Therapy 29510.5 Platforms for Deep Learning and Tools for Radiotherapy 29710.6 Radiation Therapy Implementation in Deep Learning 30010.6.1 Deep Learning and Imaging Techniques 30110.6.2 Image Segmentation 30110.6.3 Lesion Segmentation 30210.6.4 Computer-Aided Diagnosis 30210.6.5 Computer-Aided Detection 30310.6.6 Quality Assurance 30410.6.7 Treatment Planning 30510.6.8 Treatment Delivery 30510.6.9 Response to Treatment 30610.7 Prediction of Outcomes 30710.7.1 Toxicity 30910.7.2 Survival and the Ability to Respond 31010.8 Deep Learning in Conjunction With Radiomoic 31210.9 Planning for Treatment 31410.9.1 Optimization of Beam Angle 31510.9.2 Prediction of Dose 31510.10 Deep Learning’s Challenges and Future Potential 31610.11 Conclusion 317References 31811 DEEP LEARNING FRAMEWORK FOR CANCER 333Pratishtha11.1 Introduction 33411.2 Brief History of Deep Learning 33511.3 Types of Deep Learning Methods 33611.4 Applications of Deep Learning 33911.4.1 Toxicity Detection for Different Chemical Structures 33911.4.2 Mitosis Detection 34011.4.3 Radiology or Medical Imaging 34111.4.4 Hallucination 34211.4.5 Next-Generation Sequencing (NGS) 34211.4.6 Drug Discovery 34311.4.7 Sequence or Video Generation 34311.4.8 Other Applications 34311.5 Cancer 34311.5.1 Factors 34411.5.1.1 Heredity 34511.5.1.2 Ionizing Radiation 34511.5.1.3 Chemical Substances 34511.5.1.4 Dietary Factors 34511.5.1.5 Estrogen 34611.5.1.6 Viruses 34611.5.1.7 Stress 34711.5.1.8 Age 34711.5.2 Signs and Symptoms of Cancer 34711.5.3 Types of Cancer Treatment Available 34811.5.3.1 Surgery 34811.5.3.2 Radiation Therapy 34811.5.3.3 Chemotherapy 34811.5.3.4 Immunotherapy 34811.5.3.5 Targeted Therapy 34911.5.3.6 Hormone Therapy 34911.5.3.7 Stem Cell Transplant 34911.5.3.8 Precision Medicine 34911.5.4 Types of Cancer 34911.5.4.1 Carcinoma 34911.5.4.2 Sarcoma 34911.5.4.3 Leukemia 35011.5.4.4 Lymphoma and Myeloma 35011.5.4.5 Central Nervous System (CNS) Cancers 35011.5.5 The Development of Cancer (Pathogenesis) Cancer 35011.6 Role of Deep Learning in Various Types of Cancer 35011.6.1 Skin Cancer 35111.6.1.1 Common Symptoms of Melanoma 35111.6.1.2 Types of Skin Cancer 35211.6.1.3 Prevention 35311.6.1.4 Treatment 35311.6.2 Deep Learning in Skin Cancer 35411.6.3 Pancreatic Cancer 35411.6.3.1 Symptoms of Pancreatic Cancer 35511.6.3.2 Causes or Risk Factors of Pancreatic Cancer 35511.6.3.3 Treatments of Pancreatic Cancer 35511.6.4 Deep Learning in Pancreatic Cancer 35511.6.5 Tobacco-Driven Lung Cancer 35711.6.5.1 Symptoms of Lung Cancer 35711.6.5.2 Causes or Risk Factors of Lung Cancer 35811.6.5.3 Treatments Available for Lung Cancer 35811.6.5.4 Deep Learning in Lung Cancer 35811.6.6 Breast Cancer 35911.6.6.1 Symptoms of Breast Cancer 36011.6.6.2 Causes or Risk Factors of Breast Cancer 36011.6.6.3 Treatments Available for Breast Cancer 36111.6.7 Deep Learning in Breast Cancer 36111.6.8 Prostate Cancer 36211.6.9 Deep Learning in Prostate Cancer 36211.7 Future Aspects of Deep Learning in Cancer 36311.8 Conclusion 363References 36312 CARDIOVASCULAR DISEASE PREDICTION USING DEEP NEURAL NETWORK FOR OLDER PEOPLE 369Nagarjuna Telagam, B.Venkata Kranti and Nikhil Chandra Devarasetti12.1 Introduction 37012.2 Proposed System Model 37512.2.1 Decision Tree Algorithm 37512.2.1.1 Confusion Matrix 37612.3 Random Forest Algorithm 38112.4 Variable Importance for Random Forests 38312.5 The Proposed Method Using a Deep Learning Model 38412.5.1 Prevention of Overfitting 38612.5.2 Batch Normalization 38612.5.3 Dropout Technique 38612.6 Results and Discussions 38612.6.1 Linear Regression 38612.6.2 Decision Tree Classifier 38812.6.3 Voting Classifier 38912.6.4 Bagging Classifier 38912.6.5 Naïve Bayes 39012.6.6 Logistic Regression 39012.6.7 Extra Trees Classifier 39112.6.8 K-Nearest Neighbor [KNN] Algorithm 39112.6.9 Adaboost Classifier 39212.6.10 Light Gradient Boost Classifier 39312.6.11 Gradient Boosting Classifier 39312.6.12 Stochastic Gradient Descent Algorithm 39312.6.13 Linear Support Vector Classifier 39412.6.14 Support Vector Machines 39412.6.15 Gaussian Process Classification 39512.6.16 Random Forest Classifier 39512.7 Evaluation Metrics 39612.8 Conclusion 401References 40213 MACHINE LEARNING: THE CAPABILITIES AND EFFICIENCY OF COMPUTERS IN LIFE SCIENCES 407Shalini Yadav, Saurav Yadav, Shobhit Prakash Srivastava, Saurabh Kumar Gupta and Sudhanshu Mishra13.1 Introduction 40813.2 Supervised Learning 41013.2.1 Workflow of Supervised Learning 41013.2.2 Decision Tree 41013.2.3 Support Vector Machine (SVM) 41113.2.4 Naive Bayes 41313.3 Deep Learning: A New Era of Machine Learning 41413.4 Deep Learning in Artificial Intelligence (AI) 41613.5 Using ML to Enhance Preventive and Treatment Insights 41713.6 Different Additional Emergent Machine Learning Uses 41813.6.1 Education 41813.6.2 Pharmaceuticals 41913.6.3 Manufacturing 41913.7 Machine Learning 41913.7.1 Neuroscience Research Advancements 42013.7.2 Finding Patterns in Astronomical Data 42013.8 Ethical and Social Issues Raised ! ! ! 42113.8.1 Reliability and Safety 42113.8.2 Transparency and Accountability 42113.8.3 Data Privacy and Security 42113.8.4 Malicious Use of AI 42213.8.5 Effects on Healthcare Professionals 42213.9 Future of Machine Learning in Healthcare 42213.9.1 A Better Patient Journey 42213.9.2 New Ways to Deliver Care 42413.10 Challenges and Hesitations 42413.10.1 Not Overlord Assistant Intelligent 42413.10.2 Issues with Unlabeled Data 42513.11 Concluding Thoughts 425Acknowledgments 426References 426Index 431
Rechnerarchitektur für Dummies
Dieses Buch bietet eine kompakte, verständliche Einführung in das Thema "Rechnerarchitektur". Alle heute essenziellen Themengebiete werden behandelt. Der Schwerpunkt des Buches liegt auf der systemtechnischen funktionalen Beschreibung von Rechnern, ihren Komponenten und Prozessen, ohne auf die unzähligen Details dedizierter Systeme einzugehen. Die funktionale Darstellung mittels geeigneter Modellierungstechniken erlaubt das grundsätzliche Verständnis dieser Systeme, unabhängig von der jeweiligen Art der Realisierung und dem aktuellen Stand der Technologie. So müssen Sie keine Sorge haben, dass Sie sich bei der Prüfungsvorbereitung in den Details verlieren. Jürgen Neuschwander ist Professor für Informatik mit dem Schwerpunkt "Technik der Informations- und Kommunikationssysteme". Er lehrt an der Hochschule für Technik, Wirtschaft und Gestaltung in Konstanz.Über den Autor 7EINLEITUNG19Über dieses Buch 20Törichte Annahmen über den Leser 21Was Sie nicht lesen müssen 22Wie dieses Buch aufgebaut ist 22Teil I: Grundlegendes zu Rechnerarchitekturen 22Teil II: Das Kernelement: Der Prozessor 22Teil III: Das Konzept der Speicherhierarchie 23Teil IV: Vom Nutzen der Parallelverarbeitung 23Teil V: Der Top-Ten-Teil 23Symbole, die in diesem Buch verwendet werden 23Wie es weitergeht 24TEIL I: GRUNDLEGENDES ZU RECHNERARCHITEKTUREN25KAPITEL 1 BLICK AUS DER VOGELPERSPEKTIVE27Das Zeitalter der Computer 27Embedded Systems und Ubiquitous Computing 28Klassen von Rechnern 30Der Begriff »Rechnerarchitektur« 33Die Instruction Set Architecture (ISA) 34Die Mikro-Architekturebene 38Die Definition der Rechnerarchitektur aus meiner Sicht 38Höchstintegration und die Grenzen des Wachstums 39Steigerung der Anzahl der Transistoren auf einem Chip 39Der Performance-Gap 42Alternativen zur Steigerung der Taktrate 44Fortschritte beim automatisierten Entwurf solcher Chips 45Modellierungstechnik 47Aufbaupläne (Instanzennetze) 48Ablaufpläne (Petri-Netze) 51KAPITEL 2 LEISTUNGSBEWERTUNG VON RECHNERN55Überblick über Leistungsmaße 56MIPS und MFLOPS 57Die Prozessorausführungszeit 58Vereinfachung durch den CPI-Wert 61Benchmarkprogramme 63Das Gesetz von Amdahl 67TEIL II: DAS KERNELEMENT: DER PROZESSOR71KAPITEL 3 DIE VON NEUMANN-MASCHINE73Die Komponenten eines von Neumann-Rechners 76Der Prozessor 76Die Ein-/Ausgabeeinheit 77Der Hauptspeicher 78Der Systembus 79Charakteristika der von Neumann-Maschine 80Interpretation der Informationskomponenten 80Befehlszählerprinzip und die Abwicklung eines Programms 84Prozesse und Strukturelemente in einem Rechner 87Komponenten des Operationswerks 94Speicherorganisation 104Klassen von Prozessorarchitekturen 118Das Steuerwerk 124KAPITEL 4 PROGRAMMIERMODELL UND ASSEMBLERPROGRAMMIERUNG139Charakteristische Merkmale der Hochsprachen-Programmierung 140Charakteristische Merkmale der maschinennahen Programmierung 140Das Programmiermodell 141Assemblerprogrammierung 143Der Befehlssatz des Prozessors 144Adressierungsarten 150Assemblerprogrammierung am Beispiel 158Ablauf der Assemblierung 166Unterprogrammtechnik 179KAPITEL 5 KOMMUNIKATION UND AUSNAHMEVERARBEITUNG193Datenaustausch über den Systembus 193Zeitverhalten am Systembus 194Synchroner Bus 194Semi-synchroner Systembus 197Asynchroner Systembus 198Bussysteme in der heutigen Praxis 202Bus-Arbitration 203Lokale Bus-Arbitration 203Globale Bus-Arbitration 206Priorisierung mehrerer Master 207Zentrales Verfahren zur Busvergabe 207Dezentrales Verfahren zur Busvergabe 208Erweiterung des Prozessormodells 209Ausnahmeverarbeitung 210Einführung des Abwicklerverwalters 211Klassifizierung von Unterbrechungen und Ausnahmeverarbeitungen 213Priorisierung in einer Interruptebene 222Modellierung des Hardware-Dispatchers 228Ein-/Ausgabe-Interfaces und Synchronisation 234Ein-/ Ausgabe-Interfaces 234Informationelle Struktur eines Interface-Bausteins 235Synchronisation mit peripheren Instanzen 236Synchronisation durch Busy-Waiting 238Synchronisation durch Programmunterbrechung 241Synchronisation durch Handshake-Betrieb 241Direkter Speicherzugriff DMA (Direct Memory Access) 243Der DMA-Controller 246Aufbau eines DMA-Controllers 247Das Kommunikations-Interface PCI-Express 251TEIL III: DAS KONZEPT DER SPEICHERHIERARCHIE253KAPITEL 6 SPEICHERSYSTEME IM RECHNER255Der optimale Rechner 255Die Speicherhierarchie 257Inhomogenität und Organisation der Speicherhierarchie 259Lokalitätseigenschaften von Programmen 262Prinzipieller Aufbau von Halbleiter-Speicherbausteinen 263Festwertspeicher 264Schreib-/Lesespeicher 265Speicherzugriffe mittels Blockbuszyklen 271Verschränkung von Speicherbänken (Interleaving) 272Modularer Speicheraufbau 275Organisation des Hauptspeichers 276Praktische Ausprägung des Hauptspeicherzugriffs 278Eine Lösung durch spezielle Chipsätze 280Weitere Bausteintypen für Schreib-/Lesespeicher 283Sekundärspeicher 285Festplatten 286Redundant Array of Inexpensive Disks (RAID) 288Solid State Disks (SSD) 290Unternehmensweite Speichersysteme (NAS und SAN) 290Archivspeicher 292Optische Plattenspeicher 292Magnetbandspeicher 293KAPITEL 7 CACHESPEICHER295Das Problem der Zykluszeit 296Die Idee des Cachings 296Systemstrukturen für Caches 299Look-aside-Cache 299Look-through-Cache 300Zugriff auf den Cachespeicher 301Lesezugriffe 301Schreibzugriffe 303Die Idee des Assoziativspeichers 306Verdrängungsstrategie und Alterungsinformation 308Arbeitsweise des Cachespeichers 309Trefferrate und Zugriffszeiten 309Cache-Kohärenzproblem 310Kohärenzproblem bei einem Cache-Hit 312Kohärenzproblem bei Copy-back-Verfahren 312Lösung des Kohärenzproblems 313Strukturen von Cachespeichern 315Der voll-assoziative Cachespeicher 316Direkt zuordnender Cache (Direct-mapped Cache) 319Mehrwege-Satz-assoziativer Cache (n-way Set-associative Cache) 322Das MESI-Protokoll 325Cachespeicher-Hierarchie 331KAPITEL 8 VIRTUELLE SPEICHERVERWALTUNG337Die Idee des virtuellen Speichers 337Das Problem der Speicherzuweisung 339Die Memory Management Unit (MMU) 343Zusammenfassung: Das Prinzip der virtuellen Speicherverwaltung 344Segmentverwaltung 346Das Seitenverfahren 356Die zweistufige Adressumsetzung 365Virtuelle und reale Cache-Adressierung 368Virtuelle Cache-Adressierung 368Reale Cache-Adressierung 370TEIL IV: VOM NUTZEN DER PARALLELVERARBEITUNG373KAPITEL 9 DIE IDEE DER PARALLELISIERUNG375Der Einfluss der Parallelisierung auf die Rechnerarchitektur 376Charakteristika für eine Parallelisierung auf Hardwareebene 377Mikroskopische oder makroskopische Parallelität 378Symmetrische oder asymmetrische Strukturen 379Feinkörnige oder grobkörnige Parallelität 380Implizite oder explizite Parallelität 381Typen paralleler Architekturen – die Taxonomie von Flynn 381Single Instruction Single Data (SISD) 383Single Instruction Multiple Data (SIMD) 383Multiple Instruction Single Data (MISD) 384Multiple Instruction Multiple Data (MIMD) 384Performance von Multiprozessorsystemen und von Multicore-Prozessoren 386Die Harvard-Architektur 389CISC-Rechner und deren Probleme 389Die Idee der RISC-Maschinen 390Die Architektur eines RISC-Prozessors 391Erweiterung der ALU um einen Bypass 395Zusammenfassung 397Synchronisation von Prozessen 398Sequenzielle und parallele Prozesse 398Gegenseitiger Ausschluss von Prozessen 400Binäre Semaphore zur Synchronisation 402Der Test-and-Set- Befehl für Semaphorvariablen 404KAPITEL 10 FLIEẞBANDVERARBEITUNG409Die DLX-Pipeline 412Leistungssteigerung durch Pipelining 415Hardwarestruktur einer k-stufigen Pipeline 419Pipeline-Hemmnisse 422Datenabhängigkeiten 423Lösung der Datenkonflikte 429Strukturkonflikte 433Lösungen von Strukturkonflikten 434Steuerflusskonflikte 435KAPITEL 11 PARALLELE PIPELINES UND SUPERSKALARITÄT 441Parallelisierung der Programmabwicklung 441Pipelines mit mehreren Ausführungseinheiten 442VLIW-Prozessoren 446Superskalare Prozessoren 449Prinzipielle Architektur einer superskalaren Pipeline 450Sprungzielvorhersage 457Statische Sprungzielvorhersage 459Dynamische Sprungzielvorhersage 459Superskalare Pipeline – eine Verbesserung 464Core-Architektur 466Leistungssteigerung durch Multithreading 468Prozesse 468Threads 469Vorteile des Multithreading 470Simultaneous Multithreading bei superskalaren Pipelines 474Nachteile der auf Performance optimierten Prozessor-Hardware 475KAPITEL 12 VOM PROZESSOR ZU RECHNERSYSTEMEN479Cluster 480Supercomputer 481Grid Computing 482Virtuelle Maschinen 484Cloud Computing 486Liefermodelle 487Dienstleistungsmodelle 488KAPITEL 13 DIE ZUKÜNFTIGE ENTWICKLUNG491Taktfrequenzen und Miniaturisierung 491Ein neuer Hoffnungsträger – Graphen 493Nanotubes als Speichertechnologie 493Optische Prozessoren 494Architektur und Mikroarchitektur 495Processing-in-Memory 496Neuromorphe Hardware 497Memristor 499Quantencomputer 500Qubits und Quantengatter 500Superposition und Quantenparallelismus 501Quantenverschränkung 503Einsatzgebiete für Quantencomputer 505Ein kurzes Resümee 506TEIL V: DER TOP-TEN-TEIL 507KAPITEL 14 ZEHN KERNTHEMEN ZUR RECHNERARCHITEKTUR509Höchstintegration für Rechnerchips 509Die von Neumann-Architektur 510Die Harvard-Architektur und die RISC-Maschinen 511Die Instruction Set Architecture (ISA) 511Die Speicherhierarchie 512Cachespeicher 513Virtueller Speicher 514Fließbandverarbeitung 514Superskalare Prozessoren 516Quantencomputer 516KAPITEL 15 MÖGLICHE TRUGSCHLÜSSE519Assemblerprogrammierung ist immer schneller 519Die übertragenen Daten pro Zeiteinheit 520Lange Pipelines ergeben eine bessere Performance 520Höhere Cache-Kapazität ergibt bessere Performance 521Von Neumann-Architektur als Auslaufmodell 522Multi-Prozessorsysteme sind stets performanter als Mono-Prozessorsysteme 522Amdahls Gesetz bei parallelen Rechnern 523Flash-Speicher statt Hauptspeicher 523Multicore-Prozessoren in der Taxonomie von Flynn 524Hoher Parallelitätsgrad bei Anwendungssoftware 524KAPITEL 16 ZEHN SELBSTTESTS ZUM FACHGEBIET527Selbsttest zu Kapitel 1 527Selbsttest zu Kapitel 2 und 3 528Selbsttest zu Kapitel 4 529Selbsttest zu Kapitel 4 und 5 529Selbsttest zu Kapitel 3 und 6 530Selbsttest zu Kapitel 9 und 10 531Selbsttest zu Kapitel 10 und 11 531Selbsttest zu Kapitel 7 und 11 532Selbsttest zu Kapitel 8 533Selbsttest zu Kapitel 13 533Lösungen zu den Selbsttests 534Wichtige Literatur 535Abbildungsverzeichnis 537Stichwortverzeichnis 545
Battery-less NFC Sensors for the Internet of Things
The implementation of near-field communication (NFC) technology in smartphones has grown rapidly, especially due to the use of this technology as a payment system. In addition, the ability to use the energy transmitted not only for communication, but also for feeding other devices, which together with the low cost of NFC chips and the internet connectivity of the smartphones, allows the design of battery-less RF tags with sensing capabilities, whose information can be sent to the cloud. This is of great interest in the increasing amount of IoT (Internet of Things) scenarios.This book studies the feasibility of these sensors, analyzing the different parameters that have an influence on performance and in the range of operation. It also presents techniques to increase the range and analyzes the effects of certain materials when they are close to the antenna. The design and analysis of several sensors that can be powered and read by any NFC enabled device are presented in this work.MARTÍ BOADA is a postdoctoral researcher in the Department of Electronic, Electric and Automatic Engineering (URV), Tarragona, Spain, and has a PhD in telecommunication engineering.ANTONIO LAZARO is a full-time professor in the Department of Electronic, Electric and Automatic Engineering (URV), Tarragona, Spain, and has a PhD in telecommunication engineering.DAVID GIRBAU is a full-time professor in the Department of Electronic, Electric and Automatic Engineering (URV), Tarragona, Spain, and has a PhD in telecommunication engineering.RAMÓN VILLARINO is an assistant professor in the Department of Electronic, Electric and Automatic Engineering (URV), Tarragona, Spain, and has a PhD in telecommunication engineering.1. Wireless Power Transfer Applied to NFC2. Case Study 1: Soil Moisture Sensor3. Case Study 2: Smart Diaper4. Case Study 3: NFC Sensor for pH Monitoring5. Case Study 4: Fruit Ripeness Sensor
Hybrid Intelligent Approaches for Smart Energy
HYBRID INTELLIGENT APPROACHES FOR SMART ENERGYGREEN TECHNOLOGIES AND CLEANER ENERGY ARE TWO OF THE MOST IMPORTANT TOPICS FACING OUR WORLD TODAY, AND THE MARCH TOWARD EFFICIENT ENERGY SYSTEMS, SMART CITIES, AND OTHER GREEN TECHNOLOGIES, HAS BEEN, AND CONTINUES TO BE, A LONG AND INTRICATE ONE. BOOKS LIKE THIS ONE KEEP THE VETERAN ENGINEER AND STUDENT, ALIKE, UP TO DATE ON CURRENT TRENDS IN THE TECHNOLOGY AND OFFER A REFERENCE FOR THE INDUSTRY FOR ITS PRACTICAL APPLICATIONS.Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas. The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library. JOHN A, PHD, is an assistant professor at Galgotias University, Greater Noida, India, and he received his PhD in computer science and engineering from Manonmaniam Sundaranar University, Tirunelveli, India. He has presented papers in various national and international conferences and has published papers in scientific journals. SENTHIL KUMAR MOHAN, PHD, is an associate professor in the Department of Software and System Engineering at the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India. He received his PhD in engineering and technology from Vellore Institute of Technology, and he has contributed to many research articles in various technical journals and conferences. SANJEEVIKUMAR PADMANABAN, PHD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching. YASIR HAMID, PHD, is an assistant professor in the Department of Information Security Engineering Technology at Abu Dhabi Polytechnic. He earned his PhD in 2019 from Pondicherry University in Computer Science and Engineering. Before joining ADPOLY, he was an assistant professor in the Department of Computer Science, Islamic University of Science and Technology, India. He is an editorial board member on many scientific and technical journals. List of Contributors xiiiPreface xvAcknowledgements xix1 REVIEW AND ANALYSIS OF MACHINE LEARNING BASED TECHNIQUES FOR LOAD FORECASTING IN SMART GRID SYSTEM 1Shihabudheen KV and Sheik Mohammed S1.1 Introduction 21.2 Forecasting Methodology 41.3 AI-Based Prediction Methods 51.3.1 Single Prediction Methods 51.3.1.1 Linear Regression 51.3.1.2 Artificial Neural Networks (ANN) 71.3.1.3 Support Vector Regression (SVR) 81.3.1.4 Extreme Learning Machine 91.3.1.5 Neuro-Fuzzy Techniques 101.3.1.6 Deep Learning Techniques 111.3.2 Hybrid Prediction Methods 121.3.2.1 Combined AI-Based Prediction Techniques 121.3.2.2 Signal Decomposition Based Prediction Techniques 131.3.2.3 EMD Based Decomposition 141.3.2.4 Wavelet Based Decomposition 141.4 Results and Discussions 151.4.1 Description of Dataset 151.4.2 Performance Analysis of Single Prediction Methods for Load Forecasting 161.4.2.1 Feature Selection 161.4.2.2 Optimal Parameter Selection 171.4.2.3 Prediction Results of Single Prediction Methods 171.4.3 Performance Analysis of Hybrid Prediction Methods for Load Forecasting 171.4.4 Comparative Analysis 211.5 Conclusion 22References 232 ENERGY OPTIMIZED TECHNIQUES IN CLOUD AND FOG COMPUTING 27N.M. Balamurugan, TKS Rathish babu, K Maithili and M. Adimoolam2.1 Introduction 282.2 Fog Computing and Its Applications 332.3 Energy Optimization Techniques in Cloud Computing 382.4 Energy Optimization Techniques in Fog Computing 422.5 Summary and Conclusions 44References 453 ENERGY-EFFICIENT CLOUD COMPUTING TECHNIQUES FOR NEXT GENERATION: WAYS OF ESTABLISHING AND STRATEGIES FOR FUTURE DEVELOPMENTS 49Praveen Mishra, M. Sivaram, M. Arvindhan, A. Daniel and Raju Ranjan3.1 Introduction 503.2 A Layered Model of Cloud Computing 523.2.1 System of Architecture 533.3 Energy and Cloud Computing 543.3.1 Performance of Network 553.3.2 Reliability of Servers 553.3.3 Forward Challenges 553.3.4 Quality of Machinery 563.4 Saving Electricity Prices 563.4.1 Renewable Energy 573.4.2 Cloud Freedom 573.5 Energy-Efficient Cloud Usage 583.6 Energy-Aware Edge OS 583.7 Energy Efficient Edge Computing Based on Machine Learning 593.8 Energy Aware Computing Offloading 613.8.1 Energy Usage Calculation and Simulation 633.9 Comments and Directions for the Future 63References 644 ENERGY OPTIMIZATION USING SILICON DIOXIDE COMPOSITE AND ANALYSIS OF WIRE ELECTRICAL DISCHARGE MACHINING CHARACTERISTICS 67M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan4.1 Introduction 674.2 Materials and Methods 694.3 Results and Discussion 724.3.1 XRD Analysis 724.3.2 SEM Analysis 734.3.3 Grey Relational Analysis (GRA) 734.3.4 Main Effects Graph 764.3.5 Analysis of Variance (ANOVA) 774.3.6 Confirmatory Test 784.4 Conclusion 80Acknowledgement 80References 805 OPTIMAL PLANNING OF RENEWABLE DG AND RECONFIGURATION OF DISTRIBUTION NETWORK CONSIDERING MULTIPLE OBJECTIVES USING PSO TECHNIQUE FOR DIFFERENT SCENARIOS 83Balmukund Kumar and Aashish Kumar Bohre5.1 Introduction 845.2 Literature Review for Recent Development in DG Planning and Network Reconfiguration 845.3 System Performance Parameters and Index 875.4 Proposed Method 885.4.1 Formulation of Multi-Objective Fitness Function 885.4.2 Backward-Forward-Sweep Load Flow Based on BIBC-BCBV Method 895.5 PSO Based Optimization 905.6 Test Systems 925.7 Results and Discussions 925.8 Conclusions 101References 1026 INVESTIGATION OF ENERGY OPTIMIZATION FOR SPECTRUM SENSING IN DISTRIBUTED COOPERATIVE IOT NETWORK USING DEEP LEARNING TECHNIQUES 107M. Pavithra, R. Rajmohan, T. Ananth Kumar, S. Usharani and P. Manju Bala6.1 Introduction 1086.2 IoT Architecture 1116.3 Cognitive Spectrum Sensing for Distributed Shared Network 1136.4 Intelligent Distributed Sensing 1156.5 Heuristic Search Based Solutions 1176.6 Selecting IoT Nodes Using Framework 1186.7 Training With Reinforcement Learning 1196.8 Model Validation 1206.9 Performance Evaluations 1236.10 Conclusion and Future Work 125References 1267 ROAD NETWORK ENERGY OPTIMIZATION USING IOT AND DEEP LEARNING 129N. M. Balamurugan, N. Revathi and R. Gayathri7.1 Introduction 1297.2 Road Network 1327.2.1 Types of Road 1327.2.2 Road Structure Representation 1347.2.3 Intelligent Road Lighting System 1357.3 Road Anomaly Detection 1397.4 Role of IoT in Road Network Energy Optimization 1417.5 Deep Learning of Road Network Traffic 1427.6 Road Safety and Security 1427.7 Conclusion 144References 1448 ENERGY OPTIMIZATION IN SMART HOMES AND BUILDINGS 147S. Sathya, G. Karthi, A. Suresh Kumar and S. Prakash8.1 Introduction 1488.2 Study of Energy Management 1508.3 Energy Optimization in Smart Home 1508.3.1 Power Spent in Smart-Building 1538.3.2 Hurdles of Execution in Energy Optimization 1568.3.3 Barriers to Assure SH Technologies 1568.4 Scope and Study Methodology 1578.4.1 Power Cost of SH 1588.5 Conclusion 159References 1599 MACHINE LEARNING BASED APPROACH FOR ENERGY MANAGEMENT IN THE SMART CITY REVOLUTION 161Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny9.1 Introduction 1629.1.1 Smart City: What is the Need? 1629.1.2 Development of Smart City 1639.2 Need for Energy Optimization 1669.3 Methods for Energy Effectiveness in Smart City 1669.3.1 Smart Electricity Grids 1669.3.2 Smart Transportation and Smart Traffic Management 1699.3.3 Natural Ventilation Effect 1729.4 Role of Machine Learning in Smart City Energy Optimization 1739.4.1 Machine Learning: An Overview 1739.5 Machine Learning Applications in Smart City 1759.6 Conclusion 177References 17810 DESIGN OF AN ENERGY EFFICIENT IOT SYSTEM FOR POULTRY FARM MANAGEMENT 181G. Rajakumar, G. Gnana Jenifer, T. Ananth Kumar and T. S. Arun Samuel10.1 Introduction 18210.2 Literature Survey 18310.3 Proposed Methodology 18710.3.1 Monitoring and Control Module 18810.3.2 Monitoring Temperature 18810.3.3 Monitoring Humidity 18910.3.4 Monitoring Air Pollutants 18910.3.5 Artificial Lightning 19010.3.6 Monitoring Water Level 19010.4 Hardware Components 19010.4.1 Arduino UNO 19010.4.2 Temperature Sensor 19010.4.3 Humidity Sensor 19110.4.4 Gas Sensor 19210.4.5 Water Level Sensor 19210.4.6 LDR Sensor 19310.4.7 GSM (Global System for Mobile Communication) Modem 19410.5 Results and Discussion 19510.5.1 Hardware Module 19510.5.2 Monitoring Temperature 19610.5.3 Monitoring Gas Content 19810.5.4 Monitoring Humidity 19810.5.5 Artificial Lighting 19810.5.6 Monitoring Water Level 19810.5.7 Poultry Energy-Efficiency Tips 19910.6 Conclusion 201References 20311 IOT BASED ENERGY OPTIMIZATION IN SMART FARMING USING AI 205N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan, P. Kanimozhi and M. Pavithra11.1 Introduction 20611.2 IoT in Smart Farming 20811.2.1 Benefits of Using IoT in Agriculture 20811.2.2 The IoT-Based Smart Farming Cycle 20911.3 AI in Smart Farming 21011.3.1 Artificial Intelligence Revolutionises Agriculture 21011.4 Energy Optimization in Smart Farming 21111.4.1 Energy Optimization in Smart Farming Using IoT and AI 21211.5 Experimental Results 21511.5.1 Analysis of Network Throughput 21611.5.2 Analysis of Network Latency 21711.5.3 Analysis of Energy Consumption 21811.5.4 Applications of IoT and AI in Smart Farming 21911.6 Conclusion 220References 22112 SMART ENERGY MANAGEMENT TECHNIQUES IN INDUSTRIES 5.0 225S. Usharani, P. Manju Bala, T. Ananth Kumar, R. Rajmohan and M. Pavithra12.1 Introduction 22612.2 Related Work 22712.3 General Smart Grid Architecture 22912.3.1 Energy Sub-Sectors 23012.3.1.1 Smart Grid: State-of-the-Art Inside Energy Sector 23012.3.2 EV and Power-to-Gas: State-of-the-Art within Biomass and Transport 23112.3.3 Constructing Zero Net Energy (CZNE): State-of-the-Art Inside Field of Buildings 23312.3.4 Manufacturing Industry: State-of-the-Art 23412.3.5 Smart Energy Systems 23512.4 Smart Control of Power 23612.4.1 Smart Control Thermal System 23612.4.2 Smart Control Cross-Sector 23712.5 Subsector Solutions 23812.6 Smart Energy Management Challenges in Smart Factories 23912.7 Smart Energy Management Importance 24012.8 System Design 24112.9 Smart Energy Management for Smart Grids 24112.10 Experimental Results 24712.11 Conclusions 250References 25113 ENERGY OPTIMIZATION TECHNIQUES IN TELEMEDICINE USING SOFT COMPUTING 253R. Indrakumari13.1 Introduction 25313.2 Essential Features of Telemedicine 25513.3 Issues Related to Telemedicine Networks 25613.4 Telemedicine Contracts 25713.5 Energy Efficiency: Policy and Technology Issue 25813.5.1 Soft Computing 25813.5.2 Fuzzy Logic 26013.5.3 Artificial Intelligence 26013.5.4 Genetic Algorithms 26313.5.5 Expert System 26313.5.6 Expert System Based on Fuzzy Logic Rules 26413.6 Patient Condition Monitoring 26613.7 Analysis of Physiological Signals and Data Processing 27113.8 M-Health Monitoring System Architecture 27213.9 Conclusions 275References 27614 HEALTHCARE: ENERGY OPTIMIZATION TECHNIQUES USING IOT AND MACHINE LEARNING 279G. Vallathan, Senthilkumar Meyyappan and T. Rajani14.1 Introduction 28014.2 Energy Optimization Process 28114.3 Energy Optimization Techniques in Healthcare 28314.3.1 Energy Optimization in Building 28314.3.2 Machine Learning for Energy Optimization 28414.3.3 Reinforcement Learning for Energy Optimization 28614.3.4 Energy Optimization of Sustainable Internet of Things (IoT) 28714.4 Future Direction of Energy Optimizations 28814.5 Conclusion 289References 28915 CASE STUDY OF ENERGY OPTIMIZATION: ELECTRIC VEHICLE ENERGY CONSUMPTION MINIMIZATION USING GENETIC ALGORITHM 291Pedram Asef15.1 Introduction 29215.2 Vehicle Modelling to Optimisation 29515.2.1 Vehicle Mathematical Modelling 29515.2.2 Vehicle Model Optimisation Process: Applied Genetic Algorithm 29815.2.3 GA Optimisation Results and Discussion 30115.3 Conclusion 305References 305About the Editors 307Index 309
Practical Database Auditing for Microsoft SQL Server and Azure SQL
Know how to track changes and key events in your SQL Server databases in support of application troubleshooting, regulatory compliance, and governance. This book shows how to use key features in SQL Server ,such as SQL Server Audit and Extended Events, to track schema changes, permission changes, and changes to your data. You’ll even learn how to track queries run against specific tables in a database.Not all changes and events can be captured and tracked using SQL Server Audit and Extended Events, and the book goes beyond those features to also show what can be captured using common criteria compliance, change data capture, temporal tables, or querying the SQL Server log. You will learn how to audit just what you need to audit, and how to audit pretty much anything that happens on a SQL Server instance. This book will also help you set up cloud auditing with an emphasis on Azure SQL Database, Azure SQL Managed Instance, and AWS RDS SQL Server.You don’t need expensive, third-party auditing tools to make auditing work for you, and to demonstrate and provide value back to your business. This book will help you set up an auditing solution that works for you and your needs. It shows how to collect the audit data that you need, centralize that data for easy reporting, and generate audit reports using built-in SQL Server functionality for use by your own team, developers, and organization’s auditors.WHAT YOU WILL LEARN* Understand why auditing is important for troubleshooting, compliance, and governance* Track changes and key events using SQL Server Audit and Extended Events* Track SQL Server configuration changes for governance and troubleshooting* Utilize change data capture and temporal tables to track data changes in SQL Server tables* Centralize auditing data from all your databases for easy querying and reporting* Configure auditing on Azure SQL, Azure SQL Managed Instance, and AWS RDS SQL Server WHO THIS BOOK IS FORDatabase administrators who need to know what’s changing on their database servers, and those who are making the changes; database-savvy DevOps engineers and developers who are charged with troubleshooting processes and applications; developers and administrators who are responsible for generating reports in support of regulatory compliance reporting and auditingJOSEPHINE BUSH has more than 10 years of experience as a database administrator. Her experience is extensive and broad-based, including experience in financial, business, and energy data systems using SQL Server, MySQL, Oracle, and PostgreSQL. She is a Microsoft Certified Solutions Expert: Data Management and Analytics. She holds a BS in Information Technology, an MBA in IT Management, and an MS in Data Analytics. She is the author of Learn SQL Database Programming. You can reach her on Twitter @hellosqlkitty.IntroductionPART I. GETTING STARTED WITH AUDITINGChapter 1. Why Auditing is ImportantChapter 2. Types of AuditingPART II. IMPLEMENTING AUDITINGChapter 3. What is SQL Server Audit?Chapter 4. Implementing SQL Server Audit via the GUIChapter 5. Implementing SQL Server Audit via SQL ScriptsChapter 6: What is Extended Events?Chapter 7: Implementing Extended Events via the GUIChapter 8: Implementing Extended Events via SQL ScriptsChapter 9. Tracking SQL Server Configuration ChangesChapter 10. Additional SQL Server Auditing and Tracking MethodsPART III. CENTRALIZING AND REPORTING ON AUDITING DATAChapter 11. Centralizing Audit DataChapter 12. Create Reports from Audit DataPART IV. CLOUD AUDITING OPTIONSChapter 13. Auditing Azure SQL DatabasesChapter 14. Auditing Azure SQL Managed InstanceChapter 15. Other Cloud Provider Auditing OptionsPART V. APPENDIXESAppendix A. Database Auditing Options Comparison
The Art of Site Reliability Engineering (SRE) with Azure
Gain a foundational understanding of SRE and learn its basic concepts and architectural best practices for deploying Azure IaaS, PaaS, and microservices-based resilient architectures.The book starts with the base concepts of SRE operations and developer needs, followed by definitions and acronyms of Service Level Agreements in real-world scenarios. Moving forward, you will learn how to build resilient IaaS solutions, PaaS solutions, and microservices architecture in Azure. Here you will go through Azure reference architecture for high-available storage, networking and virtual machine computing, describing Availability Sets and Zones and Scale Sets as main scenarios. You will explore similar reference architectures for Platform Services such as App Services with Web Apps, and work with data solutions like Azure SQL and Azure Cosmos DB.Next, you will learn automation to enable SRE with Azure DevOps Pipelines and GitHub Actions. You’ll also gain an understanding of how an open culture around post-mortems dramatically helps in optimizing SRE and the overall company culture around managing and running IT systems and application workloads. You’ll be exposed to incent management and monitoring practices, by making use of Azure Monitor/Log Analytics/Grafana, which forms the foundation of monitoring Azure and Hybrid-running workloads.As an extra, the book covers two new testing solutions: Azure Chaos Studio and Azure Load Testing. These solutions will make it easier to test the resilience of your services.After reading this book, you will understand the underlying concepts of SRE and its implementation using Azure public cloud.WHAT WILL YOU LEARN:* Learn SRE definitions and metrics like SLI/SLO/SLA, Error Budget, toil, MTTR, MTTF, and MTBF* Understand Azure Well-Architected Framework (WAF) and Disaster Recovery scenarios on Azure* Understand resiliency and how to design resilient solutions in Azure for different architecture types and services* Master core DevOps concepts and the difference between SRE and tools like Azure DevOps and GitHub* Utilize Azure observability tools like Azure Monitor, Application Insights, KQL or Grafana* Understand Incident Response and Blameless Post-Mortems and how to improve collaboration using ChatOps practices with Microsoft toolsWHO IS THIS BOOK FOR:IT operations administrators, engineers, security team members, as well as developers or DevOps engineers.UNAI HUETE BELOKI is a Microsoft Technical Trainer (MTT) working at Microsoft, based in San Sebastian (Spain).From February 2017 to July 2020 he worked as a PFE (Premier Field Engineer), offering support and education as a DevOps Expert to Microsoft customers all around EMEA , mainly focused in the following technologies: GitHub, Azure DevOps, Azure Cloud Architecture and Monitoring, Azure AI/Cognitive Services.Since July 2020, he has worked as a Microsoft Technical Trainer (MTT) on the technologies mentioned above, and served as the MTT lead for the AZ-400 DevOps Solutions exam, helping shape content of the exam/course.In his free time, he loves traveling, water sports like surfing and spearfishing, and mountain-related activities such as MTB and snowboarding.CHAPTER 1: THE FOUNDATION OF SREThis chapter lays out the foundation of Site Resiliency Engineering, founded by Google. From the base concepts of how IT Operations and Developers need to collaborate, to how SRE helps organizations in running business-critical workloads without major downtimeCHAPTER 2: SERVICE LEVEL MANAGEMENT DEFINITIONS AND ACRONYMS AND THEIR MEANING IN A REAL-LIFE CONTEXTThis Chapter describes all common Service Level Agreements (SLA) definitions and acronyms, looked at from a real-world scenario to provide a clear understandingo Some examples, SLA, SLO, MTTF, MTBF, MTTR,…CHAPTER 3: ARCHITECTING RESILIENT INFRASTRUCTURE AS A SERVICE (IAAS) SOLUTIONS IN AZURESRE is all about providing ultimate uptime of your organization’s workloads, and this chapter will cover that in relation to Azure IaaS Compute solutions. Explaining the Azure reference architecture for high-available storage, networking and Virtual Machine computing, describing Availability Sets and Zones and ScaleSets as main scenarios. It will also touch on preparing for Disaster Recovery with Azure Backup and Azure Site Recovery, helping you to quickly mitigate outages in case of a failureCHAPTER 4: ARCHITECTING RESILIENT PLATFORM AS A SERVICE (PAAS) SOLUTIONS IN AZUREFollowing on the scenario of Virtual Machines, this chapter details similar reference architectures for Platform Services such as App Services with Web Apps, but also touching on data solutions like Azure SQL and Azure Cosmos DBCHAPTER 5: ARCHITECTING RESILIENT SERVERLESS AND MICROSERVICES ARCHITECTURES IN AZUREThis third chapter in the reference architecture topic describes how to build high-available, business-critical scenarios using Serverless Functions and Azure LogicApps, as well as Microservices scenarios using Azure Container Instance and Azure Kubernetes Service (AKS).CHAPTER 6: AUTOMATION TO ENABLE SRE WITH AZURE DEVOPS PIPELINES / GITHUB ACTIONSAutomation is the cornerstone to SRE, allowing businesses to not only deploy new workloads in a easy way, but also relying on SRE to avoid critical outages or, when an outage occurs, relying on automation to mitigate the problem as fast as possible. Sharing several examples from both Azure DevOps Pipelines and GitHub Actions, this chapter provides the reader a lot of real-life examples to reuse in their own environmentCHAPTER 7: EFFICIENTLY HANDLING BLAMELESS POST-MORTEMSPost-Mortems are the way to look back at what caused the outage, and describe any lessons learned for the future, helping in avoiding a similar outage in the future, or assist in quickly fixing an identical incident. Blameless is where the focus is on finding the root-cause of the problem, without pinpointing any individual or team as being the victim. This chapter describes how an open culture around post-mortems dramatically helps in optimizing SRE and the overall company culture around managing and running IT systems and application workloads.CHAPTER 8: MONITORING AS THE KEY TO KNOWLEDGEBesides the automated deployments, monitoring is the 2nd big technical topic in any SRE scenario. You can’t manage what you don’t know. This chapter provides an overview of Azure Monitor and Log Analytics, which forms the foundation of monitoring Azure and Hybrid-running workloads. Starting from metrics for the different Azure services touched on in earlier chapters, this chapter also covers how to export logs to 3rd party solutions such as Splunk or integrating dashboarding tools like Grafana
Enterprise-Grade IT Security for Small and Medium Businesses
Understand the IT security features that are needed to secure the IT infrastructure of a small to medium-size business. This book will give IT managers and executives a solid understanding of the different technology solutions that their business relies upon–or should be employing–in order to make reasoned decisions regarding the implementation of those features. Coverage includes multi-factor authentication, firewalls, zero-trust environments, network segmentation, remote access solutions, and the people aspects of security that are often overlooked and represent an organization’s biggest vulnerability.Chapters on the various technologies such as multi-factor authentication and zero-trust environments explain in plain English the values and benefits that each technology provides. Clear technical explanations are accompanied by business case explanations that explain the “why” of each technology and when each technology should be implemented. You will come away equipped to have business-driven discussions with your IT staff that allow for a productive balancing of the need for security with the need to do business and drive profits.YOU WILL LEARN* The importance of multi-factor authentication* The limits of what multi-factor authentication can protect* How firewalls are used to protect your company from attackers* What zero-trust environments are and what they mean* Whether zero-trust networks are what is needed to secure your own environment* The security benefits from implementing a network segmentation policy* The best ways to access files and resources from remote locations outside the officeWHO THIS BOOK IS FORManagers and executives at small to medium-size businesses who want to understand the core aspects of IT security on which their business relies, business leaders who want to be able to follow along with and engage in discussions with IT professionals about security features, and leaders who are tasked with making decisions on which IT security features to implementDENNY CHERRY has been working in the information technology field for ore than 20 years and has written almost a dozen IT books and hundreds of articles for various publications, including Inc., MSDN Magazine, and SQL Server Magazine, as well as having spoken at dozens of conferences around the world. He holds the Microsoft Certified Master certification, has been awarded the Microsoft Most Valuable Professional (MVP) award annually for more than 15 years, and has been awarded the VMware vExpert award six times. His public speaking started at local events in Southern California where he lives with his wife and their pet bunny rabbits and has expanded out to events worldwide. Speaking at conferences has taken Denny to six continents and over a dozen countries where he speaks at both small local events as well as large industry and corporate events. IntroductionPART I. THE INFRASTRUCTURE1. Why IT Security Matters2. Network Design3. Firewalls4. Distributed Denial of Service5. Remote ConnectivityPART II. THE COMPUTERS6. Operating System Security7. Multi-Factor Authentication8. Zero Trust EnvironmentsPART III. THE PEOPLE9. Protection Against the Weakest Security Link10. Employee Training
CASP+ CompTIA Advanced Security Practitioner Study Guide
PREPARE TO SUCCEED IN YOUR NEW CYBERSECURITY CAREER WITH THE CHALLENGING AND SOUGHT-AFTER CASP+ CREDENTIALIn the newly updated Fourth Edition of CASP+ CompTIA Advanced Security Practitioner Study Guide Exam CAS-004, risk management and compliance expert Jeff Parker walks you through critical security topics and hands-on labs designed to prepare you for the new CompTIA Advanced Security Professional exam and a career in cybersecurity implementation. Content and chapter structure of this Fourth edition was developed and restructured to represent the CAS-004 Exam Objectives.From operations and architecture concepts, techniques and requirements to risk analysis, mobile and small-form factor device security, secure cloud integration, and cryptography, you’ll learn the cybersecurity technical skills you’ll need to succeed on the new CAS-004 exam, impress interviewers during your job search, and excel in your new career in cybersecurity implementation.This comprehensive book offers:* Efficient preparation for a challenging and rewarding career in implementing specific solutions within cybersecurity policies and frameworks * A robust grounding in the technical skills you’ll need to impress during cybersecurity interviews * Content delivered through scenarios, a strong focus of the CAS-004 Exam * Access to an interactive online test bank and study tools, including bonus practice exam questions, electronic flashcards, and a searchable glossary of key terms Perfect for anyone preparing for the CASP+ (CAS-004) exam and a new career in cybersecurity, CASP+ CompTIA Advanced Security Practitioner Study Guide Exam CAS-004 is also an ideal resource for current IT professionals wanting to promote their cybersecurity skills or prepare for a career transition into enterprise cybersecurity.ABOUT THE AUTHORSNADEAN H. TANNER has been in the technology industry for over 20 years in a variety of positions from marketing to training to web development to hardware. She has worked in academia as an IT director and a postgraduate technology instructor. She has also been a trainer and consultant in advanced cybersecurity for Fortune 500 companies as well as the U.S. Department of Defense. Nadean is the author of CASP+ Practices Tests: Exam CAS-004 and Cybersecurity Blue Team Toolkit.JEFF T. PARKER, CISSP, COMPTIA PROJECT+, CYSA+, is a certified technical trainer and consultant specializing in governance, risk management and compliance. Jeff’s infosec roots began as a security engineer, a member of a HP consulting group in Boston, USA. Prior to becoming an author, Jeff was a Global IT Risk Manager residing for several years in Prague, Czech Republic, where he rolled out a new risk management strategy for a multinational logistics firm. Introduction xxvAssessment Test xxxvCHAPTER 1 RISK MANAGEMENT 1Risk Terminology 4The Risk Assessment Process 6Asset Identification 6Information Classification 8Risk Assessment 9Risk Assessment Options 14Implementing Controls 16Policies Used to Manage Employees 17Pre-Employment Policies 18Employment Policies 18End of Employment and Termination Procedures 20Cost-Benefit Analysis 21Continuous Monitoring 22Enterprise Security Architecture Frameworks and Governance 23Training and Awareness for Users 24Best Practices for Risk Assessments 25Business Continuity Planning and Disaster Recovery 27Reviewing the Effectiveness of Existing Security Controls 28Conducting Lessons Learned and After-Action Reviews 30Creation, Collection, and Analysis of Metrics 31Metrics 31Trend Data 32Analyzing Security Solutions to Ensure They Meet Business Needs 32Testing Plans 33Internal and External Audits 34Using Judgment to Solve Difficult Problems 35Summary 35Exam Essentials 36Review Questions 38CHAPTER 2 CONFIGURE AND IMPLEMENT ENDPOINT SECURITY CONTROLS 43Hardening Techniques 45Address Space Layout Randomization Use 47Hardware Security Module and Trusted Platform Module 48Trusted Operating Systems 52Compensating Controls 55Summary 57Exam Essentials 58Review Questions 59CHAPTER 3 SECURITY OPERATIONS SCENARIOS 63Threat Management 66Types of Intelligence 66Threat Hunting 67Threat Emulation 67Actor Types 67Intelligence Collection Methods 71Open-SourceIntelligence 71Human Intelligence and Social Engineering 73Frameworks 74MITRE Adversarial Tactics, Techniques and Common Knowledge 74ATT&CK for Industrial Control Systems 75Cyber Kill Chain 76Diamond Model of Intrusion Analysis 76Indicators of Compromise 77Reading the Logs 77Intrusion Detection and Prevention 78Notifications and Responses to IoCs 79Response 80Summary 85Exam Essentials 85Review Questions 86CHAPTER 4 SECURITY OPS: VULNERABILITY ASSESSMENTS AND OPERATIONAL RISK 91Terminology 97Vulnerability Management 98Security Content Automation Protocol 103Self-Assessment vs. Third-Party Vendor Assessment 105Patch Management 108Information Sources 110Tools 112Assessments 124Penetration Testing 129Assessment Types 131Vulnerabilities 134Buffer Overflow 134Integer Overflow 135Memory Leaks 136Race Conditions (TOC/TOU) 136Resource Exhaustion 137Data Remnants 138Use of Third-Party Libraries 138Code Reuse 138Cryptographic Vulnerabilities 138Broken Authentication 139Security Misconfiguration 140Inherently Vulnerable System/Application 140Client-Side Processing vs. Server-Side Processing 141Attacks 145Proactive Detection 153Incident Response 153Countermeasures 153Deceptive Technology 154USB Key Drops 155Simulation 155Security Data Analytics 155Application Control 156Allow and Block Lists 157Security Automation 157Physical Security 158Summary 159Exam Essentials 160Review Questions 161CHAPTER 5 COMPLIANCE AND VENDOR RISK 165Shared Responsibility in Cloud Computing 168Cloud Service/Infrastructure Models 169Cloud Computing Providers and Hosting Options 169Benefits of Cloud Computing 171Security of On-Demand/Elastic Cloud Computing 174Geographic Location 175Infrastructure 175Compute 175Storage 175Networking 176Managing and Mitigating Risk 182Security Concerns of Integrating Diverse Industries 185Regulations, Accreditations, and Standards 187PCI DSS 187GDPR 190ISO 192CMMI 193NIST 194COPPA 195CSA-STAR 196HIPAA, SOX, and GLBA 197Contract and Agreement Types 198Third-Party Attestation of Compliance 202Legal Considerations 203Summary 204Exam Essentials 205Review Questions 206CHAPTER 6 CRYPTOGRAPHY AND PKI 211The History of Cryptography 216Cryptographic Goals and Requirements 217Supporting Security Requirements 218Compliance and Policy Requirements 219Privacy and Confidentiality Requirements 219Integrity Requirements 220Nonrepudiation 220Risks with Data 221Data at Rest 221Data in Transit 222Data in Process/Data in Use 222Hashing 223Message Digest 225Secure Hash Algorithm 225Message Authentication Code 226Hashed Message Authentication Code 226RACE Integrity Primitives Evaluation Message Digest 226Poly1305 226Symmetric Algorithms 227Data Encryption Standard 230Triple DES 231Rijndael and the Advanced Encryption Standard 231ChaCha 232Salsa20 232International Data Encryption Algorithm 232Rivest Cipher Algorithms 233Counter Mode 233Asymmetric Encryption 233Diffie–Hellman 235RSA 236Elliptic Curve Cryptography 237ElGamal 238Hybrid Encryption and Electronic Data Exchange (EDI) 238Public Key Infrastructure Hierarchy 239Certificate Authority 240Registration Authority 241Digital Certificates 241Certificate Revocation List 243Certificate Types 243Certificate Distribution 244The Client’s Role in PKI 245Implementation of Cryptographic Solutions 247Application Layer Encryption 248Transport Layer Encryption 249Internet Layer Controls 250Additional Authentication Protocols 251Cryptocurrency 252Digital Signatures 252Recognizing Cryptographic Attacks 254Troubleshooting Cryptographic Implementations 256Summary 259Exam Essentials 259Review Questions 261CHAPTER 7 INCIDENT RESPONSE AND FORENSICS 265The Incident Response Framework 268Event Classifications 268Triage Events 269Pre-Escalation Tasks 270The Incident Response Process 270Response Playbooks and Processes 273Communication Plan and Stakeholder Management 274Forensic Concepts 277Principles, Standards, and Practices 278The Forensic Process 279Forensic Analysis Tools 283File Carving Tools 284Binary Analysis Tools 284Analysis Tools 286Imaging Tools 288Hashing Utilities 289Live Collection vs. Postmortem Tools 290Summary 294Exam Essentials 294Review Questions 295CHAPTER 8 SECURITY ARCHITECTURE 301Security Requirements and Objectives for a Secure Network Architecture 310Services 310Segmentation 334Deperimeterization/Zero Trust 344Merging Networks from Various Organizations 352Software-Defined Networking 357Organizational Requirements for Infrastructure Security Design 358Scalability 358Resiliency 359Automation 359Containerization 360Virtualization 361Content Delivery Network 361Integrating Applications Securely into an Enterprise Architecture 362Baseline and Templates 362Software Assurance 367Considerations of Integrating Enterprise Applications 370Integrating Security into the Development Life Cycle 373Data Security Techniques for Securing Enterprise Architecture 384Data Loss Prevention 384Data Loss Detection 387Data Classification, Labeling, and Tagging 388Obfuscation 390Anonymization 390Encrypted vs. Unencrypted 390Data Life Cycle 391Data Inventory and Mapping 391Data Integrity Management 391Data Storage, Backup, and Recovery 392Security Requirements and Objectives for Authentication and Authorization Controls 394Credential Management 394Password Policies 396Federation 398Access Control 399Protocols 401Multifactor Authentication 403One-Time Passwords 404Hardware Root of Trust 404Single Sign-On 405JavaScript Object Notation Web Token 405Attestation and Identity Proofing 406Summary 406Exam Essentials 407Review Questions 410CHAPTER 9 SECURE CLOUD AND VIRTUALIZATION 415Implement Secure Cloud and Virtualization Solutions 418Virtualization Strategies 419Deployment Models and Considerations 425Service Models 429Cloud Provider Limitations 433Extending Appropriate On-Premises Controls 433Storage Models 439How Cloud Technology Adoption Impacts Organization Security 445Automation and Orchestration 445Encryption Configuration 445Logs 446Monitoring Configurations 447Key Ownership and Location 448Key Life-Cycle Management 448Backup and Recovery Methods 449Infrastructure vs. Serverless Computing 450Software-Defined Networking 450Misconfigurations 451Collaboration Tools 451Bit Splitting 461Data Dispersion 461Summary 461Exam Essentials 462Review Questions 463CHAPTER 10 MOBILITY AND EMERGING TECHNOLOGIES 467Emerging Technologies and Their Impact on Enterprise Security and Privacy 471Artificial Intelligence 472Machine Learning 472Deep Learning 472Quantum Computing 473Blockchain 473Homomorphic Encryption 474Distributed Consensus 475Big Data 475Virtual/Augmented Reality 4753D Printing 476Passwordless Authentication 476Nano Technology 477Biometric Impersonation 477Secure Enterprise Mobility Configurations 478Managed Configurations 479Deployment Scenarios 486Mobile Device Security Considerations 487Security Considerations for Technologies, Protocols, and Sectors 495Embedded Technologies 495ICS/Supervisory Control and Data Acquisition 496Protocols 498Sectors 499Summary 500Exam Essentials 500Review Questions 501Appendix Answers to Review Questions 505Chapter 1: Risk Management 506Chapter 2: Configure and Implement Endpoint Security Controls 507Chapter 3: Security Operations Scenarios 509Chapter 4: Security Ops: Vulnerability Assessments and Operational Risk 511Chapter 5: Compliance and Vendor Risk 513Chapter 6: Cryptography and PKI 514Chapter 7: Incident Response and Forensics 516Chapter 8: Security Architecture 519Chapter 9: Secure Cloud and Virtualization 522Chapter 10: Mobility and Emerging Technologies 524Index 529
MCA Microsoft Certified Associate Azure Network Engineer Study Guide
PREPARE TO TAKE THE NEW EXAM AZ-700 WITH CONFIDENCE AND LAUNCH YOUR CAREER AS AN AZURE NETWORK ENGINEERNot only does MCA Microsoft Certified Associate Azure Network Engineer Study Guide: Exam AZ-700 help you prepare for your certification exam, it takes a deep dive into the role and responsibilities of an Azure Network Engineer, so you can learn what to expect in your new career. You’ll also have access to additional online study tools, including hundreds of bonus practice exam questions, electronic flashcards, and a searchable glossary of important terms. Prepare smarter with Sybex's superior interactive online learning environment and test bank.Exam AZ-700, Designing and Implementing Microsoft Azure Networking Solutions, measures your ability to design, implement, manage, secure, and monitor technical tasks such as hybrid networking; core networking infrastructure; routing; networks; and private access to Azure services. With this in-demand certification, you can qualify for jobs as an Azure Network Engineer, where you will work with solution architects, cloud administrators, security engineers, application developers, and DevOps engineers to deliver Azure solutions. This study guide covers 100% of the objectives and all key concepts, including:* Design, Implement, and Manage Hybrid Networking* Design and Implement Core Networking Infrastructure* Design and Implement Routing* Secure and Monitor Networks* Design and Implement Private Access to Azure ServicesIf you’re ready to become the go-to person for recommending, planning, and implementing Azure networking solutions, you’ll need certification with Exam AZ-700. This is your one-stop study guide to feel confident and prepared on test day. Trust the proven Sybex self-study approach to validate your skills and to help you achieve your career goals!ABOUT THE AUTHORSPUTHIYAVAN UDAYAKUMAR is an infrastructure architect with over 14 years of experience in modernizing and securing IT infrastructure, including the Cloud. He has been writing technical books for more than ten years on various infrastructure and security domains. He has designed, deployed, and secured IT infrastructure out of on-premises and Cloud, including virtual servers, networks, storage, and desktops for various industries, including pharmaceutical, banking, healthcare, aviation, federal entities, etc. He is an open group certified Master certified architect. KATHIRAVAN UDAYAKUMAR is Head of Delivery and Chief Architect for Oracle Digital Technologies (Europe Practice) at Cognizant, covering various elements of technology stack in on-prem and cloud. He has over 18 years of experience in architecture, design, implementation, administration and integration with Green-field IT Systems, ERP, Cloud Platforms and Solutions across various business domains and Industries. He has had a passion for networking since he was an undergraduate and becoming a Cisco Certified Network Associate (CCNA). Introduction xxvAssessment Test xxxviiCHAPTER 1 GETTING STARTED WITH AZ-700 CERTIFICATION FOR AZURE NETWORKING 1Basics of Cloud Computing and Networking 2The Need for Networking Infrastructure 3The Need for the Cloud 3Basics of Networking 6Enterprise Cloud Networking 10Microsoft Azure Overview 11Azure Cloud Foundation 12Azure Global Infrastructure 14Azure Networking Terminology 20Azure Networking Overview 21Azure Networking Services 23Azure Virtual Network 26VNet Concepts and Best Practices 28Deploying a Virtual Network with Azure PowerShell 35Configure Public IP Services 37Basic SKUs 38Standard SKUs 39Configure a Basic SKU Public IP 40Configure a Standard SKU Public IP with Zones 40Configuring Domain Name Services 40Configure an Azure DNS Zone and Record Using Azure PowerShell 42Configuring Cross-Virtual Network Connectivity with Peering 43Configuring Peering between Two Virtual Networks in the Same Region 45Configuring Virtual Network Traffic Routing 46Using Forced Tunneling to Secure the VNet Route 52Configuring Internet Access with Azure Virtual NAT 53Deploy the NAT Gateway Using Azure PowerShell 54Summary 56Exam Essentials 56Hands-On Lab: Design and Deploy a Virtual Network via the Azure Portal 57Activity 1: Prepare the Network Schema 58Activity 2: Build the Aviation Resource Group 60Activity 3a: Build the CoreInfraVnet Virtual Network and Subnets 60Activity 3b: Build the EngineeringVnet Virtual Network and Subnets 64Activity 3c: Build the BranchofficeVnet Virtual Network and Subnets 66Activity 4: Validate the Build of VNets and Subnets 68Review Questions 70CHAPTER 2 DESIGN, DEPLOY, AND MANAGE A SITE-TO-SITE VPN CONNECTION AND POINT-TO-SITE VPN CONNECTION 75Overview of Azure VPN Gateway 76Designing an Azure VPN Connection 79Design Pattern 1 86Design Pattern 2 87Design Pattern 3 88Choosing a Virtual Network Gateway SKU for Site-to-Site VPN 89Using Policy-Based VPNs vs. Route-Based VPNs 92Building and Configuring a Virtual Network Gateway 94Building and Configuring a Local Network Gateway 97Building and Configuring an IPsec/IKE Policy 101Configuration Workflow 104Diagnosing and Resolving VPN Gateway Connectivity Issues 109Choosing a VNet Gateway SKU for Point-to-Site VPNs 112Configuring RADIUS, Certificate-Based, and Azure AD Authentication 116Configuration Workflow for Native Azure Certification Authentication 117Configuration Workflow for Native Azure Active Directory 124Configuration Workflow for Windows Active Directory 127Diagnosing and Resolving Client-Side and Authentication Issues 133Summary 136Exam Essentials 136Review Questions 140CHAPTER 3 DESIGN, DEPLOY, AND MANAGE AZURE EXPRESSROUTE 145Getting Started with Azure ExpressRoute 146Key Use Case for ExpressRoute 151ExpressRoute Deployment Model 151Choosing Between the Network Service Provider and ExpressRoute Direct 153Designing and Deploying Azure Cross-Region Connectivity between Multiple ExpressRoute Locations 156Selecting ExpressRoute Circuit SKUs 156Estimating Price Based on ExpressRoute SKU 156Select a Peering Location 157Select the Proper ExpressRoute Circuit 157Select a Billing Model 159Select a High Availability Design 159Pick a Business Continuity and Disaster Recovery Design Pattern 162Choosing an Appropriate ExpressRoute SKU and Tier 169Designing and Deploying ExpressRoute Global Reach 171Deploying ExpressRoute Global Reach 173Use Case 1: Enabling Circuits in the Same Region 173Use Case 2: Enabling Circuits in Different Regions 174Designing and Deploying ExpressRoute FastPath 175Evaluate Private Peering Only, Microsoft Peering Only, or Both 176Setting Up Private Peering 178Setting Up Microsoft Peering 181Building and Configuring an ExpressRoute Gateway 182Connect a Virtual Network to an ExpressRoute Circuit 186Recommend a Route Advertisement Configuration 190Configure Encryption over ExpressRoute 191Deploy Bidirectional Forwarding Detection 192Diagnose and Resolve ExpressRoute Connection Issues 193Summary 196Exam Essentials 196Review Questions 199CHAPTER 4 DESIGN AND DEPLOY CORE NETWORKING INFRASTRUCTURE: PRIVATE IP AND DNS 203Designing Private IP Addressing for VNets 204Deploying a VNet 210Preparing Subnetting for Services 213Subnetting Design Considerations 214Example Case Study: Preparing Subnetting for Services 218Configuring Subnetting for Services 220Preparing and Configuring a Subnet Delegation 223Configure Subnet Delegation 225Planning and Configuring Subnetting for Azure Route Server 226Designing and Configuring Public DNS Zones 231Creating an Azure DNS Zone and Record Using PowerShell 233Designing and Configuring Private DNS Zones 235Creating a Private DNS Zone and Record Using PowerShell 238Designing Name Resolution Inside a VNet 240VMs and Role Instances 243Web Apps 243Linking a Private DNS Zone to a VNet 245Summary 248Exam Essentials 249Review Questions 251CHAPTER 5 DESIGN AND DEPLOY CORE NETWORKING INFRASTRUCTURE AND VIRTUAL WANS 255Overview of Virtual Network Peering, Service Chaining, and Gateway Transit 256Configure VPN Gateway Transit for Virtual Network Peering 258Design VPN Connectivity between VNets 263Deploy VNet Peering 266Deployment Model 1: Running in the Same Azure Subscription and Deployed Using Azure Resource Manager 267Deployment Model 2: Running in Different Subscriptions and Deploying Using Resource Manager 270Deployment Model 3: Running in the Same Subscription and Deploying One VNet Using Resource Manager and Another Using the Classic Model 273Deployment Model 4: Running in Different Subscriptions and Deploying One VNet Using Resource Manager and Another Using the Classic Model 275Design an Azure Virtual WAN Architecture 277Choosing SKUs and Services for Virtual WANs 289Connect a VNet Gateway to an Azure Virtual WAN and Build a Hub in a Virtual WAN 291Build a Virtual Network Appliance (NVA) in a Virtual Hub 299Set Up Virtual Hub Routing 304Build a Connection Unit 306Summary 309Exam Essentials 310Review Questions 312CHAPTER 6 DESIGN AND DEPLOY VNET ROUTING AND AZURE LOAD BALANCER 317Design and Deploy User-Defined Routes 318Basic Routing Concepts 318Azure Routes 321Associate a Route Table with a Subnet 328Set Up Forced Tunneling 329Diagnose and Resolve Routing Issues 334Design and Deploy Azure Route Server 336Route Server Design Pattern 1 338Route Server Design Pattern 2 339Choosing an Azure Load Balancer SKU 344Choosing Between Public and Internal Load Balancers 349Build and Configure an Azure Load Balancer (Including Cross-Region) 353Build and Configure Cross-Region Load Balancer Resources 361Deploy a Load Balancing Rule 366Build and Configure Inbound NAT Rules 370Build Explicit Outbound Rules for a Load Balancer 371Summary 374Exam Essentials 375Review Questions 377CHAPTER 7 DESIGN AND DEPLOY AZURE APPLICATION GATEWAY, AZURE FRONT DOOR, AND VIRTUAL NAT 381Azure Application Gateway Overview 383How Application Gateway Works 385Scaling Options for Application Gateway and WAF 389Overview of Application Gateway Deployment 390Front-End Setup 390Back-End Setup 390Health Probes Setup 391Configuring Listeners 393Redirection Overview 394Application Gateway Request Routing Rules 395Redirection Setting 397Application Gateway Rewrite Policies 397Features and Capabilities of Azure Front Door SKUs 409Health Probe Characteristics and Operation 411Secure Front Door with SSL 412Front Door for Web Applications with a High-Availability Design Pattern 413SSL Termination and End-to-End SSL Encryption 421Multisite Listeners 423Back-Ends, Back-End Pools, Back-End Host Headers, and Back-End Health Probes 424Routing and Routing Rules 426URL Redirection and URL Rewriting in Front Door Standard and Premium 427Design and Deploy Traffic Manager Profiles 429How Traffic Manager Works 430Traffic Manager Routing Methods 432Priority-Based Traffic Routing 433Weighted-Based Traffic Routing 433Performance-Based Traffic Routing 435Geographic-Based Traffic Routing 436Multivalue-Based Traffic Routing 437Subnet-Based Traffic Routing 437Building a Traffic Manager Profile 438Virtual Network NAT 442Using a Virtual Network NAT 443Allocate Public IP or Public IP Prefixes for a NAT Gateway 445Associate a Virtual Network NAT with a Subnet 447Summary 451Exam Essentials 451Review Questions 455CHAPTER 8 DESIGN, DEPLOY, AND MANAGE AZURE FIREWALL AND NETWORK SECURITY GROUPS 459Azure Firewall and Firewall Manager Features 460How Azure Firewall Manager Works 467How Azure Firewall and Firewall Manager Protect VNets 468Build and Configure an Azure Firewall Deployment 476Azure Firewall Policy 495Build and Configure a Secure Hub within an Azure Virtual WAN Hub 501Build and Configure a Secure Hub within an Azure Virtual WAN Hub Using Azure PowerShell 503Integrate an Azure Virtual WAN Hub with a Third-Party Network Virtual Appliance 507High-Level Use Case for Network Virtual Appliances 508Create and Attach a Network Security Group to a Resource 509Create an Application Security Group and Attach It to a NIC 519Create and Configure NSG Rules and Read Network Security Group Flow Logs 524Validate NSG Flow Rules 531Verify IP Flow 534Summary 536Exam Essentials 536Review Questions 539CHAPTER 9 DESIGN AND DEPLOY AZURE WEB APPLICATION FIREWALL AND MONITOR NETWORKS 543Azure Web Application Firewall Functions and Features 544WAF on Application Gateway 547WAF on Front Door 549WAF on Azure CDN from Microsoft 550Set Up Detection or Prevention Mode 551Azure Front Door WAF Policy Rule Sets 553Managed Rule Sets 555Custom Rule Sets 558WAF Policies 560Application Gateway WAF Policy Rule Sets 566Per-Site WAF Policy 568Per-URI Policy 568Managed Rules 568WAF Policies 572Custom Rules 573Deploy and Attach WAF Policies 580Set Up Network Health Alerts and Logging Using Azure Monitor 582Build and Configure Azure Network Watcher 591Build and Configure a Connection Monitor Instance 595Build, Configure, and Use Traffic Analytics 600Build and Configure NSG Flow Logs 604Enable and Set Up Diagnostic Logging 607Enabling Diagnostic Logging 608Summary 609Exam Essentials 609Review Questions 611CHAPTER 10 DESIGN AND DEPLOY PRIVATE ACCESS TO AZURE SERVICES 615Overview of Private Link Services and Private Endpoints 616Key Benefits of Private Link 618How Private Link Integrates into an Azure Virtual Network 619How Azure Private Endpoint Works 619Plan Private Endpoints 628Configure Access to Private Endpoints 632Azure Private Link RBAC Permissions 634Integrate Private Link with DNS and Private Link Services with On-Premises Clients 634Use Case 1: Workloads on Virtual Networks without a Custom DNS Server 635Use Case 2: Workloads That Use a DNS Forwarder On-Premises 637Use Case 3: Using a DNS Forwarder for Virtual Network Workloads and On-Premises Workloads 640Set Up Service Endpoints and Configure Service Endpoint Policies 642Overview of Service Tags and Access to Service Endpoints 646Configure Access to Service Endpoints 651Integrating App Services into Regional VNets 657Azure Regional VNet Integration 658How Azure Regional VNet Integration Works 659Subnet Requirements 660Access Management 661Route Management 661Application Route Management 662Configure Azure Kubernetes Service (AKS) for Regional VNet Integration 665Configure Clients to Access the App Service Environment 670Summary 673Exam Essentials 673Review Questions 675Appendix Answers to Review Questions 679Chapter 1: Getting Started with AZ-700 Certification for Azure Networking 680Chapter 2: Design, Deploy, and Manage a Site-to-Site VPN Connection and Point-to-Site VPN Connection 681Chapter 3: Design, Deploy, and Manage Azure ExpressRoute 683Chapter 4: Design and Deploy Core Networking Infrastructure: Private IP and DNS 685Chapter 5: Design and Deploy Core Networking Infrastructure and Virtual WANs 686Chapter 6: Design and Deploy VNet Routing and Azure Load Balancer 688Chapter 7: Design and Deploy Azure application gateway, Azure front door, and Virtual NAT 690Chapter 8: Design, Deploy, and Manage Azure Firewall and Network Security Groups 691Chapter 9: Design and Deploy Azure Web Application Firewall and Monitor Networks 693Chapter 10: Design and Deploy Private Access to Azure Services 694Index 697
AWS Certified Solutions Architect Study Guide with 900 Practice Test Questions
MASTER AMAZON WEB SERVICES SOLUTION DELIVERY AND EFFICIENTLY PREPARE FOR THE AWS CERTIFIED SAA-C03 EXAM WITH THIS ALL-IN-ONE STUDY GUIDEThe AWS Certified Solutions Architect Study Guide: Associate (SAA-C03) Exam, 4th Edition comprehensively and effectively prepares you for the challenging SAA-C03 Exam. This Study Guide contains efficient and accurate study tools that will help you succeed on the exam. It offers access to the Sybex online learning environment and test bank, containing hundreds of test questions, bonus practice exams, a glossary of key terms, and electronic flashcards.In this complete and authoritative exam prep blueprint, Ben Piper and David Clinton show you how to:* Design resilient AWS architectures* Create high-performing solutions* Craft secure applications and architectures* Design inexpensive and cost-optimized architecturesAn essential resource for anyone trying to start a new career as an Amazon Web Services cloud solutions architect, the AWS Certified Solutions Architect Study Guide: Associate (SAA-C03) Exam, 4th Edition will also prove invaluable to currently practicing AWS professionals looking to brush up on the fundamentals of their work.ABOUT THE AUTHORBEN PIPER is an IT consultant who has created more than 30 educational courses covering Amazon Web Services, networking, and automation. Learn more about Ben from his website at www.benpiper.com. DAVID CLINTON is a system administrator, teacher, and writer. He has created training material for subjects including Linux systems, cloud computing (AWS in particular), and container technologies like Docker. David has written books on AWS, Linux administration, and server virtualization. Many of his video training courses can be found on Pluralsight.com. Introduction xxvAssessment Test xxxiAnswers to Assessment Test xxxviiPART I THE CORE AWS SERVICES 1CHAPTER 1 INTRODUCTION TO CLOUD COMPUTING AND AWS 3Cloud Computing and Virtualization 4Cloud Computing Architecture 4Cloud Computing Optimization 5The AWS Cloud 6AWS Platform Architecture 10AWS Reliability and Compliance 13The AWS Shared Responsibility Model 13The AWS Service Level Agreement 14Working with AWS 14AWS Organizations 14AWS Control Tower 15AWS Service Catalog 15AWS License Manager 16AWS Artifact 16The AWS CLI 16AWS SDKs 17Technical Support and Online Resources 17Support Plans 17Other Support Resources 18Migrating Existing Resources to AWS 18AWS Migration Hub 19AWS Application Migration Service 19AWS Database Migration Service 19AWS Application Discovery Service 20Summary 20Exam Essentials 21Review Questions 22CHAPTER 2 COMPUTE SERVICES 25Introduction 26EC2 Instances 27Provisioning Your Instance 27Configuring Instance Behavior 32Placement Groups 33Instance Pricing 33Instance Life Cycle 34Resource Tags 35Service Limits 36EC2 Storage Volumes 36Elastic Block Store Volumes 36Instance Store Volumes 38Accessing Your EC2 Instance 39Securing Your EC2 Instance 41Security Groups 41IAM Roles 41NAT Devices 42Key Pairs 42EC2 Auto Scaling 43Launch Configurations 43Launch Templates 43Auto Scaling Groups 45Auto Scaling Options 46AWS Systems Manager 49Actions 50Insights 52AWS Systems Manager Inventory 53Running Containers 54Amazon Elastic Container Service 54Amazon Elastic Kubernetes Service 55Other Container-Oriented Services 55AWS CLI Example 56Summary 57Exam Essentials 58Review Questions 60CHAPTER 3 AWS STORAGE 67Introduction 68S3 Service Architecture 69Prefixes and Delimiters 69Working with Large Objects 69Encryption 71Logging 71S3 Durability and Availability 72Durability 72Availability 73Eventually Consistent Data 73S3 Object Life Cycle 74Versioning 74Life Cycle Management 74Accessing S3 Objects 75Access Control 75Presigned URLs 77Static Website Hosting 77Amazon S3 Glacier 79Storage Pricing 80Other Storage-Related Services 81Amazon Elastic File System 81Amazon FSx 81AWS Storage Gateway 81AWS Snow Family 82AWS DataSync 82AWS CLI Example 83Summary 84Exam Essentials 85Review Questions 86CHAPTER 4 AMAZON VIRTUAL PRIVATE CLOUD (VPC) 91Introduction 92VPC CIDR Blocks 92Secondary CIDR Blocks 93IPv6 CIDR Blocks 93Subnets 95Subnet CIDR Blocks 96Availability Zones 97IPv6 CIDR Blocks 99Elastic Network Interfaces 99Primary and Secondary Private IP Addresses 100Attaching Elastic Network Interfaces 100Enhanced Networking 101Internet Gateways 102Route Tables 102Routes 103The Default Route 104Security Groups 106Inbound Rules 106Outbound Rules 107Sources and Destinations 108Stateful Firewall 108Default Security Group 109Network Access Control Lists 110Inbound Rules 110Outbound Rules 113Using Network Access Control Lists and Security Groups Together 114AWS Network Firewall 115Public IP Addresses 115Elastic IP Addresses 116AWS Global Accelerator 118Network Address Translation 119Network Address Translation Devices 120Configuring Route Tables to Use NAT Devices 121NAT Gateway 121NAT Instance 122AWS PrivateLink 123VPC Peering 123Hybrid Cloud Networking 124AWS Site-to-Site VPN 125AWS Transit Gateway 125AWS Direct Connect 133High-Performance Computing 134Elastic Fabric Adapter 135AWS ParallelCluster 136Summary 136Exam Essentials 137Review Questions 138CHAPTER 5 DATABASE SERVICES 143Introduction 144Relational Databases 144Columns and Attributes 144Using Multiple Tables 145Structured Query Language 146Online Transaction Processing vs. Online Analytic Processing 147Amazon Relational Database Service 148Database Engines 148Licensing Considerations 149Database Option Groups 150Database Instance Classes 150Storage 151Read Replicas 154High Availability (Multi-AZ) 155Single-Master 156Multi-Master 157Backup and Recovery 157Automated Snapshots 157Maintenance Items 158Amazon RDS Proxy 158Amazon Redshift 159Compute Nodes 159Data Distribution Styles 159Redshift Spectrum 160AWS Database Migration Service 160Nonrelational (NoSQL) Databases 161Storing Data 161Querying Data 161Types of Nonrelational Databases 162DynamoDB 162Partition and Hash Keys 163Attributes and Items 164Throughput Capacity 165Reading Data 167Global Tables 168Backups 168Summary 168Exam Essentials 169Review Questions 170CHAPTER 6 AUTHENTICATION AND AUTHORIZATION—AWS IDENTITY AND ACCESS MANAGEMENT 175Introduction 176IAM Identities 176IAM Policies 177User and Root Accounts 178Access Keys 180Groups 181Roles 182Authentication Tools 183Amazon Cognito 183AWS Managed Microsoft AD 183AWS Single Sign-On 184AWS Key Management Service 184AWS Secrets Manager 184AWS CloudHSM 185AWS Resource Access Manager (AWS RAM) 185AWS CLI Example 185Summary 187Exam Essentials 187Review Questions 189CHAPTER 7 CLOUDTRAIL, CLOUDWATCH, AND AWS CONFIG 193Introduction 194CloudTrail 195Management Events 195Data Events 196Event History 196Trails 196Log File Integrity Validation 198CloudWatch 199CloudWatch Metrics 200Graphing Metrics 201Metric Math 203CloudWatch Logs 205CloudWatch Alarms 208Amazon EventBridge 211AWS Config 212The Configuration Recorder 213Configuration Items 213Configuration History 213Configuration Snapshots 213Monitoring Changes 214Summary 216Exam Essentials 216Review Questions 218CHAPTER 8 THE DOMAIN NAME SYSTEM AND NETWORK ROUTING: AMAZON ROUTE 53 AND AMAZON CLOUDFRONT 223Introduction 224The Domain Name System 224Namespaces 225Name Servers 225Domains and Domain Names 226Domain Registration 226Domain Layers 226Fully Qualified Domain Names 227Zones and Zone Files 227Record Types 227Alias Records 228Amazon Route 53 228Domain Registration 229DNS Management 229Availability Monitoring 231Routing Policies 232Traffic Flow 234Route 53 Resolver 234Amazon CloudFront 235AWS CLI Example 237Summary 238Exam Essentials 238Review Questions 239CHAPTER 9 DATA INGESTION, TRANSFORMATION, AND ANALYTICS 243Introduction 244AWS Lake Formation 244Ingestion 245Transformation 245Analytics 245AWS Transfer Family 246Kinesis 246Kinesis Video Streams 246Kinesis Data Streams 247Kinesis Data Firehose 248Kinesis Data Firehose vs. Kinesis Data Streams 248Summary 249Exam Essentials 249Review Questions 250PART II ARCHITECTING FOR REQUIREMENTS 255CHAPTER 10 RESILIENT ARCHITECTURES 257Introduction 258Calculating Availability 258Availability Differences in Traditional vs. Cloud-Native Applications 259Know Your Limits 262Increasing Availability 262EC2 Auto Scaling 263Launch Configurations 263Launch Templates 263Auto Scaling Groups 265Auto Scaling Options 266Data Backup and Recovery 270S3 270Elastic File System 271Elastic Block Storage 271Database Resiliency 271Creating a Resilient Network 272VPC Design Considerations 272External Connectivity 273Simple Queue Service 273Queues 274Queue Types 275Polling 276Dead-Letter Queues 276Designing for Availability 276Designing for 99 Percent Availability 277Designing for 99.9 Percent Availability 278Designing for 99.99 Percent Availability 279Summary 280Exam Essentials 281Review Questions 282CHAPTER 11 HIGH-PERFORMING ARCHITECTURES 289Introduction 290Optimizing Performance for the Core AWS Services 290Compute 291Storage 295Database 298Network Optimization and Load Balancing 299Infrastructure Automation 302CloudFormation 302Third-Party Automation Solutions 309Reviewing and Optimizing Infrastructure Configurations 310AWS Well-Architected Tool 311Load Testing 311Visualization 312Optimizing Data Operations 313Caching 313Partitioning/Sharding 315Compression 315Summary 316Exam Essentials 316Review Questions 318CHAPTER 12 SECURE ARCHITECTURES 323Introduction 324Identity and Access Management 324Protecting AWS Credentials 325Fine-GrainedAuthorization 325Permissions Boundaries 327Roles 328Enforcing Service-Level Protection 334Detective Controls 335CloudTrail 335CloudWatch Logs 335Searching Logs with Athena 336Auditing Resource Configurations with AWS Config 338Amazon GuardDuty 339Amazon Inspector 342Amazon Detective 343Security Hub 344Amazon Fraud Detector 344AWS Audit Manager 344Protecting Network Boundaries 344Network Access Control Lists and Security Groups 345AWS Web Application Firewall 345AWS Shield 345AWS Firewall Manager 346Data Encryption 346Data at Rest 346Data in Transit 348Macie 349Summary 349Exam Essentials 350Review Questions 351CHAPTER 13 COST-OPTIMIZED ARCHITECTURES 357Introduction 358Planning, Tracking, and Controlling Costs 358AWS Budgets 359Monitoring Tools 360AWS Trusted Advisor 361Online Calculator Tools 362Cost-OptimizingCompute 363Maximizing Server Density 364EC2 Reserved Instances 364EC2 Spot Instances 365Auto Scaling 368Elastic Block Store Lifecycle Manager 368Summary 368Exam Essentials 369Review Questions 370APPENDIX A ANSWERS TO REVIEW QUESTIONS 375Chapter 1: Introduction to Cloud Computing and AWS 376Chapter 2: Compute Services 377Chapter 3: AWS Storage 380Chapter 4: Amazon Virtual Private Cloud (VPC) 381Chapter 5: Database Services 383Chapter 6: Authentication and Authorization—AWS Identity and Access Management 386Chapter 7: CloudTrail, CloudWatch, and AWS Config 388Chapter 8: The Domain Name System and Network Routing: Amazon Route 53 and Amazon CloudFront 390Chapter 9: Data Ingestion, Transformation, and Analytics 392Chapter 10: Resilient Architectures 393Chapter 11: High-Performing Architectures 397Chapter 12: Secure Architectures 399Chapter 13: Cost-Optimized Architectures 401APPENDIX B ADDITIONAL SERVICES 405Deployment Tools 406AWS Amplify 406AWS Serverless Application Repository 406AWS Proton 407Developer Tools 407Amazon API Gateway 407AWS Device Farm 407AWS Step Functions 407Infrastructure Tools 408AWS Outposts 408AWS Wavelength 408VMware Cloud on AWS 408Connectivity Tools 409Amazon Pinpoint 409AWS Transfer Family 409AWS AppSync 409Database Tools 410Amazon DocumentDB (with MongoDB Compatibility) 410Amazon Keyspaces (for Apache Cassandra) 410Amazon Quantum Ledger Database (QLDB) 410Data Streaming Tools 410Amazon Managed Streaming for Apache Kafka (MSK) 410Amazon MQ 411AWS Data Exchange 411Amazon Timestream 411AWS Data Pipeline 411Amazon AppFlow 411Machine Learning and Artificial Intelligence 412Amazon Comprehend 412Amazon Forecast 412Amazon Lex 412Amazon Polly 412Amazon Rekognition 413Amazon Textract 413Amazon Transcribe 413Amazon Translate 413Other Tools 413AWS Batch 413AWS X-Ray 414Amazon Kendra 414Amazon OpenSearch Service (Amazon Elasticsearch Service) 414Amazon Managed Grafana 414Amazon Managed Service for Prometheus 415Index 417
Tools, Languages, Methodologies for Representing Semantics on the Web of Things
This book is a guide to the combination of the Internet of Things (IoT) and the Semantic Web, covering a variety of tools, technologies and applications that serve the myriad needs of the researchers in this field. It provides a multi dimensional view of the concepts, tools, techniques and issues that are involved in the development of semantics for the Web of Things.The various aspects studied in this book include Multi-Model Multi-Platform (SHM3P) databases for the IoT, clustering techniques for discovery services for the semantic IoT, dynamic security testing methods for the Semantic Web of Things, Semantic Web-enabled IoT integration for a smart city, IoT security issues, the role of the Semantic Web of Things in Industry 4.0, the integration of the Semantic Web and the IoT for e-health, smart healthcare systems to monitor patients, Semantic Web-based ontologies for the water domain, science fiction and searching for a job. SHIKHA MEHTA is Associate Professor in the Department of CSE & IT, Jaypee Institute of Information Technology, India. Her research interests include machine/deep learning algorithms, nature-inspired computing and social networks analytics.SANJU TIWARI is Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico, DAAD Post-Doc-Net AI Fellow and PhD co-supervisor at Rai University, India, and has worked as a post-doctoral researcher in OEG, Universidad Politecnica de Madrid, Spain. Her research interests include artificial intelligence, knowledge graphs and ontology engineering.PATRICK SIARRY is Professor in automatics and informatics at University Paris Est Créteil, France. His research interests include the design of stochastic global optimization heuristics and their applications to various engineering fields. M.A. Jabbar is Professor and Head of the Department of CSE (AI & ML), Vardhaman College of Engineering, India. His research interests include artificial intelligence, Big Data analytics, bio-informatics and machine learning.Preface xiShikha MEHTA, Sanju TIWARI, Patrick SIARRY and M.A JABBARCHAPTER 1 THE ROLE OF SEMANTIC HYBRID MULTI-MODEL MULTI-PLATFORM (SHM3P) DATABASES FOR IOT 1Sven GROPPE, Jinghua GROPPE and Tobias GROTH1.1 Introduction 11.2 Databases for multi-model data 51.3 Platforms 71.4 Variations of SHM3P DBMS 131.5 What are the benefits of SHM3P databases for IoT? 141.5.1 Data storage and placement 141.5.2 Data processing 151.5.3 IoT applications 151.6 Summary and conclusions 161.7 References 16CHAPTER 2 A SYSTEMATIC REVIEW OF ONTOLOGIES FOR THE WATER DOMAIN 21Sanju TIWARI and Raúl GARCÍA-CASTRO2.1 Introduction 212.2 Literature review 232.2.1 Features in the water domain 232.2.2 Semantic models in the water domain 242.2.3 A comprehensive review of ontologies in the water domain 242.3 Applications of ontologies in the water domain 322.4 Discussion and conclusion 352.5 References 36CHAPTER 3 SEMANTIC WEB APPROACH FOR SMART HEALTH TO ENHANCE PATIENT MONITORING IN RESUSCITATION 41Fatima Zahra AMARA, Mounir HEMAM, Meriem DJEZZAR and Moufida MAIMOUR3.1 Introduction 423.2 Background 433.2.1 Semantic Web 433.2.2 SSN (Semantic Sensor Network) ontology 443.3 IoT Smart Health applications and semantics 453.4 Proposed approach and implementation 463.4.1 Knowledge representation 473.4.2 Ontology evaluation 513.4.3 Reasoning and querying 513.4.4 Linked Data 553.5 Conclusion 563.6 References 57CHAPTER 4 ROLE OF CLUSTERING IN DISCOVERY SERVICES FOR THE SEMANTIC INTERNET OF THINGS 61Shachi SHARMA4.1 Introduction 614.2 Discovery services in IoT 644.2.1 Directory-based architectures 644.2.2 Directory-less architectures 664.3 Semantic-based architectures 674.3.1 Search engine-based 674.3.2 ONS DNS-based 684.4 Discovery services and clustering 684.5 Clustering methods in IoT 694.6 Conclusion 714.7 References 71CHAPTER 5 DYNAMIC SECURITY TESTING TECHNIQUES FOR THE SEMANTIC WEB OF THINGS: MARKET AND INDUSTRY PERSPECTIVE 75Dhananjay SINGH CHAUHAN, Gaurav CHOUDHARY, Shishir Kumar SHANDILYA and Vikas SIHAG5.1 Introduction 755.2 Related studies 775.3 Background of dynamic security testing techniques 795.3.1 Black Box testing techniques 805.4 DAST using static analysis 825.4.1 Current implementation 825.5 DAST using user session 845.5.1 Current implementation 845.6 DAST using Extended Tainted Mode Model 865.6.1 Current implementation 875.7 Current issues and research directions 885.8 Conclusion 895.9 References 89CHAPTER 6 SCIFIONTO: MODELING, VISUALIZATION AND EVALUATION OF SCIENCE FICTION ONTOLOGIES BASED ON INDIAN CONTEXTUALIZATION WITH AUTOMATIC KNOWLEDGE ACQUISITION 93Gerard DEEPAK, Ayush A KUMAR and Sheeba J PRIYADARSHINI6.1 Introduction 946.2 Literature survey 976.2.1 Formulation and modeling of ontologies for varied domains of importance 976.2.2 Auxiliary automatic and semi-automatic models in ontology synthesis 976.2.3 Ontology-driven systems and applications 986.2.4 Automatic Knowledge Acquisition systems 996.2.5 Science fiction as an independent domain of existence 996.3 Modeling and evaluation of the ontology 1006.3.1 Ontology modeling 1006.3.2 Ontology visualization 1046.3.3 Ontology evaluation 1076.4 Automatic Knowledge Acquisition model 1116.4.1 System architecture 1116.4.2 Acquisition algorithm 1136.5 Conclusion 1196.6 References 119CHAPTER 7 SEMANTIC WEB-ENABLED IOT INTEGRATION FOR A SMART CITY 123Ronak PANCHAL and Fernando ORTIZ-RODRIGUEZ7.1 Introduction: Semantic Web and sensors 1237.2 Motivation and challenge 1247.3 Literature review 1247.4 Implementation of forest planting using SPARQL queries 1257.4.1 Architecture sketch with conceptual diagram 1257.4.2 Implementation ontology from the dataset 1267.4.3 Technologies and tools 1297.5 Conclusion 1367.6 References 136CHAPTER 8 HEART RATE MONITORING USING IOT AND AI 139Kalpana MURUGAN, Cherukuri NIKHIL KUMAR, Donthu Sai SUBASH and Sangam DEVA KISHORE REDDY8.1 Introduction 1408.2 Literature survey 1428.3 Heart rate monitoring system 1458.4 Results and discussion 1498.5 Conclusion and future works 1528.6 References 152CHAPTER 9 IOT SECURITY ISSUES AND ITS DEFENSIVE METHODS 155Keshavi NALLA and Seshu VARDHAN POTHABATHULA9.1 Introduction 1559.2 IoT security architecture 1589.2.1 Typical IoT architecture 1589.2.2 Centralized and distributed approaches over the IoT security architecture 1619.2.3 IoT security architecture based on blockchain 1639.2.4 Internet of Things security architecture: trust zones and boundaries 1649.2.5 Threat modeling in IoT security architecture 1689.3 Specific security challenges and approaches 1709.3.1 Identity and authentication 1709.3.2 Access control 1719.3.3 Protocol and network security 1729.3.4 Privacy 1729.3.5 Trust and governance 1739.3.6 Fault tolerance 1739.4 Methodologies used for securing the systems 1749.4.1 PKI and digital certificates 1749.4.2 Network security 1749.4.3 API security 1749.4.4 Network access control 1759.4.5 Segmentation 1759.4.6 Security gateways 1759.4.7 Patch management and software updates 1759.5 Conclusion 1769.6 References 176CHAPTER 10 ELUCIDATING THE SEMANTIC WEB OF THINGS FOR MAKING THE INDUSTRY 4.0 REVOLUTION A SUCCESS 179Deepika CHAUDHARY and Jaiteg SINGH10.1 Introduction 17910.2 Correlation of the Semantic Web of Things with IR4.0 18010.2.1 Smart machines 18110.2.2 Smart products 18210.2.3 Augmented operators 18210.2.4 The Web of Things 18310.2.5 Semantic Web of Things 18410.3 Smart manufacturing system and ontologies 18510.3.1 Vertical level integration 18510.3.2 Horizontal level of integration 18510.3.3 End-to-end integration 18510.4 Literature survey 18810.5 Conclusion and future work 19010.6 References 190CHAPTER 11 SEMANTIC WEB AND INTERNET OF THINGS IN E-HEALTH FOR COVID-19 195ANURAG and Naren JEEVA11.1 Introduction 19611.2 Dataset 19711.3 Application of IoT for Covid-19 19811.3.1 Continuous real-time remote monitoring 19811.3.2 Remote monitoring using W-kit 19811.3.3 Early identification and monitoring 19811.3.4 Continuous and reliable health monitoring 19811.3.5 ANN-assisted patient monitoring 19911.3.6 City lockdown monitoring 19911.3.7 Technologies for tracking and tracing 19911.3.8 Tracking and tracing suspected cases 19911.3.9 Anonymity preserving contact tracing model 20011.3.10 Cognitive radio-based IoT architecture 20011.3.11 Analyzing reasons for the outbreak 20011.3.12 Analyzing Covid-19 cases using disruptive technology 20011.3.13 Post-Covid applications 20111.4 Semantic Web applications for Covid-19 20111.4.1 Ontological approach for drug development 20211.4.2 Early detection and diagnosis 20211.4.3 Knowledge-based pre-diagnosis system 20211.4.4 Semantic-based searching for online learning resources 20311.4.5 Ontology-based physiological monitoring of students 20311.4.6 Analysis of clinical trials 20311.4.7 Data annotation of EHRs 20411.4.8 Disease pattern study 20411.4.9 Surveillance in primary care 20411.4.10 Performance assessment of healthcare services 20511.4.11 Vaccination drives and rollout strategies 20511.5 Limitations and challenges of IoT and SW models 20511.6 Discussion 20611.7 Conclusion 20611.8 References 207CHAPTER 12 DEVELOPMENT OF A SEMANTIC WEB ENABLED JOB_SEARCH ONTOLOGY SYSTEM 211Hina J CHOKSHI, Dhaval VYAS and Ronak PANCHAL12.1 Introduction 21112.1.1 Ontology 21212.1.2 Importance of ontology 21312.1.3 Semantic Web and its solutions 21412.1.4 Online recruitment scenarios 21412.2 Review of the related work done for online recruitment 21512.3 Design of “SearchAJob” ontology for the IT domain 21712.3.1 Ontology structure 21812.4 Implementing the proposed ontology 22212.4.1 Architecture of semantics-based job ontology 22312.5 Benefits of Semantic Web enabled SearchAJob system 23112.6 Conclusion and future scope 23212.7 References 233List of Authors 237Index 241
Practical MATLAB Deep Learning
Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning.Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include:* Aircraft navigation* An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning* Stock market prediction* Natural language processing* Music creation usng generative deep learning* Plasma control* Earth sensor processing for spacecraft* MATLAB Bluetooth data acquisition applied to dance physics WHAT YOU WILL LEARN* Explore deep learning using MATLAB and compare it to algorithms* Write a deep learning function in MATLAB and train it with examples* Use MATLAB toolboxes related to deep learning* Implement tokamak disruption prediction* Now includes reinforcement learningWHO THIS BOOK IS FOREngineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.MICHAEL PALUSZEK is the co-author of MATLAB Recipes published by Apress. He is President of Princeton Satellite Systems, Inc. (PSS) in Plainsboro, New Jersey. Mr. Paluszek founded PSS in 1992 to provide aerospace consulting services. He used MATLAB to develop the control system and simulation for the Indostar-1 geosynschronous communications satellite, resulting in the launch of PSS' first commercial MATLAB toolbox, the Spacecraft Control Toolbox, in 1995. Since then he has developed toolboxes and software packages for aircraft, submarines, robotics, and fusion propulsion, resulting in PSS' current extensive product line. He is currently leading an Army research contract for precision attitude control of small satellites and working with the Princeton Plasma Physics Laboratory on a compact nuclear fusion reactor for energy generation and propulsion. Prior to founding PSS, Mr. Paluszek was an engineer at GE Astro Space in East Windsor, NJ. At GE he designed the Global Geospace Science Polar despun platform control system and led the design of the GPS IIR attitude control system, the Inmarsat-3 attitude control systems and the Mars Observer delta-V control system, leveraging MATLAB for control design. Mr. Paluszek also worked on the attitude determination system for the DMSP meteorological satellites. Mr. Paluszek flew communication satellites on over twelve satellite launches, including the GSTAR III recovery, the first transfer of a satellite to an operational orbit using electric thrusters. At Draper Laboratory Mr. Paluszek worked on the Space Shuttle, Space Station and submarine navigation. His Space Station work included designing of Control Moment Gyro based control systems for attitude control. Mr. Paluszek received his bachelors in Electrical Engineering, and master's and engineer’s degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology. He is author of numerous papers and has over a dozen U.S. Patents.STEPHANIE THOMAS is the co-author of MATLAB Recipes, published by Apress. She received her bachelor's and master's degrees in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 1999 and 2001. Ms. Thomas was introduced to PSS' Spacecraft Control Toolbox for MATLAB during a summer internship in 1996 and has been using MATLAB for aerospace analysis ever since. She built a simulation of a lunar transfer vehicle in C++, LunarPilot, during the same internship. In her nearly 20 years of MATLAB experience, she has developed many software tools including the Solar Sail Module for the Spacecraft Control Toolbox; a proximity satellite operations toolbox for the Air Force; collision monitoring Simulink blocks for the Prisma satellite mission; and launch vehicle analysis tools in MATLAB and Java, to name a few. She has developed novel methods for space situation assessment such as a numeric approach to assessing the general rendezvous problem between any two satellites implemented in both MATLAB and C++. Ms. Thomas has contributed to PSS' Attitude and Orbit Control textbook, featuring examples using the Spacecraft Control Toolbox, and written many software User's Guides. She has conducted SCT training for engineers from diverse locales such as Australia, Canada, Brazil, and Thailand and has performed MATLAB consulting for NASA, the Air Force, and the European Space Agency.ERIC HAM is a a Technical Specialist, Princeton Satellite Systems. His expertise lies with deep learning, programming using MATLAB, C++ and related.1. What is deep learning? – no changes except editoriala. Machine learning vs. deep learningb. Approaches to deep learningc. Recurrent deep learningd. Convolutional deep learning2. MATLAB machine and deep learning toolboxesa. Describe the functionality and applications of each toolboxb. Demonstrate MATLAB toolboxes related to Deep Learningc. Include the text toolbox generative toolbox and reinforcement learning toolboxd. Add more detail on each3. Finding Circles – no changes except editorial.4. Classifying movies – no changes except editorial.5. Tokamak disruption detection – this would be updated.6. Classifying a pirouette – no changes except editorial.7. Completing sentences - This would be revamped using the MATLAB Text Processing Toolbox.8. Terrain based navigation-The example in the original book would be changed to a regression approach that can interpolate position. We would switch to a terrestrial example applicable to drones.9. Stock prediction – this is a very popular chapter. We would improve the algorithm.10. Image classification – no changes except editorial.11. Orbit Determination – add inclination to the algorithm.12. Earth Sensors – a new example on how to use neural networks to measure roll and yaw from any Earth sensor.13. Generative deep learning example. This would be a neural network that generates pictures after learning an artist’s style.14. Reinforcement learning. This would be a simple quadcopter hovering control system. It would be simulation based although readers would be able to apply this to any programmable quadcopter.
The Official (ISC)2 CCSP CBK Reference
THE ONLY OFFICIAL BODY OF KNOWLEDGE FOR CCSP—THE MOST POPULAR CLOUD SECURITY CREDENTIAL—FULLY REVISED AND UPDATED. Certified Cloud Security Professional (CCSP) certification validates the advanced technical skills needed to design, manage, and secure data, applications, and infrastructure in the cloud. This highly sought-after global credential has been updated with revised objectives. The new third edition of The Official (ISC)2 Guide to the CCSP CBK is the authoritative, vendor-neutral common body of knowledge for cloud security professionals. This comprehensive resource provides cloud security professionals with an indispensable working reference to each of the six CCSP domains: Cloud Concepts, Architecture and Design; Cloud Data Security; Cloud Platform and Infrastructure Security; Cloud Application Security; Cloud Security Operations; and Legal, Risk and Compliance. Detailed, in-depth chapters contain the accurate information required to prepare for and achieve CCSP certification. Every essential area of cloud security is covered, including implementation, architecture, operations, controls, and immediate and long-term responses. Developed by (ISC)2, the world leader in professional cybersecurity certification and training, this indispensable guide:* Covers the six CCSP domains and over 150 detailed objectives* Provides guidance on real-world best practices and techniques* Includes illustrated examples, tables, and diagrams The Official (ISC)2 Guide to the CCSP CBK is a vital ongoing resource for IT and information security leaders responsible for applying best practices to cloud security architecture, design, operations and service orchestration. Foreword to the Fourth Edition xixIntroduction xxiCHAPTER 1 CLOUD CONCEPTS, ARCHITECTURE, AND DESIGN 1Understand Cloud Computing Concepts 2Cloud Computing Definitions 2Cloud Computing Roles and Responsibilities 3Key Cloud Computing Characteristics 7Building Block Technologies 11Describe Cloud Reference Architecture 14Cloud Computing Activities 14Cloud Service Capabilities 15Cloud Service Categories 17Cloud Deployment Models 18Cloud Shared Considerations 21Impact of Related Technologies 27Understand Security Concepts Relevant to Cloud Computing 33Cryptography and Key Management 33Identity and Access Control 34Data and Media Sanitization 36Network Security 37Virtualization Security 39Common Threats 41Security Hygiene 41Understand Design Principles of Secure Cloud Computing 43Cloud Secure Data Lifecycle 43Cloud-BasedBusiness Continuity and Disaster Recovery Plan 44Business Impact Analysis 45Functional Security Requirements 46Security Considerations for Different Cloud Categories 48Cloud Design Patterns 49DevOps Security 51Evaluate Cloud Service Providers 51Verification against Criteria 52System/Subsystem Product Certifications 54Summary 56CHAPTER 2 CLOUD DATA SECURITY 57Describe Cloud Data Concepts 58Cloud Data Lifecycle Phases 58Data Dispersion 61Data Flows 62Design and Implement Cloud Data Storage Architectures 63Storage Types 63Threats to Storage Types 66Design and Apply Data Security Technologies and Strategies 67Encryption and Key Management 67Hashing 70Data Obfuscation 71Tokenization 73Data Loss Prevention 74Keys, Secrets, and Certificates Management 77Implement Data Discovery 78Structured Data 79Unstructured Data 80Semi-structuredData 81Data Location 82Implement Data Classification 82Data Classification Policies 83Mapping 85Labeling 86Design and Implement Information Rights Management 87Objectives 88Appropriate Tools 89Plan and Implement Data Retention, Deletion, and Archiving Policies 89Data Retention Policies 90Data Deletion Procedures and Mechanisms 93Data Archiving Procedures and Mechanisms 94Legal Hold 95Design and Implement Auditability, Traceability, and Accountability of Data Events 96Definition of Event Sources and Requirement of Event Attribution 97Logging, Storage, and Analysis of Data Events 99Chain of Custody and Nonrepudiation 100Summary 101CHAPTER 3 CLOUD PLATFORM AND INFRASTRUCTURE SECURITY 103Comprehend Cloud Infrastructure and Platform Components 104Physical Environment 104Network and Communications 106Compute 107Virtualization 108Storage 110Management Plane 111Design a Secure Data Center 113Logical Design 114Physical Design 116Environmental Design 117Analyze Risks Associated with Cloud Infrastructure and Platforms 119Risk Assessment 119Cloud Vulnerabilities, Threats, and Attacks 122Risk Mitigation Strategies 123Plan and Implementation of Security Controls 124Physical and Environmental Protection 124System, Storage, and Communication Protection 125Identification, Authentication, and Authorization in Cloud Environments 127Audit Mechanisms 128Plan Disaster Recovery and Business Continuity 131Business Continuity/Disaster Recovery Strategy 131Business Requirements 132Creation, Implementation, and Testing of Plan 134Summary 138CHAPTER 4 CLOUD APPLICATION SECURITY 139Advocate Training and Awareness for Application Security 140Cloud Development Basics 140Common Pitfalls 141Common Cloud Vulnerabilities 142Describe the Secure Software Development Life Cycle Process 144NIST Secure Software Development Framework 145OWASP Software Assurance Maturity Model 145Business Requirements 145Phases and Methodologies 146Apply the Secure Software Development Life Cycle 149Cloud-Specific Risks 149Threat Modeling 153Avoid Common Vulnerabilities during Development 156Secure Coding 156Software Configuration Management and Versioning 157Apply Cloud Software Assurance and Validation 158Functional and Non-functional Testing 159Security Testing Methodologies 160Quality Assurance 164Abuse Case Testing 164Use Verified Secure Software 165Securing Application Programming Interfaces 165Supply-Chain Management 166Third-Party Software Management 166Validated Open-Source Software 167Comprehend the Specifics of Cloud Application Architecture 168Supplemental Security Components 169Cryptography 171Sandboxing 172Application Virtualization and Orchestration 173Design Appropriate Identity and Access Management Solutions 174Federated Identity 175Identity Providers 175Single Sign-on 176Multifactor Authentication 176Cloud Access Security Broker 178Summary 179CHAPTER 5 CLOUD SECURITY OPERATIONS 181Build and Implement Physical and Logical Infrastructure for Cloud Environment 182Hardware-Specific Security Configuration Requirements 182Installation and Configuration of Virtualization Management Tools 185Virtual Hardware–Specific Security Configuration Requirements 186Installation of Guest Operating System Virtualization Toolsets 188Operate Physical and Logical Infrastructure for Cloud Environment 188Configure Access Control for Local and Remote Access 188Secure Network Configuration 190Operating System Hardening through the Application of Baselines 195Availability of Stand-Alone Hosts 196Availability of Clustered Hosts 197Availability of Guest Operating Systems 199Manage Physical and Logical Infrastructure for Cloud Environment 200Access Controls for Remote Access 201Operating System Baseline Compliance Monitoring and Remediation 202Patch Management 203Performance and Capacity Monitoring 205Hardware Monitoring 206Configuration of Host and Guest Operating System Backup and Restore Functions 207Network Security Controls 208Management Plane 212Implement Operational Controls and Standards 212Change Management 213Continuity Management 214Information Security Management 216Continual Service Improvement Management 217Incident Management 218Problem Management 221Release Management 221Deployment Management 222Configuration Management 224Service Level Management 225Availability Management 226Capacity Management 227Support Digital Forensics 228Forensic Data Collection Methodologies 228Evidence Management 230Collect, Acquire, and Preserve Digital Evidence 231Manage Communication with Relevant Parties 234Vendors 235Customers 236Partners 238Regulators 238Other Stakeholders 239Manage Security Operations 239Security Operations Center 240Monitoring of Security Controls 244Log Capture and Analysis 245Incident Management 248Summary 253CHAPTER 6 LEGAL, RISK, AND COMPLIANCE 255Articulating Legal Requirements and Unique Risks within the Cloud Environment 256Conflicting International Legislation 256Evaluation of Legal Risks Specific to Cloud Computing 258Legal Frameworks and Guidelines 258eDiscovery 265Forensics Requirements 267Understand Privacy Issues 267Difference between Contractual and Regulated Private Data 268Country-Specific Legislation Related to Private Data 272Jurisdictional Differences in Data Privacy 277Standard Privacy Requirements 278Privacy Impact Assessments 280Understanding Audit Process, Methodologies, and Required Adaptations for a Cloud Environment 281Internal and External Audit Controls 282Impact of Audit Requirements 283Identify Assurance Challenges of Virtualization and Cloud 284Types of Audit Reports 285Restrictions of Audit Scope Statements 288Gap Analysis 289Audit Planning 290Internal Information Security Management System 291Internal Information Security Controls System 292Policies 293Identification and Involvement of Relevant Stakeholders 296Specialized Compliance Requirements for Highly Regulated Industries 297Impact of Distributed Information Technology Model 298Understand Implications of Cloud to Enterprise Risk Management 299Assess Providers Risk Management Programs 300Differences between Data Owner/Controller vs. Data Custodian/Processor 301Regulatory Transparency Requirements 302Risk Treatment 303Risk Frameworks 304Metrics for Risk Management 307Assessment of Risk Environment 307Understand Outsourcing and Cloud Contract Design 309Business Requirements 309Vendor Management 311Contract Management 312Supply Chain Management 314Summary 316Index 317
Up and Running on Microsoft Viva Connections
Leverage the collaboration capabilities of Microsoft Viva Connections as an employee experience platform to build a gateway to your digital workplace. This book helps you set up Microsoft Viva connections via easy-to-follow steps and extend it to target your business scenarios.The book starts with an introduction to Microsoft Viva and its modules and it discusses Viva Connections for desktop and mobile users. You will learn about the intranet landing experience with SharePoint where you will plan, build, and launch a home site. You will know how to use the app bar and global navigation in Viva Connections and understand the importance of the dashboard and dashboard cards. You will learn how to enable Viva Connections in MS Teams and define a rollout strategy. You will gain experience with Viva Connections on mobile devices and go through end-user guidance. And you will learn to extend Viva Connections with the SharePoint Framework and deploy SPFx solutions.After reading this book, you will be able to set up Microsoft Viva Connections for your digital workplace and empower your employees to search and discover relevant news, information, content, and sites from across the organization.WHAT WILL YOU LEARN* Understand the modern experience in SharePoint with Microsoft Viva Connect* Know best practices for your home site in SharePoint* Get your content ready for feed with SharePoint and Yammer* Build Adaptive Card Extensions (ACEs) with SPFx* Define governance for Viva ConnectionsWHO THIS BOOK IS FORMicrosoft professionals and business users who want to leverage the collaboration capabilities of Microsoft Viva ConnectionsNANDDEEP NACHAN is a two-time Microsoft MVP (Office Apps & Services), and Microsoft Certified Trainer. He is a results-oriented Technology Architect with experience in Microsoft Technologies, especially with Microsoft 365, SharePoint, MS Azure, and Power Platform. He is experienced in the design, implementation, configuration, and maintenance of several large-scale projects. He focuses on architectural design and implementation, website design and development, complete application development cycles, with an intense focus on .NET technologies. He is an active contributor to the PnP Microsoft 365 Platform Community.SMITA NACHAN is a Microsoft MVP (Office Development), and Microsoft Certified Trainer. She is a Certified ScrumMaster (CSM), Certified M365 professional with experience in SharePoint, Microsoft Teams, Power Platform, Groups, Yammer, Forms, and Microsoft Viva. She has 13+ years of experience in the design, implementation, configuration, and adoption of Microsoft 365 across the organization. She focuses on architectural design and implementation, development, and complete application development cycles, with an intense focus on Microsoft 365 apps and services. She is a frequent speaker at various community events, including SPS, Global Microsoft 365 Developer Bootcamp, and Global Power Platform Bootcamp. She is an active contributor to Microsoft 365 Community. She is a travel, fashion, and food blogger.CHAPTER 1: MICROSOFT VIVA FOR EVERYONECHAPTER GOAL: GET INTRODUCED TO MICROSOFT VIVA AND MODULES OF MICROSOFT VIVANO OF PAGES 5SUB -TOPICS1. Microsoft Viva Overview2. Explore Microsoft Viva modules3. Microsoft Viva Connections Experiencebegins toCHAPTER 2: MICROSOFT VIVA CONNECTIONS EXPERIENCE AND SET UPCHAPTER GOAL: UNDERSTAND THE MICROSOFT VIVA CONNECTION EXPERIENCE FOR DESKTOP AND MOBILE USERSNO OF PAGES: 5SUB - TOPICS1. Viva Connections desktop experience2. Viva Connections mobile experience3. Branding4. Understand the setup processCHAPTER 3: MODERN EXPERIENCE IN SHAREPOINTCHAPTER GOAL: UNDERSTAND THE MODERN EXPERIENCE IN SHAREPOINTNO OF PAGES : 10SUB - TOPICS:1. Modern experience in SharePoint2. Classic to modern experience journey3. Features of SharePoint4. Flat hierarchy structure and its importance5. Plan for hub sites6. Navigation experienceCHAPTER 4: HOME SITE SUPERPOWERSCHAPTER GOAL: UNDERSTAND THE INTRANET LANDING EXPERIENCE WITH SHAREPOINT HOME SITENo of pages: 10SUB - TOPICS:1. Plan, build, and launch a home site2. Home site superpowers3. SharePoint templates for home site4. Best practices for home siteCHAPTER 5: APP BAR AND GLOBAL NAVIGATIONCHAPTER GOAL: UNDERSTAND THE IMPORTANCE OF APP BAR AND GLOBAL NAVIGATION.NO OF PAGES: 10SUB - TOPICS:1. Enable SharePoint app bar2. Customize global navigation3. Localization scenariosCHAPTER 6: DASHBOARD AND CARDSCHAPTER GOAL: UNDERSTAND THE IMPORTANCE OF DASHBOARD AND ADDING CARDS TO DASHBOARD.NO OF PAGES: 10SUB - TOPICS:1. Basics of Dashboard2. Dashboard anatomy3. Dashboard authoring4. Dashboard cards5. 3rd party supportCHAPTER 7: DEFINE YOUR CONTENT FEEDSChapter Goal: Get your content ready for feed with SharePoint and Yammer.NO OF PAGES: 10SUB - TOPICS:1. Prepare content for your feeds2. Create engaging experience in feedCHAPTER 8: ENABLE VIVA CONNECTIONS IN MS TEAMSCHAPTER GOAL: PREPARE STRATEGY TO ENABLE VIVA CONNECTIONS AND ROLL OUT IN MS TEAMS.NO OF PAGES: 10SUB - TOPICS:1. Plan for Viva Connections in MS Teams2. Define roll out strategyCHAPTER 9: MOBILE SETTINGSCHAPTER GOAL: DEFINE VIVA CONNECTIONS EXPERIENCE FOR MOBILE.NO OF PAGES: 3SUB - TOPICS:1. Mobile settings guidance2. Viva Connections Dashboard on a mobile deviceCHAPTER 10: END-USER GUIDANCECHAPTER GOAL: DEFINE END USER ROLL OUT PLAN AND GUIDANCE.NO OF PAGES: 3SUB - TOPICS:1. Define roll out plan2. Educate end users for Viva ConnectionsCHAPTER 11: EXTEND VIVA CONNECTIONSCHAPTER GOAL: LEARN TO MEET CUSTOM BUSINESS SCENARIOS BY EXTENDING VIVA CONNECTIONS WITH SHAREPOINT FRAMEWORK (SPFX).NO OF PAGES: 15SUB - TOPICS:1. SPFx to extend Viva Connections2. Build Adaptive Card Extensions (ACEs) with SPFx3. Deploy SPFx solutions
Deep Learning for Targeted Treatments
DEEP LEARNING FOR TREATMENTSTHE BOOK PROVIDES THE DIRECTION FOR FUTURE RESEARCH IN DEEP LEARNING IN TERMS OF ITS ROLE IN TARGETED TREATMENT, BIOLOGICAL SYSTEMS, SITE-SPECIFIC DRUG DELIVERY, RISK ASSESSMENT IN THERAPY, ETC.Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. AUDIENCEThe book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry. RISHABHA MALVIYA, PHD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents are published/under evaluation. GHEORGHITA GHINEA, PHD, is a professor in Computing, Department of Computer Science Brunel University London. His research activities lie at the confluence of computer science, media, and psychology, and particularly interested in building semantically underpinned human-centered e-systems, particularly integrating human perceptual requirements. Has published more than 30+ articles and received 10+ research grants. RAJESH KUMAR DHANARAJ, PHD, is an associate professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed 20+ books on various technologies and 35+ articles and papers in various refereed journals and international conferences and contributed chapters to the books. His research interests include machine learning, cyber-physical systems, and wireless sensor networks. He is an Expert Advisory Panel Member of Texas Instruments Inc USA. BALAMURUGAN BALUSAMY, PHD, is a professor at Galgotias University. He has published 30+ books on various technologies as well as more than 150 journal articles, conferences, and book chapters. SONALI SUNDRAM completed B. Pharm & M. Pharm (pharmacology) from AKTU, Lucknow, and is working at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has more than 8 patents to her credit. Preface xviiAcknowledgement xix1 DEEP LEARNING AND SITE-SPECIFIC DRUG DELIVERY: THE FUTURE AND INTELLIGENT DECISION SUPPORT FOR PHARMACEUTICAL MANUFACTURING SCIENCE 1Dhanalekshmi Unnikrishnan Meenakshi, Selvasudha Nandakumar, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan and Shah Alam Khan1.1 Introduction 21.2 Drug Discovery, Screening and Repurposing 51.3 DL and Pharmaceutical Formulation Strategy 111.3.1 DL in Dose and Formulation Prediction 111.3.2 DL in Dissolution and Release Studies 151.3.3 DL in the Manufacturing Process 161.4 Deep Learning Models for Nanoparticle-Based Drug Delivery 191.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory 201.4.2 Artificial Intelligence and Drug Delivery Algorithms 211.4.3 Nanoinformatics 221.5 Model Prediction for Site-Specific Drug Delivery 231.5.1 Prediction of Mode and a Site-Specific Action 231.5.2 Precision Medicine 261.6 Future Scope and Challenges 271.7 Conclusion 29References 302 ROLE OF DEEP LEARNING, BLOCKCHAIN AND INTERNET OF THINGS IN PATIENT CARE 39Akanksha Sharma, Rishabha Malviya and Sonali Sundram2.1 Introduction 402.2 IoT and WBAN in Healthcare Systems 422.2.1 IoT in Healthcare 422.2.2 WBAN 442.2.2.1 Key Features of Medical Networks in the Wireless Body Area 442.2.2.2 Data Transmission & Storage Health 452.2.2.3 Privacy and Security Concerns in Big Data 452.3 Blockchain Technology in Healthcare 462.3.1 Importance of Blockchain 462.3.2 Role of Blockchain in Healthcare 472.3.3 Benefits of Blockchain in Healthcare Applications 482.3.4 Elements of Blockchain 492.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling 512.3.6 Mobile Health and Remote Monitoring 532.3.7 Different Mobile Health Application with Description of Usage in Area of Application 542.3.8 Patient-Centered Blockchain Mode 552.3.9 Electronic Medical Record 572.3.9.1 The Most Significant Barriers to Adoption Are 602.3.9.2 Concern Regarding Negative Unintended Consequences of Technology 602.4 Deep Learning in Healthcare 622.4.1 Deep Learning Models 632.4.1.1 Recurrent Neural Networks (RNN) 632.4.1.2 Convolutional Neural Networks (CNN) 642.4.1.3 Deep Belief Network (DBN) 652.4.1.4 Contrasts Between Models 662.4.1.5 Use of Deep Learning in Healthcare 662.5 Conclusion 702.6 Acknowledgments 70References 703 DEEP LEARNING ON SITE-SPECIFIC DRUG DELIVERY SYSTEM 77Prem Shankar Mishra, Rakhi Mishra and Rupa Mazumder3.1 Introduction 783.2 Deep Learning 813.2.1 Types of Algorithms Used in Deep Learning 813.2.1.1 Convolutional Neural Networks (CNNs) 823.2.1.2 Long Short-Term Memory Networks (LSTMs) 833.2.1.3 Recurrent Neural Networks 833.2.1.4 Generative Adversarial Networks (GANs) 843.2.1.5 Radial Basis Function Networks 843.2.1.6 Multilayer Perceptron 853.2.1.7 Self-Organizing Maps 853.2.1.8 Deep Belief Networks 853.3 Machine Learning and Deep Learning Comparison 863.4 Applications of Deep Learning in Drug Delivery System 873.5 Conclusion 90References 904 DEEP LEARNING ADVANCEMENTS IN TARGET DELIVERY 101Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma4.1 Introduction: Deep Learning and Targeted Drug Delivery 1024.2 Different Models/Approaches of Deep Learning and Targeting Drug 1044.3 QSAR Model 1054.3.1 Model of Deep Long-Term Short-Term Memory 1054.3.2 RNN Model 1074.3.3 CNN Model 1084.4 Deep Learning Process Applications in Pharmaceutical 1094.5 Techniques for Predicting Pharmacotherapy 1094.6 Approach to Diagnosis 1104.7 Application 1134.7.1 Deep Learning in Drug Discovery 1144.7.2 Medical Imaging and Deep Learning Process 1154.7.3 Deep Learning in Diagnostic and Screening 1164.7.4 Clinical Trials Using Deep Learning Models 1164.7.5 Learning for Personalized Medicine 1174.8 Conclusion 121Acknowledgment 122References 1225 DEEP LEARNING AND PRECISION MEDICINE: LESSONS TO LEARN FOR THE PREEMINENT TREATMENT FOR MALIGNANT TUMORS 127Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi5.1 Introduction 1285.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis 1325.2.1 Gene Identification and Genome Data 1335.2.2 Image Diagnosis 1355.2.3 Radiomics, Radiogenomics, and Digital Biopsy 1375.2.4 Medical Image Analysis in Mammography 1385.2.5 Magnetic Resonance Imaging 1395.2.6 CT Imaging 1405.3 dl in Next-Generation Sequencing, Biomarkers, and Clinical Validation 1415.3.1 Next-Generation Sequencing 1415.3.2 Biomarkers and Clinical Validation 1425.4 dl and Translational Oncology 1445.4.1 Prediction 1445.4.2 Segmentation 1465.4.3 Knowledge Graphs and Cancer Drug Repurposing 1475.4.4 Automated Treatment Planning 1495.4.5 Clinical Benefits 1505.5 DL in Clinical Trials—A Necessary Paradigm Shift 1525.6 Challenges and Limitations 1555.7 Conclusion 157References 1576 PERSONALIZED THERAPY USING DEEP LEARNING ADVANCES 171Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas6.1 Introduction 1726.2 Deep Learning 1746.2.1 Convolutional Neural Networks 1756.2.2 Autoencoders 1806.2.3 Deep Belief Network (DBN) 1826.2.4 Deep Reinforcement Learning 1846.2.5 Generative Adversarial Network 1866.2.6 Long Short-Term Memory Networks 188References 1917 TELE-HEALTH MONITORING USING ARTIFICIAL INTELLIGENCE DEEP LEARNING FRAMEWORK 199Swati Verma, Rishabha Malviya, Md Aftab Alam and Bhuneshwar Dutta Tripathi7.1 Introduction 2007.2 Artificial Intelligence 2007.2.1 Types of Artificial Intelligence 2017.2.1.1 Machine Intelligence 2017.2.1.2 Types of Machine Intelligence 2037.2.2 Applications of Artificial Intelligence 2047.2.2.1 Role in Healthcare Diagnostics 2057.2.2.2 AI in Telehealth 2057.2.2.3 Role in Structural Health Monitoring 2057.2.2.4 Role in Remote Medicare Management 2067.2.2.5 Predictive Analysis Using Big Data 2077.2.2.6 AI’s Role in Virtual Monitoring of Patients 2087.2.2.7 Functions of Devices 2087.2.2.8 Clinical Outcomes Through Remote Patient Monitoring 2107.2.2.9 Clinical Decision Support 2117.2.3 Utilization of Artificial Intelligence in Telemedicine 2117.2.3.1 Artificial Intelligence–Assisted Telemedicine 2127.2.3.2 Telehealth and New Care Models 2137.2.3.3 Strategy of Telecare Domain 2147.2.3.4 Role of AI-Assisted Telemedicine in Various Domains 2167.3 AI-Enabled Telehealth: Social and Ethical Considerations 2187.4 Conclusion 219References 2208 DEEP LEARNING FRAMEWORK FOR CANCER DIAGNOSIS AND TREATMENT 229Shiv Bahadur and Prashant Kumar8.1 Deep Learning: An Emerging Field for Cancer Management 2308.2 Deep Learning Framework in Diagnosis and Treatment of Cancer 2328.3 Applications of Deep Learning in Cancer Diagnosis 2338.3.1 Medical Imaging Through Artificial Intelligence 2348.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning 2348.3.3 Digital Pathology Through Deep Learning 2358.3.4 Application of Artificial Intelligence in Surgery 2368.3.5 Histopathological Images Using Deep Learning 2378.3.6 MRI and Ultrasound Images Through Deep Learning 2378.4 Clinical Applications of Deep Learning in the Management of Cancer 2388.5 Ethical Considerations in Deep Learning–Based Robotic Therapy 2398.6 Conclusion 240Acknowledgments 240References 2419 APPLICATIONS OF DEEP LEARNING IN RADIATION THERAPY 247Akanksha Sharma, Ashish Verma, Rishabha Malviya and Shalini Yadav9.1 Introduction 2489.2 History of Radiotherapy 2509.3 Principal of Radiotherapy 2519.4 Deep Learning 2519.5 Radiation Therapy Techniques 2549.5.1 External Beam Radiation Therapy 2579.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) 2599.5.3 Intensity Modulated Radiation Therapy (IMRT) 2609.5.4 Image-Guided Radiation Therapy (IGRT) 2619.5.5 Intraoperative Radiation Therapy (IORT) 2639.5.6 Brachytherapy 2659.5.7 Stereotactic Radiosurgery (SRS) 2689.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist 2699.6.1 Deep Learning in Patient Assessment 2699.6.1.1 Radiotherapy Results Prediction 2699.6.1.2 Respiratory Signal Prediction 2719.6.2 Simulation Computed Tomography 2719.6.3 Targets and Organs-at-Risk Segmentation 2739.6.4 Treatment Planning 2749.6.4.1 Beam Angle Optimization 2749.6.4.2 Dose Prediction 2769.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists 2779.7 Conclusion 280References 28110 APPLICATION OF DEEP LEARNING IN RADIATION THERAPY 289Shilpa Rawat, Shilpa Singh, Md. Aftab Alam and Rishabha Malviya10.1 Introduction 29010.2 Radiotherapy 29110.3 Principle of Deep Learning and Machine Learning 29310.3.1 Deep Neural Networks (DNN) 29410.3.2 Convolutional Neural Network 29510.4 Role of AI and Deep Learning in Radiation Therapy 29510.5 Platforms for Deep Learning and Tools for Radiotherapy 29710.6 Radiation Therapy Implementation in Deep Learning 30010.6.1 Deep Learning and Imaging Techniques 30110.6.2 Image Segmentation 30110.6.3 Lesion Segmentation 30210.6.4 Computer-Aided Diagnosis 30210.6.5 Computer-Aided Detection 30310.6.6 Quality Assurance 30410.6.7 Treatment Planning 30510.6.8 Treatment Delivery 30510.6.9 Response to Treatment 30610.7 Prediction of Outcomes 30710.7.1 Toxicity 30910.7.2 Survival and the Ability to Respond 31010.8 Deep Learning in Conjunction With Radiomoic 31210.9 Planning for Treatment 31410.9.1 Optimization of Beam Angle 31510.9.2 Prediction of Dose 31510.10 Deep Learning’s Challenges and Future Potential 31610.11 Conclusion 317References 31811 DEEP LEARNING FRAMEWORK FOR CANCER 333Pratishtha11.1 Introduction 33411.2 Brief History of Deep Learning 33511.3 Types of Deep Learning Methods 33611.4 Applications of Deep Learning 33911.4.1 Toxicity Detection for Different Chemical Structures 33911.4.2 Mitosis Detection 34011.4.3 Radiology or Medical Imaging 34111.4.4 Hallucination 34211.4.5 Next-Generation Sequencing (NGS) 34211.4.6 Drug Discovery 34311.4.7 Sequence or Video Generation 34311.4.8 Other Applications 34311.5 Cancer 34311.5.1 Factors 34411.5.1.1 Heredity 34511.5.1.2 Ionizing Radiation 34511.5.1.3 Chemical Substances 34511.5.1.4 Dietary Factors 34511.5.1.5 Estrogen 34611.5.1.6 Viruses 34611.5.1.7 Stress 34711.5.1.8 Age 34711.5.2 Signs and Symptoms of Cancer 34711.5.3 Types of Cancer Treatment Available 34811.5.3.1 Surgery 34811.5.3.2 Radiation Therapy 34811.5.3.3 Chemotherapy 34811.5.3.4 Immunotherapy 34811.5.3.5 Targeted Therapy 34911.5.3.6 Hormone Therapy 34911.5.3.7 Stem Cell Transplant 34911.5.3.8 Precision Medicine 34911.5.4 Types of Cancer 34911.5.4.1 Carcinoma 34911.5.4.2 Sarcoma 34911.5.4.3 Leukemia 35011.5.4.4 Lymphoma and Myeloma 35011.5.4.5 Central Nervous System (CNS) Cancers 35011.5.5 The Development of Cancer (Pathogenesis) Cancer 35011.6 Role of Deep Learning in Various Types of Cancer 35011.6.1 Skin Cancer 35111.6.1.1 Common Symptoms of Melanoma 35111.6.1.2 Types of Skin Cancer 35211.6.1.3 Prevention 35311.6.1.4 Treatment 35311.6.2 Deep Learning in Skin Cancer 35411.6.3 Pancreatic Cancer 35411.6.3.1 Symptoms of Pancreatic Cancer 35511.6.3.2 Causes or Risk Factors of Pancreatic Cancer 35511.6.3.3 Treatments of Pancreatic Cancer 35511.6.4 Deep Learning in Pancreatic Cancer 35511.6.5 Tobacco-Driven Lung Cancer 35711.6.5.1 Symptoms of Lung Cancer 35711.6.5.2 Causes or Risk Factors of Lung Cancer 35811.6.5.3 Treatments Available for Lung Cancer 35811.6.5.4 Deep Learning in Lung Cancer 35811.6.6 Breast Cancer 35911.6.6.1 Symptoms of Breast Cancer 36011.6.6.2 Causes or Risk Factors of Breast Cancer 36011.6.6.3 Treatments Available for Breast Cancer 36111.6.7 Deep Learning in Breast Cancer 36111.6.8 Prostate Cancer 36211.6.9 Deep Learning in Prostate Cancer 36211.7 Future Aspects of Deep Learning in Cancer 36311.8 Conclusion 363References 36312 CARDIOVASCULAR DISEASE PREDICTION USING DEEP NEURAL NETWORK FOR OLDER PEOPLE 369Nagarjuna Telagam, B.Venkata Kranti and Nikhil Chandra Devarasetti12.1 Introduction 37012.2 Proposed System Model 37512.2.1 Decision Tree Algorithm 37512.2.1.1 Confusion Matrix 37612.3 Random Forest Algorithm 38112.4 Variable Importance for Random Forests 38312.5 The Proposed Method Using a Deep Learning Model 38412.5.1 Prevention of Overfitting 38612.5.2 Batch Normalization 38612.5.3 Dropout Technique 38612.6 Results and Discussions 38612.6.1 Linear Regression 38612.6.2 Decision Tree Classifier 38812.6.3 Voting Classifier 38912.6.4 Bagging Classifier 38912.6.5 Naïve Bayes 39012.6.6 Logistic Regression 39012.6.7 Extra Trees Classifier 39112.6.8 K-Nearest Neighbor [KNN] Algorithm 39112.6.9 Adaboost Classifier 39212.6.10 Light Gradient Boost Classifier 39312.6.11 Gradient Boosting Classifier 39312.6.12 Stochastic Gradient Descent Algorithm 39312.6.13 Linear Support Vector Classifier 39412.6.14 Support Vector Machines 39412.6.15 Gaussian Process Classification 39512.6.16 Random Forest Classifier 39512.7 Evaluation Metrics 39612.8 Conclusion 401References 40213 MACHINE LEARNING: THE CAPABILITIES AND EFFICIENCY OF COMPUTERS IN LIFE SCIENCES 407Shalini Yadav, Saurav Yadav, Shobhit Prakash Srivastava, Saurabh Kumar Gupta and Sudhanshu Mishra13.1 Introduction 40813.2 Supervised Learning 41013.2.1 Workflow of Supervised Learning 41013.2.2 Decision Tree 41013.2.3 Support Vector Machine (SVM) 41113.2.4 Naive Bayes 41313.3 Deep Learning: A New Era of Machine Learning 41413.4 Deep Learning in Artificial Intelligence (AI) 41613.5 Using ML to Enhance Preventive and Treatment Insights 41713.6 Different Additional Emergent Machine Learning Uses 41813.6.1 Education 41813.6.2 Pharmaceuticals 41913.6.3 Manufacturing 41913.7 Machine Learning 41913.7.1 Neuroscience Research Advancements 42013.7.2 Finding Patterns in Astronomical Data 42013.8 Ethical and Social Issues Raised ! ! ! 42113.8.1 Reliability and Safety 42113.8.2 Transparency and Accountability 42113.8.3 Data Privacy and Security 42113.8.4 Malicious Use of AI 42213.8.5 Effects on Healthcare Professionals 42213.9 Future of Machine Learning in Healthcare 42213.9.1 A Better Patient Journey 42213.9.2 New Ways to Deliver Care 42413.10 Challenges and Hesitations 42413.10.1 Not Overlord Assistant Intelligent 42413.10.2 Issues with Unlabeled Data 42513.11 Concluding Thoughts 425Acknowledgments 426References 426Index 431
From Sequences to Graphs
In order to study living organisms, scientists not only study them at an overall macroscopic scale but also on a more detailed microscopic scale. This observation, pushed to its limits, consists of investigating the very center of each cell, where we find the molecules that determine the way it functions: DNA (deoxyribonucleic acid) and RNA (ribonucleic acid).In an organism, DNA carries the genetic information, which is called the genome. It is represented as four-letter sequences using the letters A, C, G and T; based on these sequences, computer methods described in this book can answer fundamental questions in bioinformatics.This book explores how to quickly find sequences of a few hundred nucleotides within a genome that may be made up of several billion, how to compare those sequences and how to reconstruct the complete sequence of a genome. It also discusses the problems of identifying bacteria in a given environment and predicting the structure of RNA based on its sequence.ANNIE CHATEAU is a lecturer at the University of Montpellier, France. Her research interests include algorithms and combinatorial structures.MIKAËL SALSON is a lecturer at the University of Lille, France. His work focuses mainly on indexing and sequence comparison.Preface xiAuthor Biographies xviiCHAPTER 1 METHODOLOGICAL CONCEPTS: ALGORITHMIC SOLUTIONS OF BIOINFORMATICS PROBLEMS 1Annie CHATEAU and Tom DAVOT-GRANGÉ1.1 Data, Models, Problem Formalism in Bioinformatics 11.1.1 Data 11.1.2 Genome Modeling 41.1.3 Problems in Bioinformatics 51.2 Mathematical Preliminaries 61.2.1 Propositional Logic Preliminaries 61.2.2 Preliminaries on Sets 71.3 Vocabulary in Text Algorithmics 91.4 Graph Theory 101.4.1 Subgraphs 121.4.2 Path in a Graph 131.4.3 Matching 131.4.4 Planarity 141.4.5 Tree Decomposition 151.5 Algorithmic Problems 161.5.1 Definition 161.5.2 Graph Problem 171.5.3 Satisfiability Problems 191.6 Problem Solutions 201.6.1 Algorithm 201.6.2 Complexity 211.6.3 Runtime 241.7 Complexity Classes 261.7.1 Generality 261.7.2 Exact Algorithms 281.7.3 Approximation Algorithms 321.7.4 Solvers 341.8 Some Algorithmic Techniques 351.8.1 Dynamic Programming 351.8.2 Tree Traversal 381.9 Validation 411.9.1 The Different Types of Errors 421.9.2 Quality Measures 441.9.3 And in the Non-Binary Case? 461.10 Conclusion 471.11 References 47CHAPTER 2 SEQUENCE INDEXING 49Thierry LECROQ and Mikaël SALSON2.1 Introduction 492.1.1 What is Indexing? 502.1.2 When to Index? 512.1.3 What to Index? 512.1.4 Indexing Structures and Queries Considered 522.1.5 Basic Notions and Vocabulary 532.2 Word Indexing 542.2.1 Bloom Filters 542.2.2 Inverted List 562.2.3 De Bruijn Graphs 602.2.4 Efficient Structures for Targeted Queries 612.3 Full-Text Indexing 622.3.1 Suffix Tree 622.3.2 (Extended) Suffix Array 642.3.3 Burrows–Wheeler Transform 672.4 Indexing Choice Criteria 762.4.1 Based on the Type of the Necessary Query 772.4.2 Based on the Space-Time and Data Quantity Trade-Off 772.4.3 Based on the Need to Add or Modify Indexed Data 792.4.4 Indexing Choices According to Applications 802.5 Conclusion and Perspectives 812.5.1 Efficient Methods for Indexing a Few Genomes or Sequencing Sets 812.5.2 Methods that Struggle to Take Advantage of Data Redundancy 822.6 References 83CHAPTER 3 SEQUENCE ALIGNMENT 87Laurent NOÉ3.1 Introduction 873.1.1 What is Pairwise Alignment? 873.1.2 How to Evaluate an Alignment? 883.2 Exact Alignment 903.2.1 Representation in Edit Graph Form 903.2.2 Global Alignment and Needleman–Wunsch Algorithm 933.2.3 Local Alignment and Smith–Waterman Algorithm 943.2.4 Alignment with Affine Indel Function and the Gotoh Algorithm 963.3 Heuristic Alignment 983.3.1 Seeds 993.3.2 Min-Hash and Global Sampling 1053.3.3 Minimizing and Local Sampling 1063.4 References 109CHAPTER 4 GENOME ASSEMBLY 113Dominique LAVENIER4.1 Introduction 1134.2 Sequencing Technologies 1164.2.1 Short Reads 1174.2.2 Long Reads 1184.2.3 Linked Reads 1184.2.4 Hi-C Reads 1194.2.5 Optical Mapping 1194.3 Assembly Strategies 1204.3.1 The Main Steps 1204.3.2 Cleaning and Correction of Reads 1214.3.3 Scaffold Construction 1224.3.4 Scaffold Ordering 1234.4 Scaffold Construction Methods 1244.4.1 Greedy Assembly 1244.4.2 OLC Assembly 1264.4.3 DBG Assembly 1274.4.4 Constrained Assembly 1304.5 Scaffold-Ordering Methods 1324.5.1 Hi-C Data-Based Methods 1324.5.2 Optical Mapping-Based Methods 1374.6 Assembly Validation 1394.6.1 Metrics 1404.6.2 Read Realignment 1404.6.3 Gene Prediction 1414.6.4 Competitions 1414.7 Conclusion 1424.8 References 143CHAPTER 5 METAGENOMICS AND METATRANSCRIPTOMICS 147Cervin GUYOMAR and Claire LEMAITRE5.1 What is Metagenomics? 1475.1.1 Motivations and Historical Context 1475.1.2 The Metagenomics Data 1485.1.3 Bioinformatics Challenges for Metagenomics 1515.2 “Who Are They”: Taxonomic Characterization of Microbial Communities 1535.2.1 Methods for Targeted Metagenomics 1545.2.2 Whole-Genome Methods with Reference 1555.2.3 Reference-Free Methods 1605.3 “What Are They Able To Do?”: Functional Metagenomics 1665.3.1 Gene Prediction and Annotation 1665.3.2 Metatranscriptomics 1675.3.3 Reconstruction of Metabolic Networks 1685.4 Comparative Metagenomics 1695.4.1 Comparative Metagenomics with Diversity Estimation 1705.4.2 De Novo Comparative Metagenomics 1705.5 Conclusion 1755.6 References 176CHAPTER 6 RNA FOLDING 185Yann PONTY And Vladimir REINHARZ6.1 Introduction 1856.1.1 RNA Folding 1866.1.2 Secondary Structure 1896.2 Optimization for Structure Prediction 1926.2.1 Computing the Minimum Free-Energy (MFE) Structure 1926.2.2 Listing (Sub)optimal Structures 1986.2.3 Comparative Prediction: Simultaneous Alignment/Folding of RNAs 2036.2.4 Joint Alignment/Folding Model 2046.3 Analyzing the Boltzmann Ensemble 2106.3.1 Computing the Partition Function 2106.3.2 Statistical Sampling 2156.3.3 Boltzmann Probability of Structural Patterns 2206.4 Studying RNA Structure in Practice 2256.4.1 The Turner Model 2256.4.2 Tools 2286.5 References 228Conclusion 233List of Authors 237Index 239
The Official (ISC)2 CCSP CBK Reference
THE ONLY OFFICIAL BODY OF KNOWLEDGE FOR CCSP—THE MOST POPULAR CLOUD SECURITY CREDENTIAL—FULLY REVISED AND UPDATED. Certified Cloud Security Professional (CCSP) certification validates the advanced technical skills needed to design, manage, and secure data, applications, and infrastructure in the cloud. This highly sought-after global credential has been updated with revised objectives. The new third edition of The Official (ISC)2 Guide to the CCSP CBK is the authoritative, vendor-neutral common body of knowledge for cloud security professionals. This comprehensive resource provides cloud security professionals with an indispensable working reference to each of the six CCSP domains: Cloud Concepts, Architecture and Design; Cloud Data Security; Cloud Platform and Infrastructure Security; Cloud Application Security; Cloud Security Operations; and Legal, Risk and Compliance. Detailed, in-depth chapters contain the accurate information required to prepare for and achieve CCSP certification. Every essential area of cloud security is covered, including implementation, architecture, operations, controls, and immediate and long-term responses. Developed by (ISC)2, the world leader in professional cybersecurity certification and training, this indispensable guide:* Covers the six CCSP domains and over 150 detailed objectives* Provides guidance on real-world best practices and techniques* Includes illustrated examples, tables, and diagrams The Official (ISC)2 Guide to the CCSP CBK is a vital ongoing resource for IT and information security leaders responsible for applying best practices to cloud security architecture, design, operations and service orchestration. Foreword to the Fourth Edition xixIntroduction xxiCHAPTER 1 CLOUD CONCEPTS, ARCHITECTURE, AND DESIGN 1Understand Cloud Computing Concepts 2Cloud Computing Definitions 2Cloud Computing Roles and Responsibilities 3Key Cloud Computing Characteristics 7Building Block Technologies 11Describe Cloud Reference Architecture 14Cloud Computing Activities 14Cloud Service Capabilities 15Cloud Service Categories 17Cloud Deployment Models 18Cloud Shared Considerations 21Impact of Related Technologies 27Understand Security Concepts Relevant to Cloud Computing 33Cryptography and Key Management 33Identity and Access Control 34Data and Media Sanitization 36Network Security 37Virtualization Security 39Common Threats 41Security Hygiene 41Understand Design Principles of Secure Cloud Computing 43Cloud Secure Data Lifecycle 43Cloud-BasedBusiness Continuity and Disaster Recovery Plan 44Business Impact Analysis 45Functional Security Requirements 46Security Considerations for Different Cloud Categories 48Cloud Design Patterns 49DevOps Security 51Evaluate Cloud Service Providers 51Verification against Criteria 52System/Subsystem Product Certifications 54Summary 56CHAPTER 2 CLOUD DATA SECURITY 57Describe Cloud Data Concepts 58Cloud Data Lifecycle Phases 58Data Dispersion 61Data Flows 62Design and Implement Cloud Data Storage Architectures 63Storage Types 63Threats to Storage Types 66Design and Apply Data Security Technologies and Strategies 67Encryption and Key Management 67Hashing 70Data Obfuscation 71Tokenization 73Data Loss Prevention 74Keys, Secrets, and Certificates Management 77Implement Data Discovery 78Structured Data 79Unstructured Data 80Semi-structuredData 81Data Location 82Implement Data Classification 82Data Classification Policies 83Mapping 85Labeling 86Design and Implement Information Rights Management 87Objectives 88Appropriate Tools 89Plan and Implement Data Retention, Deletion, and Archiving Policies 89Data Retention Policies 90Data Deletion Procedures and Mechanisms 93Data Archiving Procedures and Mechanisms 94Legal Hold 95Design and Implement Auditability, Traceability, and Accountability of Data Events 96Definition of Event Sources and Requirement of Event Attribution 97Logging, Storage, and Analysis of Data Events 99Chain of Custody and Nonrepudiation 100Summary 101CHAPTER 3 CLOUD PLATFORM AND INFRASTRUCTURE SECURITY 103Comprehend Cloud Infrastructure and Platform Components 104Physical Environment 104Network and Communications 106Compute 107Virtualization 108Storage 110Management Plane 111Design a Secure Data Center 113Logical Design 114Physical Design 116Environmental Design 117Analyze Risks Associated with Cloud Infrastructure and Platforms 119Risk Assessment 119Cloud Vulnerabilities, Threats, and Attacks 122Risk Mitigation Strategies 123Plan and Implementation of Security Controls 124Physical and Environmental Protection 124System, Storage, and Communication Protection 125Identification, Authentication, and Authorization in Cloud Environments 127Audit Mechanisms 128Plan Disaster Recovery and Business Continuity 131Business Continuity/Disaster Recovery Strategy 131Business Requirements 132Creation, Implementation, and Testing of Plan 134Summary 138CHAPTER 4 CLOUD APPLICATION SECURITY 139Advocate Training and Awareness for Application Security 140Cloud Development Basics 140Common Pitfalls 141Common Cloud Vulnerabilities 142Describe the Secure Software Development Life Cycle Process 144NIST Secure Software Development Framework 145OWASP Software Assurance Maturity Model 145Business Requirements 145Phases and Methodologies 146Apply the Secure Software Development Life Cycle 149Cloud-Specific Risks 149Threat Modeling 153Avoid Common Vulnerabilities during Development 156Secure Coding 156Software Configuration Management and Versioning 157Apply Cloud Software Assurance and Validation 158Functional and Non-functional Testing 159Security Testing Methodologies 160Quality Assurance 164Abuse Case Testing 164Use Verified Secure Software 165Securing Application Programming Interfaces 165Supply-Chain Management 166Third-Party Software Management 166Validated Open-Source Software 167Comprehend the Specifics of Cloud Application Architecture 168Supplemental Security Components 169Cryptography 171Sandboxing 172Application Virtualization and Orchestration 173Design Appropriate Identity and Access Management Solutions 174Federated Identity 175Identity Providers 175Single Sign-on 176Multifactor Authentication 176Cloud Access Security Broker 178Summary 179CHAPTER 5 CLOUD SECURITY OPERATIONS 181Build and Implement Physical and Logical Infrastructure for Cloud Environment 182Hardware-Specific Security Configuration Requirements 182Installation and Configuration of Virtualization Management Tools 185Virtual Hardware–Specific Security Configuration Requirements 186Installation of Guest Operating System Virtualization Toolsets 188Operate Physical and Logical Infrastructure for Cloud Environment 188Configure Access Control for Local and Remote Access 188Secure Network Configuration 190Operating System Hardening through the Application of Baselines 195Availability of Stand-Alone Hosts 196Availability of Clustered Hosts 197Availability of Guest Operating Systems 199Manage Physical and Logical Infrastructure for Cloud Environment 200Access Controls for Remote Access 201Operating System Baseline Compliance Monitoring and Remediation 202Patch Management 203Performance and Capacity Monitoring 205Hardware Monitoring 206Configuration of Host and Guest Operating System Backup and Restore Functions 207Network Security Controls 208Management Plane 212Implement Operational Controls and Standards 212Change Management 213Continuity Management 214Information Security Management 216Continual Service Improvement Management 217Incident Management 218Problem Management 221Release Management 221Deployment Management 222Configuration Management 224Service Level Management 225Availability Management 226Capacity Management 227Support Digital Forensics 228Forensic Data Collection Methodologies 228Evidence Management 230Collect, Acquire, and Preserve Digital Evidence 231Manage Communication with Relevant Parties 234Vendors 235Customers 236Partners 238Regulators 238Other Stakeholders 239Manage Security Operations 239Security Operations Center 240Monitoring of Security Controls 244Log Capture and Analysis 245Incident Management 248Summary 253CHAPTER 6 LEGAL, RISK, AND COMPLIANCE 255Articulating Legal Requirements and Unique Risks within the Cloud Environment 256Conflicting International Legislation 256Evaluation of Legal Risks Specific to Cloud Computing 258Legal Frameworks and Guidelines 258eDiscovery 265Forensics Requirements 267Understand Privacy Issues 267Difference between Contractual and Regulated Private Data 268Country-Specific Legislation Related to Private Data 272Jurisdictional Differences in Data Privacy 277Standard Privacy Requirements 278Privacy Impact Assessments 280Understanding Audit Process, Methodologies, and Required Adaptations for a Cloud Environment 281Internal and External Audit Controls 282Impact of Audit Requirements 283Identify Assurance Challenges of Virtualization and Cloud 284Types of Audit Reports 285Restrictions of Audit Scope Statements 288Gap Analysis 289Audit Planning 290Internal Information Security Management System 291Internal Information Security Controls System 292Policies 293Identification and Involvement of Relevant Stakeholders 296Specialized Compliance Requirements for Highly Regulated Industries 297Impact of Distributed Information Technology Model 298Understand Implications of Cloud to Enterprise Risk Management 299Assess Providers Risk Management Programs 300Differences between Data Owner/Controller vs. Data Custodian/Processor 301Regulatory Transparency Requirements 302Risk Treatment 303Risk Frameworks 304Metrics for Risk Management 307Assessment of Risk Environment 307Understand Outsourcing and Cloud Contract Design 309Business Requirements 309Vendor Management 311Contract Management 312Supply Chain Management 314Summary 316Index 317
Erklärvideos
An jedem Büroarbeitsplatz ist ein Werkzeug vorhanden, komplexe Zusammenhänge leicht zu erklären: PowerPoint. Aber viele Anwender wissen gar nicht, was es tatsächlich alles kann: nicht nur einfache Bildschirmvorgänge aufzeichnen, sondern auch komplex animierte Videos erstellen. Elemente können ein- oder ausgeblendet werden, schrittweise aufgebaut werden oder ineinander übergehen. So wird die Konzentration der Zuschauer fokussiert und Zusammenhänge klarer. Dieses Heft konzentriert sich auf den Bereich der Videoerstellung zu Lehr- und Demonstrationszwecken. Weitere Hard- oder Software ist nicht nötig. Die Ergebnisse können an beliebigem Ort abgespeichert oder verteilt werden, damit einmal geleistete Arbeit je nach Bedarf abgerufen werden kann.Ina Koys ist langjährige Trainerin für MS-Office-Produkte. Viele Fragen werden in den Kursen immer wieder gestellt, aber selten in Fachbüchern behandelt. Einige davon beantwortet sie jetzt in der Reihe "kurz & knackig".
Practical CockroachDB
Get a practical introduction to CockroachDB. This book starts with installation and foundational concepts and takes you through to creating clusters that are ready for production environments. You will learn how to create, optimize, and operate CockroarchDB clusters in single and multi-region environments. You will encounter anti-patterns to avoid, as well as testing techniques for integration and load testing.The book explains why CockroachDB exists, goes over its major benefits, and quickly transitions into installing and configuring CockroachDB. Just as quickly, you’ll be creating databases, getting data into those databases, and querying that data from your applications. You’ll progress to data privacy laws such as GDPR and CCPA, and learn how CockroachDB’s global distribution features can help you comply with ever-shifting data sovereignty regulations. From there, you’ll move into deployment topologies, guidance on integration testing and load testing, best practices, and a readiness checklist for production deployments.WHAT YOU WILL LEARN* Deploy and interact with CockroachDB* Design and optimize databases and tables* Choose the correct data types for modeling your data* Protect data with database and table encryption* Achieve compliance with international data privacy regulations* Scale your databases in a way that enhances their performance* Monitor changes to the data and health of your databasesWHO THIS BOOK IS FORDevelopers and database administrators who want to provide a secure, reliable, and effortlessly distributed home for their data; those who wish to use a modern tool to tackle the kinds of scaling challenges that have previously required dedicated teams of people to solve; anyone who wants to leverage their database to solve non-trivial, real-world challenges while protecting their data and users ROB REID is a software developer from London, England. In his career, he has written backend, frontend, and messaging software for the police, travel, finance, commodity, sports betting, telecom, retail, and aerospace industries. He is an avid user of CockroachDB and has worked with the Cockroach Labs team in recent years to promote the database and embed it into development teams in the US and UK. 1. The Reason for CockroachDB2. Installing CockroachDB3. Concepts4. Managing CockroachDB from the Command Line5. Interacting with CockroachDB Instances6. Data Privacy7. Deployment Topologies8. Testing9. Production