Allgemein
Internet-Modellierung mit Julia
Das Buch verbindet die Internetmodellierung mit der neuen Programmiersprache Julia. Die Eignung von Julia für die Modellierung sowohl etablierter Internet-Modelle als auch für forschungsnahe Themen wie die Car-to-Infrastructure-Kommunikation und von Blackbox-Modellen für Lastprognosen mit neuronalen Netzen wird vermittelt. Nach dem Studium des Buches und den Anregungen für weitere selbständige Analysen ist der Leser in der Lage, auch komplexere Modellierungsaufgaben in Forschung und Entwicklung mit Julia zu bearbeiten. ULRICH HOFMANN hat über 30 Jahre in Wirtschaft, Lehre und Forschung am Thema der Internet-Technologien mit den Schwerpunkten QoS (Quality of Service) gearbeitet. Einleitung.- Leistungsparameter, Optimierung Ressourceneinsatz.- Modellmodule.- Lastgeneratoren.- Bedienmodule.- Simulation von Bediensystemen.- Simulationsbasierte Optimierung mit KI.
Beginning Database Design Solutions
A CONCISE INTRODUCTION TO DATABASE DESIGN CONCEPTS, METHODS, AND TECHNIQUES IN AND OUT OF THE CLOUDIn the newly revised second edition of Beginning Database Design Solutions: Understanding and Implementing Database Design Concepts for the Cloud and Beyond, Second Edition, award-winning programming instructor and mathematician Rod Stephens delivers an easy-to-understand guide to designing and implementing databases both in and out of the cloud. Without assuming any prior database design knowledge, the author walks you through the steps you’ll need to take to understand, analyze, design, and build databases.In the book, you’ll find clear coverage of foundational database concepts along with hands-on examples that help you practice important techniques so you can apply them to your own database designs, as well as:* Downloadable source code that illustrates the concepts discussed in the book* Best practices for reliable, platform-agnostic database design* Strategies for digital transformation driven by universally accessible database designAn essential resource for database administrators, data management specialists, and database developers seeking expertise in relational, NoSQL, and hybrid database design both in and out of the cloud, Beginning Database Design Solutions is a hands-on guide ideal for students and practicing professionals alike.ROD STEPHENS is a professional programmer, sought-after technical speaker, instructor, and author. He’s written 37 books and over 250 magazine articles on C#, Java, and other programming languages. He runs the popular C# Helper and VB Helper websites which have helped millions of programmers with tips, tricks, and useful example code. Introduction xxvPART 1: INTRODUCTION TO DATABASES AND DATABASE DESIGNChapter 1: Database Design Goals 3Chapter 2: Relational Overview 29Chapter 3: NoSQL OVERVIEW 47PART 2: DATABASE DESIGN PROCESS AND TECHNIQUESChapter 4: Understanding User Needs 83Chapter 5: Translating User Needs Into Data Models 111Chapter 6: Extracting Business Rules 145Chapter 7: Normalizing Data 163Chapter 8: Designing Databases to Support Software 203Chapter 9: Using Common Design Patterns 215Chapter 10: Avoiding Common Design Pitfalls 241PART 3: A DETAILED CASE STUDYChapter 11: Defining User Needs and Requirements 263Chapter 12: Building a Data Model 283Chapter 13: Extracting Business Rules 303Chapter 14: Normalizing and Refining 313PART 4: EXAMPLE PROGRAMSChapter 15: Example Overview 327Chapter 16: MariaDB IN PYTHON 339Chapter 17: MariaDB IN C# 355Chapter 18: PostgreSQL IN PYTHON 369Chapter 19: PostgreSQL IN C# 389Chapter 20: Neo4j AuraDB IN PYTHON 401Chapter 21: Neo4j AuraDB IN C# 417Chapter 22: MongoDB ATLAS IN PYTHON 431Chapter 23: MongoDB ATLAS IN C# 453Chapter 24: Apache Ignite in Python 467Chapter 25: Apache Ignite in C# 477PART 5: ADVANCED TOPICSChapter 26: Introduction to Sql 489Chapter 27: Building Databases with Sql Scripts 519Chapter 28: Database Maintenance 533Chapter 29: Database Security 545Appendix A: Exercise Solutions 557Appendix B: Sample Relational Designs 649Glossary 671Index 683
Software Engineering
Das Handbuch fürs Selbststudium, für den Job oder vorlesungsbegleitend* erfahrungsbasierter Über- und Einblick ins Software Engineering, der sowohl die Theorie als auch die Praxis abdeckt* umfassend, verständlich und praxiserprobtDas Buch vermittelt die Grundlagen, Erfahrungen und Techniken, die den Kern des Software Engineerings bilden. Es ist als Material zu Vorlesungen über Software Engineering konzipiert. Auch für Praktiker, die mit der Softwareentwicklung und -bearbeitung und den dabei auftretenden Problemen vertraut sind, ist das Buch sehr gut geeignet, um die Kenntnisse im Selbststudium zu ergänzen und zu vertiefen. Der Inhalt des Buches ist in fünf Hauptteile gegliedert:- Grundlagen- Menschen und Prozesse- Daueraufgaben im Softwareprojekt- Techniken der Softwarebearbeitung- Verwaltung und Erhaltung von SoftwareAuch auf die Ausbildung zukünftiger Software Engineers wird eingegangen. Ergänzende Informationen sind auf der Webseite der Autoren verfügbar: https://se-buch.de.Prof. Dr. rer. nat. Jochen Ludewig geboren 1947 in Hannover. Studium der Elektrotechnik (TU Hannover) und Informatik (TU München); Promotion 1981. 1975 bis 1980 Gesellschaft für Kernforschung, Karlsruhe, dann Brown Boveri Forschungszentrum in Baden/Schweiz. 1986 Assistenzprofessor an der ETH Zürich, 1988 Ruf auf den neuen Lehrstuhl Software Engineering an der Universität Stuttgart. Arbeitsgebiete: Softwareprojekt-Management, Software-Prüfung und Software-Qualität, Software-Wartung. Ab 1996 Konzeption und Aufbau des Diplomstudiengangs Softwaretechnik, der inzwischen in einen Bachelor- und einen Masterstudiengang umgewandelt wurde. Seit 2009 Fellow der Gesellschaft für Informatik (GI). Prof. Dr. rer. nat. Horst Lichter geboren 1960 in Trier. Studium der Informatik und Betriebswirtschaftslehr (TU Kaiserslautern). Wissenschaftlicher Mitarbeiter (ETH Zürich und Universität Stuttgart), Promotion 1993. Anschließend Schweizerische Bankgesellschaft Zürich und ABB Forschungszentrum Heidelberg. 1998 Ruf an die RWTH Aachen University, Leiter des Lehr- und Forschungsgebiets Software-Konstruktion. Arbeitsgebiete: Software-Architektur, Qualitätssicherung, Software-Evolution. Seit 2005 Lecturer an der Thai German Graduate School of Engineering (TGGS) in Bangkok. Von 2018-2021 Adjunct Lecturer an der Universiti Teknologi Petronas (UTP) Malaysia.
Developing Web Components with Svelte
Leverage the power of Svelte to quickly produce the foundations of a framework-agnostic component library that can extend and develop over time. This is a project-oriented book that simplifies setting up the skeleton of the library and adding components, using little more than a text editor or free software.You'll gain a starting baseline that can be used to develop future projects or incorporated into an existing workflow. You'll take development to the next level and can use this knowledge to create components with any framework, such as React, Angular or Vue.js, not just Svelte. Developing Web Components with Svelte is an excellent resource for getting acquainted with creating and maintaining a component library using a phased approach. It takes the view that you don't have to create something complex and unwieldy. Instead, you can start building something quickly, then extend it as needs dictate over time, without sacrificing speed or features.You'll see how to develop cleaner components in a quick, clear and straightforward way. The components you create in this project can be made available in one of the world's most extensive component ecosystems to be usable by other frameworks, making them genuinely reusable. In short, Svelte offers plenty of opportunities as it is based on Node.js and JavaScript making Svelte a powerful package to work from.WHAT YOU'LL LEARN* Use the Svelte framework to rapidly create and deploy the foundation of a component library that is versatile and performant* Review developing and customizing components based on our needs* Work through a real-world project to help solidify skills learned from the book and put them into practiceWHO THIS BOOK IS FOR* Website developers, familiar with JavaScript, who are keen to learn how to leverage the Svelte framework fast* Agile development teams, where time is of the essence, and the pressure is on to deliver results quickly* Developers who want to focus on simplicity, to produce efficient and optimized content in modern browsers using tools readily availableALEX LIBBY is a frontend engineer and seasoned book author who hails from England. His passion for all things Open Source dates back to the days of his degree studies, where he first came across web development and has been hooked ever since. His daily work involves extensive use of React, NodeJS, JavaScript, HTML, and CSS; Alex enjoys tinkering with different open source libraries to see how they work. He has spent a stint maintaining the jQuery Tools library and enjoys writing about Open Source technologies, principally for front end UI development.1: Getting Started2: Creating Basic Components3: Building Action Components4: Building the Navigation Components5: Creating Notification Components6: Creating Grid Components7: Writing Documentation8: Documenting More Components9. Testing Components10: Deploying into Production11: Taking Things Further
Microsoft Power BI Data Analyst Certification Companion
Use this book to study for the PL-300 Microsoft Power BI Data Analyst exam. The book follows the “Skills Measured” outline provided by Microsoft to help focus your study. Each topic area from the outline corresponds to an area covered by the exam, and the book helps you build a good base of knowledge in each area. Each topic is presented with a blend of practical explanations, theory, and best practices.Power BI is more than just the Power BI Desktop or the Power BI Service. It is two distinct applications and an online service that, together, enable business users to gather, shape, and analyze data to generate and present insights. This book clearly delineates the purpose of each component and explains the key concepts necessary to use each component effectively. Each chapter provides best practices and tips to help an inexperienced Power BI practitioner develop good habits that will support larger or more complex analyses.Many business analysts come to Power BI with a wealth of experience in Excel and particularly with pivot tables. Some of this experience translates readily into Power BI concepts. This book leverages that overlap in skill sets to help seasoned Excel users overcome the initial learning curve in Power BI, but no prior knowledge of any kind is assumed, terminology is defined in non-technical language, and key concepts are explained using analogies and ideas from experiences common to any reader. After reading this book, you will have the background and capability to learn the skills and concepts necessary both to pass the PL-300 exam and become a confident Power BI practitioner.WHAT YOU WILL LEARN* Create user-friendly, responsive reports with drill-throughs, bookmarks, and tool tips* Construct a star schema with relationships, ensuring that your analysis will be both accurate and responsive* Publish reports and datasets to the Power BI Service, enabling the report (and the dataset) to be viewed and used by your colleagues * Extract data from a variety of sources, enabling you to leverage the data that your organization has collected and stored in a variety of sources* Schedule data refreshes for published datasets so your reports and dashboards stay up to date* Develop dashboards with visuals from different reports and streaming contentWHO THIS BOOK IS FORPower BI users who are planning to take the PL-300 exam, Power BI users who want help studying the topic areas listed in Microsoft’s outline for the PL-300 exam, and those who are not planning to take the exam but want to close any knowledge gaps they might haveJESSICA JOLLY, MCT, MCSE, DA-100, PL-100, PL-300, DP-500, is a Microsoft certified trainer who helps businesses, non-profits, and individuals improve their business intelligence skills, bit by byte. She runs her own business, ALT-Enter, LLC. Before her reinvention as an entrepreneur (after 50!), she worked for Unilever, a global consumer products company, for 27 years, in a variety of managerial roles. Her business practice focuses on data visualization tools, specifically Microsoft's Power BI platform. She is living proof that you can cry through every math class you ever had, and still fashion a career that focuses on data, charts, visuals, and code. When she is not training other adults, she is knitting and quilting (a refuge from all of the technology!), reading about the Civil War, swimming, gardening, biking, hiking, and canoeing.PART I. PREPARE TO STUDY1. Exam Overview2. PL-300 CoveragePART II. PREPARE THE DATA3. Get Data from Different Data Sources4. Clean, Transform, and Load the DataPART III. MODEL THE DATA5. Design a Data Model6. Develop a Data Model7. Create Model Calculations using DAX8. Optimize Model PerformancePART IV. VISUALIZE AND ANALYZE THE DATA9. Create Reports10. Enhance Reports11. Identify Patterns and TrendsPART V. DEPLOY AND MAINTAIN ASSETS12. Manage Files and Datasets13. Create Dashboards14. Manage WorkspacesPART VI. CONTINUE YOUR LEARNING15. Where Do You Go From Here?
Practical Highcharts with Angular
Learn to create stunning animated and interactive charts using Highcharts and Angular. This updated edition will build on your existing knowledge of HTML, CSS, and JavaScript to develop impressive dashboards that will work in all modern browsers.You will learn how to use Highcharts, call backend services for data, and easily construct real-time data dashboards so you can club your code with jQuery and Angular. This book provides the best solutions for real-time challenges and covers a wide range of charts including line, area, maps, plot, and much more. You will also learn about arc diagrams, bubble series, dependency wheels, and error bar series. After reading this book, you'll be able to export your charts in different formats for project-based learning.Highcharts is one the most useful products worldwide for developing charting on the web, and Angular is well known for speed. Using Highcharts with Angular developers can build fast, interactive dashboards. Get up to speed using this book today.WHAT YOU’LL LEARN* Create interactive animated styling themes and colors for a dashboard* Work on a real-time data project using Web API and Ajax calls through different data sources* Export charts in various formatsWHO THIS BOOK IS FORDevelopers, dev leads, software architects, students or enthusiasts who are already familiar with HTML, CSS, and JavaScript.Sourabh Mishra is an Entrepreneur, Developer, Speaker, International Author, Corporate Trainer, and Animator. He is a Microsoft guy; he is very passionate about Microsoft technologies and a true .Net Warrior. Sourabh started his career when he was just 15 years old. He’s loved computers from childhood. His programming experience includes C/C++, Asp.Net, C#, Vb.net, WCF, Sqlserver, Entity Framework, MVC, Web API, Azure, Jquery, Highcharts, and Angular. Sourabh has been awarded a Most Valuable Professional (MVP) status. He has the zeal to learn new technologies, sharing his knowledge on several online community forums.He is a founder of “IECE Digital” and “Sourabh Mishra Notes”, an online knowledge-sharing platform where one can learn new technologies very easily and comfortably.1. Getting Started with Highcharts.- 2. Concept of Highcharts.- 3. Integrating Highcharts with Angular.- 4. Different Charting Types.- 6. Working with Real-time Data.- 6. Themes and Additional Features in Highcharts.- 7. Building a Real-time Dashboard.
Beginning Database Design Solutions
A CONCISE INTRODUCTION TO DATABASE DESIGN CONCEPTS, METHODS, AND TECHNIQUES IN AND OUT OF THE CLOUDIn the newly revised second edition of Beginning Database Design Solutions: Understanding and Implementing Database Design Concepts for the Cloud and Beyond, Second Edition, award-winning programming instructor and mathematician Rod Stephens delivers an easy-to-understand guide to designing and implementing databases both in and out of the cloud. Without assuming any prior database design knowledge, the author walks you through the steps you’ll need to take to understand, analyze, design, and build databases.In the book, you’ll find clear coverage of foundational database concepts along with hands-on examples that help you practice important techniques so you can apply them to your own database designs, as well as:* Downloadable source code that illustrates the concepts discussed in the book* Best practices for reliable, platform-agnostic database design* Strategies for digital transformation driven by universally accessible database designAn essential resource for database administrators, data management specialists, and database developers seeking expertise in relational, NoSQL, and hybrid database design both in and out of the cloud, Beginning Database Design Solutions is a hands-on guide ideal for students and practicing professionals alike.ROD STEPHENS is a professional programmer, sought-after technical speaker, instructor, and author. He’s written 37 books and over 250 magazine articles on C#, Java, and other programming languages. He runs the popular C# Helper and VB Helper websites which have helped millions of programmers with tips, tricks, and useful example code. Introduction xxvPART 1: INTRODUCTION TO DATABASES AND DATABASE DESIGNCHAPTER 1: DATABASE DESIGN GOALS 3The Importance of Design 4Information Containers 6Strengths and Weaknesses of Information Containers 8Desirable Database Features 9Crud 10Retrieval 10Consistency 11Validity 11Easy Error Correction 12Speed 13Atomic Transactions 13Acid 14Base 16NewSQL 17Persistence and Backups 17Low Cost and Extensibility 18Ease of Use 19Portability 19Security 20Sharing 21Ability to Perform Complex Calculations 21CAP Theorem 22Cloud Considerations 22Legal and Security Considerations 23Consequences of Good and Bad Design 24Summary 26CHAPTER 2: RELATIONAL OVERVIEW 29Picking a Database 30Relational Points of View 31Table, Rows, and Columns 32Relations, Attributes, and Tuples 34Keys 34Indexes 36Constraints 37Domain Constraints 37Check Constraints 37Primary Key Constraints 38Unique Constraints 38Foreign Key Constraints 38Database Operations 40Popular RDBs 41Spreadsheets 43Summary 44CHAPTER 3: NOSQL OVERVIEW 47The Cloud 47Picking a Database 50NoSQL Philosophy 50NoSQL Databases 50Document Databases 51Key- Value Database 52Column- Oriented Databases 53Graph Databases 53Street Networks 54Communication Networks 55Social Media Apps 55E- Commerce Programs 55Algorithms 56Hierarchical Databases 56Less Exotic Options 59Flat Files 59XML Files 60XML Basics 61XML Practices 64XML Summary 66JSON Files 67Spreadsheets 69More Exotic Options 70Object 70Deductive 70Dimensional 70Temporal 71Database Pros and Cons 72Relational 72General NoSQL 73Quick Guidelines 74Summary 76PART 2: DATABASE DESIGN PROCESS AND TECHNIQUESCHAPTER 4: UNDERSTANDING USER NEEDS 83Make a Plan 84Bring a List of Questions 85Functionality 85Data Needs 86Data Integrity 86Security 87Environment 88Meet the Customers 88Learn Who’s Who 89Pick the Customers’ Brains 93Walk a Mile in the User’s Shoes 93Study Current Operations 94Brainstorm 94Look to the Future 95Understand the Customers’ Reasoning 96Learn What the Customers Really Need 97Prioritize 98Verify Your Understanding 99Create the Requirements Document 101Make Use Cases 102Decide Feasibility 106Summary 106CHAPTER 5: TRANSLATING USER NEEDS INTO DATA MODELS 111What Are Data Models? 112User Interface Models 114Semantic Object Models 118Classes and Objects 119Cardinality 120Identifiers 120Putting It Together 121Semantic Views 122Class Types 124Simple Objects 124Composite Objects 124Compound Objects 125Hybrid Objects 125Association Objects 126Inherited Objects 128Comments and Notes 129Entity- Relationship Models 130Entities, Attributes, and Identifiers 131Relationships 132Cardinality 133Inheritance 134Additional Conventions 136Comments and Notes 137Relational Models 137Converting Semantic Object Models 138Converting ER Diagrams 140Summary 142CHAPTER 6: EXTRACTING BUSINESS RULES 145What Are Business Rules? 145Identifying Key Business Rules 147Extracting Key Business Rules 152Multi- Tier Applications 154Summary 158CHAPTER 7: NORMALIZING DATA 163What Is Normalization? 163First Normal Form (1NF) 164Second Normal Form (2NF) 173Third Normal Form (3NF) 177Stopping at Third Normal Form 181Boyce- Codd Normal Form (BCNF) 181Fourth Normal Form (4NF) 185Fifth Normal Form (5NF) 190Domain/Key Normal Form (DKNF) 193Essential Redundancy 195The Best Level of Normalization 197NoSQL Normalization 197Summary 199CHAPTER 8: DESIGNING DATABASES TO SUPPORT SOFTWARE 203Plan Ahead 204Document Everything 204Consider Multi- Tier Architecture 205Convert Domains into Tables 205Keep Tables Focused 206Use Three Kinds of Tables 207Use Naming Conventions 209Allow Some Redundant Data 210Don’t Squeeze in Everything 211Summary 212CHAPTER 9: USING COMMON DESIGN PATTERNS 215Associations 216Many- to- Many Associations 216Multiple Many- to- Many Associations 216Multiple- Object Associations 218Repeated Attribute Associations 221Reflexive Associations 222One- to- One Reflexive Associations 223One- to- Many Reflexive Associations 224Hierarchical Data 225Hierarchical Data with NoSQL 228Network Data 229Network Data with NoSQL 231Temporal Data 232Effective Dates 232Deleted Objects 233Deciding What to Temporalize 234Logging and Locking 236Audit Trails 236Turnkey Records 237Summary 238CHAPTER 10: AVOIDING COMMON DESIGN PITFALLS 241Lack of Preparation 241Poor Documentation 242Poor Naming Standards 242Thinking Too Small 244Not Planning for Change 245Too Much Normalization 248Insufficient Normalization 248Insufficient Testing 249Performance Anxiety 249Mishmash Tables 250Not Enforcing Constraints 253Obsession with IDs 253Not Defining Natural Keys 256Summary 257PART 3: A DETAILED CASE STUDYCHAPTER 11: DEFINING USER NEEDS AND REQUIREMENTS 263Meet the Customers 263Pick the Customers’ Brains 265Determining What the System Should Do 265Determining How the Project Should Look 267Determining What Data Is Needed for the User Interface 268Determining Where the Data Should Come From 269Determining How the Pieces of Data Are Related 269Determining Performance Needs 271Determining Security Needs 272Determining Data Integrity Needs 273Write Use Cases 275Write the Requirements Document 279Demand Feedback 280Summary 281CHAPTER 12: BUILDING A DATA MODEL 283Semantic Object Modeling 283Building an Initial Semantic Object Model 283Improving the Semantic Object Model 286Entity- Relationship Modeling 289Building an ER Diagram 289Building a Combined ER Diagram 291Improving the Entity- Relationship Diagram 293Relational Modeling 294Putting It All Together 298Summary 299CHAPTER 13: EXTRACTING BUSINESS RULES 303Identifying Business Rules 303Courses 304CustomerCourses 306Customers 307Pets 307Employees 307Orders 307OrderItems 308InventoryItems 308TimeEntries 308Shifts 309Persons 309Phones 309Vendors 309Drawing a New Relational Model 310Summary 310CHAPTER 14: NORMALIZING AND REFINING 313Improving Flexibility 313Verifying First Normal Form 315Verifying Second Normal Form 318Pets 319TimeEntries 320Verifying Third Normal Form 321Summary 323PART 4: EXAMPLE PROGRAMSCHAPTER 15: EXAMPLE OVERVIEW 327Tool Choices 327Jupyter Notebook 329Visual Studio 331Database Adapters 332Packages in Jupyter Notebook 333Packages in Visual Studio 334Program Passwords 336Summary 336CHAPTER 16: MARIADB IN PYTHON 339Install MariaDB 340Run HeidiSQL 340Create the Program 343Install pymysql 344Create the Database 344Define Tables 346Create Data 348Fetch Data 350Summary 352CHAPTER 17: MARIADB IN C# 355Create the Program 355Install MySqlConnector 356Create the Database 356Define Tables 358Create Data 360Fetch Data 364Summary 366CHAPTER 18: POSTGRESQL IN PYTHON 369Install PostgreSQL 370Run pgAdmin 371Design the Database 371Create a User 371Create the Database 373Define the Tables 374Define the customers Table 374Define the orders Table 376Define the order_items Table 377Create the Program 378Install Psycopg 379Connect to the Database 379Delete Old Data 380Create Customer Data 380Create Order Data 382Create Order Item Data 383Close the Connection 384Perform Queries 384Summary 386CHAPTER 19: POSTGRESQL IN C# 389Create the Program 389Install Npgsql 389Connect to the Database 390Delete Old Data 391Create Customer Data 392Create Order Data 393Create Order Item Data 395Display Orders 396Summary 399CHAPTER 20: NEO4J AURADB IN PYTHON 401Install Neo4j AuraDB 402Nodes and Relationships 404Cypher 404Create the Program 405Install the Neo4j Database Adapter 405Action Methods 405delete_all_nodes 406make_node 407make_link 407execute_node_query 408find_path 409Org Chart Methods 410build_org_chart 410query_org_chart 411Main Program 412Summary 414CHAPTER 21: NEO4J AURADB IN C# 417Create the Program 418Install the Neo4j Driver 418Action Methods 419DeleteAllNodes 419MakeNode 420MakeLink 421ExecuteNodeQuery 422FindPath 422Org Chart Methods 423BuildOrgChart 424QueryOrgChart 424Main 426Summary 428CHAPTER 22: MONGODB ATLAS IN PYTHON 431Not Normal but Not Abnormal 432XML, JSON, and BSON 432Install MongoDB Atlas 434Find the Connection Code 436Create the Program 439Install the PyMongo Database Adapter 439Helper Methods 440person_string 440connect_to_db 441delete_old_data 442create_data 442query_data 444Main Program 449Summary 450CHAPTER 23: MONGODB ATLAS IN C# 453Create the Program 454Install the MongoDB Database Adapter 454Helper Methods 454PersonString 455DeleteOldData 456CreateData 457QueryData 458Main Program 462Summary 465CHAPTER 24: APACHE IGNITE IN PYTHON 467Install Apache Ignite 468Start a Node 468Without Persistence 469With Persistence 470Create the Program 470Install the pyignite Database Adapter 471Define the Building Class 471Save Data 471Read Data 473Demonstrate Volatile Data 473Demonstrate Persistent Data 474Summary 474CHAPTER 25: APACHE IGNITE IN C# 477Create the Program 477Install the Ignite Database Adapter 478The Main Program 479The Building Class 480The WriteData Method 480The ReadData Method 482Demonstrate Volatile Data 483Demonstrate Persistent Data 483Summary 483PART 5: ADVANCED TOPICSChapter 26: Introduction to Sql 489Background 491Finding More Information 491Standards 492Multistatement Commands 493Basic Syntax 495Command Overview 495Create Table 498Create Index 503Drop 504Insert 504Select 506SELECT Clause 506FROM Clause 507WHERE Clause 511GROUP BY Clause 511ORDER BY Clause 512Update 513Delete 514Summary 515CHAPTER 27: BUILDING DATABASES WITH SQL SCRIPTS 519Why Bother with Scripts? 519Script Categories 520Database Creation Scripts 520Basic Initialization Scripts 520Data Initialization Scripts 520Cleanup Scripts 521Saving Scripts 521Ordering SQL Commands 522Summary 531CHAPTER 28: DATABASE MAINTENANCE 533Backups 533Data Warehousing 537Repairing the Database 538Compacting the Database 538Performance Tuning 538Summary 542Chapter 29: Database Security 545The Right Level of Security 545Passwords 546Single- Password Databases 546Individual Passwords 546Operating System Passwords 547Good Passwords 547Privileges 548Initial Configuration and Privileges 553Too Much Security 553Physical Security 554Summary 555Appendix A: Exercise Solutions 557Appendix B: Sample Relational Designs 649Glossary 671Index 683
Expert Performance Indexing in Azure SQL and SQL Server 2022
Take a deep dive into perhaps the single most important facet of query performance—indexes—and how to best use them. Newly updated for SQL Server 2022 and Azure SQL, this fourth edition includes new guidance and features related to columnstore indexes, improved and consolidated content on Query Store, deeper content around Intelligent Query Processing, and other updates to help you optimize query execution and make performance improvements to even the most challenging workloads.The book begins with explanations of the types of indexes and how they are stored in a database. Moving further into the book, you will learn how statistics are critical for optimal index usage and how the Index Advisor can assist in reviewing and optimizing index health. This book helps you build a clear understanding of how indexes work, how to implement and use them, and the many options available to tame even the most large and complex workloads.WHAT YOU WILL LEARN* Properly index row store, columnstore, and memory-optimized tables* Make use of Intelligent Query Processing for faster query results* Review statistics to understand indexing choices made by the optimizer* Apply indexing strategies such as covering indexes, included columns, and index intersections* Recognize and remove unnecessary indexes* Design effective indexes for full-text, spatial, and XML data typesWHO THIS BOOK IS FORAzure SQL and SQL Server administrators and developers who are ready to improve the performance of their database environment by thoughtfully building indexes to speed up queries that matter the most and make a difference to the businessEDWARD POLLACK has over 20 years of experience in database and systems administration, architecture, and development, becoming an advocate for designing efficient data structures that can withstand the test of time. He has spoken at many events, such as SQL Saturdays, PASS Data Community Summit, Dativerse, and at many user groups and is the organizer of SQL Saturday Albany. Ed has authored many articles, as well as the following Apress books: Dynamic SQL: Applications, Performance, and Security in Microsoft SQL Server Analytics Optimization with Columnstore Indexes in Microsoft SQL Server: Optimizing OLAP Workloads; and a chapter in Expert T-SQL Window Functions in SQL Server. His first patent was issued in 2021, focused on the compression of geographical data for use by analytic systems.In his free time, Ed enjoys video games, sci-fi and fantasy, traveling, and baking. He lives in the sometimes-frozen icescape of Albany, NY, with his wife Theresa and sons Nolan and Oliver, and a mountain of (his) video game plushies that help break the fall when tripping on (their) toys.JASON STRATE is a database architect and administrator with more than 15 years of experience. He has been a recipient of Microsoft's Most Valuable Professional designation for SQL Server since July 2009. His experience includes design and implementation of both OLTP and OLAP solutions, as well as assessment and implementation of SQL Server environments for best practices, performance, and high availability solutions. He is a SQL Server MCITP and participated in the development of Microsoft Certification exams for SQL Server 2008.Jason is actively involved with his local PASS chapter (SQL Server User Group) and serves as its director of program development. He worked with the board to organize the PASSMN SQL Summit 2009 for the local community. He enjoys helping others in the SQL Server community and does this by presenting at technical conferences and user group meetings. Most recently, he has presented at the SSWUG Virtual Conferences, TechFuse, numerous SQL Saturdays, and at PASSMN user group meetings.Jason is a contributing author for the Microsoft whitepaper on "Empowering Enterprise Solutions with SQL Server 2008 Enterprise Edition." He is an active blogger with a focus on SQL Server and related technologies.1. Index Fundamentals2. Index Storage Fundamentals3. Examining Index Contents4. Fragmentation5. Index Metadata and Statistics6. XML Indexes7. Spatial Indexing8. Indexing Memory-Optimized Tables9. Full-Text Indexing10. Indexing Myths and Best Practices11. Index Maintenance12. Indexing Tools13. Indexing Strategies14. Query Strategies15. Monitoring Indexes16. Index Analysis17. Indexing Methodology
Building With Ethereum
Build products on top of Ethereum's new and expansive technological stack.Writing any good web application requires planning, care, and deft technical skills, but Ethereum's execution model presents its own challenges for engineers wishing to build applications on top of its smart contract layer. Building performant and engaging product experiences is one of the most important – and often underappreciated – roles in any company.This book looks at the full product stack needed to build such experiences on top of Ethereum smart contracts, weaving tutorials and case studies through more conversational discussions of the various constraints, trade-offs, and complexities involved in doing so. You’ll learn about the fundamentals of Ethereum from a new perspective, developing a strong understanding of how the Ethereum Virtual Machine (EVM) works and how it affects product engineering, as well as all the pieces of technology that go into decentralized apps (dapps) behind the front end: RPC nodes, wallets, indexers, application hosts, and more. You’ll be exposed to plenty of UI, JavaScript code, and idiomatic ways to bring on-chain data into your front ends. And you’ll be given up-to-date knowledge of the best practices and future possibilities that decentralized computation might offer the product engineer.WHAT YOU WILL LEARN* Understand the EVM and how it works* Gain insight into smart contracts and how apps connect to them* Understand the difference between live data and indexed data* How decentralization affects the UI of applications* Build engaging, tasteful product experiences on top of EthereumWHO THIS BOOK IS FORA confident – mid-level or senior – software engineer or web developer who hasn’t properly branched out into Ethereum; someone who might have scratched the surface, but wants a deeper understanding of the principles behind dapps, and who wants a head start on the hurdles faced while building them.JAMIE RUMBELOW is a software engineer and writer based in London, U.K. Until recently, he was a product engineer at Fei Labs, a major Ethereum protocol, where he built technologies and products at the intersection between the web2 and web3 stacks. He worked as a Founding Engineer and Senior Software Engineer in startups, responsible for training and mentoring as well as writing code. He has worked in the tech industry for over a decade, seeing organizations at varying levels of scale and the ways that teams and technologies get shaped by product decisions. He has also studied philosophy at the graduate level, which has given him the tools to think about computing in a deeper way. Over the past few years, he has built up a set of principles and opinions on what makes for great software and a great engineering culture.CHAPTER 1. INTRODUCTIONWe introduce the core concepts discussed in the book, and situate them with respect to the foundational principles of cryptocurrencies and web3. We discuss decentralisation. We describe the key design decisions of the EVM (Ethereum Virtual Machine) and how they raise some problems for product engineers. We describe the book ahead and what will be covered, chapter by chapter.CHAPTER 2. THE LIFECYCLE OF AN ETHEREUM TRANSACTIONWe discuss the lifecycle of a request on Ethereum, guiding the user through: how is a request initiated by a UI and confirmed by a user; sent to a node, validated, how can UIs display its status, when is a transaction confirmed. We ask: what is a smart contract? We frame smart contracts as APIs. We also set up the following three chapters with the tripartite structure of write, read, and side-effect.CHAPTER 3. WALLETSWe discuss the role of wallets as both stores and signers. We discuss the popular MetaMask model for wallet injection, WalletConnect, EIP-1193, and other attempts to standardise the wallet interface. We explore wallets as identities, and describe approaches to 'logging in' with Ethereum. We explore different attack vectors that integrating wallets might raise.CHAPTER 4. NODES AND INDEXESWe talk about nodes – the programmatic gateway to the blockchain – in more detail: how to connect to them and use them to retrieve information. We discuss the difference between live data and indexed data. We discuss how external RPC nodes allow external function calls with and friends. We discuss ABIs and contract interfaces.CHAPTER 5. EVENTSWe investigate Ethereum's event model, and how to reconstruct a contract's state from the event log. We talk about Bloom filters and how to process many events efficiently.CHAPTER 6. TRANSACTIONSThis chapter builds more on the first chapter, diving into detail about the implementation of transactions. We discuss interfaces for creating, signing, and viewing transactions. We frame transactions theoretically and practically. We discuss the RPC call and its interface. We break down the minutiae of cryptographic signing and nonces, and consider how it affects the UI of applications. We discuss transaction statuses and estimation.CHAPTER 7. ERROR HANDLINGIn this chapter, we discuss the various failure modes of smart contracts and how to build rich, useful user interfaces around them. We consider gas, nonces, and transaction synchronisation. We talk about ways of building error-resilience into your product with input validation. We talk about standardising error messages at the smart contract level.CHAPTER 8. TOOLINGIn this chapter, we investigate the sorts of tools available to the product engineer, drawing on problems faced in the previous chapters. We discuss running manual tests, declarative provisioning of infrastructure, and keeping private keys safe. We also discuss current lacunas and places where existing tooling could be improved.CHAPTER 9. DATA-FIRST APPLICATIONSWe wrap-up the preceding discussion with a more opinionated chapter on a data-first approach to application design. In particular, I'll make the case for single-page applications backed by a data scraper as a strong model for building application infrastructure around smart contracts, tying together the themes of the preceding few chapters.CHAPTER 10. CONCLUSION: SMART CONTRACTS, SMARTER PRODUCTSWe discuss the ways that the surface area of smart contracts rubs against the surface area of web applications, with an emphasis on the more commercial and conceptual. We explore how detaching state from interface allows for more decentralised and censorship-resistant protocols – and how product engineers can profit from this split. We talk about the many ways that thoughtful product engineering can improve the user experience of crypto, and ruminate on the future direction of the ecosystem.Appendix 1: A Common-Sense Crypto-Native ChecklistWe provide a summary of the main questions and action points found in the book, with an easy reference checklist for engineers to work through when building their crypto-native applications.Appendix 2: Resources and BibliographyWe provide one or two pages of QR codes, with links to relevant resources. We provide a more comprehensive, traditional bibliography.
Modern Oracle Database Programming
Level up your skill set to the latest that Oracle Database can offer. This book introduces features that are not well known that can transform your development efforts. You’ll discover built-in functionality that can save you massive amounts of time that otherwise would be spent reinventing the wheel. You’ll find that what used to take a lot of programming some years ago can be done with less code in a more reliable way today. Anyone using Oracle Database without the knowledge in this book is leaving valuable functionality–that their company has paid for–on the table, and this book opens the door to that functionality so that you can deliver reliable and performant solutions faster and more easily than ever.Part I looks at features in SQL and PL/SQL that are underused and not well known. You’ll learn about new join types, pattern matching across rows, Top N pagination (useful in reporting!), qualified expressions, and enhancements to iterators that reduce code complexity and make your logic easier to understand.Part II covers how and when to invoke PL/SQL from SQL while maintaining performance. You'll learn about SQL macro functions for creating reusable SQL fragments, polymorphic table functions with return types determined by incoming argument types, and constructing and parsing JSON documents for data interchange with other systems.Part III introduces a vast array of built-in functionality that Oracle provides that is just waiting to be used. Edition-based redefinition enables zero-downtime application and schema upgrades. Data redaction enables easier compliance with privacy laws and similar regulations by protecting sensitive data from those who have no need to see it. Virtual private databases provide the appearance of giving each user their own database, again helping to secure sensitive data. These features are just a taste of what the book provides. Soon you’ll be improving your skills and wondering why you ever worked so hard to solve problems that Oracle Database already solves for you.WHAT YOU WILL LEARN* Write more powerful code by incorporating underused features in SQL and PL/SQL* Optimize your integration between SQL and PL/SQL for best performance* Take advantage of enhanced set operators, lateral joins, row-based pattern matching, and other advanced features in SQL* Make your code easier to understand through your use of newer PL/SQL features, such as qualified expressions and iterator enhancements* Integrate with web services and external data sources directly from the database* Create and parse JSON documents for easy data exchange and flexible schema designWHO THIS BOOK IS FORAny developer who is writing SQL or PL/SQL, PL/SQL experts who want to level up their knowledge and skills to the latest features that Oracle Database provides, and developers who don’t want to write their own solutions only to find out later that they’ve wasted their time by building something that Oracle Database provides out of the boxALEX NUIJTEN is an independent consultant specializing in Oracle database development with PL/SQL and Oracle Application Express (APEX). Besides his consultancy work, he conducts training classes in APEX, SQL, and PL/SQL. He is a speaker at numerous international conferences, such as ODTUG, Oracle Open World, HrOUG, UKOUG, IOUG, OUGF, BGOUG, NLOUG APEX World, OBUG, and many more. He has received several Best Speaker awards and writes at regular intervals about Oracle Application Express and Oracle database development on his blog "Notes on Oracle." He is co-author of Oracle APEX Best Practices and Real World SQL and PL/SQL. Because of his contributions to the Oracle community, Alex was awarded the Oracle ACE Director membership in August 2010.PATRICK BAREL is a PL/SQL developer for Qualogy in The Netherlands. Besides working with SQL and PL/SQL Developer, he has written different plug-ins for PL/SQL. He publishes articles on his own blog and on the Qualogy blog. He is a speaker at international conferences, such as ODTUG, UKOUG, AUSOUG, NZOUG, IOUG, OUGN, NLOUG, DOUG, HrOUG, and many more. Patrick was awarded the Oracle ACE membership. In 2015, he received the Oracle Developer Choice Award in the PL/SQL Category. In 2019, he was promoted to Oracle ACE Director.IntroductionPART I. THE ADVANCED BASICS1. Underutilized Functionality and Enhancements2. Analytic Functions, Model, and Pivoting3. Joins4 Finding Patterns5. Pagination and Set Operators6. Conditional Compilation7. Iterations and Qualified ExpressionsPART II. MULTIPLE TECHNIQUES AND LANGUAGES8. SQL Macro and Polymorphic Table Functions9. Subquery Factoring, the WITH Clause Explained10. Calling PL/SQL from SQL11. Storing JSON in the Database12. Creating and Parsing JSON in SQL13. Creating and Parsing JSON in PL/SQLPART III. ORACLE-PROVIDED FUNCTIONALITY14. Comparing and Manipulating JSON15. Useful APEX Packages16. Processing Data in the Background17. Introspecting PL/SQL18. See What You Need to See19. Upgrade your Application with Zero Downtime20. Choosing the Right Table
AWS for Public and Private Sectors
Assess, plan, develop, implement, and manage AWS EC2 Instances, Cloud Formation using JSON Template with Bash programming language, and Cloud Watch monitoring. This book helps the public and private sectors comprehend how to assess and evaluate AWS cloud software as a service (SaaS), infrastructure as a service (IaaS), and platform as a service (PaaS).Government and business sector entities are looking for strategies to upgrade on-premises information systems to virtual cloud infrastructure orchestration and automation. You'll gain a step-by-step approach to planning, developing, implementing, and managing cloud infrastructure, services, and platforms that help reduce cost increases, scalability, and improves security. Outline your strategy to research how cloud infrastructure is planned and developed before being deployed and managed by on-premises IT team members. This book also supports cloud services for AWS and helps you understand why supporting and using AWS for cloud services is beneficial both short and long-term.Once you complete this book, you'll be able to make logical decisions regarding AWS use cases for public and private sector entities, including disaster recovery and backup, IT self-service, Web applications, and messaging.WHAT YOU'LL LEARN* Assess different cloud services provided by Amazon* Look at Cloud as a Service (CAAS)* Understand internet protocol, packet switching, authoritative, recursive, and open-flow* Review cloud infrastructure planning methods* Examine Cloud orchestration and automation* Work with the AWS total cost of ownership calculatorWHO THIS BOOK IS FORThis book is aimed at business, government, non-profit organizations, academic institutions, and financial institutions interested in upgrading to AWS cloud architect infrastructure as a primary mode of data transmission, storage, and security at a scalable and economical annual cost.BRADLEY FOWLER has worked in the technology industry since 2012, when he became Webmaster for the American Alliance of Paralegals, Inc. In 2015, Bradley became co-owner of Construction eMarketing a Web site development and eMarketing company that provides services remotely, including Cybersecurity Analysis, Web Page security integration, and cloud computing architecture consulting.Bradley earned a Master of Science in Cloud Computing Architecture from the University of Maryland Global Campus-Cum Laude, where he began working with BalletOnline as the Intern Cloud Architect, and climbed up to become Chief Cloud Architect. Bradley also earned a Master of Public Policy in Cybersecurity Policy from American Public University System-Summa Cum Laude, a Master of Science in Cybersecurity, and a Master of Science in Managing Information Systems in Information Security Management, both from Bellevue University, both Summa Cum Laude. Bradley acquired his Bachelor of Arts in eMarketing from the University of Arizona Global Campus. He is currently completing a Master of Science in Technology Management at University of Arizona Global Campus as well as a Doctor of Management in Information Systems & Technology at University of Phoenix.Chapter I Cloud Services and TechnologiesChapter goal: This chapter enables readers to understand the value of assessing and evaluating cloud services provided by Amazon Web Services (AWS) and Microsoft Azure, prior to making an executive decision to partner with either service provider. Readers will comprehend how to calculate the total cost of ownership for usage of cloud services rendered by both service providers, and acquire knowledge of the advantages and disadvantage of services rendered by both services providers. Readers will also acquire knowledge of applicable laws regarding usage of cloud services in alignment with federal laws and guidelines as well as gain knowledge regarding policy enforcement mechanisms, system monitoring and audit mechanisms, and finally complete discussion questions that help readers comprehend their study of this chapter and the valued components that should be remembered.Sub-topics:1. Total Cost of Ownership2. Functional requirements3. Nonfunctional requirements4. Risk analysis and management guidelines5. Six components of GDPR6. Understand physical security issues7. Critical IT requirements related to data storage8. Potential privacy issues and migration strategiesChapter 2: Network EngineeringChapter goal: This chapter helps readers gain knowledge about Internet protocol and its impact in cloud architecture. Readers will also learn about packet switching, IP addressing, DNS, IP subnetting, IP address classes, CIDR Notation, multiple subnets in a LAN, subnetting proposal, Transport Control Protocol (TCP), transport reliability, TCP sliding windows, software defined networking, networking in the cloud, cloud command line interfaces, and cloud APIs. Having this knowledge helps improve decision making regarding what strategies best serve the needs of an enterprise migrating from an on-premises legacy information system to a cloud infrastructure. By the time readers conclude this chapter, they will understand the meaning of internet protocol, packet switching, authoritative, recursive, and what makes open-flow so popular. Most importantly, readers acquire knowledge about subnetting proposals and their role in supporting enterprises.Subtopics:1. TCP Connections2. SDN Enables BalletOnline Cloud Deployment3. Declarative resource definitions4. AWS Migration Environment and Configure Web ServicesChapter 3: Infrastructure Planning and MigrationChapter goal: This chapter will educate readers about cloud infrastructure planning methods that increase organizational structure that aligns with business acumen and helps readers understand migration prerequisites. This enables a reduction of setbacks arises during the migration from on-premises to cloud infrastructure. Furthermore, readers will be able to effectively assess migration tools and comprehend AWS Application Discovery. Most important, readers will acquire methods that have proven beneficial and provides a return on the initial investment.Sub-topics:1. Cloud premigration considerations2. Cloud migration tool assessment3. AWS Application Discovery Services4. Agentbase options5. Agentless options6. Evaluation of discovery application and infrastructure data7. Migration recommendationsChapter 4: Computing Development and ManagementChapter goal: Readers will gain knowledge about cloud service offering for email and how AWS services help reduce concerns of vulnerability. Readers will also learn about AWS Cloud Watch and how this tool helps capture metrics and reports statistics regarding the consumption for backup and archiving. Additionally, readers will learn about AWS backup and archiving as well as AWS virtual private cloud and the value of relying on VPN (Virtual Private Networks). Furthermore, readers will attain information about AWS S3 buckets and cloud synchronization services.Subtopics:1. Data Backups and Archiving to Cloud Using Cloud Sync Services2. AWS Cloud Watch Monitoring3. AWS Service Catalog4. Cloud operations end-users’ guides5. Cloud operations administrative guides6. LimitationsChapter V Cloud Computing Orchestration and AutomationChapter goals: This chapters prepares readers for the orchestration and automation of AWS cloud services. Readers will also learn about cloud advanced features as well as advanced data solutions in the cloud. Most importantly, readers will acquire knowledge about Symantec Cloud Workload Protection for Storage Overview. Attaining this knowledge will increase readers trust with AWS services and the ability to effectively manage their cloud virtual private infrastructure with security strategies that are impenetrable.Subtopics:1. Monthly cost analysis2. Total costs of ownership analysis3. Use casesa. Web applicationsb. Messagingc. Disaster recovery and backupd. IT service planning4. Return on investment analysisGlossaryIndex
MATLAB® meets MicroPython
Dieses essential behandelt die Verknüpfung von MicroPython betriebenen Mikrocontroller mit MATLAB®. Anhand eines Praxisbeispiels werden die Aspekte der Planung, der elektronischen Umsetzung, der Programmierung in MicroPython, die Programmierung in MATLAB® und die Erstellung einer graphischen Oberfläche handelt. Planung.- Hardware - Konstruktion der Anpassungselektronik.- MCU-Software in MicroPython.- MATLAB.
Artificial Intelligence Applications and Reconfigurable Architectures
ARTIFICIAL INTELLIGENCE APPLICATIONS AND RECONFIGURABLE ARCHITECTURESTHE PRIMARY GOAL OF THIS BOOK IS TO PRESENT THE DESIGN, IMPLEMENTATION, AND PERFORMANCE ISSUES OF AI APPLICATIONS AND THE SUITABILITY OF THE FPGA PLATFORM.This book covers the features of modern Field Programmable Gate Arrays (FPGA) devices, design techniques, and successful implementations pertaining to AI applications. It describes various hardware options available for AI applications, key advantages of FPGAs, and contemporary FPGA ICs with software support. The focus is on exploiting parallelism offered by FPGA to meet heavy computation requirements of AI as complete hardware implementation or customized hardware accelerators. This is a comprehensive textbook on the subject covering a broad array of topics like technological platforms for the implementation of AI, capabilities of FPGA, suppliers’ software tools and hardware boards, and discussion of implementations done by researchers to encourage the AI community to use and experiment with FPGA. Readers will benefit from reading this book because* It serves all levels of students and researcher’s as it deals with the basics and minute details of Ecosystem Development Requirements for Intelligent applications with reconfigurable architectures whereas current competitors’ books are more suitable for understanding only reconfigurable architectures.* It focuses on all aspects of machine learning accelerators for the design and development of intelligent applications and not on a single perspective such as only on reconfigurable architectures for IoT applications.* It is the best solution for researchers to understand how to design and develop various AI, deep learning, and machine learning applications on the FPGA platform.* It is the best solution for all types of learners to get complete knowledge of why reconfigurable architectures are important for implementing AI-ML applications with heavy computations.AUDIENCEResearchers, industrial experts, scientists, and postgraduate students who are working in the fields of computer engineering, electronics, and electrical engineering, especially those specializing in VLSI and embedded systems, FPGA, artificial intelligence, Internet of Things, and related multidisciplinary projects. ANURADHA THAKARE, PHD, is a Dean of International Relations and Professor in the Department of Computer Engineering at Pimpri Chinchwad College of Engineering, Pune, India. She has more than 22 years of experience in academics and research and has published more than 80 research articles in SCI journals as well several books. SHEETAL BHANDARI,PHD, received her degree in the area of reconfigurable computing. She is a postgraduate in electronics engineering from the University of Pune with a specialization in digital systems. She is working as a professor in the Department of Electronics and Telecommunication Engineering and Dean of Academics at Pimpri Chinchwad College of Engineering. Her research area concerns reconfigurable computing and embedded system design around FPGA HW-SW Co-Design. Preface xiii1 STRATEGIC INFRASTRUCTURAL DEVELOPMENTS TO REINFORCE RECONFIGURABLE COMPUTING FOR INDIGENOUS AI APPLICATIONS 1Deepti Khurge1.1 Introduction 21.2 Infrastructural Requirements for AI 21.3 Categories in AI Hardware 41.3.1 Comparing Hardware for Artificial Intelligence 81.4 Hardware AI Accelerators to Support RC 91.4.1 Computing Support for AI Application: Reconfigurable Computing to Foster the Adaptation 91.4.2 Reconfiguration Computing Model 101.4.3 Reconfigurable Computing Model as an Accelerator 111.5 Architecture and Accelerator for AI-Based Applications 151.5.1 Advantages of Reconfigurable Computing Accelerators 201.5.2 Disadvantages of Reconfigurable Computing Accelerators 211.6 Conclusion 22References 222 REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND ARCHITECTURES 25Rashmi Mahajan, Dipti Sakhare and Rohini Gadgil2.1 Introduction 252.2 Technological Platforms for AI Implementation—Graphics Processing Unit 272.3 Technological Platforms for AI Implementation—Field Programmable Gate Array (FPGA) 282.3.1 Xilinx Zynq 282.3.2 Stratix 10 NX Architecture 292.4 Design Implementation Aspects 302.5 Conclusion 32References 323 AN ORGANIZED LITERATURE REVIEW ON VARIOUS CUBIC ROOT ALGORITHMIC PRACTICES FOR DEVELOPING EFFICIENT VLSI COMPUTING SYSTEM—UNDERSTANDING COMPLEXITY 35Siba Kumar Panda, Konasagar Achyut, Swati K. Kulkarni, Akshata A. Raut and Aayush Nayak3.1 Introduction 363.2 Motivation 373.3 Numerous Cubic Root Methods for Emergent VLSI Computing System—Extraction 453.4 Performance Study and Discussion 503.5 Further Research 503.6 Conclusion 59References 594 AN OVERVIEW OF THE HIERARCHICAL TEMPORAL MEMORY ACCELERATORS 63Abdullah M. Zyarah and Dhireesha Kudithipudi4.1 Introduction 634.2 An Overview of Hierarchical Temporal Memory 654.3 HTM on Edge 674.4 Digital Accelerators 684.4.1 Pim Htm 684.4.2 Pen Htm 694.4.3 Classic 704.5 Analog and Mixed-Signal Accelerators 724.5.1 Rcn Htm 724.5.2 Rbm Htm 734.5.3 Pyragrid 744.6 Discussion 764.6.1 On-Chip Learning 764.6.2 Data Movement 774.6.3 Memory Requirements 794.6.4 Scalability 804.6.5 Network Lifespan 824.6.6 Network Latency 834.6.6.1 Parallelism 844.6.6.2 Pipelining 854.6.7 Power Consumption 864.7 Open Problems 884.8 Conclusion 89References 905 NLP-BASED AI-POWERED SANSKRIT VOICE BOT 95Vedika Srivastava, Arti Khaparde, Akshit Kothari and Vaidehi Deshmukh5.1 Introduction 965.2 Literature Survey 965.3 Pipeline 985.3.1 Collect Data 985.3.2 Clean Data 985.3.3 Build Database 985.3.4 Install Required Libraries 985.3.5 Train and Validate 985.3.6 Test and Update 985.3.7 Combine All Models 1005.3.8 Deploy the Bot 1005.4 Methodology 1005.4.1 Data Collection and Storage 1005.4.1.1 Web Scrapping 1005.4.1.2 Read Text from Image 1015.4.1.3 MySQL Connectivity 1015.4.1.4 Cleaning the Data 1015.4.2 Various ML Models 1025.4.2.1 Linear Regression and Logistic Regression 1025.4.2.2 SVM – Support Vector Machine 1035.4.2.3 PCA – Principal Component Analysis 1045.4.3 Data Pre-Processing and NLP Pipeline 1055.5 Results 1065.5.1 Web Scrapping and MySQL Connectivity 1065.5.2 Read Text from Image 1075.5.3 Data Pre-Processing 1085.5.4 Linear Regression 1095.5.5 Linear Regression Using TensorFlow 1095.5.6 Bias and Variance for Linear Regression 1125.5.7 Logistic Regression 1135.5.8 Classification Using TensorFlow 1145.5.9 Support Vector Machines (SVM) 1155.5.10 Principal Component Analysis (PCA) 1165.5.11 Anomaly Detection and Speech Recognition 1175.5.12 Text Recognition 1195.6 Further Discussion on Classification Algorithms 1195.6.1 Using Maximum Likelihood Estimator 1195.6.2 Using Gradient Descent 1225.6.3 Using Naive Bayes’ Decision Theory 1235.7 Conclusion 123Acknowledgment 123References 1236 AUTOMATED ATTENDANCE USING FACE RECOGNITION 125Kapil Tajane, Vinit Hande, Rohan Nagapure, Rohan Patil and Rushabh Porwal6.1 Introduction 1266.2 All Modules Details 1276.2.1 Face Detection Model 1276.2.2 Image Preprocessing 1286.2.3 Trainer Model 1306.2.4 Recognizer 1306.3 Algorithm 1316.4 Proposed Architecture of System 1316.4.1 Face Detection Model 1326.4.2 Image Enhancement 1326.4.3 Trainer Model 1326.4.4 Face Recognition Model 1336.5 Conclusion 134References 1347 A SMART SYSTEM FOR OBSTACLE DETECTION TO ASSIST VISUALLY IMPAIRED IN NAVIGATING AUTONOMOUSLY USING MACHINE LEARNING APPROACH 137Vijay Dabhade, Dnyaneshwar Dhawalshankh, Anuradha Thakare, Maithili Kulkarni and Priyanka Ambekar7.1 Introduction 1387.2 Related Research 1387.3 Evaluation of Related Research 1417.4 Proposed Smart System for Obstacle Detection to Assist Visually Impaired in Navigating Autonomously Using Machine Learning Approach 1417.4.1 System Description 1417.4.2 Algorithms for Proposed Work 1427.4.3 Devices Required for the Proposed System 1467.5 Conclusion and Future Scope 148References 1488 CROP DISEASE DETECTION ACCELERATED BY GPU 151Abhishek Chavan, Anuradha Thakare, Tulsi Chopade, Jessica Fernandes and Omkar Gawari8.1 Introduction 1528.2 Literature Review 1558.3 Algorithmic Study 1618.4 Proposed System 1628.5 Dataset 1638.6 Existing Techniques 1638.7 Conclusion 164References 1649 A RELATIVE STUDY ON OBJECT AND LANE DETECTION 167Rakshit Jha, Shruti Sonune, Mohammad Taha Shahid and Santwana Gudadhe9.1 Introduction 1689.2 Algorithmic Survey 1689.2.1 Object Detection Using Color Masking 1699.2.1.1 Color Masking 1699.2.1.2 Modules/Libraries Used 1699.2.1.3 Algorithm for Color Masking 1699.2.1.4 Advantages and Disadvantages 1709.2.1.5 Verdict 1709.2.2 Yolo v3 Object Detection 1719.2.2.1 Yolo V 3 1719.2.2.2 Algorithm Architecture 1719.2.2.3 Advantages and Disadvantages 1729.2.2.4 Verdict 1729.3 Yolo v/s Other Algorithms 1739.3.1 OverFeat 1739.3.2 Region Convolutional Neural Networks 1739.3.3 Very Deep Convolutional Networks for Large-Scale Image Recognition 1739.3.4 Deep Residual Learning for Image Recognition 1749.3.5 Deep Neural Networks for Object Detection 1749.4 Yolo and Its Version History 1749.4.1 Yolo V 1 1749.4.2 Fast YOLO 1759.4.3 Yolo V 2 1769.4.4 Yolo 9000 1769.4.5 Yolo V 3 1769.4.6 Yolo V 4 1779.4.7 Yolo V 5 1789.4.8 Pp-yolo 1789.5 A Survey in Lane Detection Approaches 1799.5.1 Lidar vs. Other Sensors 1829.6 Conclusion 182References 18310 FPGA-BASED AUTOMATIC SPEECH EMOTION RECOGNITION USING DEEP LEARNING ALGORITHM 187Rupali Kawade, Triveni Dhamale and Dipali Dhake10.1 Introduction 18810.2 Related Work 18910.2.1 Machine Learning–Based SER 18910.2.2 Deep Learning–Based SER 19310.3 FPGA Implementation of Proposed SER 19510.4 Implementation and Results 19910.5 Conclusion and Future Scope 201References 20211 HARDWARE IMPLEMENTATION OF RNN USING FPGA 205Nikhil Bhosale, Sayali Battuwar, Gunjan Agrawal and S.D. Nagarale11.1 Introduction 20611.1.1 Motivation 20611.1.2 Background 20711.1.3 Literature Survey 20711.1.4 Project Specification 20911.2 Proposed Design 21011.3 Methodology 21011.3.1 Block Diagram Explanation 21311.3.2 Block Diagram for Recurrent Neural Network 21511.3.3 Textual Input Data (One Hot Encoding) 21511.4 PYNQ Architecture and Functions 21611.4.1 Hardware Specifications 21611.5 Result and Discussion 21611.6 Conclusion 217References 217Index 219
Machine Intelligence, Big Data Analytics, and IoT in Image Processing
MACHINE INTELLIGENCE, BIG DATA ANALYTICS, AND IOT IN IMAGE PROCESSINGDISCUSSES BOTH THEORETICAL AND PRACTICAL ASPECTS OF HOW TO HARNESS ADVANCED TECHNOLOGIES TO DEVELOP PRACTICAL APPLICATIONS SUCH AS DRONE-BASED SURVEILLANCE, SMART TRANSPORTATION, HEALTHCARE, FARMING SOLUTIONS, AND ROBOTICS USED IN AUTOMATION.The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, academicians, and practitioners worldwide. It identifies and discusses various advanced technologies, such as artificial intelligence, machine learning, IoT, image processing, network security, cloud computing, and sensors, to provide effective solutions to the lifestyle challenges faced by humankind. Machine Intelligence, Big Data Analytics, and IoT in Image Processing is a significant addition to the body of knowledge on practical applications emerging from machine intelligence, big data analytics, and IoT. The chapters deal with specific areas of applications of these technologies. This deliberate choice of covering a diversity of fields was to emphasize the applications of these technologies in almost every contemporary aspect of real life to assist working in different sectors by understanding and exploiting the strategic opportunities offered by these technologies. AUDIENCEThe book will be of interest to a range of researchers and scientists in artificial intelligence who work on practical applications using machine learning, big data analytics, natural language processing, pattern recognition, and IoT by analyzing images. Software developers, industry specialists, and policymakers in medicine, agriculture, smart cities development, transportation, etc. will find this book exceedingly useful. ASHOK KUMAR, PHD, is an assistant professor at Lovely Professional University, Phagwara, Punjab, India. He has 15+ years of teaching and research experience, filed 3 patents, and published many articles in international journals and conferences. His current areas of research interest include cloud computing, the Internet of Things, and mist computing. MEGHA BHUSHAN, PHD, is an assistant professor at the School of Computing, DIT University, Dehradun, Uttarakhand, India. She has filed 4 patents and published many research articles in international journals and conferences. Her research interest includes software quality, software reuse, ontologies, artificial intelligence, and expert systems. JOSE GALINDO, PHD, is currently in the Department of Computer Languages and Systems, University of Seville, Spain. He has developed many tools such as FaMa, FaMaDEB, FaMaOVM, TESALIA, and VIVID, and his research interests include recommender systems, software visualization, variability-intensive systems, and software product lines. LALIT GARG, PHD, is a Senior Lecturer in the Department of Computer Information Systems, University of Malta, and an honorary lecturer at the University of Liverpool, UK. He has edited four books and published over 110 papers in refereed journals, conferences, and books. He has 12 patents and delivered more than twenty keynote speeches in different countries, and organized/chaired/co-chaired many international conferences. YU-CHEN HU, PHD, is a distinguished professor in the Department of Computer Science and Information Management, Providence University, Taichung City, Taiwan. His research interests include image and signal processing, data compression, information hiding, information security, computer network, and artificial network. Preface xvPART I: DEMYSTIFYING SMART HEALTHCARE 11 DEEP LEARNING TECHNIQUES USING TRANSFER LEARNING FOR CLASSIFICATION OF ALZHEIMER’S DISEASE 3Monika Sethi, Sachin Ahuja and Puneet Bawa1.1 Introduction 41.2 Transfer Learning Techniques 61.3 AD Classification Using Conventional Training Methods 91.4 AD Classification Using Transfer Learning 121.5 Conclusion 16References 162 MEDICAL IMAGE ANALYSIS OF LUNG CANCER CT SCANS USING DEEP LEARNING WITH SWARM OPTIMIZATION TECHNIQUES 23Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao2.1 Introduction 242.2 The Major Contributions of the Proposed Model 262.3 Related Works 282.4 Problem Statement 322.5 Proposed Model 332.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis 332.5.2 Deep Learning with PSO 342.5.3 Proposed CNN Architectures 352.6 Dataset Description 372.7 Results and Discussions 392.7.1 Parameters for Performance Evaluation 392.8 Conclusion 47References 483 LIVER CANCER CLASSIFICATION WITH USING GRAY-LEVEL CO-OCCURRENCE MATRIX USING DEEP LEARNING TECHNIQUES 51Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao3.1 Introduction 523.1.1 Liver Roles in Human Body 533.1.2 Liver Diseases 533.1.3 Types of Liver Tumors 553.1.3.1 Benign Tumors 553.1.3.2 Malignant Tumors 573.1.4 Characteristics of a Medical Imaging Procedure 583.1.5 Problems Related to Liver Cancer Classification 603.1.6 Purpose of the Systematic Study 613.2 Related Works 623.3 Proposed Methodology 663.3.1 Gaussian Mixture Model 683.3.2 Dataset Description 693.3.3 Performance Metrics 703.3.3.1 Accuracy Measures 703.3.3.2 Key Findings 743.3.3.3 Key Issues Addressed 753.4 Conclusion 77References 774 TRANSFORMING THE TECHNOLOGIES FOR RESILIENT AND DIGITAL FUTURE DURING COVID-19 PANDEMIC 81Garima Kohli and Kumar Gourav4.1 Introduction 824.2 Digital Technologies Used 844.2.1 Artificial Intelligence 854.2.2 Internet of Things 854.2.3 Telehealth/Telemedicine 874.2.4 Cloud Computing 874.2.5 Blockchain 884.2.6 5g 894.3 Challenges in Transforming Digital Technology 904.3.1 Increasing Digitalization 914.3.2 Work From Home Culture 914.3.3 Workplace Monitoring and Techno Stress 914.3.4 Online Fraud 924.3.5 Accessing Internet 924.3.6 Internet Shutdowns 924.3.7 Digital Payments 924.3.8 Privacy and Surveillance 934.4 Implications for Research 934.5 Conclusion 94References 95PART II: PLANT PATHOLOGY 1015 PLANT PATHOLOGY DETECTION USING DEEP LEARNING 103Sangeeta V., Appala S. Muttipati and Brahmaji Godi5.1 Introduction 1045.2 Plant Leaf Disease 1055.3 Background Knowledge 1095.4 Architecture of ResNet 512 V 2 1115.4.1 Working of Residual Network 1125.5 Methodology 1135.5.1 Image Resizing 1135.5.2 Data Augmentation 1135.5.2.1 Types of Data Augmentation 1145.5.3 Data Normalization 1145.5.4 Data Splitting 1165.6 Result Analysis 1165.6.1 Data Collection 1175.6.2 Feature Extractions 1175.6.3 Plant Leaf Disease Detection 1175.7 Conclusion 119References 1206 SMART IRRIGATION AND CULTIVATION RECOMMENDATION SYSTEM FOR PRECISION AGRICULTURE DRIVEN BY IOT 123N. Marline Joys Kumari, N. Thirupathi Rao and Debnath Bhattacharyya6.1 Introduction 1246.1.1 Background of the Problem 1276.1.1.1 Need of Water Management 1276.1.1.2 Importance of Precision Agriculture 1276.1.1.3 Internet of Things 1286.1.1.4 Application of IoT in Machine Learning and Deep Learning 1296.2 Related Works 1316.3 Challenges of IoT in Smart Irrigation 1336.4 Farmers’ Challenges in the Current Situation 1356.5 Data Collection in Precision Agriculture 1366.5.1 Algorithm 1366.5.1.1 Environmental Consideration on Stage Production of Crop 1406.5.2 Implementation Measures 1416.5.2.1 Analysis of Relevant Vectors 1416.5.2.2 Mean Square Error 1416.5.2.3 Potential of IoT in Precision Agriculture 1416.5.3 Architecture of the Proposed Model 1436.6 Conclusion 147References 1477 MACHINE LEARNING-BASED HYBRID MODEL FOR WHEAT YIELD PREDICTION 151Haneet Kour, Vaishali Pandith, Jatinder Manhas and Vinod Sharma7.1 Introduction 1527.2 Related Work 1537.3 Materials and Methods 1557.3.1 Methodology for the Current Work 1557.3.1.1 Data Collection for Wheat Crop 1557.3.1.2 Data Pre-Processing 1567.3.1.3 Implementation of the Proposed Hybrid Model 1577.3.2 Techniques Used for Feature Selection 1597.3.2.1 ReliefF Algorithm 1597.3.2.2 Genetic Algorithm 1617.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction 1627.3.3.1 K-Nearest Neighbor 1627.3.3.2 Artificial Neural Network 1637.3.3.3 Logistic Regression 1647.3.3.4 Naïve Bayes 1647.3.3.5 Support Vector Machine 1657.3.3.6 Linear Discriminant Analysis 1667.4 Experimental Result and Analysis 1677.5 Conclusion 173Acknowledgment 173References 1748 A STATUS QUO OF MACHINE LEARNING ALGORITHMS IN SMART AGRICULTURAL SYSTEMS EMPLOYING IOT-BASED WSN: TRENDS, CHALLENGES AND FUTURISTIC COMPETENCES 177Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang8.1 Introduction 1788.2 Types of Wireless Sensor for Smart Agriculture 1798.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture 1798.4 ml and WSN-Based Techniques for Smart Agriculture 1858.5 Future Scope in Smart Agriculture 1888.6 Conclusion 190References 190PART III: SMART CITY AND VILLAGES 1979 IMPACT OF DATA PRE-PROCESSING IN INFORMATION RETRIEVAL FOR DATA ANALYTICS 199Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja9.1 Introduction 2009.1.1 Tasks Involved in Data Pre-Processing 2009.2 Related Work 2029.3 Experimental Setup and Methodology 2059.3.1 Methodology 2059.3.2 Application of Various Data Pre-Processing Tasks on Datasets 2069.3.3 Applied Techniques 2079.3.3.1 Decision Tree 2079.3.3.2 Naive Bayes 2079.3.3.3 Artificial Neural Network 2089.3.4 Proposed Work 2089.3.4.1 PIMA Diabetes Dataset (PID) 2089.3.5 Cleveland Heart Disease Dataset 2119.3.6 Framingham Heart Study 2159.3.7 Diabetic Dataset 2179.4 Experimental Result and Discussion 2209.5 Conclusion and Future Work 222References 22210 CLOUD COMPUTING SECURITY, RISK, AND CHALLENGES: A DETAILED ANALYSIS OF PREVENTIVE MEASURES AND APPLICATIONS 225Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth Sinha10.1 Introduction 22610.2 Background 22810.2.1 History of Cloud Computing 22810.2.1.1 Software-as-a-Service Model 23010.2.1.2 Infrastructure-as-a-Service Model 23010.2.1.3 Platform-as-a-Service Model 23210.2.2 Types of Cloud Computing 23210.2.3 Cloud Service Model 23210.2.4 Characteristics of Cloud Computing 23410.2.5 Advantages of Cloud Computing 23410.2.6 Challenges in Cloud Computing 23510.2.7 Cloud Security 23610.2.7.1 Foundation Security 23610.2.7.2 SaaS and PaaS Host Security 23710.2.7.3 Virtual Server Security 23710.2.7.4 Foundation Security: The Application Level 23810.2.7.5 Supplier Data and Its Security 23810.2.7.6 Need of Security in Cloud 23910.2.8 Cloud Computing Applications 23910.3 Literature Review 24110.4 Cloud Computing Challenges and Its Solution 24210.4.1 Solution and Practices for Cloud Challenges 24610.5 Cloud Computing Security Issues and Its Preventive Measures 24810.5.1 General Security Threats in Cloud 24910.5.2 Preventive Measures 25410.6 Cloud Data Protection and Security Using Steganography 25810.6.1 Types of Steganography 25910.6.2 Data Steganography in Cloud Environment 26010.6.3 Pixel Value Differencing Method 26110.7 Related Study 26310.8 Conclusion 263References 26411 INTERNET OF DRONE THINGS: A NEW AGE INVENTION 269Prachi Dahiya11.1 Introduction 26911.2 Unmanned Aerial Vehicles 27111.2.1 UAV Features and Working 27411.2.2 IoDT Architecture 27511.3 Application Areas 28011.3.1 Other Application Areas 28411.4 IoDT Attacks 28511.4.1 Counter Measures 29111.5 Fusion of IoDT With Other Technologies 29611.6 Recent Advancements in IoDT 29911.7 Conclusion 302References 30312 COMPUTER VISION-ORIENTED GESTURE RECOGNITION SYSTEM FOR REAL-TIME ISL PREDICTION 305Mukul Joshi, Gayatri Valluri, Jyoti Rawat and Kriti12.1 Introduction 30512.2 Literature Review 30712.3 System Architecture 30912.3.1 Model Development Phase 30912.3.2 Development Environment Phase 31112.4 Methodology 31212.4.1 Image Pre-Processing Phase 31212.4.2 Model Building Phase 31312.5 Implementation and Results 31412.5.1 Performance 31412.5.2 Confusion Matrix 31812.6 Conclusion and Future Scope 318References 31913 RECENT ADVANCES IN INTELLIGENT TRANSPORTATION SYSTEMS IN INDIA: ANALYSIS, APPLICATIONS, CHALLENGES, AND FUTURE WORK 323Elamurugan Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu13.1 Introduction 32413.2 A Primer on ITS 32513.3 The ITS Stages 32613.4 Functions of ITS 32713.5 ITS Advantages 32813.6 ITS Applications 32913.7 ITS Across the World 33113.8 India’s Status of ITS 33313.9 Suggestions for Improving India’s ITS Position 33413.10 Conclusion 335References 33514 EVOLUTIONARY APPROACHES IN NAVIGATION SYSTEMS FOR ROAD TRANSPORTATION SYSTEM 341Noopur Tyagi, Jaiteg Singh and Saravjeet Singh14.1 Introduction 34214.1.1 Navigation System 34314.1.2 Genetic Algorithm 34714.1.3 Differential Evolution 34814.2 Related Studies 34914.2.1 Related Studies of Evolutionary Algorithms 35114.3 Navigation Based on Evolutionary Algorithm 35214.3.1 Operators and Terms Used in Evolutionary Algorithms 35314.3.2 Operator and Terms Used in Evolutionary Algorithm 35714.4 Meta-Heuristic Algorithms for Navigation 35914.4.1 Drawbacks of DE 36214.5 Conclusion 362References 36315 IOT-BASED SMART PARKING SYSTEM FOR INDIAN SMART CITIES 369E. Fantin Irudaya Raj, M. Appadurai, M. Chithamabara Thanu and E. Francy Irudaya Rani15.1 Introduction 37015.2 Indian Smart Cities Mission 37115.3 Vehicle Parking and Its Requirements in a Smart City Configuration 37315.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities 37515.5 Sensors for Vehicle Parking System 38315.5.1 Active Sensors 38415.5.2 Passive Sensors 38615.6 IoT-Based Vehicle Parking System for Indian Smart Cities 38715.6.1 Guidance to the Customers Through Smart Devices 38915.6.2 Smart Parking Reservation System 39115.7 Advantages of IoT-Based Vehicle Parking System 39215.8 Conclusion 392References 39316 SECURITY OF SMART HOME SOLUTION BASED ON SECURE PIGGYBACKED KEY EXCHANGE MECHANISM 399Jatin Arora and Saravjeet Singh16.1 Introduction 40016.2 IoT Challenges 40416.3 IoT Vulnerabilities 40516.4 Layer-Wise Threats in IoT Architecture 40616.4.1 Sensing Layer Security Issues 40716.4.2 Network Layer Security Issues 40816.4.3 Middleware Layer Security Issues 40916.4.4 Gateways Security Issues 41016.4.5 Application Layer Security Issues 41116.5 Attack Prevention Techniques 41116.5.1 IoT Authentication 41216.5.2 Session Establishment 41316.6 Conclusion 414References 41417 MACHINE LEARNING MODELS IN PREDICTION OF STRENGTH PARAMETERS OF FRP-WRAPPED RC BEAMS 419Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor and Ashok Kumar17.1 Introduction 42017.1.1 Defining Fiber-Reinforced Polymer 42117.1.2 Types of FRP Composites 42217.1.2.1 Carbon Fiber–Reinforced Polymer 42217.1.2.2 Glass Fiber 42317.1.2.3 Aramid Fiber 42417.1.2.4 Basalt Fiber 42417.2 Strengthening of RC Beams With FRP Systems 42517.2.1 FRP-to-Concrete Bond 42617.2.2 Flexural Strengthening of Beams With FRP Composite 42717.2.3 Shear Strengthening of Beams With FRP Composite 42717.3 Machine Learning Models 42817.3.1 Prediction of Bond Strength 43017.3.2 Estimation of Flexural Strength 43417.3.3 Estimation of Shear Strength 43417.4 Conclusion 441References 44118 PREDICTION OF INDOOR AIR QUALITY USING ARTIFICIAL INTELLIGENCE 447Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Aman Kumar and Harish Chandra Arora18.1 Introduction 44818.2 Indoor Air Quality Parameters 45018.2.1 Physical Parameters 45318.2.1.1 Humidity 45318.2.1.2 Air Changes (Ventilation) 45418.2.1.3 Air Velocity 45418.2.1.4 Temperature 45418.2.2 Particulate Matter 45518.2.3 Chemical Parameters 45618.2.3.1 Carbon Dioxide 45618.2.3.2 Carbon Monoxide 45618.2.3.3 Nitrogen Dioxide 45618.2.3.4 Sulphur Dioxide 45718.2.3.5 Ozone 45718.2.3.6 Gaseous Ammonia 45818.2.3.7 Volatile Organic Compounds 45818.2.4 Biological Parameters 45918.3 AI in Indoor Air Quality Prediction 45918.4 Conclusion 464References 465Index 471
Convergence of Cloud with AI for Big Data Analytics
CONVERGENCE OF CLOUD WITH AI FOR BIG DATA ANALYTICSTHIS BOOK COVERS THE FOUNDATIONS AND APPLICATIONS OF CLOUD COMPUTING, AI, AND BIG DATA AND ANALYSES THEIR CONVERGENCE FOR IMPROVED DEVELOPMENT AND SERVICES.The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. AUDIENCEResearchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals. DANDA B RAWAT, PHD, is a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of DoD Center of Excellence in Artificial Intelligence & Machine Learning, Director of Cyber-security and Wireless Networking Innovations Research Lab, Graduate Program Director of Howard CS Graduate Programs, and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat has published more than 250 scientific/technical articles and 11 books. LALIT K AWASTHI, PHD, is the Director of Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India). He received his PhD degree from the Indian Institute of Technology Roorkee in computer science and engineering. He has published more than 150 research papers in various journals and conferences of international repute and guided many PhDs in these areas. VALENTINA E BALLAS, PHD, is aFull Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. Dr. Ballas is the author of more than 280 research papers in refereed journals and international conferences. She is the Editor-in-Chief of International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering. MOHIT KUMAR, PHD, is an assistant professor in the Department of Information Technology at Dr. B R Ambedkar National Institute of Technology, Jalandhar, India. He received his PhD degree from the Indian Institute of Technology Roorkee in the field of cloud computing in 2018. His research topics cover the areas of cloud computing, fog computing, edge computing, Internet of Things, soft computing, and blockchain. He has published more than 25 research articles in international journals and conferences. JITENDRA KUMAR SAMRIYA, PHD, has afaculty position in the Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar. His research interest is cloud computing, artificial intelligence, and multi-objective evolutionary optimization techniques. He has published 15 research articles in international journals and has published five Indian and international patents. Preface xv1 INTEGRATION OF ARTIFICIAL INTELLIGENCE, BIG DATA, AND CLOUD COMPUTING WITH INTERNET OF THINGS 1Jaydip Kumar1.1 Introduction 21.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT 31.3 Integration of Artificial Intelligence with the Internet of Things Devices 41.4 Integration of Big Data with the Internet of Things 61.5 Integration of Cloud Computing with the Internet of Things 61.6 Security of Internet of Things 81.7 Conclusion 10References 102 CLOUD COMPUTING AND VIRTUALIZATION 13Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri2.1 Introduction to Cloud Computing 142.1.1 Need of Cloud Computing 142.1.2 History of Cloud Computing 142.1.3 Definition of Cloud Computing 152.1.4 Different Architectures of Cloud Computing 162.1.4.1 Generic Architecture of Cloud Computing 162.1.4.2 Market Oriented Architecture of Cloud Computing 172.1.5 Applications of Cloud Computing in Different Domains 182.1.5.1 Cloud Computing in Healthcare 182.5.1.2 Cloud Computing in Education 192.5.1.3 Cloud Computing in Entertainment Services 192.5.1.4 Cloud Computing in Government Services 192.1.6 Service Models in Cloud Computing 192.1.7 Deployment Models in Cloud Computing 212.2 Virtualization 222.2.1 Need of Virtualization in Cloud Computing 222.2.2 Architecture of a Virtual Machine 232.2.3 Advantages of Virtualization 242.2.4 Different Implementation Levels of Virtualization 252.2.4.1 Instruction Set Architecture Level 252.2.4.2 Hardware Level 262.2.4.3 Operating System Level 262.2.4.4 Library Level 262.2.4.5 Application Level 262.2.5 Server Consolidation Using Virtualization 262.2.6 Task Scheduling in Cloud Computing 272.2.7 Proposed System Architecture 312.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm 312.2.9 Multi Objective Optimization 342.2.10 Chaotic Social Spider Algorithm 342.2.11 Proposed Task Scheduling Algorithm 352.2.12 Simulation and Results 362.2.12.1 Calculation of Makespan 362.2.12.2 Calculation of Energy Consumption 372.3 Conclusion 37References 383 TIME AND COST-EFFECTIVE MULTI-OBJECTIVE SCHEDULING TECHNIQUE FOR CLOUD COMPUTING ENVIRONMENT 41Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed3.1 Introduction 423.2 Literature Survey 443.3 Cloud Computing and Cloudlet Scheduling Problem 463.4 Problem Formulation 473.5 Cloudlet Scheduling Techniques 493.5.1 Heuristic Methods 503.5.2 Meta-Heuristic Methods 513.6 Cloudlet Scheduling Approach (CSA) 523.6.1 Proposed CSA 523.6.2 Time Complexity 533.6.3 Case Study 543.7 Simulation Results 563.7.1 Simulation Environment 563.7.2 Evaluation Metrics 563.7.2.1 Performance Evaluation with Small Number of Cloudlets 573.7.2.2 Performance Evaluation with Large Number of Cloudlets 573.8 Conclusion 64References 644 CLOUD-BASED ARCHITECTURE FOR EFFECTIVE SURVEILLANCE AND DIAGNOSIS OF COVID- 19 69Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta4.1 Introduction 704.2 Related Work 714.2.1 Proposed Cloud-Based Network for Management of COVID- 19 734.3 Research Methodology 754.3.1 Sample Size and Target 764.3.1.1 Sampling Procedures 774.3.1.2 Response Rate 774.3.1.3 Instrument and Measures 774.3.2 Reliability and Validity Test 784.3.3 Exploratory Factor Analysis 784.4 Survey Findings 804.4.1 Outcomes of the Proposed Scenario 824.4.1.1 Online Monitoring 824.4.1.2 Location Tracking 824.4.1.3 Alarm Linkage 824.4.1.4 Command and Control 824.4.1.5 Plan Management 824.4.1.6 Security Privacy 834.4.1.7 Remote Maintenance 834.4.1.8 Online Upgrade 834.4.1.9 Command Management 834.4.1.10 Statistical Decision 834.4.2 Experimental Setup 834.5 Conclusion and Future Scope 85References 865 SMART AGRICULTURE APPLICATIONS USING CLOUD AND IOT 89Keshav Kaushik5.1 Role of IoT and Cloud in Smart Agriculture 895.2 Applications of IoT and Cloud in Smart Agriculture 945.3 Security Challenges in Smart Agriculture 975.4 Open Research Challenges for IoT and Cloud in Smart Agriculture 1005.5 Conclusion 103References 1036 APPLICATIONS OF FEDERATED LEARNING IN COMPUTING TECHNOLOGIES 107Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra6.1 Introduction 1086.1.1 Federated Learning in Cloud Computing 1086.1.1.1 Cloud-Mobile Edge Computing 1096.1.1.2 Cloud Edge Computing 1116.1.2 Federated Learning in Edge Computing 1126.1.2.1 Vehicular Edge Computing 1136.1.2.2 Intelligent Recommendation 1136.1.3 Federated Learning in IoT (Internet of Things) 1146.1.3.1 Federated Learning for Wireless Edge Intelligence 1146.1.3.2 Federated Learning for Privacy Protected Information 1156.1.4 Federated Learning in Medical Computing Field 1166.1.4.1 Federated Learning in Medical Healthcare 1176.1.4.2 Data Privacy in Healthcare 1176.1.5 Federated Learning in Blockchain 1186.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data 1186.2 Advantages of Federated Learning 1196.3 Conclusion 119References 1197 ANALYZING THE APPLICATION OF EDGE COMPUTING IN SMART HEALTHCARE 121Parul Verma and Umesh Kumar7.1 Internet of Things (IoT) 1227.1.1 IoT Communication Models 1227.1.2 IoT Architecture 1247.1.3 Protocols for IoT 1257.1.3.1 Physical/Data Link Layer Protocols 1257.1.3.2 Network Layer Protocols 1277.1.3.3 Transport Layer Protocols 1287.1.3.4 Application Layer Protocols 1297.1.4 IoT Applications 1307.1.5 IoT Challenges 1327.2 Edge Computing 1337.2.1 Cloud vs. Fog vs. Edge 1347.2.2 Existing Edge Computing Reference Architecture 1357.2.2.1 FAR-EDGE Reference Architecture 1357.2.2.2 Intel-SAP Joint Reference Architecture (RA) 1357.2.3 Integrated Architecture for IoT and Edge 1367.2.4 Benefits of Edge Computing Based IoT Architecture 1387.3 Edge Computing and Real Time Analytics in Healthcare 1407.4 Edge Computing Use Cases in Healthcare 1487.5 Future of Healthcare and Edge Computing 1517.6 Conclusion 151References 1528 FOG-IOT ASSISTANCE-BASED SMART AGRICULTURE APPLICATION 157Pawan Whig, Arun Velu and Rahul Reddy Nadikattu8.1 Introduction 1588.1.1 Difference Between Fog and Edge Computing 1598.1.1.1 Bandwidth 1638.1.1.2 Confidence 1648.1.1.3 Agility 1648.1.2 Relation of Fog with IoT 1658.1.3 Fog Computing in Agriculture 1678.1.4 Fog Computing in Smart Cities 1698.1.5 Fog Computing in Education 1708.1.6 Case Study 171Conclusion and Future Scope 173References 1739 INTERNET OF THINGS IN THE GLOBAL IMPACTS OF COVID-19: A SYSTEMATIC STUDY 177Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma9.1 Introduction 1789.2 COVID-19 – Misconceptions 1819.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic 1839.3.1 Impact on Healthcare and Major Contributions of IoT 1839.3.2 Social Impacts of COVID-19 and Role of IoT 1879.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses 1889.3.4 Impact on Education and Part Played by IoT 1919.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT 1949.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future 1979.4 Conclusions 198References 19810 AN EFFICIENT SOLAR ENERGY MANAGEMENT USING IOT-ENABLED ARDUINO-BASED MPPT TECHNIQUES 205Rita Banik and Ankur BiswasList of Symbols 20610.1 Introduction 20610.2 Impact of Irradiance on PV Efficiency 21010.2.1 PV Reliability and Irradiance Optimization 21110.2.1.1 PV System Level Reliability 21110.2.1.2 PV Output with Varying Irradiance 21110.2.1.3 PV Output with Varying Tilt 21210.3 Design and Implementation 21210.3.1 The DC to DC Buck Converter 21510.3.2 The Arduino Microcontroller 21710.3.3 Dynamic Response 21910.4 Result and Discussions 22010.5 Conclusions 223References 22411 AXIOMATIC ANALYSIS OF PRE-PROCESSING METHODOLOGIES USING MACHINE LEARNING IN TEXT MINING: A SOCIAL MEDIA PERSPECTIVE IN INTERNET OF THINGS 229Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak11.1 Introduction 23011.2 Text Pre-Processing – Role and Characteristics 23211.3 Modern Pre-Processing Methodologies and Their Scope 23411.4 Text Stream and Role of Clustering in Social Text Stream 24111.5 Social Text Stream Event Analysis 24211.6 Embedding 24411.6.1 Type of Embeddings 24411.7 Description of Twitter Text Stream 25011.8 Experiment and Result 25111.9 Applications of Machine Learning in IoT (Internet of Things) 25111.10 Conclusion 252References 25212 APP-BASED AGRICULTURE INFORMATION SYSTEM FOR RURAL FARMERS IN INDIA 257Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar12.1 Introduction 25812.2 Motivation 25912.3 Related Work 26012.4 Proposed Methodology and Experimental Results Discussion 26212.4.1 Mobile Cloud Computing 26612.4.2 XML Parsing and Computation Offloading 26612.4.3 Energy Analysis for Computation Offloading 26712.4.4 Virtual Database 26912.4.5 App Engine 27012.4.6 User Interface 27212.4.7 Securing Data 27312.5 Conclusion and Future Work 274References 27413 SSAMH – A SYSTEMATIC SURVEY ON AI-ENABLED CYBER PHYSICAL SYSTEMS IN HEALTHCARE 277Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa13.1 Introduction 27813.2 The Architecture of Medical Cyber-Physical Systems 27813.3 Artificial Intelligence-Driven Medical Devices 28213.3.1 Monitoring Devices 28213.3.2 Delivery Devices 28313.3.3 Network Medical Device Systems 28313.3.4 IT-Based Medical Device Systems 28413.3.5 Wireless Sensor Network-Based Medical Driven Systems 28513.4 Certification and Regulation Issues 28513.5 Big Data Platform for Medical Cyber-Physical Systems 28613.6 The Emergence of New Trends in Medical Cyber-Physical Systems 28813.7 Eminence Attributes and Challenges 28913.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion 29013.9 Role of the Software Platform in the Interoperability of Medical Devices 29113.10 Clinical Acceptable Decision Support Systems 29113.11 Prevalent Attacks in the Medical Cyber-Physical Systems 29213.12 A Suggested Framework for Medical Cyber-Physical System 29413.13 Conclusion 295References 29614 ANN-AWARE METHANOL DETECTION APPROACH WITH CUO-DOPED SNO 2 IN GAS SENSOR 299Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra14.1 Introduction 30014.1.1 Basic ANN Model 30014.1.2 ANN Data Pre- and Post-Processing 30314.1.2.1 Activation Function 30414.2 Network Architectures 30514.2.1 Feed Forward ANNs 30514.2.2 Recurrent ANNs Topologies 30714.2.3 Learning Processes 30814.2.3.1 Supervised Learning 30814.2.3.2 Unsupervised Learning 30814.2.4 ANN Methodology 30914.2.5 1%CuO–Doped SnO 2 Sensor for Methanol 30914.2.6 Experimental Result 311References 32715 DETECTING HEART ARRHYTHMIAS USING DEEP LEARNING ALGORITHMS 331Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni15.1 Introduction 33215.1.1 Deep Learning 33315.2 Motivation 33415.3 Literature Review 33415.4 Proposed Approach 36615.4.1 Dataset Descriptions 36715.4.2 Algorithms Description 36915.4.2.1 Dense Neural Network 36915.4.2.2 Convolutional Neural Network 37015.4.2.3 Long Short-Term Memory 37215.5 Experimental Results of Proposed Approach 37615.6 Conclusion and Future Scope 379References 38016 ARTIFICIAL INTELLIGENCE APPROACH FOR SIGNATURE DETECTION 387Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash16.1 Introduction 38716.2 Literature Review 39016.3 Problem Definition 39216.4 Methodology 39216.4.1 Data Flow Process 39416.4.2 Algorithm 39516.5 Result Analysis 39716.6 Conclusion 399References 39917 COMPARISON OF VARIOUS CLASSIFICATION MODELS USING MACHINE LEARNING TO PREDICT MOBILE PHONES PRICE RANGE 401Chinu Singla and Chirag Jindal17.1 Introduction 40217.2 Materials and Methods 40317.2.1 Dataset 40317.2.2 Decision Tree 40317.2.2.1 Basic Algorithm 40417.2.3 Gaussian Naive Bayes (GNB) 40417.2.3.1 Basic Algorithm 40517.2.4 Support Vector Machine 40517.2.4.1 Basic Algorithm 40617.2.5 Logistic Regression (LR) 40717.2.5.1 Basic Algorithm 40717.2.6 K-Nearest Neighbor 40817.2.6.1 Basic Algorithm 40917.2.7 Evaluation Metrics 40917.3 Application of the Model 41017.3.1 Decision Tree (DT) 41117.3.2 Gaussian Naive Bayes 41117.3.3 Support Vector Machine 41217.3.4 Logistic Regression 41217.3.5 K Nearest Neighbor 41317.4 Results and Comparison 41317.5 Conclusion and Future Scope 418References 418Index 421
Einführung in das Lightning Netzwerk
Das Second-Layer-Blockchain-Protokoll für effiziente Bitcoin-Zahlungen verstehen und nutzenDas Lightning-Netzwerk (LN) ist ein schnell wachsendes Second-Layer-Zahlungsprotokoll, das auf Bitcoin aufsetzt, um nahezu sofortige Transaktionen zwischen zwei Parteien zu ermöglichen. In diesem Praxisbuch erklären die Autoren Andreas M. Antonopoulos, Olaoluwa Osuntokun und René Pickhardt, wie diese Weiterentwicklung die nächste Stufe der Skalierung von Bitcoin ermöglicht, die Geschwindigkeit und den Datenschutz erhöht und gleichzeitig die Gebühren reduziert.Dieses Buch ist ideal für Entwickler*innen, Systemarchitekt*innen, Investor*innen und Unternehmer*innen, die ein besseres Verständnis von LN anstreben. Es zeigt, warum Expertinnen und Experten das LN als entscheidende Lösung für das Skalierbarkeitsproblem von Bitcoin sehen. Nach der Lektüre werden Sie verstehen, warum LN in der Lage ist, weit mehr Transaktionen zu verarbeiten als die heutigen Finanznetzwerke.Dieses Buch behandelt:wie das Lightning-Netzwerk die Herausforderung der Blockchain-Skalierung angehtdie BOLT-Standarddokumente (Basis of Lightning Technology)die fünf Schichten der Lightning-Network-ProtokollsuiteLN-Grundlagen, einschließlich Wallets, Nodes und wie man sie betreibtLightning-Zahlungskanäle, Onion-Routing und das Gossip-Protokolldie Wegfindung über Zahlungskanäle, um Bitcoin off-chain vom Absender zum Empfänger zu sendenAutoren:Andreas M. Antonopoulos ist ein Bestsellerautor, Speaker, Pädagoge und gefragter Experte für Bitcoin und offene Blockchain-Technologien. Er ist dafür bekannt, komplexe Themen leicht verständlich zu erklären und sowohl die positiven als auch die negativen Auswirkungen, die diese Technologien auf unsere globale Gesellschaft haben können, zu verdeutlichen.Andreas hat zwei weitere technische Bestseller für Programmierer bei O’Reilly geschrieben, Mastering Bitcoin (in deutscher Übersetzung: Bitcoin & Blockchain – Grundlagen und Programmierung) und Mastering Ethereum (in deutscher Übersetzung: Ethereum – Grundlagen und Programmierung). Andreas produziert wöchentlich kostenlose Bildungsinhalte auf seinem YouTube-Kanal und hält virtuelle Workshops auf seiner Website. Erfahren Sie mehr unter aantonop.com.Olaoluwa Osuntokun ist Mitbegründer und CTO von Lightning Labs und außerdem der leitende Entwickler von lnd, einer der wichtigsten Implementierungen von Lightning. Er erwarb seinen BS und MS in Informatik an der University of California, Santa Barbara und war Mitglied der Forbes „30-Under-30-Klasse“ von 2019.Während seines Studiums konzentrierte er sich auf den Bereich der angewandten Kryptographie, insbesondere auf die verschlüsselte Suche. Er ist seit über fünf Jahren aktiver Bitcoin-Entwickler und Autor mehrerer Bitcoin-Verbesserungsvorschläge (BIP-157 und 158). Derzeit liegt sein Hauptaugenmerk auf dem Aufbau, dem Design und der Weiterentwicklung von privaten, skalierbaren Off-Chain-Blockchain-Protokollen wie Lightning.René Pickhardt ist ein ausgebildeter Mathematiker und Data Science Consultant, der sein Wissen nutzt, um mit der Norwegian University of Science and Technology über Pfadfindung, Datenschutz, Zuverlässigkeit von Zahlungen und Service Level Agreements des Lightning-Netzwerks zu forschen. René unterhält einen technischen und entwicklerorientierten YouTube-Kanal (https://www.youtube.com/renepickhardt) zum Lightning-Netzwerk und hat etwa die Hälfte der Fragen zum Lightning-Netzwerk auf Bitcoin Stack Exchange beantwortet, was ihn zur Anlaufstelle für fast alle neuen Entwicklerinnen und Entwickler macht, die sich in diesem Bereich engagieren wollen. René hat zahlreiche öffentliche und private Workshops zum Lightning-Netzwerk gehalten, unter anderem für die Studenten der Chaincode Labs Residency 2019 zusammen mit anderen Lightning-Entwicklern.Zielgruppe:Enwickler*innenSoftwarearchitekt*innenInvestor*innen & Unternehmer*innen
Data Mesh
Eine dezentrale Datenarchitektur entwerfenWir befinden uns an einem Wendepunkt im Umgang mit Daten. Unser bisheriges Datenmanagement wird den komplexen Organisationsstrukturen, den immer zahlreicheren Datenquellen und dem zunehmenden Einsatz von künstlicher Intelligenz nicht mehr gerecht. Dieses praxisorientierte Buch führt Sie in Data Mesh ein, ein dezentrales soziotechnisches Konzept basierend auf modernen verteilten Architekturen. Data Mesh ist ein neuer Ansatz für die Beschaffung, Bereitstellung, den Zugriff und die Verwaltung analytischer Daten, der auch skaliert.Zhamak Dehghani begleitet Softwarearchitekt*innen, Entwickler*innen und Führungskräfte auf ihrem Weg von einer traditionellen, zentralen Big-Data-Architektur hin zu einer verteilten, dezentralen Organisationsstruktur für das Managen analytischer Daten. Data Mesh behandelt dabei Daten als Produkt, ist stark domänengetrieben und zielt auf eine Self-Serve-Datenplattform ab. Das Buch erläutert technische Migrationsstrategien, aber auch die organisatorischen Veränderungen von Teamstrukturen, Rollen und Verantwortlichkeiten, die mit dezentralen Architekturen einhergehen.Lernen Sie die Prinzipien von Data Mesh und ihre Bestandteile kennenEntwerfen Sie eine Data-Mesh-ArchitekturDefinieren Sie Ihre Data-Mesh-Strategie und begleiten Sie deren UmsetzungSteuern Sie den organisatorischen Wandel hin zu dezentraler Data OwnershipMigrieren Sie von traditionellen Data Warehouses und Data Lakes hin zu einem verteilten Data MeshAutor:Zhamak DehghaniZhamak Dehghani ist Director of Technology bei Thoughtworks und Spezialistin für verteilte Systeme und Datenarchitektur in großen Unternehmen. Sie ist Mitglied in mehreren Beratungsgremien für Technologie, unter anderem bei Thoughtworks. Zhamak ist eine Verfechterin der Dezentralisierung aller Dinge, einschließlich der Architektur, der Daten und letztlich von Macht. Sie ist die Begründerin des Data-Mesh-Konzepts.Zielgruppe:Softwarearchitekt*innenSoftwareentwickler*innenData EngineersData ScientistsDatenanalyst*innen
Brain-Computer Interface
BRAIN-COMPUTER INTERFACEIT COVERS ALL THE RESEARCH PROSPECTS AND RECENT ADVANCEMENTS IN THE BRAIN-COMPUTER INTERFACE USING DEEP LEARNING.The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). AUDIENCEResearchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists. M. G. SUMITHRA, PHD, is a professor at Anna University Chennai, India. With 25 years of teaching experience, she has published more than 70 technical papers in refereed journals, 3 book chapters, and 130 research papers in national and international conferences. She is a Nvidia Deep Learning Institute Certified Instructor for "Computer Vision".RAJESH KUMAR DHANARAJ, PHD, is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed around 25 authored and edited books on various technologies, 17 patents, and more than 40 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).MARIOFANNA MILANOVA, PHD, is a professor in the Department of Computer Science at the University of Arkansas, Little Rock, USA. She is an IEEE Senior Member and Nvidia’s Deep Learning Institute University Ambassador. She has published more than 120 publications, more than 53 journal papers, 35 book chapters, and numerous conference papers. She also has two patents.BALAMURUGAN BALUSAMY, PHD, is a professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He is a Pioneer Researcher in the areas of big data and IoT and has published more than 70 articles in various top international journals.V. CHANDRAN holds an M.E degree in VLSI Design from Government College of Technology, Coimbatore, and is a Nvidia Certified Instructor for Deep learning for Computer Vision.
Constructed Truths
In a world in which more and more fake news is being spread, it is becoming increasingly difficult to distinguish truth from lies, knowledge from opinion. Disinformation campaigns are not only perceived as a political problem, but the fake news debate is also about fundamental philosophical questions: What is truth? How can we recognize it? Is there such a thing as objective facts oris everything socially constructed? This book explains how echo chambers and alternative worldviews emerge, it blames post-factual thinking for the current truth crisis, and it shows how we can escape the threat of truth relativism.Thomas Zoglauer (Dr. phil. habil.) teaches philosophy at the Brandenburg University of Technology Cottbus-Senftenberg and at the Graduate Academy of the University of Stuttgart and is the author of numerous books on the philosophy of technology and applied ethics.Filter bubbles and echo chambers.- Conspiracy theories.- Fake news.- Epistemology of the post-factual.- Theories of truth.- Information and knowledge.
Agile Software Development
AGILE SOFTWARE DEVELOPMENTA UNIQUE TITLE THAT INTRODUCES THE WHOLE RANGE OF AGILE SOFTWARE DEVELOPMENT PROCESSES FROM THE FUNDAMENTAL CONCEPTS TO THE HIGHEST LEVELS OF APPLICATIONS SUCH AS REQUIREMENT ANALYSIS, SOFTWARE TESTING, QUALITY ASSURANCE, AND RISK MANAGEMENT. Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community. Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliability but also test cases, resulting in cost-effective and time-effective software. However, many significant research challenges must be addressed to put such ASD capabilities into practice. With the use of diverse techniques, guiding principles, artificial intelligence, soft computing, and machine learning, this book seeks to study theoretical and technological research findings on all facets of ASD. Also, it sheds light on the latest trends, challenges, and applications in the area of ASD. This book explores the theoretical as well as the technical research outcomes on all the aspects of Agile Software Development by using various methods, principles, artificial intelligence, soft computing, and machine learning. AUDIENCEThe book is designed for computer scientists and software engineers both in research and industry. Graduate and postgraduate students will find the book accessible as well. SUSHEELA HOODA,PHD, is an assistant professor in the Department of Computer Science & Engineering, Chitkara University Institute of Engineering & Technology, Punjab, India. VANDANA MOHINDRU SOOD,PHD, is an assistant professor in the Department of Computer Science & Engineering, Chitkara University Institute of Engineering & Technology, Punjab, India. YASHWANT SINGH, PHD, is an associate professor & Head of the Department of Computer Science and Information Technology, Central University of Jammu, J&K, India. SANDEEP DALAL,PHD, is an assistant professor in the Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India. MANU SOOD,PHD. is a professor in the Department of Computer Science, Himachal Pradesh University, Shimla, India.
Explainable AI Recipes
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution.After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.WHAT YOU WILL LEARN* Create code snippets and explain machine learning models using Python* Leverage deep learning models using the latest code with agile implementations* Build, train, and explain neural network models designed to scale* Understand the different variants of neural network models WHO THIS BOOK IS FORAI engineers, data scientists, and software developers interested in XAIPRADEEPTA MISHRA is the Director of AI, Fosfor at L&T Infotech (LTI). He leads a large group of data scientists, computational linguistics experts, and machine learning and deep learning experts in building the next-generation product—Leni—which is the world’s first virtual data scientist. He has expertise across core branches of artificial intelligence, including autonomous ML and deep learning pipelines, ML ops, image processing, audio processing, natural language processing (NLP), natural language generation (NLG), design and implementation of expert systems, and personal digital assistants (PDAs). In 2019 and 2020, he was named one of "India's Top 40 Under 40 Data Scientists" by Analytics India magazine. Two of his books have been translated into Chinese and Spanish, based on popular demand.Pradeepa delivered a keynote session at the Global Data Science Conference 2018, USA. He delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2,000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI at various universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in artificial intelligence.Chapter 1: Introduction to Explainability Library InstallationsChapter Goal: This chapter is to understand various XAI library installations process and initialization of libraries to set up the explainability environment.No of pages: 15-20 pagesChapter 2: Linear Supervised Model ExplainabilityChapter Goal: This chapter aims at explaining the supervised linear models as regression and classification and related issues.No of pages: 20-25Chapter 3: Non-Linear Supervised Learning Model ExplainabilityChapter Goal: This chapter explains the use of XAI libraries to explain the decisions made by non-linear models for regression and classification.No of pages : 20-25Chapter 4: Ensemble Model for Supervised Learning ExplainabilityChapter Goal: This chapter explains the use of XAI to explain the decisions made by ensemble models in regression and classification scenarios.No of pages: 20-25Chapter 5: Explainability for Natural Language ModelingChapter Goal: In this chapter, we are going to use XAI for natural language processing, pre-processing, and feature engineering.No of pages: 15-20Chapter 6: Time Series Model ExplainabilityGoal: The objective of this chapter is to explain the forecast using XAI librariesNo of Pages: 10-15Chapter 7: Deep Neural Network Model ExplainabilityGoal: Using XAI libraries to explain the decisions made by Deep Learning modelsNo of Pages: 20-25
Swarm Intelligence
SWARM INTELLIGENCETHIS IMPORTANT AUTHORED BOOK PRESENTS VALUABLE NEW INSIGHTS BY EXPLORING THE BOUNDARIES SHARED BY COGNITIVE SCIENCE, SOCIAL PSYCHOLOGY, ARTIFICIAL LIFE, ARTIFICIAL INTELLIGENCE, AND EVOLUTIONARY COMPUTATION BY APPLYING THESE INSIGHTS TO SOLVING COMPLEX ENGINEERING PROBLEMS.Motivated by the capability of the biologically inspired algorithms, “Swarm Intelligence: An Approach from Natural to Artificial” focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with swarm intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of swarm intelligence and foundations of engineering swarm intelligent systems; as well as applications and interesting experiences using particle swarm optimization, which is at the heart of computational intelligence. Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms. AUDIENCEThe book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and swarm intelligence. KULDEEP SINGH KASWAN, PHD, is working in the School of Computing Science & Engineering, Galgotias University, Uttar Pradesh, India. He received his PhD in computer science from Banasthali Vidyapith, Rajasthan, and D. Engg. from Dana Brain Health Institute, Iran. His research interests are in brain-computer interface, cyborg, and data sciences. JAGJIT SINGH DHATTERWAL, PHD, is an associate professor in the Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. He completed his doctorate in computer science from Mewar University, Rajasthan, India. He has numerous publications in international/national journals and conferences. AVADHESH KUMAR, PHD, is Pro Vice-Chancellor at Galgotias University, India. He obtained his doctorate in computer science with a specialization in software engineering from Thapar University, Patiala, Punjab. He has more than 22 years of teaching and research experience and has published more than 40 research papers in SCI international journals/conferences. His research areas are aspect-oriented programming (AOP), software metrics, software quality, component-based software development (CBSD), artificial intelligence, and autonomic computing. Preface xi1 INTRODUCTION OF SWARM INTELLIGENCE 11.1 Introduction to Swarm Behavior 11.1.1 Individual vs. Collective Behaviors 11.2 Concepts of Swarm Intelligence 21.3 Particle Swarm Optimization (PSO) 21.3.1 Main Concept of PSO 31.4 Meaning of Swarm Intelligence 31.5 What Is Swarm Intelligence? 41.5.1 Types of Communication Between Swarm Agents 41.5.2 Examples of Swarm Intelligence 41.6 History of Swarm Intelligence 51.7 Taxonomy of Swarm Intelligence 61.8 Properties of Swarm Intelligence 101.8.1 Models of Swarm Behavior 111.8.2 Self-Propelled Particles 111.9 Design Patterns in Cyborg Swarm 121.9.1 Design Pattern Creation 141.9.2 Design Pattern Primitives and Their Representation 161.10 Design Patterns Updating in Cyborg 191.10.1 Behaviors and Data Structures 201.10.2 Basics of Cyborg Swarming 201.10.3 Information Exchange at Worksites 211.10.4 Information Exchange Center 221.10.5 Working Features of Cyborg 231.10.6 Highest Utility of Cyborg 241.10.7 Gain Extra Reward 251.11 Property of Design Cyborg 251.12 Extending the Design of Cyborg 311.12.1 Information Storage in Cyborg 321.12.2 Information Exchange Any Time 341.12.3 The New Design Pattern Rules in Cyborg 341.13 Bee-Inspired Cyborg 351.14 Conclusion 362 FOUNDATION OF SWARM INTELLIGENCE 372.1 Introduction 372.2 Concepts of Life and Intelligence 382.2.1 Intelligence: Good Minds in People and Machines 402.2.2 Intelligence in People: The Boring Criterion 412.2.3 Intelligence in Machines: The Turing Criterion 422.3 Symbols, Connections, and Optimization by Trial and Error 432.3.1 Problem Solving and Optimization 432.3.2 A Super-Simple Optimization Problem 442.3.3 Three Spaces of Optimization 452.3.4 High-Dimensional Cognitive Space and Word Meanings 462.4 The Social Organism 492.4.1 Flocks, Herds, Schools and Swarms: Social Behavior as Optimization 502.4.2 Accomplishments of the Social Insects 512.4.3 Optimizing with Simulated Ants: Computational Swarm Intelligence 522.5 Evolutionary Computation Theory and Paradigms 542.5.1 The Four Areas of Evolutionary Computation 542.5.2 Evolutionary Computation Overview 572.5.3 Evolutionary Computing Technologies 572.6 Humans – Actual, Imagined, and Implied 582.6.1 The Fall of the Behaviorist Empire 592.7 Thinking is Social 612.7.1 Adaptation on Three Levels 622.8 Conclusion 623 THE PARTICLE SWARM AND COLLECTIVE INTELLIGENCE 653.1 The Particle Swarm and Collective Intelligence 653.1.1 Socio-Cognitive Underpinnings: Evaluate, Compare, and Imitate 663.1.2 A Model of Binary Decision 683.1.3 The Particle Swarm in Continuous Numbers 703.1.4 Pseudocode for Particle Swarm Optimization in Continuous Numbers 713.2 Variations and Comparisons 723.2.1 Variations of the Particle Swarm Paradigm 723.2.2 Parameter Selection 723.2.3 Vmax 723.2.4 Controlling the Explosion 733.2.5 Simplest Constriction 733.2.6 Neighborhood Topology 743.2.7 Sociometric of the Particle Swarm 743.2.8 Selection and Self-Organization 763.2.9 Ergodicity: Where Can It Go from Here? 773.2.10 Convergence of Evolutionary Computation and Particle Swarms 783.3 Implications and Speculations 783.3.1 Assertions in Cuckoo Search 793.3.2 Particle Swarms Are a Valuable Soft Intelligence (Machine Learning Intelligent) Approach 803.3.3 Information and Motivation 823.3.4 Vicarious vs. Direct Experience 833.3.5 The Spread of Influence 833.3.6 Machine Adaptation 843.3.7 Learning or Adaptation? 853.4 Conclusion 864 ALGORITHM OF SWARM INTELLIGENCE 894.1 Introduction 894.1.1 Methods for Alternate Stages of Model Parameter Reform 904.1.2 Ant Behavior 904.2 Ant Colony Algorithm 924.3 Artificial Bee Colony Optimization 954.3.1 The Artificial Bee Colony 964.4 Cat Swarm Optimization 984.4.1 Original CSO Algorithm 984.4.2 Description of the Global Version of CSO Algorithm 1004.4.3 Seeking Mode (Resting) 1004.4.4 Tracing Mode (Movement) 1014.4.5 Description of the Local Version of CSO Algorithm 1014.5 Crow Search Optimization 1034.5.1 Original CSA 1044.6 Elephant Intelligent Behavior 1054.6.1 Elephant Herding Optimization 1074.6.2 Position Update of Elephants in a Clan 1084.6.3 Pseudocode of EHO Flowchart 1094.7 Grasshopper Optimization 1094.7.1 Description of the Grasshopper Optimization Algorithm 1114.8 Conclusion 1125 NOVEL SWARM INTELLIGENCE OPTIMIZATION ALGORITHM (SIOA) 1135.1 Water Wave Optimization 1135.1.1 Objective Function 1155.1.2 Power Balance Constraints 1155.1.3 Generator Capacity Constraints 1165.1.4 Water Wave Optimization Algorithm 1165.1.5 Mathematical Model of WWO Algorithm 1175.1.6 Implementation of WWO Algorithm for ELD Problem 1185.2 Brain Storm Optimization 1195.2.1 Multi-Objective Brain Storm Optimization Algorithm 1205.2.2 Clustering Strategy 1205.2.3 Generation Process 1215.2.4 Mutation Operator 1225.2.5 Selection Operator 1225.2.6 Global Archive 1235.3 Whale Optimization Algorithm 1235.3.1 Description of the WOA 1245.4 Conclusion 1256 SWARM CYBORG 1276.1 Introduction 1276.1.1 Swarm Intelligence Cyborg 1296.2 Swarm Cyborg Taxis Algorithms 1326.2.1 Cyborg Alpha Algorithm 1356.2.2 Cyborg Beta Algorithm 1366.2.3 Cyborg Gamma Algorithm 1386.3 Swarm Intelligence Approaches to Swarm Cyborg 1396.4 Swarm Cyborg Applications 1406.4.1 Challenges and Issues 1456.5 Conclusion 1467 IMMUNE-INSPIRED SWARM CYBERNETIC SYSTEMS 1497.1 Introduction 1497.1.1 Understanding the Problem Domain in Swarm Cybernetic Systems 1507.1.2 Applying Conceptual Framework in Developing Immune-Inspired Swarm Cybernetic Systems Solutions 1517.2 Reflections on the Development of Immune-Inspired Solution for Swarm Cybernetic Systems 1557.2.1 Reflections on the Cyborg Conceptual Framework 1557.2.2 Immunology and Probes 1577.2.3 Simplifying Computational Model and Algorithm Framework/Principle 1587.2.4 Reflections on Swarm Cybernetic Systems 1597.3 Cyborg Static Environment 1617.4 Cyborg Swarm Performance 1627.4.1 Solitary Cyborg Swarms 1627.4.2 Local Cyborg Broadcasters 1627.4.3 Cyborg Bee Swarms 1637.4.4 The Performance of Swarm Cyborgs 1637.5 Information Flow Analysis in Cyborgs 1657.5.1 Cyborg Scouting Behavior 1657.5.2 Information Gaining by Cyborg 1667.5.3 Information Gain Rate of Cyborgs 1697.5.4 Evaluation of Information Flow in Cyborgs 1707.6 Cost Analysis of Cyborgs 1707.6.1 The Cyborg Work Cycle 1717.6.2 Uncertainty Cost of Cyborgs 1727.6.3 Cyborg Opportunity Cost 1757.6.4 Costs and Rewards Obtained by Cyborgs 1767.7 Cyborg Swarm Environment 1797.7.1 Cyborg Scouting Efficiency 1797.7.2 Cyborg Information Gain Rate 1807.7.3 Swarm Cyborg Costs 1807.7.4 Solitary Swarm Cyborg Costs 1817.7.5 Information-Cost-Reward Framework 1817.8 Conclusion 1838 APPLICATION OF SWARM INTELLIGENCE 1858.1 Swarm Intelligence Robotics 1858.1.1 What is Swarm Robotics? 1868.1.2 System-Level Properties 1868.1.3 Coordination Mechanisms 1878.2 An Agent-Based Approach to Self-Organized Production 1898.2.1 Ingredients Model 1908.3 Organic Computing and Swarm Intelligence 1938.3.1 Organic Computing Systems 1958.4 Swarm Intelligence Techniques for Cloud Services 1978.4.1 Context 1988.4.2 Model Formulation 1988.4.3 Decision Variable 1988.4.4 Objective Functions 1998.4.5 Solution Evaluation 2018.4.6 Genetic Algorithm (GA) 2038.4.7 Particle Swarm Optimization (PSO) 2048.4.8 Harmony Search (HS) 2068.5 Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies 2068.5.1 Classification Features of Network Routing Protocols 2098.5.2 Nearest Neighbor Behavior in Ant Colonies and the ACO Metaheuristic to Network Routing Protocols Inspired by Insect Societies 2138.5.3 Useful Ideas from Honeybee Colonies 2148.5.4 Colony and Workers Recruitment Communications 2158.5.5 Stochastic Food Site Selection 2158.6 Swarm Intelligence in Data Mining 2168.6.1 Steps of Knowledge Discovery 2168.7 Swarm Intelligence and Knowledge Discovery 2178.8 Ant Colony Optimization and Data Mining 2218.9 Conclusion 222References 223Index 231
Computer und Künstliche Intelligenz
Das Buch beginnt mit einer Erklärung der menschlichen Intelligenz und der Beschreibung von Intelligenztests. Die Künstliche Intelligenz, die auf Computerprogrammen beruht, beginnt mit der Dartmouth – Konferenz 1956, an der sich berühmte Informatiker dieser Zeit beteiligten. Diese damit eingeleitete Entwicklung wurde von großen Fortschritten der Kybernetik und der Spieltheorie begleitet.Es folgen Beschreibungen wichtiger Methoden und Anwendungen:* Maschinelles Lernen und Neuronale Netze * Sehr publikumswirksam waren die Entwicklungen von Programmen für strategische Spiele, die nach einem kurzen Training die jeweiligen Weltmeister besiegen konnten. * Die Sprachübersetzer von Google und DeepL sind mittlerweile vielen bekannt. * Es wird erklärt, wie intelligente Systeme mit Datenbanken zusammenarbeiten, wie beliebige Daten digitalisiert werden können. Große Mengen an Daten werden unter dem Stichwort „Big Data“ behandelt. * Ausführlich beschrieben werden die Bildverarbeitung, die Erkennung von Tumoren und Viren. * Robotik ist ein weiterer Punkt, der ausführlich dargestellt wird. Roboter in der Chirurgie und in der Pflege sind ebenfalls sehr bedeutsam. * „Exotische Ausreißer“ sind die Anwendungen in der Kunst. * Sehr bedeutsam für die zukünftige Entwicklung sind Anwendungen in der Rechtssprechung.DR. CHRISTIAN POSTHOFF war von 1983 bis 1993 Professor für Theoretische Informatik und Künstliche Intelligenz an der TU in Karl-Marx-Stadt (Chemnitz) und von 1994 bis 2010 Professor of Computer Science an der University of The West Indies in Trinidad & Tobago.Während dieser Zeit hat er etwa 30 Bücher geschrieben über Computerschach, binäre Logik und Künstliche Intelligenz.Definitionen der menschlichen Intelligenz - Intelligenztests - Die Anfänge der Entwicklung - Die Dartmouth – Konferenz - Algorithmen und Programmierung - Die Turing-Maschine - Spieltheorie - Kybernetik - Simulation - Beispiele aus der realen Welt, interessante Systeme und die Konsequenzen ihrer Anwendung - Lernprozesse und Neuronale NetzeDie Bedeutung der Mathematik - Das Problem der interdisziplinären Zusammenarbeit - Die Komplexität der Probleme und der Systeme - Anwendungsbereiche - Roboter und autonomes Fahren - Juristische Probleme, die sich aus diesen Systemen ergeben (Urheberrecht, Garantien, Haftung etc.) - Das Zusammenwachsen von Digitalisierung und KI - Probleme der Aus- und Weiterbildung
Firewalls Don't Stop Dragons
Rely on this practical, comprehensive guide to significantly improve your cyber safety and data privacy. This book was written expressly for regular, everyday people -- though even technically savvy readers will find many useful tips here. This book contains everything you need to protect yourself-step by step, without judgment, and with as little jargon as possible.Protecting your digital domain is much like defending a medieval castle. Wide moats, towering walls and trained guards provide defense in depth, safeguarding the people and property within against the most common threats. But attempting to dragon-proof your castle would be counterproductive and costly. The goal of this book is to keep your devices and data safe from the most likely and impactful hazards - not a targeted attack by the NSA. Like wearing seat belts and sunscreen in the real world, there are dozens of simple, effective precautions we need to take in the virtual world.Author Carey Parker has structured this book to give you maximum benefit with minimum effort. If you just want to know what you need to do, each chapter includes a detailed checklist of expert tips. But the book also explains why you need to do these things, using entertaining analogies and straightforward explanations. This revised and expanded fifth edition includes:* Updated for Windows 11, macOS 13 (Ventura), iOS 16 and Android 13. * Updated recommendations for most secure and private products. * Over 200 tips with complete step-by-step instructions and screenshots. WHAT YOU WILL LEARN* Maximize your computer and smartphone security. * Minimize your vulnerabilities and data footprint. * Solve your password problems and use two-factor authentication. * Browse the web safely and confidently with a secure, private browser. * Shop and bank online with maximum security and peace of mind. * Defend against identity theft, ransomware and online scams. * Safeguard your children online, at home and in school. * Block online tracking, data mining and malicious online ads. * Send files and messages with end-to-end encryption. * Secure your home network and keep your smart devices from spying on you. * Create automated backups of all your devices. * Learn how to deal with account hacks, data. breaches and viruses. * Understand how computers, the internet, VPNs and encryption really work * And much more!CAREY PARKER, CIPM was born and raised in Indiana, an only child who loved to tear apart his electronic toys and reassemble them in interesting ways. He began programming computers in middle school when personal computers were just starting to become popular. For years, these twin interests percolated until he attended Purdue University and learned that you could get paid to do this stuff—it was called electrical engineering! After obtaining both bachelor and master degrees in electrical engineering, Carey launched his career in telecommunications software development at Bell Northern Research (aka the "Big Nerd Ranch"). Over the next 20 years, he wrote software for multiple companies, large and small, and lived in various cities across the southern United States. In recent years, particularly after the Edward Snowden revelations in 2013, Carey became deeply concerned about computer security and privacy. In 2014, he began combining his passion for computers, cybersecurity,and fantasy novels with his long-time desire to write a book, and the result is Firewalls Don't Stop Dragons. This eventually launched a blog, newsletter, and weekly podcast of the same name.Chapter 1: Before We Begin.- Chapter 2: Cybersecurity 101.- Chapter 3: First Things First.- Chapter 4: Passwords.- Chapter 5: Computer Security.- Chapter 6: Lan Sweet Lan.- Chapter 7: Practice Safe Surfing.- Chapter 8: Secure Communication.- Chapter 9: Online Accounts and Social Media.- Chapter 10: Parental Guidance.- Chapter 11: Don’t Be a Smart Phone Dummy.- Chapter 12: Odds and Ends.- Chapter 13: Parting Thoughts.- Chapter 14: Glossary.