Allgemein
Visual Basic Quickstart Guide
Whether you’re an absolute beginner or an experienced developer looking to learn the Visual Basic language, this book takes a hands-on approach to guide you through the process. From the very first chapters, you'll delve into writing programs, exploring core concepts such as data types, decision branching, and iteration. Additionally, you’ll get to grips with working with data structures, file I/O, and essential object-oriented principles like inheritance and polymorphism.This book goes beyond the basics to equip you with the skills to read and write code across the entire VB family, spanning VB Script, VBA, VB Classic, and VB.NET, enabling you to handle legacy code maintenance with ease.With clear explanations, practical examples, and hands-on exercises, this book empowers you to tackle real-world software development tasks, whether you're enhancing existing projects or embarking on new ones. It addresses common challenges like distinguishing between the variations of the VB programming language to help you choose the right one for your projects.Don't let VB's extensive legacy daunt you; embrace it with this comprehensive guide that equips you with practical, up-to-date coding skills to overcome the challenges presented by Visual Basic's rich history of over two decades.
Künstliche Intelligenz und schlanke Produktion
Dieses Buch wendet künstliche Intelligenz auf die schlanke Produktion an und zeigt, wie sich die Vorteile dieser beiden Disziplinen praktisch kombinieren lassen. Die schlanke Produktion hat ihren Ursprung in Japan und ist ein bekanntes Instrument zur Verbesserung der Wettbewerbsfähigkeit der Hersteller. Zu den gängigen Werkzeugen der schlanken Produktion gehören Kanban, Pacemaker, Wertstromkarte, 5s, Just-in-Time und Pull Manufacturing. Lean Manufacturing und das Toyota-Produktionssystem wurden in verschiedenen Fabriken und Lieferketten auf der ganzen Welt erfolgreich eingesetzt. Ein schlankes Fertigungssystem kann nicht nur Verschwendung und Lagerbestände reduzieren, sondern auch unmittelbarer auf Kundenbedürfnisse reagieren. Künstliche Intelligenz ist ein Thema, das in letzter Zeit viel Aufmerksamkeit auf sich gezogen hat. Viele Forscher und praktische Entwickler arbeiten hart daran, künstliche Intelligenz in unserem täglichen Leben anzuwenden, auch in Fabriken. So wurden beispielsweise Fuzzy-Regeln zur Optimierung von Maschineneinstellungen entwickelt. Es wurden bionische Algorithmen vorgeschlagen, um Probleme bei der Produktionsreihenfolge und -planung zu lösen. Technologien des maschinellen Lernens werden eingesetzt, um mögliche Probleme mit der Produktqualität zu erkennen und den Zustand einer Maschine zu diagnostizieren. Dieses Buch ist für Produktionsingenieure, Manager sowie für Studenten und Forscher im Bereich der Fertigungstechnik von Interesse. Kapitel 1. Grundlagen des Lean Management - Kapitel 2. KI in der Fertigung.- Kapitel 3. KI-Anwendungen für das Kaizen-Management - Kapitel 4. KI-Anwendungen für Pull Manufacturing und JIT - Kapitel 5. KI-Anwendungen für das Produktions-Leveling - Kapitel 6. KI-Anwendungen für das Shop Floor Management: 5S, Kanban, SMED.- Kapitel 7. KI-Anwendungen für Value Stream Mapping.
CCST Cisco Certified Support Technician Study Guide
THE IDEAL PREP GUIDE FOR EARNING YOUR CCST NETWORKING CERTIFICATIONCCST Cisco Certified Support Technician Study Guide: Networking Exam is the perfect way to study for your certification as you prepare to start or upskill your IT career. Written by industry expert and Cisco networking guru Todd Lammle, this Sybex Study Guide uses the trusted Sybex approach, providing 100% coverage of CCST Networking exam objectives. You’ll find detailed information and examples for must-know Cisco networking topics, as well as practical insights drawn from real-world scenarios. This Study Guide provides authoritative coverage of key exam topics, including standards and concepts, addressing and subnet formats, endpoints and media types, infrastructure, diagnosing problems, and security. You also get one year of FREE access to a robust set of online learning tools, including a test bank with hundreds of questions, a practice exam, a set of flashcards, and a glossary of important terminology. The CCST Networking certification is an entry point into the Cisco certification program, and a pathway to the higher-level CCNA, so it’s a great place to start as you build a rewarding career!* Study 100% of the topics covered on the Cisco CCST Networking certification exam* Get access to flashcards, practice questions, and more great resources online* Master difficult concepts with real-world examples and clear explanations* Learn about the career paths you can follow and what comes next after the CCSTThis Sybex study guide is perfect for anyone wanting to earn their CCST Networking certification, including entry-level network technicians, networking students, interns, and IT professionals. ABOUT THE AUTHORSTODD LAMMLE is the authority on Cisco certification and internetworking, and is Cisco certified in most Cisco certification categories. He is a world-renowned author, speaker, trainer, and consultant. Todd has published over 130 books, including the very popular CCNA: Cisco Certified Network Associate Study Guide, and over a hundred more from Sybex. He runs an international consulting and training company based in northern Idaho, where he spends his free time in the mountains playing with his golden retrievers. You can reach Todd through his website at www.lammle.com. DONALD ROBB has over 15 years of experience with most areas of IT, including networking, security, collaboration, data center, cloud, SDN, and automation/devops. He’s earned many advanced certifications and specializations. Currently he’s a SDN/Cloud/DevOps SME. Visit his blog at https://www.the-packet-thrower.com and YouTube channel at https://www.youtube.com/c/ThePacketThrower.
Fintech and Cryptocurrency
FINTECH AND CRYPTOCURRENCYDIVE INTO THE WORLD OF FINTECH AND CRYPTOCURRENCY THROUGH THE ENGAGING PERSPECTIVES OF THIS DIVERSE GROUP OF AUTHORS AND UNCOVER THE INTRICATE CONNECTIONS BETWEEN TECHNOLOGY, FINANCE, AND CRYPTOCURRENCY THAT MAKE THIS A MUST-HAVE FOR ANYONE INTRIGUED BY THE FUTURE OF DIGITAL SOCIETY.Digital currencies, decentralization of money, and the growth of new technologies like blockchain, the Internet of Things, and machine learning have produced new opportunities and difficulties for banking and finance, as well as users of these services in electronic commerce. New banking and finance technologies may improve operational efficiency, risk management, compliance, and client pleasure, but they can decrease barriers and introduce new concerns, such as cybersecurity risk. Cryptocurrencies with smart contracts for payments and trading, as well as AI systems with adaptive algorithms that allow picture and speech recognition, expert judgement, group categorization, and forecasting in a variety of fields, are instances of increased automation. Simultaneously, the potentials pose risks and raise regulatory concerns. The rise of blockchain technology and its widespread use have had a significant impact on the operation and management of digital systems. At the same time, researchers and practitioners have paid close attention to digital finance. Blockchain’s first applications were limited to the production of digital currency, but it has now been expanded to include financial and commercial applications. Innovative digital finance has had a huge impact on business and society since it has been extensively adopted by businesses and consumers. As a result, the goal of this edited book is to expand and deepen our knowledge of the business possibilities of novel blockchain and digital financial applications. MOHD NAVED, PHD, is an associate professor with a career spanning over a decade in the fields of business analytics, data science, and artificial intelligence. He has over 80 publications in reputed scholarly journals and books, and his research focuses on the applications of these disciplines in various industries.V. AJANTHA DEVI, PHD, is a distinguished researcher and holds the position of Research Head at AP3 Solutions, located in Chennai, Tamil Nadu, India. She earned her Ph.D. from the University of Madras in 2015, marking the inception of her impressive academic journey. She has over 45 papers published in prestigious international journals and conference proceedings, and she has authored, co-authored and edited numerous books.She holds five Australian patents and one Indian patent, and she has won numerous awards.ADITYA KUMAR GUPTA, PHD, is an associate professor at Amity University, Noida, India, with over 20 years of experience in both academia and industry. He is the editor of the Amity Case Research Journal, and he has been a reviewer for many scientific journals.Preface xvii1 EVOLUTION OF FINTECH IN FINANCIAL ERA 1Tanya Kumar and Satveer Kaur1.1 Introduction 11.2 Review of Literature 21.3 Objectives and Research Methodology 41.4 Working of FinTech 41.5 Tools and Techniques used in FinTech 51.6 Future Framework of FinTech 71.7 Evolution of FinTech in Financial World 71.8 Discussion and Conclusion 9References 9Webliography 112 DIGITAL TRANSFORMATION OF FINANCIAL SERVICES IN THE ERA OF FINTECH 13Ayesha Siddiqui, Arti Yadav and Najib H.S. Farhan2.1 Introduction 132.2 Review of Literature 162.2.1 Studies on FinTech 162.2.2 Studies on Digital Transformation 182.3 Digital Transformation: A Conceptual Overview 202.4 FinTech Ecosystem 212.5 Role of Fintech and Digital Transformation with Respect to Financial Services 242.6 Conclusion 27References 273 RESHAPING BANKING WITH DIGITAL TECHNOLOGIES 35Ankita Srivastava and Aishwarya Kumar3.1 Banking and Artificial Intelligence (AI) 353.1.1 Basic Algorithms and Machine Intellect 373.1.2 Artificial Common Intelligence 373.1.3 Ultra Smart AI 373.2 Fintech Evolution 383.3 AI Opportunities in Fintech 403.3.1 Automation 403.3.1.1 Robotic Process Automation (RPA) 403.3.1.2 iPaaS 413.3.1.3 iSaaS 413.3.1.4 Bots 413.3.1.5 Enterprise Automation 413.3.2 Improved Decision Making 423.3.3 Customization 433.4 Reshaping the Banking 443.4.1 Payments 443.4.2 Lending & AI-Based Credit Analysis 463.4.3 Wealth Management 483.4.3.1 Portfolio Management 483.4.3.2 Compliance Management 493.4.3.3 Robo-Advisory 503.5 Insurance 523.6 Challenges Faced by Fintech in Banking 533.6.1 Regulatory Compliance 533.6.2 Customer Trust 533.6.3 Blockchain Integration 533.7 Conclusion 54References 544 ADOPTION OF FINTECH: A PARADIGM SHIFT AMONG MILLENNIALS AS A NEXT NORMAL BEHAVIOUR 59Pushpa A., Jaheer Mukthar K. P., Ramya U., Edwin Hernan Ramirez Asis and William Rene Dextre Martinez4.1 Introduction 604.1.1 Evolution of Fintech 614.1.2 Technology Innovation in the Financial Sector - Building a Digital Future 634.1.3 Taxonomy of Fintech Business Model 664.1.4 Fintech Ecosystem 694.1.5 Prepositions for Fintech Adoption 724.1.6 Challenges of the Fintech Industry 744.2 Statement of the Problem and Research Questions 754.3 Research Questions and Objectives 764.4 Conceptual Framework and Proposed Model 774.4.1 Conceptual Framework 774.4.2 The TAM Model 774.4.3 Proposed Model and Hypothesis Framed 794.5 Conclusion 83Acknowledgement 83References 835 A COMPREHENSIVE STUDY OF CRYPTOCURRENCIES AS A FINANCIAL ASSET: MAJOR TOPICS AND MARKET TRENDS 91Gioia Arnone and Ajantha Devi Vairamani5.1 Introduction 915.2 Literature Review 925.3 Methodology 955.4 Findings 965.5 Cryptocurrencies as a Major Financial Asset 975.6 What is the Value of Cryptocurrencies? Current Market Trends 985.7 Conclusion 100References 1016 CUSTOMERS’ SATISFACTION AND CONTINUANCE INTENTION TO ADOPT FINTECH SERVICES: DEVELOPING COUNTRIES’ PERSPECTIVE 105Song Bee Lian and Liew Chee Yoong6.1 Introduction 1066.2 Understanding the Fintech Phenomenon in Developing Countries 1086.3 Literature Review 1096.3.1 Technology Acceptance Model (TAM) 1096.3.2 Customer Satisfaction 1106.3.3 Customer Innovativeness 1106.3.4 Hedonic Motivation 1116.3.5 Perceived Usefulness 1116.3.6 Perceived Ease of Use 1126.3.7 System Quality 1136.3.8 Technology Self-Efficacy 1136.3.9 Continuance Intention to Adopt 1146.3.10 Hypothesis Development 1146.3.11 Conceptual Model 1156.4 Research Methodology 1156.4.1 Sample and Data Collection 1156.4.2 Measures of the Constructs 1166.4.3 Validity and Reliability Assessment 1166.5 Results 1206.5.1 Demographic Profile of the Respondent 1206.5.2 Structural Model Assessment 1206.6 Discussion 1226.7 Theoretical and Practical Implications 1266.8 Conclusion 127References 1287 FINTECH APPS: AN INTEGRAL TOOL IN TITIVATING BANKING OPERATIONS 137Arun Prakash A., Leelavathi R., Rupashree R. and V.G. Jisha7.1 Introduction 1387.2 Objectives 1427.3 Statement of the Problem 1437.4 Need for the Study 1447.5 Review of Literature 1447.6 Proposed Model 1457.7 Lending APPS 1457.8 Investment Apps 1457.9 Payment Apps 1477.10 Insurance Apps 1487.11 Persuading Factors that Increase the Usage of Fin-Tech Apps 1497.12 Methodology 1507.13 Results and Discussions 1517.14 Multiple Linear Regression 1527.15 Structural Equation Modelling 1547.16 Conclusion 155References 1558 ANALYTICAL STUDY OF FIN-TECH IN BANKING: A UTILITY MODEL 157Neha Kamboj and Mamta Sharma8.1 Introduction 1588.2 Literature Analysis and Development of Hypothesis 1608.2.1 Perceived Utility (PU) and Willingness to Adopt Fin-Tech (WUF) Services 1618.2.2 Sensible Usability (SU) and Willingness to Use Fin-Tech (WUF) Services 1618.2.3 Customer Belief (CU) and Willingness to Use Fin-Tech (WUF) Services 1628.2.4 Social Implications (SI) and Willingness to Use Fin-Tech (WUF)Services 1628.3 Research Design 1638.4 Empirical Results 1658.4.1 Effect of Perceived Utility of Fin-Tech Services (PU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 1688.4.2 Effect of Perceived Ease of Use of Fin-Tech Services (PEU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 1688.4.3 Effect of Customer Belief in Fin-Tech Services (CU) on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 1688.4.4 Social Influence (SI) Impact on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 1698.5 Conclusion 169References 1709 IS DIGITAL CURRENCY A PAYMENT DISRUPTION MECHANISM? 173Vanishree Mysore Ramkrishna and Vyshnavi Loganathan9.1 Introduction 1739.2 Review of Literature 1759.3 Methodology and Sampling 1779.4 Results and Discussion 1799.4.1 Financial Literacy & Inclusion 1809.4.2 Infrastructure 1819.4.3 Technical Know-How 1839.4.4 Trust and Belief 1849.5 Acceptance of CBDC 1859.6 Conclusion 188References 19010 INVESTOR SENTIMENT DRIVING CRYPTO-TRADE IN INDIA 193Sushant Waghmare and Dipesh Uike10.1 Introduction 19410.2 Review of Literature 19510.2.1 Finance & Sentiments 19510.2.2 Cryptocurrency 19610.2.3 Addiction or Analysis? 19710.2.4 Fear of Missing Out (FOMO) 19810.3 Research Methodology 20010.3.1 Aim of the Study 20010.3.2 Objectives of the Study 20010.3.3 Sampling Methodology & Data Analysis 20010.3.4 Limitations of the Study 20110.4 Data Analysis & Interpretation 20110.5 Conclusions, Suggestions & Recommendations 214References 21711 APPLICATIONS OF DIGITAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE IN CRYPTOCURRENCY - A MULTI-DIMENSIONAL PERSPECTIVE 221W. Jaisingh, Preethi N. and R. K. Kavitha11.1 Introduction 22211.2 State-of-the-Art Review 22311.3 Application Areas of Cryptocurrencies 22411.3.1 Fundraising and Investments 22411.3.2 Freight Transportation and Travel 22611.3.3 Education 22711.3.4 Publication and Advertising as Means of Communication 22711.3.5 E-Commerce and Entertainment 22811.3.6 Real Estate and Stock Market 22811.3.7 Trained Financial Planners 22811.4 Financial Transaction Using Blockchain Technology 22911.4.1 Introduction 22911.4.2 Technology Acceptance Model 23011.4.3 External Constructs 23011.4.3.1 Trust (T) 23011.4.3.2 Support (RS) for Regulatory Standards 23211.4.3.3 Experience (E) 23211.4.3.4 Social Influence (SI) 23311.4.3.5 Design (D) 23311.4.4 Summary 23411.5 An Analysis of Cryptocurrency Mining Using a Hybrid Approach 23411.5.1 Introduction 23411.5.2 Cryptocurrency Mining Strategies 23511.5.3 Summary and Discussion 23611.6 Forecasting Cryptocurrency Price Using Convolutional Neural Networks 23711.6.1 Introduction 23711.6.2 Assistive Technologies in Machine Learning and Deep Learning 23811.6.3 Convolutional Neural Networks with Weighted and Attentive Memory Channels 23811.6.3.1 Attentive Memory Module 23911.6.3.2 Convolution & Pooling Module 23911.6.4 Summary and Discussion 23911.7 Blockchain Technology and Cryptocurrencies for the Collaborative Economy 24011.7.1 Introduction 24011.7.2 Collaborative Economy and Digital Platforms 24111.7.3 Emergence of Collaborative Consumption 24211.7.3.1 Promoting the Diffusion of Collaborative Practices 24211.7.4 Summary 24211.8 Conclusions 243References 24312 A STUDY ON THE INFLUENCE OF PERSONALITY ON SAVINGS AND INVESTMENT IN CRYPTOS 251K. Manimekalai, T. Satheeshkumar and G. Manokaran12.1 Introduction 25212.2 Literature Review 25312.2.1 Openness to Experience 25412.2.2 Conscientiousness 25612.2.3 Extroversion 25712.2.4 Aggreeableness 25812.2.5 Neuroticism 25912.3 Objectives of the Research 26012.4 Methodolgy 26012.5 Discussion 268References 26913 DEEP NEURAL NETWORK IN SECURITY: A NOVEL ROBUST CAPTCHA MODEL 277Manasi Chhibber, Rashmi Gandhi, Aakanshi Gupta and Ashok Kumar Yadav13.1 Introduction 27713.2 Literature Review 27913.2.1 Convolutional Neural Networks 28013.2.2 Transfer Learning 28113.3 Proposed Approach 28313.3.1 Data Pre-Processing and Exploratory Analysis 28313.3.2 Data Acquisition 28313.4 Results and Discussions 28913.4.1 Cnn 28913.4.2 DenseNet 29213.4.3 MobileNet 29513.4.4 Vgg 16 29713.5 Conclusion 300References 30014 CUSTOMER’S PERCEPTION OF VOICE BOT ASSISTANCE IN THE BANKING INDUSTRY IN MALAYSIA 303Manimekalai Jambulingam, Indhumathi Sinnasamy and Magiswary Dorasamy14.1 Introduction 30314.2 Problem Statement 30414.3 What is a Voice Bot? 30514.3.1 Characteristics of a Voice Bot 30714.3.2 Why are Voice Bots Becoming Popular? 30814.3.3 Benefits of a Voice Bot from the Bank’s Perspective 30914.3.4 Benefits of a Voice Bot from the Customer’s Perspective 31114.3.5 Opportunities for Voice Bots in Banking 31214.3.6 Use Cases: Bank Voice Bots Currently Available Around the World 31314.4 Call to Action 31514.5 Literature Review 31614.6 Research Methodology 31714.7 Descriptive Analysis 31814.8 Discussion and Conclusion 321References 32215 APPLICATION OF TECHNOLOGY ACCEPTANCE MODEL (TAM) IN FINTECH MOBILE APPLICATIONS FOR BANKING 325Tabitha Durai and F. Lallawmawmi15.1 Introduction 32615.1.1 Significance of the Study 33115.1.2 Research Objectives 33115.1.3 Hypotheses of the Study 33215.1.3.1 Perceived Usefulness Toward the Usage of Fintech 33215.1.3.2 Brand Image Toward the Usage of Fintech 33315.1.3.3 Perceived Risks Toward the Usage of Fintech 33315.2 Methods and Measures 33415.2.1 Instrument Development 33515.3 Results 33615.3.1 Demographic Profile of the Respondents 33615.3.2 Factors Influencing Fintech under Technology Acceptance Model (TAM) 33815.3.2.1 Prominent Factors under Technology Acceptance Model (TAM) that Influence the Usage of Fintech 34015.3.3 Technology Acceptance Model and the Usage of Fintech 34015.3.3.1 Perceived Usefulness Toward the Usage of Fintech 34015.3.3.2 Brand Image Toward the Usage of Fintech 34215.3.3.3 Perceived Risks Toward the Usage of Fintech 34315.4 Discussion 34415.5 Conclusion 346References 34716 UPSURGE OF ROBO ADVISORS: INTEGRATING CUSTOMER ACCEPTANCE 351C. Nagadeepa, Reenu Mohan, Antonio Peregrino Huaman Osorio and Willian Josue Fernandez Celestino16.1 Introduction 35116.2 Chatbots 35516.2.1 Benefits of Using Chatbots in Banks 35516.3 Robo-Advisor 35616.3.1 Robo-Adviser – A Brief History 35716.3.2 Historic Account of Robo Advisors 35816.3.3 Robo-Advisor Versus FA (Financial Advisor) 35916.3.4 Robo Advisors in Market 35916.3.5 How Robo-Advisors Work? 36416.3.6 Types of Robo-Advisor 36516.3.7 Top Robo-Advisors 36716.3.8 Points to be Consider while Selection of Robo-Advisor 37116.3.9 Benefits of Robo-Advisors 37216.3.10 Limitations of Robo-Advisors 37316.4 Acceptance of Robo-Advisor 37416.4.1 Theoretical Background and Research Propositions 37416.4.2 A Glimpse of Earlier Research Studies 37516.4.3 Methodology and Hypothesis 37616.4.4 Collection of Data 37716.4.5 Hypotheses Testing 37716.5 Conclusion 379References 37917 SUPER APPS: THE NATURAL PROGRESSION IN FIN-TECH 383Kavitha D., Uma Maheswari B. and Sujatha R.17.1 Introduction 38317.2 Journey from an App to a Super App 38517.3 Super App vs. A Vertically Integrated App 38517.4 Architecture and Design of Super Apps 38617.4.1 Monolithic Architecture 38817.4.2 Modular Architecture 38817.4.3 Microservices Architecture 38917.5 Business Models of Super Apps 39017.6 The Super App Market Space and the Business Models 39117.6.1 WeChat 39117.6.2 Alipay 39317.6.3 Grab 39417.6.4 Paytm 39517.6.5 The Latest Entrant: Tata Neu 39617.6.6 Other Players 39617.7 Factors Contributing to the Success of Super Apps 39717.8 Super Apps in Fin-Tech and their Role in the Financial Services Segment 40017.9 Role of Super Apps in Financial Inclusion 40317.10 Benefits of Super Apps in the Financial Services Sector 40517.10.1 Economies of Scale & Cost of Financial Intermediation 40517.10.2 Size & Speed of Product and Service Offerings 40617.10.3 The Power of Data and the Speed of Responsiveness 40617.11 Risks due to Super Apps in the Financial System 40617.11.1 Financial Stability 40717.11.2 Market Concentration 40717.11.3 Dis-Intermediation of Banks from Customer 40817.12 Regulatory Measures to Mitigate the Risks 40817.13 The Future of Super Apps 40817.14 Conclusion 410References 411Index 413
LPI Web Development Essentials Study Guide
PASS THE LPI WEB DEVELOPMENT ESSENTIALS EXAM AND SET YOURSELF UP FOR SUCCESS AT A NEW WEB DEVELOPMENT JOBIn LPI Linux Professional Institute Web Development Essentials Study Guide: Exam 030-100, accomplished IT educator and systems engineer, Audrey O’Shea delivers an easy-to-follow and hands-on roadmap to passing the LPI Web Development Essentials exam and hitting the ground running at a new job as a web developer. In the book, you’ll explore the software development skills, web technologies, HTML, CSS, Node.js, and JavaScript info you need to implement modern applications and solutions in a web environment. You will find:* Introductory coverage of SQL, HTML, JavaScript, CSS, and MongoDB* A heavy emphasis on real-world job skills, as well as the technologies used every day by web developers in the field* Complimentary access to the Sybex interactive online learning environment and test bank, complete with hundreds of practice questions, electronic flashcards, and a searchable glossary of important termsAn essential and practical resource for anyone preparing for the Web Development Essentials certification exam, LPI Linux Professional Institute Web Development Essentials Study Guide: Exam 030-100 is also the ideal book for entry-level software developers seeking knowledge of web development tools and principles. ABOUT THE AUTHORAUDREY O’SHEA currently teaches electronics and information technology at a technical school in upstate New York. She has been working and educating in IT since 1989 and her career has included positions of network administrator, tech support specialist, trainer and consultant. Introduction xviiAssessment Test xxiiiCHAPTER 1 WEB DEVELOPMENT BASICS 1Developer Types 2Creating Software 3Text Editors and IDEs 6Compiled Languages 7Interpreted Languages 8Programming Paradigms 8Maintaining Software 11Version Control Systems 11Software Testing 12Summary 12Exam Essentials 13Review Questions 17CHAPTER 2 CLIENT/SERVER COMPUTING 21Client-Side 23Types of Clients 24Web Browsers 26Server- Side 28Types of Servers 28Popular Server Software 29Popular Web Page–Creating Software 29Summary 31Exam Essentials 31Review Questions 34CHAPTER 3 DATABASE MANAGEMENT SYSTEMS 39Database Structures and Languages 40Database Structures 40Database Languages 43Relational Database Concepts 44Content Maintenance and Delivery 45Summary 47Exam Essentials 47Review Questions 51CHAPTER 4 CLIENT/SERVER COMMUNICATION 55HTTP 56HTTP Client-Side 57HTTP Server-Side 60WebSocket API 61Caches and Cookies 62HTTP Security Concerns 63Summary 63Exam Essentials 64Review Questions 66CHAPTER 5 HTML INTRODUCTION 71What Is HTML? 73The HTML Skeleton 73HTML Syntax 75HTML Head 79Summary 81Exam Essentials 81Review Questions 86CHAPTER 6 CONTENT MARKUP 91The Basics 92Block and Inline Elements 93Block Elements 93Inline Elements 95Hierarchical Structure 96Lists 97Styles 100Semantic and Non-Semantic Elements 100Non-Semantic Elements 100Semantic Structural Elements 100Summary 101Exam Essentials 101Review Questions 111CHAPTER 7 REFERENCES AND EMBEDDED RESOURCES 115Page Anchors 116External Resource Links 117Using Images 118The img Tag and Its Attributes 119Inline or Background Images 120Image Maps 122File Formats 124The Tag 125Summary 126Exam Essentials 126Review Questions 131CHAPTER 8 CREATING HTML FORMS 135Anatomy of an Input Form 136Commonly Used Attributes 137Button Types 142Adding Functionality 143Radio Buttons and Check Boxes 145A Form for Reviews 146Summary 146Exam Essentials 147Review Questions 154CHAPTER 9 INTRODUCING CSS 159Applying Styles to HTML 160The style Attribute 160CSS Rules and Selectors 161The Tag 162CSS Stored in Separate Files 164CSS Accessibility Features 165Summary 166Exam Essentials 166Review Questions 171CHAPTER 10 APPLYING CSS STYLES 175Other Selectors 176CSS Inheritance 180CSS Pseudo- Classes 181CSS Order of Precedence 182Summary 183Exam Essentials 183Review Questions 188CHAPTER 11 CSS STYLING FUNDAMENTALS 193Units of Measure 194Absolute Units 194Relative Units 195Common Properties 196Color 197Background 198Borders 200Font 201Text 202List Style 204Line Height 206Summary 208Exam Essentials 209Review Questions 213CHAPTER 12 CSS LAYOUT AND BOX MODEL 217The CSS Box Model 218CSS Box Model Parts 219Element Dimensions 219CSS Website Layout 221Elements and Their Positions 221Text Flow 221Document Flow 222Other Layout Features 224Summary 226Exam Essentials 227Review Questions 233CHAPTER 13 JAVASCRIPT ESSENTIALS 237JavaScript Statements and Syntax 238Adding Comments 240Inserting JavaScript into HTML 241JavaScript as an External File 242The JavaScript Console 244Summary 245Exam Essentials 246Review Questions 249CHAPTER 14 JAVASCRIPT DATA 253Variables, Constants, and Scope 254Variables 254Constants 255Scope 255Objects and Methods 256Data Types 258Primitive Types 258Arrays 259Strings 260Operators and Precedence 261Data Conversions 263Summary 264Exam Essentials 265Review Questions 269CHAPTER 15 FUNCTIONS AND CONTROL STRUCTURES 273Functions 274Function Syntax 274Starting and Stopping Functions 275Functions and Variables 276Conditional Statements 276Comparison Operators 280Data Coercion 280Truthy and Falsy Values 281Summary 281Exam Essentials 281Review Questions 285CHAPTER 16 THE DOM 291DOM Structure 292DOM Methods and Properties 296Adding and Deleting Classes and Attributes 296Retrieving and Setting Values 297Changing CSS Styling Using the DOM 299DOM Events 300Summary 301Exam Essentials 301Review Questions 305CHAPTER 17 NODEJS BASICS 309What Is Node.js? 310Installing Node.js 311Installing a Node.js Module 312Running a Node.js App 313Summary 313Exam Essentials 314Review Questions 317CHAPTER 18 NODEJS EXPRESS 321Node.js vs. Node.js Express 322Installing Express 322Routing and Serving Files 325User Input and Validation 327Templates 331Template Engines 331HTML Templates 331Security Concerns 333Cross-Site Scripting 333Cross-Site Request Forgery 333Summary 333Exam Essentials 333Review Questions 337CHAPTER 19 MANIPULATING SQLITE WITH NODEJS 341Installing the SQLite Module 342Creating a SQLite Database 342Opening and Closing the Database 343In Terminal 343In NodeJS 344Managing SQLite Data with NodeJS 345Entering Data 346Changing Data 346Running Queries 347Security Concerns 349Summary 349Exam Essentials 350Review Questions 356APPENDIX ANSWERS TO REVIEW QUESTIONS 361Chapter 1: Web Development Basics 362Chapter 2: Client/Server Computing 363Chapter 3: Database Management Systems 365Chapter 4: Client/Server Communication 367Chapter 5: HTML Introduction 369Chapter 6: Content Markup 370Chapter 7: References and Embedded Resources 372Chapter 8: Creating HTML Forms 373Chapter 9: Introducing CSS 375Chapter 10: Applying CSS Styles 376Chapter 11: CSS Styling Fundamentals 378Chapter 12: CSS Layout and Box Model 380Chapter 13: JavaScript Essentials 381Chapter 14: JavaScript Data 383Chapter 15: Functions and Control Structures 384Chapter 16: The DOM 386Chapter 17: NodeJS Basics 388Chapter 18: NodeJS Express 389Chapter 19: Manipulating SQLite with NodeJS 391Index 395
Self-Service AI mit Power BI
In diesem Buch wird erklärt, wie Sie die in Power BI Desktop geladenen Daten durch den Zugriff auf eine Reihe von Funktionen der künstlichen Intelligenz (KI) anreichern können. Diese KI-Funktionen sind in Power BI Desktop integriert und helfen Ihnen, neue Erkenntnisse aus vorhandenen Daten zu gewinnen. Einige der Funktionen sind automatisiert und stehen Ihnen auf Knopfdruck oder durch das Schreiben von Datenanalyseausdrücken (DAX) zur Verfügung. Andere Funktionen sind durch das Schreiben von Code in den Sprachen R, Python oder M verfügbar. Dieses Buch eröffnet Ihnen die gesamte Palette der KI-Funktionen mit klaren Beispielen, die zeigen, wann sie am besten angewendet werden und wie Sie sie auf Ihre eigenen Datensätze anwenden können. Ganz gleich, ob Sie Geschäftsanwender, Analyst oder Datenwissenschaftler sind - Power BI verfügt über KI-Funktionen, die auf Sie zugeschnitten sind. In diesem Buch erfahren Sie, welche Arten von Erkenntnissen Power BI automatisch liefern kann. Sie erfahren, wie Sie die Sprachen R und Python für Statistiken integrieren und nutzen können, wie Sie beim Laden von Daten mit Cognitive Services und Azure Machine Learning Services zusammenarbeiten, wie Sie Ihre Daten durch Fragen in einfachem Englisch erkunden können ... und vieles mehr! Es gibt KI-Funktionen für die Entdeckung Ihrer Daten, die Charakterisierung unerforschter Datensätze und die Erstellung von Was-wäre-wenn-Szenarien.Es gibt viel zu mögen und von diesem Buch zu lernen, ob Sie ein Neuling in Power BI oder ein erfahrener Benutzer sind. Power BI Desktop ist ein frei verfügbares Tool zur Visualisierung und Analyse. Dieses Buch hilft Ihnen, das Beste aus diesem Tool herauszuholen, indem Sie einige seiner neuesten und fortschrittlichsten Funktionen nutzen.WAS SIE LERNEN WERDEN:- Stellen Sie Fragen in natürlicher Sprache und erhalten Sie Antworten aus Ihren Daten- Lassen Sie sich von Power BI erklären, warum sich ein bestimmter Datenpunkt von den anderen unterscheidet- Lassen Sie Power BI die wichtigsten Einflussfaktoren über Datenkategorien anzeigen- Zugriff auf die in der Azure-Cloud verfügbaren Funktionen für künstliche Intelligenz- Gehen Sie denselben Drilldown-Pfad in verschiedenen Teilen Ihrer Hierarchie- Laden Sie Visualisierungen, um Ihre Berichte intelligenter zu gestalten- Simulieren Sie Änderungen an Daten und sehen Sie sofort die Folgen- Kennen Sie Ihre Daten, noch bevor Sie Ihren ersten Bericht erstellen- Erstellen Sie neue Spalten, indem Sie Beispiele für die benötigten Daten angeben- Transformieren und visualisieren Sie Ihre Daten mit Hilfe von R- und Python-SkriptenFÜR WEN DIESES BUCH GEDACHT IST:Für den begeisterten Power BI-Anwender, der modernste Funktionen der künstlichen Intelligenz (KI) einsetzen möchte, um neue Erkenntnisse aus vorhandenen Daten zu gewinnen. Für Endanwender und IT-Fachleute, die sich nicht scheuen, in die neue Welt des maschinellen Lernens einzutauchen, und bereit sind, diesen Schritt zu tun und einen tieferen Blick in ihre Daten zu werfen. Für diejenigen, die von einfachen Berichten und Visualisierungen zu diagnostischen und prädiktiven Analysen übergehen wollen.Markus Ehrenmueller-Jensen ist ein Datenexperte, der seine Karriere als Berater für Business Intelligence-Lösungen auf einem IBM AS/400-System begann, bevor er 2006 in die Welt von Microsofts Data Platform eingeführt wurde. Seitdem hat er Data Warehouses und Business Intelligence-Lösungen für eine Vielzahl von Kunden entwickelt. Sein Portfolio umfasst Schulungen und Workshops, Architekturentwürfe und die Entwicklung von datenorientierten Lösungen. Im Jahr 2018 gründete er Savory Data, ein unabhängiges Beratungsunternehmen.Markus ist als Microsoft Certified Solution Expert (MCSE) für Data Platform und Business Intelligence sowie als Microsoft Certified Trainer (MCT) zertifiziert. Er unterrichtet Datenbanken, Informationsmanagement und Projektmanagement an der HTL Leonding, Österreich. Er ist Mitbegründer von PASS Austria und PUG Austria und Mitorganisator des jährlich stattfindenden SQL Saturday in Wien. Seit 2017 ist er jedes Jahr ein Microsoft Most Valuable Professional (MVP).1. Fragen in natürlicher Sprache stellen2. Die Insights-Funktion3. Entdeckung wichtiger Einflussfaktoren4. Drill-Down und Zerlegung von Hierarchien5. Hinzufügen intelligenter Visualisierungen6. Mit Szenarien experimentieren7. Einen Datensatz charakterisieren8. Spalten aus Beispielen erstellen9. Ausführen von R- und Python-Visualisierungen10. Datenumwandlung mit R und Python11. Ausführen von Machine Learning Modellen in der Azure Cloud
Critical Theory of AI
We live in an age of artificial intelligence. Machines think and act in ever more complex ways, making suggestions and decisions on our behalf. While AI might be seen as practical and profitable, issues of data surveillance, algorithmic control, and sexist and racist bias persist. In this rapidly changing landscape, social analysis of AI risks getting scaled down to issues of ‘ethics’, ‘responsibility’, and ‘fairness’. While these are important issues, they must be addressed not from an ‘AI first’ perspective, but more thoroughly in terms of power and contention.Approaching artificial intelligence from the often overlooked perspective of critical social theory, this book provides a much-needed intervention on how both old and new theories conceptualize the social consequences of AI. Questions are posed about the ideologies driving AI, the mythologies surrounding AI, and the complex relationship between AI and power. Simon Lindgren provides a way of defining AI as an object of social and political critique, and guides the reader through a set of contentious areas where AI and politics intersect. In relation to these topics, critical theories are drawn upon, both as an argument for and an illustration of how AI can be critiqued.Given the opportunities and challenges of AI, this book is a must-read for students and scholars in the humanities, social sciences, and STEM disciplines.SIMON LINDGREN is Professor of Sociology at Umeå University.1. AI and critical theory2. AI assemblage3. Ideology behind AI4. Ideology within AI5. Social machines6. AI at work7. AI subjects8. AI in the loopReferencesIndex
Introduction to Ansible Network Automation
This book provides a comprehensive learning journey aimed at helping you master Ansible and efficiently automate a range of enterprise network devices, such as routers, switches, firewalls, Wireless LAN Controller (WLC), and Linux servers. Introduction to Ansible Network Automation combines the fundamentals of three books into one, covering basics of Linux administration, Ansible concepts, and Ansible network automation.Authors Brendan Choi and Erwin Medina have created a structured learning path that starts with the fundamentals and allows you to progressively enhance your skill sets with each chapter. Part I serves as an Ansible primer, guiding you through Linux basics using WSL on Windows 11 and assisting in the setup of your learning environment. Part II covers essential Ansible concepts through practical lab activities involving four Linux servers. In Part III, you will learn how to apply your Linux skills from Part I and the concepts from Part II to real-world scenarios by creating Ansible automation YAML scripts.What sets this book apart is its unique focus on Ansible and Network Automation, combined with a strong emphasis on understanding Linux. It is specifically designed for novice network automation engineers and students, assuming no prior Linux expertise, and provides first-hand experience starting from scratch. It also offers practical insights by sharing real-life examples of Ansible playbooks derived from production enterprise infrastructure, allowing you to gain an understanding of how Ansible can be effectively applied in real-world enterprise network environments.Upon completion of this book, you will have developed foundational skills in Ansible network automation and general Linux administration, and will understand how to apply this newly acquired knowledge to real-life scenarios.WHAT YOU WILL LEARN* Develop a comprehensive understanding of Ansible and its essential concepts for automating enterprise network devices and applying them to real-world scenarios* Master the basics of Ansible operations within Linux automation and progress to applying Ansible concepts specifically to network device automation* Execute Ansible ad-hoc commands and playbooks for a range of network operational tasks, including configuration management, software and system updates, and upgrades* Work with real-life examples of Ansible playbooks derived from actual enterprise infrastructure, gaining practical experience in writing Ansible YAML scripts* Acquire the skills to automate network operations using Ansible, streamline network management processes, and replace manual-driven tasks with directives in Ansible playbooksWHO IS THIS BOOK FORNetwork, security, UC and systems engineers, as well as technical leaders, IT managers and network students.BRENDAN (BYONG CHOL) CHOI is a highly accomplished tech lead at Secure Agility, with over 19 years of hands-on experience in the ICT industry. He is a certified Cisco, VMware, and Fortinet Engineer, and has worked for renowned enterprises such as Cisco Systems, NTT (Dimension Data), Fujitsu, as well as reputable Australian IT integrators like Telstra and Secure Agility. Brendan specializes in optimizing enterprise IT infrastructure management and enterprise business process optimization, utilizing both open and proprietary tools. He is the author of Python Network Automation: Building an Integrated Virtual Lab as well as Introduction to Python Network Automation: The First Journey. Through these publications, Brendan shared his knowledge with the IT community. He has trained over 200 Network and Systems Engineers on Python and Ansible Network automation and enjoys sharing industry-acquired knowledge through social media, blogging, and his YouTube channel. Brendan's current interests revolve around private cloud, enterprise networking, security, virtualization, and automation technologies. His dedication and passion for enterprise infrastructure management are evident in his commitment to continuous learning, knowledge sharing, and contributing to the ICT industry as a whole.ERWIN MEDINA is an experienced Senior Security and Network Engineer in the ICT industry with over 11 years of experience. He holds certifications in Cisco, Palo Alto, Fortinet, and Juniper technologies. Currently employed at CSIRO, Erwin contributes to the organization's security, network, and system operations and optimization, utilizing both open and proprietary tools. Erwin began his career as a field engineer in telecommunications before transitioning to ICT as a network engineer. Working with diverse networks in customers' production environments, he recognizes the crucial role of automation in simplifying complex network and security challenges. Embracing Ansible as his primary IT tool, Erwin has successfully transitioned away from manual-driven tasks. During his time at Telstra, Erwin had the privilege of being mentored by Brendan for over two years, gaining invaluable experience in leveraging Ansible and Python for enterprise network and security device management. Currently, Erwin applies Ansible in real-production scenarios to drive efficiency and productivity within his organization. He thrives on technical challenges and eagerly adapts to the ever-evolving ICT landscape, actively contributing to his organization's success. Erwin's commitment, expertise, and passion to share his knowledge with others make him a valuable asset in the ICT industry.Part 1: The IntrosChapter 1: Is Ansible good for Network Automation?Chapter 2: Shall We Linux? (Part I)Chapter 3: Shall We Linux? (Part II)Chapter 4: Creating an Ansible Learning EnvironmentPart 2: The ConceptsChapter 5: Data Types and File Formats in AnsibleChapter 6: Ansible Concepts I - SSH and Ad-Hoc CommandsChapter 7: Ansible Concepts II - Practical Application of Ad-Hoc CommandsChapter 8: Ansible Concepts III - Using when, Improving Playbooks, and Targeted NodesChapter 9: Ansible Concepts IV - Git Integration, Tags, File, and Service ManagementChapter 10: Ansible Concepts V - Users, Roles, Host Variables, Templates, and Password VaultChapter 11: Building an Ansible Learning Environment for Network and SecurityPart 3: The PracticalChapter 12: Ansible Practical I - Configuring Cisco Routers and SwitchesChapter 13: Ansible Practical II - Backing Up Cisco Network Device ConfigurationsChapter 14: Ansible Practical III - Developing a Network Configuration Comparison ToolChapter 15: Ansible Practical IV - Upgrading Cisco IOS-XE RoutersChapter 16: Ansible Practical V - Upgrading Cisco Wireless LAN Controllers (WLC)Chapter 17: Ansible Practical VI - Creating User Accounts on Palo Alto and Fortinet FirewallsChapter 18: Ansible Practical VII - Creating Security Policies on Palo Alto and Fortinet FirewallsChapter 19: Ansible Practical VIII - Creating IPsec Tunnels on Palo Alto FirewallsChapter 20: Ansible Practical IX - Creating Object Addresses on Palo Alto FirewallsChapter 21: Ansible Practical X - Upgrading Palo Alto Firewalls
CompTIA DataSys+ Study Guide
YOUR ALL-IN-ONE GUIDE TO PREPARING FOR THE COMPTIA DATASYS+ EXAMIn CompTIA DataSys+ Study Guide: Exam DS0-001, a team of accomplished IT experts delivers a practical and hands-on roadmap to succeeding on the challenging DS0-001 exam and in a new or existing career as a data systems professional. In the book, you’ll explore the essentials of databases, their deployment, management, maintenance, security, and more.Whether you’re preparing for your first attempt at the CompTIA DataSys+ exam or for your first day on the job at a new database-related IT position, this book walks you through the foundational and intermediate skills you need to have to succeed. It covers every objective tested by the DS0-001 and skills commonly required in the real-world.You’ll also find:* Practice test questions that measure your readiness for the real exam and your ability to handle the challenges of a new data systems position* Examples and scenarios drawn from real life, as well as challenging chapter review questions* Complimentary access to Sybex’s interactive online learning environment and test bank, accessible from multiple devices, and including electronic flashcards and a searchable glossaryPerfect for anyone getting ready to write the DS0-001 certification exam, CompTIA DataSys+ Study Guide: Exam DS0-001 is also an essential resource for everyone seeking the foundational knowledge and skills required to move into a database administrator role.ABOUT THE AUTHORSMIKE CHAPPLE, PHD, SECURITY+, CYSA+, CISSP, is Teaching Professor of Information Technology, Analytics, and Operations at Notre Dame’s Mendoza College of Business. He is a bestselling author of over 30 books and serves as the Academic Director of the University’s Master of Science in Business Analytics program. He holds multiple additional certifications, including the CISSP (Certified Information Systems Security Professional), CompTIA Data+, CIPP/US (Certified Information Privacy Professional), CompTIA PenTest+, and CompTIA Security+. Mike provides IT certification resources at his website, CertMike.com. SHARIF NIJIM is an Associate Teaching Professor of IT, Analytics, and Operations at Notre Dame’s Mendoza College of Business, where he also serves as the Academic co-Director of the residential Master of Science in Business Analytics program. Prior to Notre Dame, Sharif co-founded and served on the board of a customer data integration company serving the airline industry. Introduction xxiAssessment Test xxviiiCHAPTER 1 TODAY’S DATA SYSTEMS PROFESSIONAL 1Data Drives the Modern Business 2Data 3Storage 3Computing Power 5Data Systems 5Database Fundamentals 6Database Deployment 7Database Management and Maintenance 8Data and Database Security 8Business Continuity 9Careers in Data Systems 10Summary 11CHAPTER 2 DATABASE FUNDAMENTALS 13Types of Databases 14The Relational Model 14Relational Databases 18Nonrelational Databases 22Linear vs. Nonlinear Format 27NoSQL Database Tools 28Programming and Database Operations 29Object- Relational Mapping 30Process to Gauge Impact 31Summary 33Exam Essentials 34Review Questions 36CHAPTER 3 SQL AND SCRIPTING 41Flavors of SQL 42Data Definition Language 43Data Manipulation Language 46Set- Based Logic 50Transaction Control Languages 54ACID Principles 58SQL 58Programmatic SQL 59SQL and Set-Based Logic 68Automating Operations 72Script Purpose and Runtime Location 74Languages 75Command- Line Scripting 76Summary 77Exam Essentials 79Review Questions 81CHAPTER 4 DATABASE DEPLOYMENT 87Planning and Design 88Requirements Gathering 88Database Architecture 99Schema Design 106Design Documentation 116Implementation, Testing, and Deployment 121Acquisition of Assets 121Phases of Deployment 122Database Connectivity 122Testing 127Database Validation 135Summary 139Exam Essentials 140Review Questions 142CHAPTER 5 DATABASE MANAGEMENT AND MAINTENANCE 147Monitoring and Reporting 148System Alerts and Notifications 149Log Files 157Deadlock Monitoring 158Connections and Sessions 159Maintenance 160Database Stability 160Database Performance 163Database Reliability 168Facilitating Operations 172Data Management Tasks 180Data Management 181Summary 189Exam Essentials 191Review Questions 193CHAPTER 6 GOVERNANCE, SECURITY, AND COMPLIANCE 199Data Governance 200Data Governance Roles 200Access Requirements 201Data Retention 206Identity and Access Management 207Identification, Authentication, and Authorization 207Authentication Techniques 208Password Policies 209Account Types 211Data Security 212Protecting Data at Rest 213Protecting Data in Transit 213Data Loss Prevention 216Data Classification 217Personally Identifiable Information 217Protected Health Information 218Payment Card Information 220Regional Requirements 221Breach Reporting Requirements 222Routine Auditing 223Expired Accounts 223Connection Requests 223SQL Code 224Credential Storage Checks 224Summary 225Exam Essentials 225Review Questions 227CHAPTER 7 DATABASE SECURITY 231Database Infrastructure Security 232Physical Security 232Logical Security 237Database Attacks 242SQL Injection 242Denial of Service 248On- Path Attacks 249Malware 250Phishing 253Password Attacks 255Summary 255Exam Essentials 256Review Questions 258CHAPTER 8 BUSINESS CONTINUITY 265The Nature of Disaster 266Natural Disasters 266Human-Made Disasters 272Disaster Recovery Planning 275Prioritizing Disaster Recovery Efforts 275Disaster Recovery Documentation 277Disaster Recovery Technology 280Disaster Recovery Plan Testing 285Lessons Learned 287Plan Maintenance 288Backup and Restore 288Summary 292Exam Essentials 293Review Questions 294APPENDIX ANSWERS TO REVIEW QUESTIONS 299Chapter 2: Database Fundamentals 300Chapter 3: SQL and Scripting 301Chapter 4: Database Deployment 304Chapter 5: Database Management and Maintenance 309Chapter 6: Governance, Security, and Compliance 312Chapter 7: Database Security 314Chapter 8: Business Continuity 316Index 319
Cybernetical Intelligence
CYBERNETICAL INTELLIGENCEHIGHLY COMPREHENSIVE, DETAILED, AND UP-TO-DATE OVERVIEW OF ARTIFICIAL INTELLIGENCE AND CYBERNETICS, WITH PRACTICAL EXAMPLES AND SUPPLEMENTARY LEARNING RESOURCESCybernetical Intelligence: Engineering Cybernetics with Machine Intelligence is a comprehensive guide to the field of cybernetics and neural networks, as well as the mathematical foundations of these technologies. The book provides a detailed explanation of various types of neural networks, including feedforward networks, recurrent neural networks, and convolutional neural networks as well as their applications to different real-world problems. This groundbreaking book presents a pioneering exploration of machine learning within the framework of cybernetics. It marks a significant milestone in the field’s history, as it is the first book to describe the development of machine learning from a cybernetics perspective. The introduction of the concept of “Cybernetical Intelligence” and the generation of new terminology within this context propel new lines of thought in the historical development of artificial intelligence. With its profound implications and contributions, this book holds immense importance and is poised to become a definitive resource for scholars and researchers in this field of study. Each chapter is specifically designed to introduce the theory with several examples. This comprehensive book includes exercise questions at the end of each chapter, providing readers with valuable opportunities to apply and strengthen their understanding of cybernetical intelligence. To further support the learning journey, solutions to these questions are readily accessible on the book’s companion site. Additionally, the companion site offers programming practice exercises and assignments, enabling readers to delve deeper into the practical aspects of the subject matter. Cybernetical Intelligence includes information on:* The history and development of cybernetics and its influence on the development of neural networks* Developments and innovations in artificial intelligence and machine learning, such as deep reinforcement learning, generative adversarial networks, and transfer learning* Mathematical foundations of artificial intelligence and cybernetics, including linear algebra, calculus, and probability theory* Ethical implications of artificial intelligence and cybernetics as well as responsible and transparent development and deployment of AI systemsPresenting a highly detailed and comprehensive overview of the field, with modern developments thoroughly discussed, Cybernetical Intelligence is an essential textbook that helps students make connections with real-life engineering problems by providing both theory and practice, along with a myriad of helpful learning aids. PROF. DR. KELVIN K. L. WONG, is a distinguished expert in medical image processing and computational science, earning his Ph.D. from The University of Adelaide. With a strong academic background from Nanyang Technological University and The University of Sydney, he has been at the forefront of merging the fields of cybernetics and artificial intelligence (AI). He is renowned for coining the term “Cybernetical Intelligence” and is the inventor and founder of Deep Red AI. Preface xvAbout the Author xixAbout the Companion Website xxi1 ARTIFICIAL INTELLIGENCE AND CYBERNETICAL LEARNING 11.1 Artificial Intelligence Initiative 11.2 Intelligent Automation Initiative 41.2.1 Benefits of IAI 51.3 Artificial Intelligence Versus Intelligent Automation 51.3.1 Process Discovery 61.3.2 Optimization 71.3.3 Analytics and Insight 81.4 The Fourth Industrial Revolution and Artificial Intelligence 91.4.1 Artificial Narrow Intelligence 101.4.2 Artificial General Intelligence 121.4.3 Artificial Super Intelligence 131.5 Pattern Analysis and Cognitive Learning 141.5.1 Machine Learning 151.5.1.1 Parametric Algorithms 161.5.1.2 Nonparametric Algorithms 171.5.2 Deep Learning 201.5.2.1 Convolutional Neural Networks in Advancing Artificial Intelligence 211.5.2.2 Future Advancement in Deep Learning 221.5.3 Cybernetical Learning 231.6 Cybernetical Artificial Intelligence 241.6.1 Artificial Intelligence Control Theory 241.6.2 Information Theory 261.6.3 Cybernetic Systems 271.7 Cybernetical Intelligence Definition 281.8 The Future of Cybernetical Intelligence 30Summary 32Exercise Questions 32Further Reading 332 CYBERNETICAL INTELLIGENT CONTROL 352.1 Control Theory and Feedback Control Systems 352.2 Maxwell’s Analysis of Governors 372.3 Harold Black 392.4 Nyquist and Bode 402.5 Stafford Beer 422.5.1 Cybernetic Control 422.5.2 Viable Systems Model 422.5.3 Cybernetics Models of Management 432.6 James Lovelock 432.6.1 Cybernetic Approach to Ecosystems 432.6.2 Gaia Hypothesis 442.7 Macy Conference 442.8 McCulloch–Pitts 452.9 John von Neumann 472.9.1 Discussions on Self-Replicating Machines 472.9.2 Discussions on Machine Learning 48Summary 48Exercise Questions 49Further Reading 503 THE BASICS OF PERCEPTRON 513.1 The Analogy of Biological and Artificial Neurons 513.1.1 Biological Neurons and Neurodynamics 523.1.2 The Structure of Neural Network 533.1.3 Encoding and Decoding 563.2 Perception and Multilayer Perceptron 573.2.1 Back Propagation Neural Network 593.2.2 Derivative Equations for Backpropagation 593.3 Activation Function 613.3.1 Sigmoid Activation Function 613.3.2 Hyperbolic Tangent Activation Function 623.3.3 Rectified Linear Unit Activation Function 623.3.4 Linear Activation Function 64Summary 65Exercise Questions 67Further Reading 674 THE STRUCTURE OF NEURAL NETWORK 694.1 Layers in Neural Network 694.1.1 Input Layer 694.1.2 Hidden Layer 704.1.3 Neurons 704.1.4 Weights and Biases 714.1.5 Forward Propagation 724.1.6 Backpropagation 724.2 Perceptron and Multilayer Perceptron 734.3 Recurrent Neural Network 754.3.1 Long Short-Term Memory 764.4 Markov Neural Networks 774.4.1 State Transition Function 774.4.2 Observation Function 784.4.3 Policy Function 784.4.4 Loss Function 784.5 Generative Adversarial Network 78Summary 79Exercise Questions 80Further Reading 815 BACKPROPAGATION NEURAL NETWORK 835.1 Backpropagation Neural Network 835.1.1 Forward Propagation 855.2 Gradient Descent 855.2.1 Loss Function 855.2.2 Parameters in Gradient Descent 885.2.3 Gradient in Gradient Descent 885.2.4 Learning Rate in Gradient Descent 895.2.5 Update Rule in Gradient Descent 895.3 Stopping Criteria 895.3.1 Convergence and Stopping Criteria 905.3.2 Local Minimum and Global Minimum 915.4 Resampling Methods 915.4.1 Cross-Validation 935.4.2 Bootstrapping 935.4.3 Monte Carlo Cross-Validation 945.5 Optimizers in Neural Network 945.5.1 Stochastic Gradient Descent 945.5.2 Root Mean Square Propagation 965.5.3 Adaptive Moment Estimation 965.5.4 AdaMax 975.5.5 Momentum Optimization 97Summary 97Exercise Questions 99Further Reading 1006 APPLICATION OF NEURAL NETWORK IN LEARNING AND RECOGNITION 1016.1 Applying Backpropagation to Shape Recognition 1016.2 Softmax Regression 1056.3 K-Binary Classifier 1076.4 Relational Learning via Neural Network 1086.4.1 Graph Neural Network 1096.4.2 Graph Convolutional Network 1116.5 Cybernetics Using Neural Network 1126.6 Structure of Neural Network for Image Processing 1156.7 Transformer Networks 1166.8 Attention Mechanisms 1166.9 Graph Neural Networks 1176.10 Transfer Learning 1186.11 Generalization of Neural Networks 1196.12 Performance Measures 1206.12.1 Confusion Matrix 1206.12.2 Receiver Operating Characteristic 1216.12.3 Area Under the ROC Curve 122Summary 123Exercise Questions 123Further Reading 1247 COMPETITIVE LEARNING AND SELF-ORGANIZING MAP 1257.1 Principal of Competitive Learning 1257.1.1 Step 1: Normalized Input Vector 1287.1.2 Step 2: Find the Winning Neuron 1287.1.3 Step 3: Adjust the Network Weight Vector and Output Results 1297.2 Basic Structure of Self-Organizing Map 1297.2.1 Properties Self-Organizing Map 1307.3 Self-Organizing Mapping Neural Network Algorithm 1317.3.1 Step 1: Initialize Parameter 1327.3.2 Step 2: Select Inputs and Determine Winning Nodes 1327.3.3 Step 3: Affect Neighboring Neurons 1327.3.4 Step 4: Adjust Weights 1337.3.5 Step 5: Judging the End Condition 1337.4 Growing Self-Organizing Map 1337.5 Time Adaptive Self-Organizing Map 1367.5.1 TASOM-Based Algorithms for Real Applications 1387.6 Oriented and Scalable Map 1397.7 Generative Topographic Map 141Summary 145Exercise Questions 146Further Reading 1478 SUPPORT VECTOR MACHINE 1498.1 The Definition of Data Clustering 1498.2 Support Vector and Margin 1528.3 Kernel Function 1558.3.1 Linear Kernel 1558.3.2 Polynomial Kernel 1568.3.3 Radial Basis Function 1578.3.4 Laplace Kernel 1598.3.5 Sigmoid Kernel 1598.4 Linear and Nonlinear Support Vector Machine 1608.5 Hard Margin and Soft Margin in Support Vector Machine 1648.6 I/O of Support Vector Machine 1678.6.1 Training Data 1678.6.2 Feature Matrix and Label Vector 1688.7 Hyperparameters of Support Vector Machine 1698.7.1 The C Hyperparameter 1698.7.2 Kernel Coefficient 1698.7.3 Class Weights 1708.7.4 Convergence Criteria 1708.7.5 Regularization 1718.8 Application of Support Vector Machine 1718.8.1 Classification 1718.8.2 Regression 1738.8.3 Image Classification 1738.8.4 Text Classification 174Summary 174Exercise Questions 175Further Reading 1769 BIO-INSPIRED CYBERNETICAL INTELLIGENCE 1779.1 Genetic Algorithm 1789.2 Ant Colony Optimization 1819.3 Bees Algorithm 1849.4 Artificial Bee Colony Algorithm 1869.5 Cuckoo Search 1899.6 Particle Swarm Optimization 1939.7 Bacterial Foraging Optimization 1969.8 Gray Wolf Optimizer 1979.9 Firefly Algorithm 199Summary 200Exercise Questions 201Further Reading 20210 LIFE-INSPIRED MACHINE INTELLIGENCE AND CYBERNETICS 20310.1 Multi-Agent AI Systems 20310.1.1 Game Theory 20510.1.2 Distributed Multi-Agent Systems 20610.1.3 Multi-Agent Reinforcement Learning 20710.1.4 Evolutionary Computation and Multi-Agent Systems 20910.2 Cellular Automata 21110.3 Discrete Element Method 21210.3.1 Particle-Based Simulation of Biological Cells and Tissues 21410.3.2 Simulation of Microbial Communities and Their Interactions 21510.3.3 Discrete Element Method-Based Modeling of Biological Fluids and Soft Materials 21610.4 Smoothed Particle Hydrodynamics 21810.4.1 SPH-Based Simulations of Biomimetic Fluid Dynamic 21910.4.2 SPH-Based Simulations of Bio-Inspired Engineering Applications 220Summary 221Exercise Questions 222Further Reading 22311 REVISITING CYBERNETICS AND RELATION TO CYBERNETICAL INTELLIGENCE 22511.1 The Concept and Development of Cybernetics 22511.1.1 Attributes of Control Concepts 22511.1.2 Research Objects and Characteristics of Cybernetics 22611.1.3 Development of Cybernetical Intelligence 22711.2 The Fundamental Ideas of Cybernetics 22711.2.1 System Idea 22711.2.2 Information Idea 22911.2.3 Behavioral Idea 23011.2.4 Cybernetical Intelligence Neural Network 23111.3 Cybernetic Expansion into Other Fields of Research 23411.3.1 Social Cybernetics 23411.3.2 Internal Control-Related Theories 23711.3.3 Software Control Theory 23711.3.4 Perceptual Cybernetics 23811.4 Practical Application of Cybernetics 24011.4.1 Research on the Control Mechanism of Neural Networks 24011.4.2 Balance Between Internal Control and Management Power Relations 24011.4.3 Software Markov Adaptive Testing Strategy 24211.4.4 Task Analysis Model 244Summary 245Exercise Questions 246Further Reading 24712 TURING MACHINE 24912.1 Behavior of a Turing Machine 25012.1.1 Computing with Turing Machines 25112.2 Basic Operations of a Turing Machine 25212.2.1 Reading and Writing to the Tape 25312.2.2 Moving the Tape Head 25412.2.3 Changing States 25412.3 Interchangeability of Program and Behavior 25512.4 Computability Theory 25612.4.1 Complexity Theory 25712.5 Automata Theory 25812.6 Philosophical Issues Related to Turing Machines 25912.7 Human and Machine Computations 26012.8 Historical Models of Computability 26112.9 Recursive Functions 26212.10 Turing Machine and Intelligent Control 263Summary 264Exercise Questions 265Further Reading 26513 ENTROPY CONCEPTS IN MACHINE INTELLIGENCE 26713.1 Relative Entropy of Distributions 26813.2 Relative Entropy and Mutual Information 26813.3 Entropy in Performance Evaluation 26913.4 Cross-Entropy Softmax 27113.5 Calculating Cross-Entropy 27213.6 Cross-Entropy as a Loss Function 27313.7 Cross-Entropy and Log Loss 27413.8 Application of Entropy in Intelligent Control 27513.8.1 Entropy-Based Control 27513.8.2 Fuzzy Entropy 27613.8.3 Entropy-Based Control Strategies 27713.8.4 Entropy-Based Decision-Making 278Summary 279Exercise Questions 279Further Reading 28014 SAMPLING METHODS IN CYBERNETICAL INTELLIGENCE 28314.1 Introduction to Sampling Methods 28314.2 Basic Sampling Algorithms 28414.2.1 Importance of Sampling Methods in Machine Intelligence 28614.3 Machine Learning Sampling Methods 28714.3.1 Random Oversampling 28814.3.2 Random Undersampling 29014.3.3 Synthetic Minority Oversampling Technique 29014.3.4 Adaptive Synthetic Sampling 29214.4 Advantages and Disadvantages of Machine Learning Sampling Methods 29314.5 Advanced Sampling Methods in Cybernetical Intelligence 29414.5.1 Ensemble Sampling Method 29514.5.2 Active Learning 29714.5.3 Bayesian Optimization in Sampling 29914.6 Applications of Sampling Methods in Cybernetical Intelligence 30214.6.1 Image Processing and Computer Vision 30214.6.2 Natural Language Processing 30414.6.3 Robotics and Autonomous Systems 30714.7 Challenges and Future Directions 30814.8 Challenges and Limitations of Sampling Methods 30914.9 Emerging Trends and Innovations in Sampling Methods 309Summary 310Exercise Questions 311Further Reading 31215 DYNAMIC SYSTEM CONTROL 31315.1 Linear Systems 31415.2 Nonlinear System 31615.3 Stability Theory 31815.4 Observability and Identification 32015.5 Controllability and Stabilizability 32115.6 Optimal Control 32315.7 Linear Quadratic Regulator Theory 32415.8 Time-Optimal Control 32615.9 Stochastic Systems with Applications 32815.9.1 Stochastic System in Control Systems 32915.9.2 Stochastic System in Robotics and Automation 32915.9.3 Stochastic System in Neural Networks 330Summary 331Exercise Questions 331Further Reading 33216 DEEP LEARNING 33316.1 Neural Network Models in Deep Learning 33516.2 Methods of Deep Learning 33616.2.1 Convolutional Neural Networks 33716.2.2 Recurrent Neural Networks 34016.2.3 Generative Adversarial Networks 34216.2.4 Deep Learning Based Image Segmentation Models 34516.2.5 Variational Auto Encoders 34816.2.6 Transformer Models 35016.2.7 Attention-Based Models 35216.2.8 Meta-Learning Models 35416.2.9 Capsule Networks 35716.3 Deep Learning Frameworks 35816.4 Applications of Deep Learning 35916.4.1 Object Detection 36016.4.2 Intelligent Power Systems 36116.4.3 Intelligent Control 362Summary 362Exercise Questions 363References 364Further Reading 36517 NEURAL ARCHITECTURE SEARCH 36717.1 Neural Architecture Search and Neural Network 36917.2 Reinforcement Learning-Based Neural Architecture Search 37117.3 Evolutionary Algorithms-Based Neural Architecture Search 37417.4 Bayesian Optimization-Based Neural Architecture Search 37617.5 Gradient-Based Neural Architecture Search 37817.6 One-shot Neural Architecture Search 37917.7 Meta-Learning-Based Neural Architecture Search 38117.8 Neural Architecture Search for Specific Domains 38317.8.1 Cybernetical Intelligent Systems: Neural Architecture Search in Real-World 38417.8.2 Neural Architecture Search for Specific Cybernetical Control Tasks 38517.8.3 Neural Architecture Search for Cybernetical Intelligent Systems in Real-World 38617.8.4 Neural Architecture Search for Adaptive Cybernetical Intelligent Systems 38817.9 Comparison of Different Neural Architecture Search Approaches 389Summary 391Exercise Questions 391Further Reading 392Final Notes on Cybernetical Intelligence 393Index 399
Cybernetical Intelligence
CYBERNETICAL INTELLIGENCEHIGHLY COMPREHENSIVE, DETAILED, AND UP-TO-DATE OVERVIEW OF ARTIFICIAL INTELLIGENCE AND CYBERNETICS, WITH PRACTICAL EXAMPLES AND SUPPLEMENTARY LEARNING RESOURCESCybernetical Intelligence: Engineering Cybernetics with Machine Intelligence is a comprehensive guide to the field of cybernetics and neural networks, as well as the mathematical foundations of these technologies. The book provides a detailed explanation of various types of neural networks, including feedforward networks, recurrent neural networks, and convolutional neural networks as well as their applications to different real-world problems. This groundbreaking book presents a pioneering exploration of machine learning within the framework of cybernetics. It marks a significant milestone in the field’s history, as it is the first book to describe the development of machine learning from a cybernetics perspective. The introduction of the concept of “Cybernetical Intelligence” and the generation of new terminology within this context propel new lines of thought in the historical development of artificial intelligence. With its profound implications and contributions, this book holds immense importance and is poised to become a definitive resource for scholars and researchers in this field of study. Each chapter is specifically designed to introduce the theory with several examples. This comprehensive book includes exercise questions at the end of each chapter, providing readers with valuable opportunities to apply and strengthen their understanding of cybernetical intelligence. To further support the learning journey, solutions to these questions are readily accessible on the book’s companion site. Additionally, the companion site offers programming practice exercises and assignments, enabling readers to delve deeper into the practical aspects of the subject matter. Cybernetical Intelligence includes information on:* The history and development of cybernetics and its influence on the development of neural networks* Developments and innovations in artificial intelligence and machine learning, such as deep reinforcement learning, generative adversarial networks, and transfer learning* Mathematical foundations of artificial intelligence and cybernetics, including linear algebra, calculus, and probability theory* Ethical implications of artificial intelligence and cybernetics as well as responsible and transparent development and deployment of AI systemsPresenting a highly detailed and comprehensive overview of the field, with modern developments thoroughly discussed, Cybernetical Intelligence is an essential textbook that helps students make connections with real-life engineering problems by providing both theory and practice, along with a myriad of helpful learning aids. PROF. DR. KELVIN K. L. WONG, is a distinguished expert in medical image processing and computational science, earning his Ph.D. from The University of Adelaide. With a strong academic background from Nanyang Technological University and The University of Sydney, he has been at the forefront of merging the fields of cybernetics and artificial intelligence (AI). He is renowned for coining the term “Cybernetical Intelligence” and is the inventor and founder of Deep Red AI. Preface xvAbout the Author xixAbout the Companion Website xxi1 ARTIFICIAL INTELLIGENCE AND CYBERNETICAL LEARNING 11.1 Artificial Intelligence Initiative 11.2 Intelligent Automation Initiative 41.2.1 Benefits of IAI 51.3 Artificial Intelligence Versus Intelligent Automation 51.3.1 Process Discovery 61.3.2 Optimization 71.3.3 Analytics and Insight 81.4 The Fourth Industrial Revolution and Artificial Intelligence 91.4.1 Artificial Narrow Intelligence 101.4.2 Artificial General Intelligence 121.4.3 Artificial Super Intelligence 131.5 Pattern Analysis and Cognitive Learning 141.5.1 Machine Learning 151.5.1.1 Parametric Algorithms 161.5.1.2 Nonparametric Algorithms 171.5.2 Deep Learning 201.5.2.1 Convolutional Neural Networks in Advancing Artificial Intelligence 211.5.2.2 Future Advancement in Deep Learning 221.5.3 Cybernetical Learning 231.6 Cybernetical Artificial Intelligence 241.6.1 Artificial Intelligence Control Theory 241.6.2 Information Theory 261.6.3 Cybernetic Systems 271.7 Cybernetical Intelligence Definition 281.8 The Future of Cybernetical Intelligence 30Summary 32Exercise Questions 32Further Reading 332 CYBERNETICAL INTELLIGENT CONTROL 352.1 Control Theory and Feedback Control Systems 352.2 Maxwell’s Analysis of Governors 372.3 Harold Black 392.4 Nyquist and Bode 402.5 Stafford Beer 422.5.1 Cybernetic Control 422.5.2 Viable Systems Model 422.5.3 Cybernetics Models of Management 432.6 James Lovelock 432.6.1 Cybernetic Approach to Ecosystems 432.6.2 Gaia Hypothesis 442.7 Macy Conference 442.8 McCulloch–Pitts 452.9 John von Neumann 472.9.1 Discussions on Self-Replicating Machines 472.9.2 Discussions on Machine Learning 48Summary 48Exercise Questions 49Further Reading 503 THE BASICS OF PERCEPTRON 513.1 The Analogy of Biological and Artificial Neurons 513.1.1 Biological Neurons and Neurodynamics 523.1.2 The Structure of Neural Network 533.1.3 Encoding and Decoding 563.2 Perception and Multilayer Perceptron 573.2.1 Back Propagation Neural Network 593.2.2 Derivative Equations for Backpropagation 593.3 Activation Function 613.3.1 Sigmoid Activation Function 613.3.2 Hyperbolic Tangent Activation Function 623.3.3 Rectified Linear Unit Activation Function 623.3.4 Linear Activation Function 64Summary 65Exercise Questions 67Further Reading 674 THE STRUCTURE OF NEURAL NETWORK 694.1 Layers in Neural Network 694.1.1 Input Layer 694.1.2 Hidden Layer 704.1.3 Neurons 704.1.4 Weights and Biases 714.1.5 Forward Propagation 724.1.6 Backpropagation 724.2 Perceptron and Multilayer Perceptron 734.3 Recurrent Neural Network 754.3.1 Long Short-Term Memory 764.4 Markov Neural Networks 774.4.1 State Transition Function 774.4.2 Observation Function 784.4.3 Policy Function 784.4.4 Loss Function 784.5 Generative Adversarial Network 78Summary 79Exercise Questions 80Further Reading 815 BACKPROPAGATION NEURAL NETWORK 835.1 Backpropagation Neural Network 835.1.1 Forward Propagation 855.2 Gradient Descent 855.2.1 Loss Function 855.2.2 Parameters in Gradient Descent 885.2.3 Gradient in Gradient Descent 885.2.4 Learning Rate in Gradient Descent 895.2.5 Update Rule in Gradient Descent 895.3 Stopping Criteria 895.3.1 Convergence and Stopping Criteria 905.3.2 Local Minimum and Global Minimum 915.4 Resampling Methods 915.4.1 Cross-Validation 935.4.2 Bootstrapping 935.4.3 Monte Carlo Cross-Validation 945.5 Optimizers in Neural Network 945.5.1 Stochastic Gradient Descent 945.5.2 Root Mean Square Propagation 965.5.3 Adaptive Moment Estimation 965.5.4 AdaMax 975.5.5 Momentum Optimization 97Summary 97Exercise Questions 99Further Reading 1006 APPLICATION OF NEURAL NETWORK IN LEARNING AND RECOGNITION 1016.1 Applying Backpropagation to Shape Recognition 1016.2 Softmax Regression 1056.3 K-Binary Classifier 1076.4 Relational Learning via Neural Network 1086.4.1 Graph Neural Network 1096.4.2 Graph Convolutional Network 1116.5 Cybernetics Using Neural Network 1126.6 Structure of Neural Network for Image Processing 1156.7 Transformer Networks 1166.8 Attention Mechanisms 1166.9 Graph Neural Networks 1176.10 Transfer Learning 1186.11 Generalization of Neural Networks 1196.12 Performance Measures 1206.12.1 Confusion Matrix 1206.12.2 Receiver Operating Characteristic 1216.12.3 Area Under the ROC Curve 122Summary 123Exercise Questions 123Further Reading 1247 COMPETITIVE LEARNING AND SELF-ORGANIZING MAP 1257.1 Principal of Competitive Learning 1257.1.1 Step 1: Normalized Input Vector 1287.1.2 Step 2: Find the Winning Neuron 1287.1.3 Step 3: Adjust the Network Weight Vector and Output Results 1297.2 Basic Structure of Self-Organizing Map 1297.2.1 Properties Self-Organizing Map 1307.3 Self-Organizing Mapping Neural Network Algorithm 1317.3.1 Step 1: Initialize Parameter 1327.3.2 Step 2: Select Inputs and Determine Winning Nodes 1327.3.3 Step 3: Affect Neighboring Neurons 1327.3.4 Step 4: Adjust Weights 1337.3.5 Step 5: Judging the End Condition 1337.4 Growing Self-Organizing Map 1337.5 Time Adaptive Self-Organizing Map 1367.5.1 TASOM-Based Algorithms for Real Applications 1387.6 Oriented and Scalable Map 1397.7 Generative Topographic Map 141Summary 145Exercise Questions 146Further Reading 1478 SUPPORT VECTOR MACHINE 1498.1 The Definition of Data Clustering 1498.2 Support Vector and Margin 1528.3 Kernel Function 1558.3.1 Linear Kernel 1558.3.2 Polynomial Kernel 1568.3.3 Radial Basis Function 1578.3.4 Laplace Kernel 1598.3.5 Sigmoid Kernel 1598.4 Linear and Nonlinear Support Vector Machine 1608.5 Hard Margin and Soft Margin in Support Vector Machine 1648.6 I/O of Support Vector Machine 1678.6.1 Training Data 1678.6.2 Feature Matrix and Label Vector 1688.7 Hyperparameters of Support Vector Machine 1698.7.1 The C Hyperparameter 1698.7.2 Kernel Coefficient 1698.7.3 Class Weights 1708.7.4 Convergence Criteria 1708.7.5 Regularization 1718.8 Application of Support Vector Machine 1718.8.1 Classification 1718.8.2 Regression 1738.8.3 Image Classification 1738.8.4 Text Classification 174Summary 174Exercise Questions 175Further Reading 1769 BIO-INSPIRED CYBERNETICAL INTELLIGENCE 1779.1 Genetic Algorithm 1789.2 Ant Colony Optimization 1819.3 Bees Algorithm 1849.4 Artificial Bee Colony Algorithm 1869.5 Cuckoo Search 1899.6 Particle Swarm Optimization 1939.7 Bacterial Foraging Optimization 1969.8 Gray Wolf Optimizer 1979.9 Firefly Algorithm 199Summary 200Exercise Questions 201Further Reading 20210 LIFE-INSPIRED MACHINE INTELLIGENCE AND CYBERNETICS 20310.1 Multi-Agent AI Systems 20310.1.1 Game Theory 20510.1.2 Distributed Multi-Agent Systems 20610.1.3 Multi-Agent Reinforcement Learning 20710.1.4 Evolutionary Computation and Multi-Agent Systems 20910.2 Cellular Automata 21110.3 Discrete Element Method 21210.3.1 Particle-Based Simulation of Biological Cells and Tissues 21410.3.2 Simulation of Microbial Communities and Their Interactions 21510.3.3 Discrete Element Method-Based Modeling of Biological Fluids and Soft Materials 21610.4 Smoothed Particle Hydrodynamics 21810.4.1 SPH-Based Simulations of Biomimetic Fluid Dynamic 21910.4.2 SPH-Based Simulations of Bio-Inspired Engineering Applications 220Summary 221Exercise Questions 222Further Reading 22311 REVISITING CYBERNETICS AND RELATION TO CYBERNETICAL INTELLIGENCE 22511.1 The Concept and Development of Cybernetics 22511.1.1 Attributes of Control Concepts 22511.1.2 Research Objects and Characteristics of Cybernetics 22611.1.3 Development of Cybernetical Intelligence 22711.2 The Fundamental Ideas of Cybernetics 22711.2.1 System Idea 22711.2.2 Information Idea 22911.2.3 Behavioral Idea 23011.2.4 Cybernetical Intelligence Neural Network 23111.3 Cybernetic Expansion into Other Fields of Research 23411.3.1 Social Cybernetics 23411.3.2 Internal Control-Related Theories 23711.3.3 Software Control Theory 23711.3.4 Perceptual Cybernetics 23811.4 Practical Application of Cybernetics 24011.4.1 Research on the Control Mechanism of Neural Networks 24011.4.2 Balance Between Internal Control and Management Power Relations 24011.4.3 Software Markov Adaptive Testing Strategy 24211.4.4 Task Analysis Model 244Summary 245Exercise Questions 246Further Reading 24712 TURING MACHINE 24912.1 Behavior of a Turing Machine 25012.1.1 Computing with Turing Machines 25112.2 Basic Operations of a Turing Machine 25212.2.1 Reading and Writing to the Tape 25312.2.2 Moving the Tape Head 25412.2.3 Changing States 25412.3 Interchangeability of Program and Behavior 25512.4 Computability Theory 25612.4.1 Complexity Theory 25712.5 Automata Theory 25812.6 Philosophical Issues Related to Turing Machines 25912.7 Human and Machine Computations 26012.8 Historical Models of Computability 26112.9 Recursive Functions 26212.10 Turing Machine and Intelligent Control 263Summary 264Exercise Questions 265Further Reading 26513 ENTROPY CONCEPTS IN MACHINE INTELLIGENCE 26713.1 Relative Entropy of Distributions 26813.2 Relative Entropy and Mutual Information 26813.3 Entropy in Performance Evaluation 26913.4 Cross-Entropy Softmax 27113.5 Calculating Cross-Entropy 27213.6 Cross-Entropy as a Loss Function 27313.7 Cross-Entropy and Log Loss 27413.8 Application of Entropy in Intelligent Control 27513.8.1 Entropy-Based Control 27513.8.2 Fuzzy Entropy 27613.8.3 Entropy-Based Control Strategies 27713.8.4 Entropy-Based Decision-Making 278Summary 279Exercise Questions 279Further Reading 28014 SAMPLING METHODS IN CYBERNETICAL INTELLIGENCE 28314.1 Introduction to Sampling Methods 28314.2 Basic Sampling Algorithms 28414.2.1 Importance of Sampling Methods in Machine Intelligence 28614.3 Machine Learning Sampling Methods 28714.3.1 Random Oversampling 28814.3.2 Random Undersampling 29014.3.3 Synthetic Minority Oversampling Technique 29014.3.4 Adaptive Synthetic Sampling 29214.4 Advantages and Disadvantages of Machine Learning Sampling Methods 29314.5 Advanced Sampling Methods in Cybernetical Intelligence 29414.5.1 Ensemble Sampling Method 29514.5.2 Active Learning 29714.5.3 Bayesian Optimization in Sampling 29914.6 Applications of Sampling Methods in Cybernetical Intelligence 30214.6.1 Image Processing and Computer Vision 30214.6.2 Natural Language Processing 30414.6.3 Robotics and Autonomous Systems 30714.7 Challenges and Future Directions 30814.8 Challenges and Limitations of Sampling Methods 30914.9 Emerging Trends and Innovations in Sampling Methods 309Summary 310Exercise Questions 311Further Reading 31215 DYNAMIC SYSTEM CONTROL 31315.1 Linear Systems 31415.2 Nonlinear System 31615.3 Stability Theory 31815.4 Observability and Identification 32015.5 Controllability and Stabilizability 32115.6 Optimal Control 32315.7 Linear Quadratic Regulator Theory 32415.8 Time-Optimal Control 32615.9 Stochastic Systems with Applications 32815.9.1 Stochastic System in Control Systems 32915.9.2 Stochastic System in Robotics and Automation 32915.9.3 Stochastic System in Neural Networks 330Summary 331Exercise Questions 331Further Reading 33216 DEEP LEARNING 33316.1 Neural Network Models in Deep Learning 33516.2 Methods of Deep Learning 33616.2.1 Convolutional Neural Networks 33716.2.2 Recurrent Neural Networks 34016.2.3 Generative Adversarial Networks 34216.2.4 Deep Learning Based Image Segmentation Models 34516.2.5 Variational Auto Encoders 34816.2.6 Transformer Models 35016.2.7 Attention-Based Models 35216.2.8 Meta-Learning Models 35416.2.9 Capsule Networks 35716.3 Deep Learning Frameworks 35816.4 Applications of Deep Learning 35916.4.1 Object Detection 36016.4.2 Intelligent Power Systems 36116.4.3 Intelligent Control 362Summary 362Exercise Questions 363References 364Further Reading 36517 NEURAL ARCHITECTURE SEARCH 36717.1 Neural Architecture Search and Neural Network 36917.2 Reinforcement Learning-Based Neural Architecture Search 37117.3 Evolutionary Algorithms-Based Neural Architecture Search 37417.4 Bayesian Optimization-Based Neural Architecture Search 37617.5 Gradient-Based Neural Architecture Search 37817.6 One-shot Neural Architecture Search 37917.7 Meta-Learning-Based Neural Architecture Search 38117.8 Neural Architecture Search for Specific Domains 38317.8.1 Cybernetical Intelligent Systems: Neural Architecture Search in Real-World 38417.8.2 Neural Architecture Search for Specific Cybernetical Control Tasks 38517.8.3 Neural Architecture Search for Cybernetical Intelligent Systems in Real-World 38617.8.4 Neural Architecture Search for Adaptive Cybernetical Intelligent Systems 38817.9 Comparison of Different Neural Architecture Search Approaches 389Summary 391Exercise Questions 391Further Reading 392Final Notes on Cybernetical Intelligence 393Index 399
Inside Abacus und die verrückte Geschichte der Schweizer IT-Branche
Die drei HSG-Absolventen Claudio Hintermann, Eliano Ramelli und Thomas Köberl hatten keinen Plan, als sie 1985 die Software-Firma Abacus gründeten – ausser, dass sie nicht angestellt sein wollten. Sie und der später dazugestossene Daniel Senn zuckten mit den Schultern, als man sie im New-Economy-Hype zu Millionären machen wollte und legten sich immer wieder mit Behörden oder grossen Konzernen an. Ihr Fokus lag auf den Mitarbeitenden und auf gutem Essen, gutem Wein, tollen Partys und Kultur. Und darauf, die beste Software zu programmieren.In der zweiten Hälfte der 1980er- Jahre gab es in der Schweiz zahlreiche Firmen, die betriebswirtschaftliche Software entwickelten. Viele gingen unter. Auch multinationale Unternehmen wie Microsoft und SAP kündigten an, den Schweizer KMU-Markt zu erobern – und scheiterten.Christoph Hugenschmidt, IT-Journalist und Autor, erzählt die aussergewöhnliche Geschichte von Abacus und der Schweizer Software-Industrie.PrologGier und Grössenwahn«Eine Packung Fischstäbchen kostete 2.80»Freitags BierHilfe! Die Multis kommen!Sackgassen, Irr- und UmwegeGeier, Gemeinden, Goldgruben … und der Neubau der SoftwareÈ un gruppo di pazziDas Prinzip Blätterteig – Eine Reportage von Lukas Tobler«Jetzt sind sie zu weit gegangen»Die neue AbacusEpilog – Das Abacus-GeheimnisAnhang
Balkonkraftwerke - Verstehen und einfach einsteigen
Mit wenig Aufwand viel Energie sparen. Ganz neu im heise shop aus Oktober 2023.Die Stromrechnung senken und gleichzeitig die Umwelt schützen? Gar nicht so schwierig, wie man vielleicht denken mag! Wie's geht, zeigt Ihnen Jörg Rippel Schritt für Schritt in diesem Ratgeber. Neben Hinweisen zum Stromsparen finden Sie praktische Hilfestellungen und Tipps rund um Balkonkraftwerke. Sie beginnen mit der Erfassung des eigenen Strombedarfs, richten Ihr persönliches Sonnenkraftwerk ein, kümmern sich um ein bisschen Papierkram und erzeugen flugs Ihren eigenen Strom. Aus dem Inhalt:Off the Grid: unterwegs Strom erzeugenDer Papierkram: Anmeldung, Förderung, VersicherungDas Balkonkraftwerk installierenDer richtige Platz: Ausrichtung und AufstellungDas passende Modell findenDo it yourself: was Sie selbst machen könnenDen Verbrauch im Blick behaltenStrom (und Geld) sparenWie viel Strom brauche ich überhaupt?Ist das wirklich alles so einfach? Einstieg in die Photovoltaik
Softwareentwicklung für Kraftfahrzeuge
Im ersten Teil dieses Lehrbuchs werden Funktions- und Softwareentwicklung für Kraftfahrzeuge eingeführt. Dabei wird auf den Prozess der strukturierten Funktions- und Softwareentwicklung von der Erhebung der Anforderungen über die Softwareerstellung bis zum Test sowie Reifegradmodelle eingegangen. Im zweiten Teil werden diese Prozesse aus Wissenschaft und Lehre mit etablierten Vorgehensweisen aus der industriellen Praxis konkretisiert. Die pragmatische Beschreibung der konkreten Vorgehensweisen ist eine Basis für die Ausgestaltung detaillierter Prozesse im Unternehmensalltag sowie deren tägliche Anwendung im Projektgeschäft. Funktions- und Softwareentwicklung.- Softwareentwicklungsprozess in der Automobilindustrie.
Künstliche Intelligenz (6. Auflg.)
Künstliche Intelligenz. Wissensverarbeitung - Neuronale Netze. 6., aktualisierte Auflage, Oktober 2023.Künstliche Intelligenz (KI) ganz praktischSymbolverarbeitende künstliche Intelligenz und künstliche neuronale Netze in einem BuchBusiness Rules und WissensnetzeConvolutional Neural Networks und Deep LearningÜbungen in PROLOG sowie mit JavaNNS und PythonDie künstliche Intelligenz hat unseren Alltag erreicht: Wir nutzen Chatbots, reden mit Sprachassistenten, KI digitalisiert die Dokumentenverarbeitung, die Muster-, Bild- oder Objekt-Erkennung. Sie ermöglicht neue, intelligentere Lösungen in vielen Bereichen, von der Medizin bis zum autonomen Fahren.Das Buch gibt eine Einführung in die KI. Es wird gezeigt, wie symbolverabeitende KI in Form von Wissensnetzen oder Geschäftsregeln heute angewendet und wie künstliche neuronale Netze in der Mustererkennung oder auch im Data Mining eingesetzt werden können. Wissensrepräsentation und -verarbeitung auf Basis der Logik wird unter Nutzung der logischen Programmiersprache PROLOG eingeführt. Logische Schlussfolgerungen lassen sich in PROLOG wesentlich leichter als in Python oder Java implementieren. Die Konzepte neuronaler Netze werden mit dem System JavaNNS und mittels Python praktisch vertieft. Fragen und Aufgaben am Ende eines Abschnittes fordern zum aktiven Lesen und Lernen auf. Die Webseiten zum Buch enthalten Demo-Programme, die diskutierte Vorgehensweisen veranschaulichen und das Verständnis fördern.Aus dem Inhalt:Überblick zur künstlichen IntelligenzDarstellung und Verarbeitung von WissenProblemlösung mittels SucheWissensverarbeitung mit PROLOGKünstliche neuronale NetzeVorwärtsgerichtete neuronale NetzeWettbewerbslernenAutoassoziative NetzeEntwicklung neuronaler NetzeNeu in der 6. Auflage sind Abschnitte zu den Themen ChatGPT sowie Decision Model and Notation (DMN) bei Prozessbeschreibungen.Leseprobe (PDF-Link)
Beginning C++23
Begin your programming journey with C++ , starting with the basics and progressing through step-by-step examples that will help you become a proficient C++ programmer. This book includes new features from the C++23 standard. All you need are Beginning C++23 and any recent C++ compiler and you'll soon be writing real C++ programs.There is no assumption of prior programming knowledge. All language concepts that are explained in the book are illustrated with working program examples, and all chapters include exercises for you to test and practice your knowledge. Free source code downloads are provided for all examples from the text and solutions to the exercises.This latest edition has been fully updated to the latest version of the language, C++23, and to all conventions and best practices of modern C++. This book also introduces elements of the C++ Standard Library that provide essential support for C++23. After completing this book, you will have the knowledge and skills needed to build your first C++ applications.WHAT YOU WILL LEARN* Begin programming with the C++23 standard* Carry out modular programming in C++* Work with arrays and loops, pointers and references, strings, and more* Write your own functions, types, and operators* Discover the essentials of object-oriented programming* Use overloading, inheritance, virtual functions, and polymorphism* Write generic function and class templates, and make them safer using concepts * Learn the ins and outs of containers, algorithms, and ranges* Use auto type declarations, exceptions, move semantics, lambda expressions, and much moreWHO THIS BOOK IS FORProgrammers new to C++ and those who may be looking for a refresh primer on C++ in general. IVOR HORTON is self-employed in consultancy and writes programming tutorials. He is the author of many programming books. Ivor worked for IBM for many years and holds a bachelor's degree, with honors, in mathematics. Horton's experience at IBM includes programming in most languages (including assembler and high-level languages on a variety of machines), real-time programming, and designing and implementing real-time closed loop industrial control systems. He has extensive experience teaching programming to engineers and scientists (Fortran, PL/1, APL, etc.). Horton is an expert in mechanical, process, and electronic CAD systems; mechanical CAM systems; and DNC/CNC systems.PETER VAN WEERT works for Danaher in its R&D unit for digital dentistry software, developing software for the dental practice of tomorrow. In his spare time, he has co-authored two books on C++ and two award-winning Windows 8 apps and is a regular expert speaker at, and board member of, the Belgian C++ Users Group. He is a software engineer whose main interests and expertise are application software development, programming languages, algorithms, and data structures.He received his master of science degree in computer science summa cum laude with congratulations of the Board of Examiners from the University of Leuven. In 2010, he completed his PhD thesis there on the design and efficient compilation of rule-based programming languages at the research group for declarative programming languages and artificial intelligence. During his doctoral studies, he was a teaching assistant for object-oriented programming (Java), software analysis and design, and declarative programming. After graduating, Peter worked at Nikon Metrology for more than six years on large-scale, industrial application software in the area of 3D laser scanning and point cloud inspection. He learned to master C++ and refactoring and debugging of very large code bases, and he gained further proficiency in all aspects of the software development process, including the analysis of functional and technical requirements, and agile and scrum-based project and team management.1. Basic Ideas2. Introducing Fundamental Types of Data3. Working Fundamental Types4. Making Decisions5. Arrays and Loops6. Pointers and References7. Working with Strings8. Defining Functions9. Vocabulary Types10. Function Templates11. Modules and Namespaces12. Defining your own Data Types13. Operator Overloading14. Inheritance15. Polymorphism16. Runtime Errors and Exceptions17. Class Templates18. Move Semantics19. First-Class Functions20. Containers and Algorithms21. Constrained Templates and ConceptsAppendix A (online only; to be offered as part of source code download)
Hands-On Web Scraping with Python
Web scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience.Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning.This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery.
TypeScript Basics
Jumpstart your working knowledge of Typescript with a quick, practical guide and start using this open source, object orientated language with confidence. This book highlights how Typescript works with JavaScript and its frameworks, giving it greater functionality, especially for larger enterprise projects. You'll start by reviewing some fundamentals such as how to run a TypeScript file and compile it to JavaScript as well as understand how it sits within the full ecosystem alongside React, Redux, Angular and Webpack. Using a practical project-led approach, you'll go through key concepts and aspects of Typescript such as compilers, arrays, classes and interfaces. Once you complete the fundamental basics, you'll move onto more complex topics like advanced types. You'll also learn about Decorators, which are a must-have feature in Angular. The book then presents a small To-do list project followed by a larger 'Drag and Drop' project. You'll see how to divide the drag and drop project into small manageable modules and learn to make the project more efficient with Webpack. Lastly, you'll create two more React projects, a party app with React and then one with React and Redux. Typescript Basics reveals how this JavaScript extension is currently the fastest growing language for web development with more and more developers and companies looking to utilize and adopt it within their current workflows and projects. You will: * Understand the particulars of Typescript and how it feeds into the JS and Web development eco-system * Implement Typescript for enterprise projects * Use Typescript with practical projects and examples * See how Typescript interacts with major frameworks like React and Angular Jumpstart your working knowledge of Typescript with a quick, practical guide and start using this open source, object orientated language with confidence. This book highlights how Typescript works with JavaScript and its frameworks, giving it greater functionality, especially for larger enterprise projects. You'll start by reviewing some fundamentals such as how to run a TypeScript file and compile it to JavaScript as well as understand how it sits within the full ecosystem alongside React, Redux, Angular and Webpack. Using a practical project-led approach, you'll go through key concepts and aspects of Typescript such as compilers, arrays, classes and interfaces. Once you complete the fundamental basics, you'll move onto more complex topics like advanced types. You'll also learn about Decorators, which are a must-have feature in Angular. The book then presents a small To-do list project followed by a larger 'Drag and Drop' project. You'll see howto divide the drag and drop project into small manageable modules and learn to make the project more efficient with Webpack. Lastly, you'll create two more React projects, a party app with React and then one with React and Redux. Typescript Basics reveals how this JavaScript extension is currently the fastest growing language for web development with more and more developers and companies looking to utilize and adopt it within their current workflows and projects. What You'll Learn * Understand the particulars of Typescript and how it feeds into the JS and Web development eco-system * Implement Typescript for enterprise projects * Use Typescript with practical projects and examples * See how Typescript interacts with major frameworks like React and Angular Who This Book Is For Those who want to learn about TypeScript and implement it in their Enterprise or hobby projects with React and Angular. Beginners will find it easy to learn the basic concepts and principles to get started and to progress onto more advanced projects and how to implement them. Nabendu Biswas is a Full Stack JavaScript developer, who has been working in the IT industry for the past 16 years for some of world's top development firms and investment banks. He is a passionate tech blogger, YouTuber, and currently runs an EdTech company, specializing in teaching students about web-app development and the JavaScript ecosystem. He is also the author of five Apress books focusing on topics such as Gatsby, MERN, and React Firebase, all of which can be found on Amazon. 1.Getting Started.- 2. TypeScript Basics.- 3. TypeScript Compilers.- 4. Classes and Interfaces 5. Advanced Types 6. Generics & Decorators.- 7. To-do List With TypeScript.- 8.Drag Drop Project.- 9.Modules and Webpack.- 10. React TypeScript Project.- 11. React Redux with TypeScript..
Practical Implementation of a Data Lake
This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions. Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need? He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you’ll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user’s perspective. You’ll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup. After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems. You will: * Understand the challenges associated with implementing a data lake * Explore the architectural patterns and processes used to design a new data lake * Design and implement data lake capabilities * Associate business requirements with technical deliverables to drive success This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions. Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need? He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you’ll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user’s perspective. You’ll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup. After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems. What You Will Learn * Understand the challenges associated with implementing a data lake * Explore the architectural patterns and processes used to design a new data lake * Design and implement data lake capabilities * Associate business requirements with technical deliverables to drive success Who This Book Is For Data Scientists and Architects, Machine Learning Engineers, and Software Engineers. Nayanjyoti Paul is an Associate Director and Chief Azure Architect for GenAI and LLM CoE for Accenture. He is the product owner and creator of a patented asset. Presently, he leads multiple projects as a lead architect around generative AI , large language models, data analytics, and machine learning. Nayan is a certified Master Technology Architect, certified Data Scientist, and certified Databricks Champion with additional AWS and Azure certifications. He is a speaker at conferences like Strata Conference, Data Works Summit, and AWS Reinvent. He also delivers guest lectures at Universities. Chapter 1: Understanding the Customer Needs.- Chapter 2: Security Model.- Chapter 3: Organizational Model.- Chapter 4: Data Lake Structure.- Chapter 5: Production Playground.- Chapter 6: Production Operationalization.- Chapter 7: Miscellaneous.
Attribute-based Encryption (ABE)
ATTRIBUTE-BASED ENCRYPTION (ABE)ENABLES READERS TO UNDERSTAND APPLICATIONS OF ATTRIBUTE-BASED ENCRYPTION SCHEMES IN CLOUD COMPUTING WITH THE SUPPORT OF BLOCKCHAIN TECHNOLOGYWith a focus on blockchain technology, Attribute-based Encryption (ABE) provides insight into the application of attribute-based encryption (ABE) schemes, discussing types of blockchains, fundamentals of blockchain, and how blockchains are constructed. Comprised of 16 chapters, the text provides an overview of the components that go into creating a dual ABE system of encryption proofs within the following: composite bilinear groups, dual pairing vector space framework, matrix pairing framework, framework for matrix pairing, and the application of cryptographic scheme on blockchain. The team of authors discuss the basic construction components of ABE and share the security models, including the selective and semi- adaptive security models, applying these to either prime order or composite order groups. The book also discusses the tools used for converting a composite order ABE scheme to prime order and an adaptive secure ABE scheme based on prime order. In Attribute-based Encryption (ABE), readers can expect to find information on:* Mathematical background of ABE, covering group and cyclic group, elliptic curves, curve selection, supersingular curves, ordinary curves, and weil and tate pairing* Basic construction components of ABE, covering access structure, monotone Boolean formula, linear secret-sharing scheme, and ordered binary decision diagram* Tools for converting composite order ABE schemes to prime order, covering security assumptions and conversion based on vectors for preliminaries, scheme construction, and security proof technique* Foundations of blockchain technology, covering blocks, miners, hash functions, and public key cryptographyAttribute-based Encryption (ABE) is an essential resource for professionals working in the field of design and cybersecurity who wish to understand how to use blockchain and the ABE scheme to provide fine-grained access control in outsourced data on third-party cloud servers. QI XIA received her PhD in Computer Science from the University of Electronic Science and Technology of China in 2010. JIANBIN GAO received his PhD in Computer Science from the University of Electronic Science and Technology of China in 2012. ISAAC AMANKONA OBIRI received his Master’s and PhD in Computer Science and Technology from the University of Electronic Science and Technology of China. KWAME OMONO ASAMOAH received his Master’s and PhD in Computer Science and Technology from the University of Electronic Science and Technology of China. DANIEL ADU WORAE is currently pursuing his Master’s degree in Computer Science and Technology at the University of Electronic Science and Technology of China. About the Authors xiiiPreface xvAcknowledgments xviiPart I Attribute-Based Encryption (ABE) 11 Foundation of Attribute-Based Encryption 31.1 Introduction 31.1.1 Symmetric Encryption 41.1.2 Asymmetric Key Encryption 41.1.3 Identity-Based Encryption 51.2 Functional Encryption 71.2.1 Applications of Attribute-Based Encryption 81.2.2 Problems with Attribute-Based Encryption 91.2.3 A Brief History of Security Proof of Functional Encryption 91.2.4 Dual System of Encryption 101.2.5 Summary 11References 122 Mathematical Background 152.1 Group Theory 152.1.1 Law of Composition 152.1.2 Groups 152.1.3 Subgroups 162.1.4 Homomorphisms 172.1.5 Cyclic Group 172.2 Ring Theory 202.2.1 Ideals and Quotient Rings 212.2.2 Euler’s Totient Function 222.2.3 Polynomial Rings 222.2.4 Irreducible and Monic Polynomials 222.2.5 Field Theory 232.2.5.1 Quotient Field 242.2.6 Field Characteristic 24Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page vi[1][1] [1][1]vi Contents2.2.7 Algebraic Extension Felds 242.3 Elliptic Curves 242.3.1 Plane Curve 242.3.2 Group Operations on Elliptic Curves 262.3.2.1 Point Addition 262.3.2.2 Point Doubling 272.4 Divisors and Bilinear Map 282.4.1 Divisors 282.4.2 The Degree and Support ofD 292.4.3 The Divisor of a Function f onE 292.4.4 Equivalence of Divisors 302.4.5 Bilinear Map 312.4.6 Weil Pairing 312.4.7 Miller’s Algorithm 322.4.8 The Tate Pairing 342.5 Summary 36References 363 Attribute-Based Encryption 373.1 Introduction 373.2 Basic Components of ABE Construction 393.2.1 Secret-Sharing Schemes 393.2.2 Polynomial Interpolation 413.2.2.1 Polynomials Over the Reals 413.2.2.2 Polynomials ModulusP 443.2.3 Shamir Secret Sharing 453.2.4 Verifiable Secret Sharing (VSS) 473.2.4.1 Algorithm for Converting Access Structure Into LSSS Matrix 473.2.4.2 Access Structure Example 483.2.4.3 Algorithms in Attribute-Based Encryption 493.2.5 Properties of Attribute-Based Encryption 513.2.6 Prime Order Group 513.3 Cryptographic Hard Assumptions 513.3.1 Composite Order Bilinear Groups 543.3.2 Complexity Assumptions 553.4 Provable Security 563.5 Security Notions 573.5.1 Summary 57References 584 Data Access Control 614.1 Introduction 614.1.1 Coarse-Grained 624.1.2 Fine-Grained Access Control 634.1.3 Importance of Fine-Grained Access Control 644.2 Concerns About Cloud-Based Access Control that Are Trustworthy 654.2.1 Encryption Access Control 65Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page vii[1][1] [1][1]Contents vii4.2.2 Requirements for Encryption-Based Access Control 674.3 Summary 67References 675 Selective Secure ABE Schemes Based on Prime Order Group 695.1 Introduction 695.1.1 Selective Security Model for KP-ABE 705.1.2 Selective Security Model for CP-ABE 705.1.3 ABE Schemes 715.2 The KP-ABE Scheme 715.2.1 Concrete Scheme Construction 715.2.2 Security Proof 735.3 The CP-ABE Scheme 745.3.1 Concrete Scheme Construction 745.3.2 Security Proof 765.4 Summary 77References 776 Fully Secure ABE Schemes Based on Composite and Prime Order Groups 796.1 Introduction 796.2 A Fully Secure CP-ABE from Composite Order Group 816.2.1 CP-ABE Construction 826.2.2 Adaptive Security Proof 836.2.2.1 Description of Hybrids 836.2.3 Security Proof 846.3 A Fully Secure KP-ABE Scheme Based on Dual Vector Space 846.3.1 KP-ABE Construction 856.3.2 Adaptive Security 876.3.3 Security Proof 886.4 KP-ABE Scheme Based on Matrix 896.4.1 The Scheme 896.4.2 Adaptive Security 906.4.3 Security Proof 916.5 Summary 91References 92Part II Concepts of Blockchain Technology 957 Blockchain Technology 977.1 Introduction 977.1.1 History 977.1.2 Preliminary Concepts of Blockchain Technology 987.1.3 Characteristics of Blockchain 1007.1.4 Evolution and Types of Blockchain 1047.1.4.1 The Blockchain 1.0 1047.1.4.2 Blockchain 2.0 104Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page viii[1][1] [1][1]viii Contents7.1.4.3 Blockchain 3.0 1057.1.5 Permissionless vs Permissioned Blockchains 1057.1.6 Types of Blockchain 1057.2 Architecture of Blockchain 1067.2.1 Architecture of Blockchain 1.0 (Cryptocurrencies) 1067.2.2 Block 1067.2.3 Node 1077.2.4 Types of Blockchain Nodes 1077.2.5 Consensus 1107.3 Architecture of Blockchain 2.0 (Smart Contracts) 1107.3.1 Introduction to Smart Contracts 1107.3.2 How Smart ContractsWork 1117.3.3 Example of Smart Contract 1117.3.4 Uses of Smart Contracts 1117.3.5 Advantages of Smart Contracts 1127.3.6 Limitations of Smart Contracts 1127.4 Architecture of Blockchain 3.0 (Blockchain Applications) 1137.4.1 Consensus Mechanism 1137.5 Blockchain 4.0 1187.5.1 Blockchain 4.0 Applications 1197.5.2 Metaverse 1197.5.3 Industrial Revolution 4.0 1207.5.4 Blockchain 4.0 for Businesses 120References 1208 Scaling-Out Blockchains with Sharding 1258.1 Introduction 1258.1.1 Scalability Trilemma 1268.1.2 Nakamoto-Based – Monoxide – Chu-ko-nu Mining 1288.1.3 Elastico 1288.1.4 OmniLedger 1298.1.5 Rapid Chain 1308.1.6 Learnings 1318.1.7 General Improvements 1328.1.7.1 Reducing Transaction Latency 1338.1.7.2 Inter-Communication Protocol 1338.1.7.3 Shards Ledger Pruning 1348.1.7.4 Decentralized Bootstrapping 1348.1.7.5 Securing the Epoch Reconfiguration 1348.1.7.6 Sharded Smart Contract 1358.1.7.7 Replay Attacks and Defenses Against Cross-Shard Protocols 1358.2 Off-Chain Solution: Layer 2 Solutions 1368.2.1 State Channels 1368.2.2 Side Chains of the Plasma 1388.2.3 Problems with Data Accessibility 1398.3 Rollups 139Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page ix[1][1] [1][1]Contents ix8.3.1 Rollups Based on Zero Knowledge 1408.3.2 Proofs of Zero-Knowledge 1408.3.3 Protocol Schnorr 1428.3.4 Protocol Pedersen 1438.3.5 zk-SNARKs 1448.4 Summary 144References 145Part III Applying Blockchain with Real-Time Technologies 1479 Blockchain Technology for Supply Management 1499.1 Introduction 1499.1.1 System Design 1539.1.2 System Architecture 1539.1.3 Entities of the System 1549.1.3.1 Users 1549.1.4 Smart Contract Control 1579.1.5 Blockchain Network 1579.1.5.1 Processing Nodes 1579.1.5.2 System Application Layer 1589.1.5.3 Storage Infrastructure 1589.1.6 System Decryption 1589.1.7 Blocks 1599.1.7.1 Block Design 1609.2 System Flow 1639.2.1 System Advantages 1639.2.2 Conclusion 164References 16510 Satellite Communication 16710.1 Introduction 16710.1.1 Low-Orbit Constellation Communication Networks 16910.1.2 Interstellar Link Length 17110.1.3 Model of Satellite Motion 17110.1.4 Edge Computing Technologies 17210.2 Analysis of Edge Computing Requirements of Low-Orbit ConstellationCommunication Networks 17510.2.1 Design of Edge Computing Architecture for Low-Orbit Constellation CommunicationNetworks 17510.2.2 Satellite 17610.2.3 System Entities 18010.2.4 System Process Flow 18010.2.5 Security Properties 18310.3 Summary 183References 183Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page x[1][1] [1][1]x Contents11 Foundation of Information-Centric Communication 18511.1 Introduction 18511.2 Information-Centric Communication 18511.3 Name-Based Routing of Content 18711.4 Benefits of Using ICN 18711.5 Cost-Efficient and Scalable Distribution of Content Design Principles 18911.6 ICN Design Challenges 19011.6.1 Content Naming 19011.6.2 Caching of Content 19111.6.3 Data Integrity 19211.6.4 Resolution System’s Scalability and Name-Based Routing 192References 19312 Security Overall in Information-Centric Networks 19512.1 Introduction 19512.2 Content-Centric Network (CCN) Architecture 19512.3 Naming System Design 19712.4 Secure Naming Scheme for Information-Centric Networks 19812.5 Data Transmission – Content Delivery 19812.6 Traffic Load in Network Caching 19912.6.1 Store Unique Naming of Content in Caches 20012.6.2 Storage Limitation in Caching Space Devices 20112.7 Content’s Freshness Detection 20112.8 ICN Security 20112.9 Attacks in ICN Architectures 20212.10 ICN Attributes to Ensure Security Threats 20412.11 Traffic Analysis and Prediction 20412.12 Some Key Problem Statements 20512.13 Blockchain-Based ICN Scheme Improvement 20612.13.1 Protection Against DDos 20612.14 A Secured Information-Centric Network Based on Blockchain 20612.14.1 Blockchain-Based ICN Structure 20712.14.1.1 Data Integrity 20712.15 Attribute-Based Encryption Scheme for the Information-Centric Network 20812.15.1 Applying Ciphertext-Policy ABE (CP-ABE) Scheme in ICN 20912.15.2 System Design of CP-ABE Scheme in ICN 210References 21213 Subscriber Data Management System Based on Blockchain 21513.1 Introduction 21513.1.1 Motivation 21613.1.2 Problem Statement 21613.1.3 Contributions 21613.2 Literature Review 21713.3 System Design Description 21713.3.1 Assumptions 217Trim Size: 7in x 10in Single Column Xia989356 ftoc.tex V1 - 06/28/2023 7:58pm Page xi[1][1] [1][1]Contents xi13.3.2 Ciphertext-Policy Attribute-Based Encryption (CP-ABE) 21813.3.3 CP-ABE Construction 21813.3.4 System Components 21913.3.4.1 Data Subscribers (DSs) 21913.3.4.2 Data Providers (DPs) 22013.3.4.3 Key Generation and Distribution Center (KGDC) 22013.3.4.4 IPFS Distributed Storage 22013.3.4.5 Blockchain Platform 22013.3.5 Process Description 22213.3.5.1 Subscriber Registration 22413.3.5.2 Subscriber Data Storage 22413.3.5.3 Subscriber Data Request 22413.3.6 Benefits of Proposed Design 22513.3.7 Security Requirements 22613.4 Summary 227References 22714 A Secure Data-Sharing Blockchain-Based Crowdfunding System 22914.1 Introduction 22914.2 Literature Review 23114.2.1 Present-Day Centralized Crowdfunding 23114.2.2 Crowdfunding Models 23314.2.3 Problems of Traditional Crowdfunding 23414.2.4 Blockchain-Based Crowdfunding 23414.2.5 Advantages of Blockchain-Based Crowdfunding 23514.3 Proposed System 23614.3.1 System Model 23614.3.1.1 Key Components 23714.3.2 System Framework Overview 23814.3.2.1 Application Layer 23914.3.2.2 Blockchain Layer 23914.3.2.3 Data Storage Layer 23914.3.3 System Assumptions and Threat Model 24014.3.4 Process Description 24014.3.5 Smart Contract Interactions 24114.3.5.1 User Registration Contract (URC) 24114.3.5.2 User Verification Contract (UVC) 24114.3.5.3 Project Data Access Contract (PDAC) 24114.3.6 Concrete Implementation 24114.3.6.1 User Register 24214.3.6.2 Data Encrypt 24214.3.6.3 Data Search 24214.3.6.4 Fine-Grained Access Authorization 24214.3.6.5 Data Decrypt 24314.3.6.6 Transaction Confirmation 24314.3.7 Security Requirements 24314.3.7.1 Fine-Grained Access Control 24314.3.7.2 Key Counterfeiting 24314.3.7.3 Data Integrity 24414.4 Summary 244References 244Index 247
Using and Administering Linux: Volume 2
Learn to be a Linux sysadmin and an expert user of the Linux operating system, even with no previous Linux experience. This second edition of the popular and highly rated Linux self-study training course has been fully updated to Fedora Linux 37 with expanded and revised content and figures as well new chapters on the BTRFS file system, using Zram for swap, NetworkManager, automation with Ansible, and systemd.Like the previous version, this edition has been reviewed for technical accuracy by a highly respected Linux expert and will prepare you to manage complex systems with ease and equip you for a new career. It has also been reviewed by a student who took this course to ensure its readability and flow for those with little or no previous experience with Linux. This second volume builds upon what you learned in the first and depends upon the virtual network and virtual machine created there.You’ll see how to manage and monitor running processes, discover the power of the special filesystems, monitor and tune the kernel while it is running – without a reboot. You’ll then turn to regular expressions and the power that using them for pattern matching can bring to the command line, and learn to manage printers and printing from the command line and unlock the secrets of the hardware on which your Linux operating system is running.Experiment with command line programming and how to automate various administrative tasks, networking, and the many services that are required in a Linux system. Use the logs and journals to look for clues to problems and confirmation that things are working correctly, and learn to enhance the security of your Linux systems and how to perform easy local and remote backups.WHAT YOU WILL LEARN* Understand Logical Volume Management, using file managers, and special filesystems* Exploit everything is a file* Perform command line programming and basic automation* Configure printers and manage other hardware* Manage system services with systemd, user management, security, and local and remote backups using simple and freely available toolsWHO THIS BOOK IS FORAnyone who wants to continue to learn Linux in depth as an advanced user and system administrator at the command line while using the GUI desktop to leverage productivity.DAVID BOTH is an Open Source Software and GNU/Linux advocate, trainer, writer, and speaker. He has been working with Linux and Open Source Software for more than 20 years and has been working with computers for over 45 years. He is a strong proponent of and evangelist for the "Linux Philosophy for System Administrators." David has been in the IT industry for over forty years.He worked for IBM for 21 years and, while working as a Course Development Representative in Boca Raton, FL, in 1981, wrote the training course for the first IBM PC. He has taught RHCE classes for Red Hat and has worked at MCI Worldcom, Cisco, and the State of North Carolina. In most of the places he has worked since leaving IBM in 1995, he has taught classes on Linux ranging from Lunch'n'Learns to full five day courses. Helping others learn about Linux and open source software is one of his great pleasures.David had some amazing teachers and mentors in his 40 years in IT and more than 20 years working with Linux. At their core, Linux and open source in general are about sharing and helping others and about contributing to the community. These books, along with “The Linux philosophy for SysAdmins,” are a continuation of his desire to pass on his knowledge and to provide mentoring to anyone interested in learning about Linux.David is the author of The Linux Philosophy for SysAdmins (Apress, 2018), co-author of Linux for Small Business Owners (Apress, 2022) and can be found on Twitter @linuxgeek46
Using and Administering Linux: Volume 3
In Using and Administering Linux: Volume 3 you’ll work with multiple VMs on a single physical host to create a network in which to sharpen your sysadmin skills. Chapters have been fully updated to Fedora Linux 38 with expanded content and figures as well brand new material on the BTRFS file system, using Zram for swap, NetworkManager, automation with Ansible, as well as systemd.Focusing on network and other advanced services, this second edition of the final series volume builds upon the skills you have learned so far in volumes 1&2 and will depend upon the virtual network and VMs created there. Start by reviewing the administration of Linux servers and install and configure various Linux server services such as DHCP, DNS, NTP, and SSH server that will be used to provide advanced network services. You’ll then learn to install and configure servers such as BIND for name services, DHCP for network host configuration, and SSH for secure logins to remote hosts.Other topics covered include public/private keypairs to further enhance security, SendMail and IMAP and antispam protection for email, using Apache and WordPress to create and manage web sites, NFS, SAMBA, and Chrony. This volume also covers SELinux and its use in making your systems even more secure., You will learn to build RPMs to be used to distribute automation scripts. All of these services are installed on a single server host over the course of the book and by the time you are finished you will have a single server that provides these services for your network.WHAT YOU WILL LEARN* Install, configure, and manage several Linux server services such as email with spam management and single and multiple web sites* Work with NTP time synchronization, DHCP, SSH, and file sharing with Unix/Linux and Windows clients* Create RPMs for distribution of scripts and administrative programs.* Understand and work with enhanced security. WHO THIS BOOK IS FORThose who are already Linux power users – SysAdmins who can administer Linux workstation hosts that are not servers – who want to learn to administer the services provided by Linux servers such as web, time, name, email, SSH, and more.DAVID BOTH is an Open Source Software and GNU/Linux advocate, trainer, writer, and speaker. He has been working with Linux and Open Source Software for more than 20 years and has been working with computers for over 45 years. He is a strong proponent of and evangelist for the "Linux Philosophy for System Administrators." David has been in the IT industry for over forty years.He worked for IBM for 21 years and, while working as a Course Development Representative in Boca Raton, FL, in 1981, wrote the training course for the first IBM PC. He has taught RHCE classes for Red Hat and has worked at MCI Worldcom, Cisco, and the State of North Carolina. In most of the places he has worked since leaving IBM in 1995, he has taught classes on Linux ranging from Lunch'n'Learns to full five day courses. Helping others learn about Linux and open source software is one of his great pleasures.David had some amazing teachers and mentors in my 40 years in IT and my more than 20 years working with Linux. At their core, Linux and open source in general are about sharing and helping others and about contributing to the community. These books, along with “The Linux philosophy for SysAdmins,” are a continuation of his desire to pass on my knowledge and to provide mentoring to anyone interested in learning about Linux.David is the author of The Linux Philosophy for SysAdmins (Apress, 2018) and can be found on Twitter @linuxgeek46.42. Server Preparation.- 43. Name services.- 44. Routing.- 45. Remote Access with SSH.- 46. Security.- 47. Backup everything - frequently.- 48. Introducing Email.- 49. Advanced Email Topics.- 50. Combating Spam.- 51. Apache Web Server.- 52. WordPress.- 53. Mailing Lists.- 54. Security.- 55. Advanced Package Management.- 56. File Sharing.- 57. Where Do I Go From Here?.- Bibliography.
Metaverse and Immersive Technologies
METAVERSE AND IMMERSIVE TECHNOLOGIESTHE BOOK COVERS THE MULTIDIMENSIONAL PERSPECTIVES OF THE METAVERSE THROUGH THE PRISM OF VIRTUAL REALITY, AUGMENTED REALITY, BLOCKCHAIN, ARTIFICIAL INTELLIGENCE, AND IOT, RANGING FROM RUDIMENTARY TO ADVANCED APPLICATIONS.This book provides a thorough explanation of how the technology behind metaverse and other virtual reality technologies are changing the world. The primary objective is to present the revolutionary innovation of the 21st century—the metaverse—and exhibit its wide range of applications in different domains. Although blockchain and VR/AR were the first popularly known applications of the metaverse, several other applications also exist. While some still believe the metaverse is overhyped, in reality, it is transforming almost every industry—healthcare, 3D, 4D, industry, game industry, business management, artificial intelligence, and IoT, just to name a few. This technological breakthrough not only paved the way for virtual reality but also provided useful solutions for other areas of technology. The unique nature of the technology, which is a single, shared, immersive, persistent, 3D virtual space where humans experience life in ways not possible in the physical world, makes it suitable for all real-world applications; it has great potential to transform business, and companies are already in the race for different product offerings. AUDIENCEAI and computer science researchers, engineers and graduate students, IT personnel in business as well as entrepreneurs and policymakers. CHANDRASHEKHAR A, PHD, is an assistant professor in the Department of Mechatronics, ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has 8 patents to his name and has published numerous research articles in international journals. SHAIK HIMAM SAHEB, PHD, is an assistant professor in the Department of Mechatronics Engineering, ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has published numerous research articles in international journals. SANDEEP KUMAR PANDA, PHD, is an associate professor in the Department of Data Science and Artificial Intelligence, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has published numerous research articles in international journals and has 18 Indian patents to his name. In 2020, he received the “Research and Innovation of the Year Award 2020” from the Govt. of India. S. BALAMURUGAN, PHD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/conferences, and 35 patents. SHENG-LUNG PENG, PHD, is a professor in the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He is an honorary professor of Beijing Information Science and Technology, University of China, and a supervisor of the Chinese Information Literacy Association and of the Association of Algorithms and Computation Theory. He has published more than 120 international journal and conference papers.
Learn Data Mining Through Excel
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how.This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You’ll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages.Over the course of Learn Data Mining Through Excel, you will learn the data mining advantages the application offers when the data sets are not too large. You’ll see how to use Excel’s built-in features to create visual representations of your data, enabling you to present your findings in an accessible format. Author Hong Zhou walks you through each step, offering not only an active learning experience, but teaching you how the mining process works and how to find hidden patterns within the data.Upon completing this book, you will have a thorough understanding of how to use an application you very likely already have to mine and analyze data, and how to present results in various formats.WHAT YOU WILL LEARN* Comprehend data mining using a visual step-by-step approach* Gain an introduction to the fundamentals of data mining* Implement data mining methods in Excel* Understand machine learning algorithms* Leverage Excel formulas and functions creatively* Obtain hands-on experience with data mining and ExcelWHO THIS BOOK IS FORAnyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.HONG ZHOU, PHD is a professor of computer science and mathematics and has been teaching courses in computer science, data science, mathematics, and informatics at the University of Saint Joseph for nearly 20 years. His research interests include bioinformatics, data mining, software agents, and blockchain. Prior to his current position, he was as a Java developer in Silicon Valley. Dr. Zhou believes that learners can develop a better foundation of data mining models when they visually experience them step-by-step, which is what Excel offers. He has employed Excel in teaching data mining and finds it an effective approach for both data mining learners and educators.Chapter 1: Excel and Data Mining.- Chapter 2: Linear Regression.- Chapter 3: K-Means Clustering.- Chapter 4: Linear Discriminant Analysis.- Chapter 5: Cross Validation and ROC.- Chapter 6: Logistic Regression.- Chapter 7: K-nearest Neighbors.- Chapter 8: Naïve Bayes Classification.- Chapter 9: Decision Trees.- Chapter 10: Association Analysis.- Chapter 11: Artificial Neural Networks.- Chapter 12: Text Mining.- Chapter 13: Hierarchical Clustering and Dendrogram.- Chapter 14 Exploratory Data Analysis (EDA).- Chapter 15: After Excel.