Allgemein
Datenschutz für Softwareentwicklung und IT
Dieses Buch beschreibt das Thema Datenschutz aus der Sicht von Softwareentwicklung und IT. Die Verantwortlichen in diesen Bereichen gestalten die praktische Umsetzung des Datenschutzes zu erheblichen Teilen mit, benötigen dafür aber entsprechende Kenntnisse über die rechtlichen Rahmenbedingungen und Möglichkeiten zu deren Umsetzung. Der Fokus dieses Buchs liegt daher auf den Aspekten des Datenschutzes, die durch Softwareentwicklung stark beeinflusst werden, wie z.B. Privacy by Design, Privacy by Default, Datenminimierung, Umsetzung von Auskunftsrechten sowie Datenlöschung. RALF KNEUPER ist Professor für Wirtschaftsinformatik und Informatik an der IUBH Internationale Hochschule im Bereich Fernstudium. Daneben arbeitet er als Berater für Softwarequalitätsmanagement und Prozessverbesserung sowie als externer Datenschutzbeauftragter bei mehreren IT-Unternehmen.Einführung - Allgemeine Grundlagen des Datenschutzes nach DSGVO - Grundsätze des Datenschutzes und deren Umsetzung - Rechte der Betroffenen und deren Umsetzung - Austausch von Daten zwischen Beteiligten - Technische und organisatorische Gestaltung des Datenschutzes - Grundbegriffe der IT-Sicherheit - Datenschutz innerhalb einer IT-Organisation
Sketchnotes in der IT
Abstrakte Themen mit Leichtigkeit visualisieren. Die praktische Einführung mit Tipps, Tricks und Symbolen.Im IT-Berufsalltag sammeln sich unzählige Notizen – zu Vorträgen, Meetings, Aufzeichnungen zu komplexen Aufgaben … Häufig sind sie hässlich, lang, unleserlich – und landen schnell im Altpapier. Sketchnotes dagegen sehen nicht nur schick aus, sie helfen auch dabei, sich an die wichtigsten Dinge zu erinnern, und erfreuen Kolleginnen und Kollegen.Dieses Buch gibt eine praktische Einführung in die Welt der Sketchnotes. Schon auf den ersten Seiten erstellst du deine erste Sketchnote – unabhängig von Vorwissen oder Talent. Nach einem Grundlagenkapitel, das Hilfen für den Einstieg bietet, zeigt die Softwareentwicklerin Lisa-Maria Moritz, in welchen Bereichen deines Arbeitsalltags in der IT du Sketchnotes einsetzen kannst. Um dabei die passende Visualisierung zu finden, hat sie eine umfangreiche Bibliothek mit zahlreichen Symbolideen zu abstrakten Begriffen der IT zusammengestellt, deren Erstellung sie in Schritt-für-Schritt-Anleitungen zeigt.
Essential Java for AP CompSci
Gain the essential skills for computer science using one of today's most popular programming languages, Java. This book will prepare you for AP CompSci Complete, but you don’t need to be sitting that class to benefit. Computer science has become a basic life skill that everyone is going to need to learn. Whether you are going into a career or side hustle in business, technology, creativity, architecture, or almost any other field, you will find coding and computer science play a role.So when we learn programming we are going to focus on three things: what is the process; what is the syntax; and what is the flow. The process is represented as a flowchart. We will learn how to make these to help you plan out what you are going to do before you write a line of code. At first, the flowcharts will be pretty simple, but then they will get more complex. The syntax is the code: this is what you write that translates the process you create in a flowchart to the instructions that the computer can understand. Finally, there is the flow. This is where you trace through the code and see how the data and information it stores along the way changes. You can see how the operation of the program cascades from line to line. You will be building charts that will capture the programming flow so you can better understand how the computer processes code to make your next program easier to conceive and code.Along the way to aid in the learning of the essential Java skills, there will be three kinds of project types throughout this book: business software projects for applications where you work for a company and need to complete an internal project for a team such as the sales, marketing, or data science teams; social good projects where you are working for non-profits or for agencies that are trying to research and provide solutions to economic, environmental, medical, or humanitarian projects; and game development projects for games based on player input, random chance, or other mechanics for the use of entertainment.What is unique about computer science is how it has become a skill, and not just a career. While there are jobs and titles of “computer scientist”, the skill of computer science, and specifically programming, are almost everywhere. After reading and using this book, you'll have the essential skills to think like a computer scientist, even if you are not. As a result you’ll be of greater value to your clients, your company, and yourself.WHAT YOU WILL LEARNDiscover the primary building blocks of programming using the Java programming language * See terminology and best practices of software development* Work with object-oriented programming concepts* Use common-language definitions and examples to help drive understanding and comprehension of computer science fundamentalsWHO THIS BOOK IS FORThose who want to learn programming and want to think like a computer scientist. Ideal for anyone taking AP CompSci Complete.Doug Winnie is director of learning experience at H&R Block, responsible for learning and development platforms supporting associates across the organization. Previously, Doug was principal program manager at Microsoft and LinkedIn leading the LinkedIn Learning instructor community, curriculum strategy for technology learning content, and as a member the Windows Insider team supporting educational and career growth for millions of Windows Insiders worldwide.Throughout his career and consulting with companies such as Adobe, PG&E, Safeway, HP, and the US Army, Doug has worked to help developers and designers through education, product management, and interactive development. Doug was honored with two Webby award nominations with projects for Industrial Light and Magic and has written multiple publications to teach beginners how to code. He is also an AP Computer Science teacher, teaching the next generation of developers. Doug lives in the Kansas City metro area and Palm Springs, California.1. WELCOME TO COMPUTER SCIENCE2. SPRINT 01: INTRODUCTION3. SPRINT 02: SETTING UP THE JAVA JDK AND INTELLIJ4. SPRINT 03: SETTING UP GITHUBa. QUIZ 01b. QUIZ 025. SPRINT 04: PROGRAMMING LANGUAGES6. SPRINT 05: HISTORY AND USES OF JAVA7. SPRINT 06: HOW JAVA WORKSa. QUIZ 038. SPRINT 07: FLOWCHARTINGa. ASSIGNMENT 01: PBJ’Db. QUIZ 049. SPRINT 08: HELLO, WORLDa. QUIZ 0510. SPRINT 09: SIMPLE JAVA PROGRAM STRUCTURE11. SPRINT 10: TEXT LITERALS AND OUTPUTa. ASSIGNMENT 02: EE’D12. SPRINT 11: VALUE LITERALS13. SPRINT 12: OUTPUT FORMATTING14. SPRINT 13: COMMENTS AND WHITESPACE15. SPRINT 14: ABSTRACTION OF NUMBERS16. SPRINT 15: BINARYa. QUIZ 0617. SPRINT 16: UNICODE18. SPRINT 17: VARIABLES19. SPRINT 18: MATH. UGH.a. QUIZ 07b. ASSIGNMENT 03: SILO’D20. SPRINT 19: MATH FUNCTIONS21. SPRINT 20: MANAGING TYPEa. ASSIGNMENT 04: SPACE’Db. QUIZ 08c. QUIZ 09d. QUIZ 10e. QUIZ 1122. SPRINT 21: RANDOM NUMBERS23. SPRINT 22: CAPTURE INPUT24. SPRINT 23: CREATING TRACE TABLES25. SPRINT 24: FUNCTIONSa. ASSIGNMENT 05: ORC’D26. SPRINT 25: NESTED FUNCTIONS27. SPRINT 26: FUNCTIONS AND VALUESa. QUIZ 1228. SPRINT 27: FUNCTIONS AND SCOPEa. QUIZ 13b. QUIZ 14c. QUIZ 15d. ASSIGNMENT 06: ULTIMA’De. ASSIGNMENT 07: CYCLONE’D29. SPRINT 28: BOOLEAN VALUES AND EQUALITYa. QUIZ 16b. ASSIGNMENT 08: SPRINT’Dc. USER STORY: 52-PICKUP30. SPRINT 29: SIMPLE CONDITIONAL STATEMENTSa. USER STORY: YAHTZEEb. USER STORY: YAHTZEE TESTINGc. QUIZ 17d. QUIZ 18e. QUIZ 1931. SPRINT 30: MATCHING CONDITIONS WITH THE SWITCH STATEMENT32. SPRINT 31: THE TERNARY OPERATOR33. SPRINT 32: THE STACK AND THE HEAP34. SPRINT 33: TESTING EQUALITY WITH STRINGSa. ASSIGNMENT 09: ESCAPE’Db. USER STORY: ESCAPE’D WHITE BOX35. SPRINT 34: DEALING WITH ERRORS36. SPRINT 35: DOCUMENTING WITH JAVADOC37. SPRINT 36: FORMATTED STRINGS38. SPRINT 37: THE WHILE LOOPa. QUIZ 20b. QUIZ 21c. QUIZ 2239. SPRINT 38: AUTOMATIC PROGRAM LOOPS40. SPRINT 39: THE DO/WHILE LOOPa. ASSIGNMENT 10: SEQUENCE’Db. USER STORY: DICEYc. USER STORY SOLUTION: DICEYd. USER STORY: CONVERTERe. USER STORY SOLUTION: CONVERTER41. SPRINT 40: PROBABILITY42. SPRINT 41: SIMPLIFIED ASSIGNMENT OPERATORS43. SPRINT 42: THE FOR LOOPa. QUIZ 23b. ASSIGNMENT 11: ODDS’D44. SPRINT 43: NESTING LOOPSa. USER STORY: MAP BUILDER45. SPRINT 44: STRINGS AS COLLECTIONSa. ASSIGNMENT 12: PALINDROME’Db. QUIZ 2446. SPRINT 45: MAKE COLLECTIONS USING ARRAYSa. QUIZ 2547. SPRINT 46: CREATING ARRAYS FROM STRINGSa. ASSIGNMENT 13: ELECTION’Db. QUIZ 2648. SPRINT 47: MULTIDIMENSIONAL ARRAYS49. SPRINT 48: LOOPING THROUGH MULTIDIMENSIONAL ARRAYSa. QUIZ 27b. QUIZ 2850. SPRINT 49: BEYOND ARRAYS WITH ARRAYLISTS51. SPRINT 50: INTRODUCING GENERICS52. SPRINT 51: LOOPING WITH ARRAYLISTSa. ASSIGNMENT 14: LIST’D53. SPRINT 52: USING FOR…EACH LOOPSa. ASSIGNMENT 15: NUMBER’Db. QUIZ 29c. QUIZ 3054. SPRINT 53: THE ROLE-PLAYING GAME CHARACTERa. ASSIGNMENT 16: ROLL’D55. SPRINT 54: POLYMORPHISMa. ASSIGNMENT 17: EXTEN’D56. SPRINT 55: MAKE ALL THE THINGS…CLASSES57. SPRINT 56: CLASS, EXTEND THYSELF!a. QUIZ 3158. SPRINT 57: I DON'T COLLECT THOSE; TOO ABSTRACT.59. SPRINT 58: ACCESS DENIED: PROTECTED AND PRIVATEa. QUIZ 32b. QUIZ 3360. SPRINT 59: INTERFACING WITH INTERFACESa. QUIZ 34b. QUIZ 35c. QUIZ 36d. QUIZ 37e. ASSIGNMENT 18: STARSHIP’D61. SPRINT 60: ALL I'M GETTING IS STATIC62. SPRINT 61: AN ALL-STAR CAST, FEATURING NULL63. ANSWER KEY
Digitale Transformation, Arbeit und Gesundheit
Die digitale Transformation verändert die Arbeitswelt. Wie wird die Digitalisierung gesundheitsgerecht in kleinen und mittleren Unternehmen umgesetzt? Der aktuelle Wissensstand wird zusammengefasst, mit detaillierten Einblicken in die Praxis und Werkzeugen zur Bewältigung betrieblicher Digitalisierungsprojekte.THOMAS ENGEL, Leiter ZeTT – Zentrum Digitale Transformation Thüringen, Friedrich-Schiller-Universität Jena, forscht zum Wandel von Arbeit und Beschäftigung in der Digitalisierung.CHRISTIAN ERFURTH, Professor für Informatik, Ernst-Abbe-Hochschule Jena, beschäftigt sich mit den technologischen und organisatorischen Gestaltungsmöglichkeiten der digitalen Arbeitswelt.STEPHANIE DRÖSSLER arbeitet am Institut und Poliklinik für Arbeits- und Sozialmedizin der Medizinischen Fakultät der TU Dresden zu gesundheitlichen Belastungen und Prävention im digitalen Wandel.SANDRA LEMANSKI arbeitet am Lehrstuhl Gesundheit und Prävention der Universität Greifswald zu Stress im Arbeitskontext und den Gestaltungsmöglichkeiten von Arbeit in der und durch die digitale Transformation.
SQL Server on Kubernetes
Build a modern data platform by deploying SQL Server in Kubernetes. Modern application deployment needs to be fast and consistent to keep up with business objectives and Kubernetes is quickly becoming the standard for deploying container-based applications, fast. This book introduces Kubernetes and its core concepts. Then it shows you how to build and interact with a Kubernetes cluster. Next, it goes deep into deploying and operationalizing SQL Server in Kubernetes, both on premises and in cloud environments such as the Azure Cloud.You will begin with container-based application fundamentals and then go into an architectural overview of a Kubernetes container and how it manages application state. Then you will learn the hands-on skill of building a production-ready cluster. With your cluster up and running, you will learn how to interact with your cluster and perform common administrative tasks. Once you can admin the cluster, you will learn how to deploy applications and SQL Server in Kubernetes. You will learn about high-availability options, and about using Azure Arc-enabled Data Services. By the end of this book, you will know how to set up a Kubernetes cluster, manage a cluster, deploy applications and databases, and keep everything up and running.WHAT YOU WILL LEARN* Understand Kubernetes architecture and cluster components* Deploy your applications into Kubernetes clusters* Manage your containers programmatically through API objects and controllers* Deploy and operationalize SQL Server in Kubernetes* Implement high-availability SQL Server scenarios on Kubernetes using Azure Arc-enabled Data Services* Make use of Kubernetes deployments for Big Data ClustersWHO THIS BOOK IS FORDBAs and IT architects who are ready to begin planning their next-generation data platform and want to understand what it takes to run SQL Server in a container in Kubernetes. SQL Server on Kubernetes is an excellent choice for those who want to understand the big picture of why Kubernetes is the next-generation deployment method for SQL Server but also want to understand the internals, or the how, of deploying SQL Server in Kubernetes. When finished with this book, you will have the vision and skills to successfully architect, build and maintain a modern data platform deploying SQL Server on Kubernetes.ANTHONY E. NOCENTINO is the Founder and President of Centino Systems as well as a Pluralsight author, a Microsoft Data Platform MVP, and an industry-recognized Kubernetes, SQL Server, and Linux expert. In his consulting practice, Anthony designs solutions, deploys the technology, and provides expertise on system performance, architecture, and security. He has bachelor's and master's degrees in computer science, with research publications in machine virtualization, high performance/low latency data access algorithms, and spatial database systems.BEN WEISSMAN is the owner and founder of Solisyon, a consulting firm based in Germany and focused on business intelligence (BI), business analytics, and data warehousing. He is a Microsoft Data Platform MVP, the first German BimlHero, and has been working with SQL Server since SQL Server 6.5. Ben is also an MCSE, Charter Member of the Microsoft Professional Program for Big Data, Artificial Intelligence and Data Science, and he is a Certified Data Vault Data Modeler. If he is not currently working with data, he is probably travelling to explore the world. You can find him online at @bweissman on Twitter.PART I. CONTAINER AND KUBERNETES FOUNDATIONS1. Getting Started2. Container Fundamentals3. Kubernetes ArchitecturePART II. KUBERNETES IN PRACTICE4. Installing Kubernetes5. Interacting with your Kubernetes Cluster6. Storing Persistent Data in KubernetesPART III. SQL SERVER IN KUBERNETES7. Deploying SQL Server in Kubernetes8. Monitoring SQL Server in Kubernetes9. Azure Arc-enabled Data Services and High Availability for SQL Server in Kubernetes10. Big Data Clusters
Towards Sustainable Artificial Intelligence
So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization’s demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles.This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems.The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term.WHAT YOU WILL LEARN* See the relevance of ethics to the practice of data science and AI* Examine the elements that enable AI within an organization* Discover the challenges of developing AI systems that meet certain human or specific standards* Explore the challenges of AI governance* Absorb the key factors to consider when evaluating AI systemsWHO THIS BOOK IS FORDecision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role.GHISLAIN TSAFACK is Head of Data Science at Elemental Concept 2016 LTD (EC), where he leads the organization’s AI strategy. As part of this, he leads the company’s work in leveraging the latest advances in AI to help clients create value from their data and auditing AI systems developed by third parties on behalf of potential investors.Ghislain’s work in the healthcare industry at EC involves supporting the development of data related healthcare products for his clients. This made him appreciate the challenges and the complexity of developing AI systems that people trust to make the right decision for them and stimulated him to write this book.Before joining EC Ghislain held positions as data scientist in the telecommunications and energy sectors. Prior to this, Ghislain worked as an academic at the French National Institute for Research and Automation (INRIA) and the University of Lyon 1. His work primarily focused on analyzing the behaviors of high performance systems to improve their energy efficiency and gave him the opportunity to co-author several scientific books presenting methodologies for improving the energy efficiency for large scale computing infrastructures. He holds a PhD in computer science from Ecole Normale Supérieure of Lyon, France.● Chapter 1: AI in our Society● Chapter goal: Reviews the place of AI within our society, discuss the various challenges that it AI faces, and introduces the foundational concepts of our sustainable AI framework○ 1.1 The Need for Artificial Intelligence○ 1.2 Challenges of Artificial Intelligence○ 1.3 Sustainable Artificial Intelligence● Chapter 2 Ethics of the Data Science Practice● Chapter goal: Reviews the human factor pillar of artificial intelligence, the relevance of ethics in AI and the source of ethical hazards in AI○ 2.1 Introduction○ 2.2 Ethics and their relevance to AI○ 2.3 Ethical nature of AI inferencing capability○ 2.4 Data – The business asset○ 2.5 AI regulatory outlook○ 2.6 Conclusion● Chapter 3 Overview of the Sustainable Artificial Intelligence Framework (SAIF)● Chapter goal: Summarises the SAIF framework for the development and deployment of AI applications● Chapter 4 Intra-organizational understanding of AI: Towards Transparency● Chapter goal: Discusses the need for understanding AI at the organization’s level and introduces concepts of AI governance○ 4.1 Introduction○ 4.2 Data Science Development Process○ 4.3 AI development process Controls○ 4.4 Governance■ 4.4.1 Expectations from AI governance■ 4.4.2 People and Values■ 4.4.3 Assessment of AI governance arrangements○ 4.5 Conclusion● Chapter 5 AI Performance Measurement: Think business values and objectives● Chapter goal: Summarises performance metrics for evaluating AI systems and introduces a framework to account for the human factor of AI○ 5.1 Introduction○ 5.2 AI performance metrics overview■ 5.2.1 Supervised problems■ 5.2.2 Unsupervised problems○ 5.3 Beyond traditional AI performance metrics■ 5.3.1 Soft performance metrics■ 5.3.2 From AI performance metrics to business objectives○ 5.4 Conclusion● Chapter 6 SAIF in Action● Chapter goal: This chapter illustrates how SAIF would work in practice through use cases● Chapter 7 Alternatives avenues for regulating AI systems● Chapter goal: Draws from experiences in academic, Telecom/Utility, and healthcare sectors to explore and examine the need for industry specific regulations.● Chapter 8 AI decision-making – from expectations to reality: The use case of healthcare● Chapter goal: Explores the use of artificial intelligence in the healthcare, its practical limitations an implications● Chapter 9 Conclusions and discussion● Chapter goal: Presents concluding remarks and discuss current lack of standards○ 9.1 Conclusions○ 9.2 Need for standards and definitions
Real-Time Twilio and Flybase
Use Flybase and Twilio with Node.js to build real-time solutions and understand how real-time web technologies work. Written by the founder of Flybase, this book offers you practical solutions for communicating effectively with users on the modern web.Flybase.io is a web platform, used to store and retrieve data in real-time, as well as to send and receive real-time events such as triggers for incoming calls, incoming messages, agents logging off, etc.You will learn to send daily SMS messages, build an SMS call center to provide support to users, and build a call center to handle incoming and outgoing phone calls from the browser. You'll also build a group calling system to let groups send messages to each other: handy for managing events.Real-Time Twilio brings to light using the winning combination of Flybase and Twilio with Node.js for anyone with basic web development skills.WHAT YOU'LL LEARN* Develop web apps with Flybase and Twilio* Build a live blogging tool and a group chat app* Create a click-to-call call center and a Salesforce-powered call center* Send daily SMS reminders* Develop a real-time call tracking dashboardWHO THIS BOOK IS FORThose who want to learn to use Twilio and who wants to learn real-time development.ROGER STRINGER is the founder of Flybase, a real-time application platform that makes it easy for developers to design, build, and scale real-time web and mobile apps in minutes instead of days using client-side code. You can find him on Twitter @freekrai.1. Introducing Real-Time Apps2. Build a real-time SMS call center3. Build a Live Blogging tool4. Build a Real-time Group Chat App5. Creating a Click to Call Call Center6. Building A Salesforce Powered Call Center7. Sending Daily SMS Reminders8. Building a real-time Call Tracking Dashboard
Quantum Machine Learning: An Applied Approach
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost.Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms.The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples.WHAT YOU WILL LEARN* Understand and explore quantum computing and quantum machine learning, and their application in science and industry* Explore various data training models utilizing quantum machine learning algorithms and Python libraries* Get hands-on and familiar with applied quantum computing, including freely available cloud-based access* Be familiar with techniques for training and scaling quantum neural networks* Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWHO THIS BOOK IS FORData scientists, machine learning professionals, and researchersSANTANU GANGULY has been working in the fields of quantum technologies, cloud computing, data networking, and security (on research, design, and delivery) for over 21 years. He works in Switzerland and the United Kingdom (UK) for various Silicon Valley vendors and ISPs. He has two postgraduate degrees (one in mathematics and another in observational astrophysics), and research experience and publications in nanoscale photonics and laser spectroscopy. He is currently leading global projects out of the UK related to quantum communication and machine learning, among other technologies.Chapter 1: IntroductionCHAPTER GOAL: Introduction to book and topics to be coveredNO OF PAGES 12SUB -TOPICS1. Rise of The Quantum Computers2. Learning from data: AI, ML and Deep Learning3. Way forward4. Bird’s Eye view of Quantum Machine Learning Algorithms5. Organisation of the book6. Software and Languages (Linux and Python libraries)Chapter 2: Quantum Computing & Information1. CHAPTER GOAL: A comprehensive understanding of key concepts related to Quantum information science and cloud based free access options for quantum computation quantum domain with examplesNO OF PAGES: 65SUB - TOPICS:2. Basics of Quantum Computing: Qubits, Bloch sphere and gates3. Quantum Circuits4. Quantum Parallelism5. Quantum Computing by Annealing6. Quantum Computing with Superconducting qubits7. Other flavours of Quantum Computing8. Algorithms: Grover, Deutsch, Deutsch-Josza9. Optimisation theory10. Hands-on exercisesChapter 3: Quantum Information EncodingChapter Goal:To understand how to encode data in quantum machine learning space with examplesNO OF PAGES: 30SUB - TOPICS:26. Initiation and selection of data27. Basis encoding28. Superposition of inputs29. Sampling Theory30. Hamiltonian31. Amplitude Encoding32. Other Encoding techniques33. Hands-on exercisesChapter 4: QML AlgorithmsCHAPTER GOAL: Understanding hardware driven algorithmic computations for quantum machine learningNo of pages: 35SUB - TOPICS:34. Hardware Interface (Quantum Processors)35. Quantum K-Means and K-Medians36. Quantum Clustering37. Quantum Classifiers (e.g., nearest neighbours)38. Support Vector Machine (SVM) in quantum space39. Hands-on exercisesChapter 5: InferenceCHAPTER GOAL: Models and methods used in Quantum Machine LearningNO OF PAGES: 35SUB - TOPICS:40. Principal Component Analysis41. Feature Maps42. Linear Models43. Probabilistic Models44. Hands-on ExercisesChapter 6: Training the DataCHAPTER GOAL: Training models and techniques of Quantum Machine LearningNO OF PAGES: 105SUB - TOPICS:45. Unsupervised and supervised learning46. Matrix inversion47. Amplitude amplification for QML48. Quantum optimization49. Travelling Salesman Problem50. Variational Algorithms51. QAOA52. Maxcut Problem53. VQE (Virtual Quantum Eigensolver)54. Varitaional Classification algorithms55. Hands-on ExercisesChapter 7: Quantum Learning ModelsCHAPTER GOAL: Learning models and techniques of Quantum Machine LearningNo of pages: 75SUB - TOPICS:56. Optimal state for learning57. Channel State duality58. Tomography59. Quantum Neural Networks60. Quantum Walk61. Tensor Network applications62. Hands-on ExercisesChapter 8: Future of QML in Research and IndustryCHAPTER GOAL: Forward looking prospects of Quantum Machine Learning in industry, enterprises and opportunitiesNO OF PAGES: 15SUB - TOPICS:1. Speed up that Big Data2. Effect of Error Correction3. Machine learning marries Quantum Computing4. QBoost5. Quantum Walk6. Mapping to hardware7. Hands-on ExercisesReferences Index
Software Testing Foundations
FUNDAMENTAL KNOWLEDGE AND BASIC EXPERIENCE – BROUGHT THROUGH PRACTICAL EXAMPLES * Thoroughly revised and updated 5th edition, following upon the success of four previous editions * Updated according to the most recent ISTQB® Syllabus for the Certified Tester Foundations Level (2018) * Authors are among the founders of the Certified Tester Syllabus Professional testing of software is an essential task that requires a profound knowledge of testing techniques. The International Software Testing Qualifications Board (ISTQB®) has developed a universally accepted, international qualification scheme aimed at software and system testing professionals, and has created the Syllabi and Tests for the Certified Tester. Today about 673,000 people have taken the ISTQB® certification exams. The authors of Software Testing Foundations, 5th Edition, are among the creators of the Certified Tester Syllabus and are currently active in the ISTQB®. This thoroughly revised and updated fifth edition covers the Foundation Level (entry level) and teaches the most important methods of software testing. It is designed for self-study and provides the information necessary to pass the Certified Tester-Foundations Level exam, version 2018, as defined by the ISTQB®. Topics covered: - Fundamentals of Testing - Testing and the Software Lifecycle - Static and Dynamic Testing Techniques - Test Management - Test Tools
Data Analytics for Organisational Development
A PRACTICAL GUIDE FOR ANYONE WHO ASPIRES TO BECOME DATA ANALYTICS–SAVVYData analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics.Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the "non-data scientists." With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work.* Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis* Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science* Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately* Work through end-of-chapter exercises to review key concepts and apply methods using sample data setsData Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools.UWE H. KAUFMANN, PHD, is the founder of the Centre for Organisational Effectiveness, a business advisory firm based in Singapore. He is Adjunct Senior Fellow at the Singapore University of Technology and Design and Affiliate Faculty at Singapore Management University Academy.AMY B.C. TAN is Director and Partner at the Centre for Organisational Effectiveness and Affiliate Faculty at Singapore Management University Academy. She has over 20 years of experience in strategic HR management, organisational development, succession planning, performance management, and leadership development.ForewordPrefaceIntroduction: Why Data Analytics is ImportantChapter 1: Introduction to Data Analytics and Data ScienceChapter 2: Customer Domain – Customer AnalyticsChapter 3: Process Domain – Operations AnalyticsChapter 4: Workforce Domain – Workforce AnalyticsChapter 5: Implementing Data Analytics for Organisational DevelopmentMaterials for DownloadIndex
External Labeling
THIS BOOK FOCUSES ON TECHNIQUES FOR AUTOMATING THE PROCEDURE OF CREATING EXTERNAL LABELINGS, ALSO KNOWN AS CALLOUT LABELINGS. In this labeling type, the features within an illustration are connected by thin leader lines (called leaders) with their labels, which are placed in the empty space surrounding the image.In general, textual labels describing graphical features in maps, technical illustrations (such as assembly instructions or cutaway illustrations), or anatomy drawings are an important aspect of visualization that convey information on the objects of the visualization and help the reader understand what is being displayed.Most labeling techniques can be classified into two main categories depending on the "distance" of the labels to their associated features. Internal labels are placed inside or in the direct neighborhood of features, while external labels, which form the topic of this book, are placed in the margins outside the illustration, where they do not occlude the illustration itself. Both approaches form well-studied topics in diverse areas of computer science with several important milestones.The goal of this book is twofold. The first is to serve as an entry point for the interested reader who wants to get familiar with the basic concepts of external labeling, as it introduces a unified and extensible taxonomy of labeling models suitable for a wide range of applications. The second is to serve as a point of reference for more experienced people in the field, as it brings forth a comprehensive overview of a wide range of approaches to produce external labelings that are efficient either in terms of different algorithmic optimization criteria or in terms of their usability in specific application domains. The book mostly concentrates on algorithmic aspects of external labeling, but it also presents various visual aspects that affect the aesthetic quality and usability of external labeling.* Bibliography* Preface* Acknowledgments* Figure Credits* Introduction* A Unified Taxonomy* Visual Aspects of External Labeling* Labeling Techniques* External Labelings with Straight-Line Leaders* External Labelings with Polyline Leaders* Conclusions and Outlook* Bibliography* Authors' Biographies* Index
Beginning Unity Editor Scripting
Learn about editor scripting in Unity, including different possible methods of editor customization to fit your custom game workflow or even to create assets that could be published on the Asset Store to earn a passive income. The knowledge of editor scripting, although rarely covered in books, gives a game developer insight into how things work in Unity under the hood, which you can leverage to create custom tools that empower your unique game idea.This book starts with the very basics of editor scripting in Unity, such as using built-in attributes to customize your component’s editor and creating custom editors and windows with IMGUI and UI Toolkit. Next, we move to a general use case example by creating an object spawner EditorTool for the scene view. Later, we dive straight to in-depth stats and detailed case studies of two Unity assets: ProArray and Rhythm Game Starter. Here you’ll get more context on how editor scripting is used in published assets.You will also learn how to set up a better workflow for editor scripting, asset publishing, maintenance, and iterative updates. You will leverage the power of modern web technology to build a documentation site with GitBook and DocFX. Finally, you will see some tips and tricks for automating asset versioning and changelogs.WHAT YOU WILL LEARN* Get started with Editor scripting in Unity * Work with advanced editor topics such as custom EditorWindows and EditorTool* Structure your C# code with namespaces and asmdef * Use IMGUI and UI Toolkit for creating editor GUIs* Master packaging and selling your own editor tools* Set up a better workflow for asset publishing, maintenance, and iterative updatesWHO THIS BOOK IS FORReaders who want to learn about editor scripting to improve their game-development process and create tools for themselves. Moderate experience with C# and a fundamental knowledge of Unity is expected.BennyKok is primarily a Unity asset publisher, indie game developer, and music producer. He is a creative individual who loves creating tools for Unity and published ProArray and Rhythm Game Starter on the Unity Asset Store. He also dedicates his time to sharing open-source Unity tools on GitHub for the community.Chapter 1: IntroductionChapter 2: Customize Editor with Attributes and CallbacksChapter 3: Custom Editor with IMGUIChapter 4: Custom Editor with UI ToolkitChapter 5: Object Spawner Tool Using EditorTool and ScriptableObject- Chapter 6: Case Study: ProArrayChapter 7: . Case Study: Rhythm Game StarterChapter 8: Asset Workflow for PublishingChapter 9: Package Distribution and PublishingChapter 10: Conclusion.
Emerging Technologies for Healthcare
“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.MONIKA MANGLA, received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab, in 2019. Currently, she is working as an assistant professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai.NONITA SHARMA is working as assistant professor, National Institute of Technology, Jalandhar. She received the B. Tech degree in Computer Science Engineering in 2002, the M. Tech degree in Computer Science engineering in 2004, and her PhD degree in Wireless Sensor Network from the National Institute of Technology, Jalandhar, India in 2017. POONAM MITTAL received her PhD from J.C Bose University of Science and Technology YMCA, Faridabad, India, in 2019. Currently, she is working as an assistant professor in the Department of Computer Engineering at J.C Bose University of Science and Technology YMCA, Faridabad, India. VAISHALI MEHTA WADHWA obtained her PhD in Facility Location Problems from Thapar University. Her research interests include approximation algorithms, location modeling, IoT, cloud computing and machine learning. She has multiple articles and 2 patents to her name. THIRUNAVAKKARASU K. is a distinguished academician with over twenty-two years of experience in teaching and working in the software industry. Curently, he is heading the Department of BCA and Specialization at Galgotias University. He has done Bachelor in computer science from the University of Madras in 1994 and received 3 master’s degrees in computer science. SHAHNAWAZ KHAN is an assistant professor and serving as Secretary-General of Scientific Research Council at University College of Bahrain. He holds a PhD (Computer Science) from the Indian Institute of Technology (BHU), India. Preface xviiPART I: BASICS OF SMART HEALTHCARE 11 AN OVERVIEW OF IOT IN HEALTH SECTORS 3Sheeba P. S.1.1 Introduction 31.2 Influence of IoT in Healthcare Systems 61.2.1 Health Monitoring 61.2.2 Smart Hospitals 71.2.3 Tracking Patients 71.2.4 Transparent Insurance Claims 81.2.5 Healthier Cities 81.2.6 Research in Health Sector 81.3 Popular IoT Healthcare Devices 91.3.1 Hearables 91.3.2 Moodables 91.3.3 Ingestible Sensors 91.3.4 Computer Vision 101.3.5 Charting in Healthcare 101.4 Benefits of IoT 101.4.1 Reduction in Cost 101.4.2 Quick Diagnosis and Improved Treatment 101.4.3 Management of Equipment and Medicines 111.4.4 Error Reduction 111.4.5 Data Assortment and Analysis 111.4.6 Tracking and Alerts 111.4.7 Remote Medical Assistance 111.5 Challenges of IoT 121.5.1 Privacy and Data Security 121.5.2 Multiple Devices and Protocols Integration 121.5.3 Huge Data and Accuracy 121.5.4 Underdeveloped 121.5.5 Updating the Software Regularly 121.5.6 Global Healthcare Regulations 131.5.7 Cost 131.6 Disadvantages of IoT 131.6.1 Privacy 131.6.2 Access by Unauthorized Persons 131.7 Applications of IoT 131.7.1 Monitoring of Patients Remotely 131.7.2 Management of Hospital Operations 141.7.3 Monitoring of Glucose 141.7.4 Sensor Connected Inhaler 151.7.5 Interoperability 151.7.6 Connected Contact Lens 151.7.7 Hearing Aid 161.7.8 Coagulation of Blood 161.7.9 Depression Detection 161.7.10 Detection of Cancer 171.7.11 Monitoring Parkinson Patient 171.7.12 Ingestible Sensors 181.7.13 Surgery by Robotic Devices 181.7.14 Hand Sanitizing 181.7.15 Efficient Drug Management 191.7.16 Smart Sole 191.7.17 Body Scanning 191.7.18 Medical Waste Management 201.7.19 Monitoring the Heart Rate 201.7.20 Robot Nurse 201.8 Global Smart Healthcare Market 211.9 Recent Trends and Discussions 221.10 Conclusion 23References 232 IOT-BASED SOLUTIONS FOR SMART HEALTHCARE 25Pankaj Jain, Sonia F Panesar, Bableen Flora Talwar and Mahesh Kumar Sah2.1 Introduction 262.1.1 Process Flow of Smart Healthcare System 262.1.1.1 Data Source 262.1.1.2 Data Acquisition 272.1.1.3 Data Pre-Processing 272.1.1.4 Data Segmentation 282.1.1.5 Feature Extraction 282.1.1.6 Data Analytics 282.2 IoT Smart Healthcare System 292.2.1 System Architecture 302.2.1.1 Stage 1: Perception Layer 302.2.1.2 Stage 2: Network Layer 322.2.1.3 Stage 3: Data Processing Layer 322.2.1.4 Stage 4: Application Layer 332.3 Locally and Cloud-Based IoT Architecture 332.3.1 System Architecture 332.3.1.1 Body Area Network (BAN) 342.3.1.2 Smart Server 342.3.1.3 Care Unit 352.4 Cloud Computing 352.4.1 Infrastructure as a Service (IaaS) 372.4.2 Platform as a Service (PaaS) 372.4.3 Software as a Service (SaaS) 372.4.4 Types of Cloud Computing 372.4.4.1 Public Cloud 372.4.4.2 Private Cloud 382.4.4.3 Hybrid Cloud 382.4.4.4 Community Cloud 382.5 Outbreak of Arduino Board 382.6 Applications of Smart Healthcare System 392.6.1 Disease Diagnosis and Treatment 412.6.2 Health Risk Monitoring 422.6.3 Voice Assistants 422.6.4 Smart Hospital 422.6.5 Assist in Research and Development 432.7 Smart Wearables and Apps 432.8 Deep Learning in Biomedical 442.8.1 Deep Learning 462.8.2 Deep Neural Network Architecture 472.8.3 Deep Learning in Bioinformatic 492.8.4 Deep Learning in Bioimaging 492.8.5 Deep Learning in Medical Imaging 502.8.6 Deep Learning in Human-Machine Interface 532.8.7 Deep Learning in Health Service Management 532.9 Conclusion 55References 553 QLATTICE ENVIRONMENT AND FEYN QGRAPH MODELS—A NEW PERSPECTIVE TOWARD DEEP LEARNING 69Vinayak Bharadi3.1 Introduction 703.1.1 Machine Learning Models 703.2 Machine Learning Model Lifecycle 713.2.1 Steps in Machine Learning Lifecycle 713.2.1.1 Data Preparation 723.2.1.2 Building the Machine Learning Model 723.2.1.3 Model Training 723.2.1.4 Parameter Selection 723.2.1.5 Transfer Learning 733.2.1.6 Model Verification 733.2.1.7 Model Deployment 743.2.1.8 Monitoring 743.3 A Model Deployment in Keras 753.3.1 Pima Indian Diabetes Dataset 753.3.2 Multi-Layered Perceptron Implementation in Keras 763.3.3 Multi-Layered Perceptron Implementation With Dropout and Added Noise 773.4 QLattice Environment 803.4.1 Feyn Models 803.4.1.1 Semantic Types 823.4.1.2 Interactions 833.4.1.3 Generating QLattice 833.4.2 QLattice Workflow 833.4.2.1 Preparing the Data 843.4.2.2 Connecting to QLattice 843.4.2.3 Generating QGraphs 843.4.2.4 Fitting, Sorting, and Updating QGraphs 853.4.2.5 Model Evaluation 863.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction 87References 914 SENSITIVE HEALTHCARE DATA: PRIVACY AND SECURITY ISSUES AND PROPOSED SOLUTIONS 93Abhishek Vyas, Satheesh Abimannan and Ren-Hung Hwang4.1 Introduction 944.1.1 Types of Technologies Used in Healthcare Industry 944.1.2 Technical Differences Between Security and Privacy 954.1.3 HIPAA Compliance 954.2 Medical Sensor Networks/Medical Internet of Things/Body Area Networks/WBANs 974.2.1 Security and Privacy Issues in WBANs/WMSNs/WMIOTs 1014.3 Cloud Storage and Computing on Sensitive Healthcare Data 1124.3.1 Security and Privacy in Cloud Computing and Storage for Sensitive Healthcare Data 1144.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data 1194.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data 1224.5.1 Differential Privacy for Preserving Privacy of Big Medical Healthcare Data and for Its Analytics 1244.6 Conclusion 124References 125PART II: EMPLOYMENT OF MACHINE LEARNING IN DISEASE DETECTION 1295 DIABETES PREDICTION MODEL BASED ON MACHINE LEARNING 131Ayush Kumar Gupta, Sourabh Yadav, Priyanka Bhartiya and Divesh Gupta5.1 Introduction 1315.2 Literature Review 1335.3 Proposed Methodology 1355.3.1 Data Accommodation 1355.3.1.1 Data Collection 1355.3.1.2 Data Preparation 1365.3.2 Model Training 1385.3.2.1 K Nearest Neighbor Classification Technique 1395.3.2.2 Support Vector Machine 1405.3.2.3 Random Forest Algorithm 1425.3.2.4 Logistic Regression 1445.3.3 Model Evaluation 1455.3.4 User Interaction 1455.3.4.1 User Inputs 1465.3.4.2 Validation Using Classifier Model 1465.3.4.3 Truth Probability 1465.4 System Implementation 1475.5 Conclusion 153References 1536 LUNG CANCER DETECTION USING 3D CNN BASED ON DEEP LEARNING 157Siddhant Panda, Vasudha Chhetri, Vikas Kumar Jaiswal and Sourabh Yadav6.1 Introduction 1576.2 Literature Review 1596.3 Proposed Methodology 1616.3.1 Data Handling 1616.3.1.1 Data Gathering 1616.3.1.2 Data Pre-Processing 1626.3.2 Data Visualization and Data Split 1626.3.2.1 Data Visualization 1626.3.2.2 Data Split 1626.3.3 Model Training 1636.3.3.1 Training Neural Network 1636.3.3.2 Model Optimization 1666.4 Results and Discussion 1686.4.1 Gathering and Pre-Processing of Data 1696.4.1.1 Gathering and Handling Data 1696.4.1.2 Pre-Processing of Data 1706.4.2 Data Visualization 1716.4.2.1 Resampling 1736.4.2.2 3D Plotting Scan 1736.4.2.3 Lung Segmentation 1736.4.3 Training and Testing of Data in 3D Architecture 1756.5 Conclusion 178References 1787 PNEUMONIA DETECTION USING CNN AND ANN BASED ON DEEP LEARNING APPROACH 181Priyanka Bhartiya, Sourabh Yadav, Ayush Gupta and Divesh Gupta7.1 Introduction 1827.2 Literature Review 1837.3 Proposed Methodology 1857.3.1 Data Gathering 1857.3.1.1 Data Collection 1857.3.1.2 Data Pre-Processing 1867.3.1.3 Data Split 1867.3.2 Model Training 1877.3.2.1 Training of Convolutional Neural Network 1897.3.2.2 Training of Artificial Neural Network 1917.3.3 Model Fitting 1937.3.3.1 Fit Generator 1937.3.3.2 Validation of Accuracy and Loss Plot 1937.3.3.3 Testing and Prediction 1937.4 System Implementation 1947.4.1 Data Gathering, Pre-Processing, and Split 1947.4.1.1 Data Gathering 1947.4.1.2 Data Pre-Processing 1957.4.1.3 Data Split 1967.4.2 Model Building 1967.4.3 Model Fitting 1977.4.3.1 Fit Generator 1977.4.3.2 Validation of Accuracy and Loss Plot 1977.4.3.3 Testing and Prediction 1987.5 Conclusion 199References 1998 PERSONALITY PREDICTION AND HANDWRITING RECOGNITION USING MACHINE LEARNING 203Vishal Patil and Harsh Mathur8.1 Introduction to the System 2048.1.1 Assumptions and Limitations 2068.1.1.1 Assumptions 2068.1.1.2 Limitations 2068.1.2 Practical Needs 2068.1.3 Non-Functional Needs 2068.1.4 Specifications for Hardware 2078.1.5 Specifications for Applications 2078.1.6 Targets 2078.1.7 Outcomes 2078.2 Literature Survey 2088.2.1 Computerized Human Behavior Identification Through Handwriting Samples 2088.2.2 Behavior Prediction Through Handwriting Analysis 2098.2.3 Handwriting Sample Analysis for a Finding of Personality Using Machine Learning Algorithms 2098.2.4 Personality Detection Using Handwriting Analysis 2108.2.5 Automatic Predict Personality Based on Structure of Handwriting 2108.2.6 Personality Identification Through Handwriting Analysis: A Review 2108.2.7 Text Independent Writer Identification Using Convolutional Neural Network 2108.2.8 Writer Identification Using Machine Learning Approaches 2118.2.9 Writer Identification from HandwrittenText Lines 2118.3 Theory 2128.3.1 Pre-Processing 2128.3.2 Personality Analysis 2158.3.3 Personality Characteristics 2168.3.4 Writer Identification 2178.3.5 Features Used 2198.4 Algorithm To Be Used 2208.5 Proposed Methodology 2248.5.1 System Flow 2258.6 Algorithms vs. Accuracy 2268.6.1 Implementation 2288.7 Experimental Results 2318.8 Conclusion 2328.9 Conclusion and Future Scope 232Acknowledgment 232References 2339 RISK MITIGATION IN CHILDREN WITH AUTISM SPECTRUM DISORDER USING BRAIN SOURCE LOCALIZATION 237Joy Karan Singh, Deepti Kakkar and Tanu Wadhera9.1 Introduction 2389.2 Risk Factors Related to Autism 2399.2.1 Assistive Technologies for Autism 2409.2.2 Functional Connectivity as a Biomarker for Autism 2419.2.3 Early Intervention and Diagnosis 2429.3 Materials and Methodology 2439.3.1 Subjects 2439.3.2 Methods 2439.3.3 Data Acquisition and Processing 2439.3.4 sLORETA as a Diagnostic Tool 2449.4 Results and Discussion 2459.5 Conclusion and Future Scope 247References 24710 PREDICTING CHRONIC KIDNEY DISEASE USING MACHINE LEARNING 251Monika Gupta and Parul Gupta10.1 Introduction 25210.2 Machine Learning Techniques for Prediction of Kidney Failure 25310.2.1 Analysis and Empirical Learning 25410.2.2 Supervised Learning 25510.2.3 Unsupervised Learning 25610.2.3.1 Understanding and Visualization 25710.2.3.2 Odd Detection 25710.2.3.3 Object Completion 25810.2.3.4 Information Acquisition 25810.2.3.5 Data Compression 25810.2.3.6 Capital Market 25810.2.4 Classification 25910.2.4.1 Training Process 26010.2.4.2 Testing Process 26010.2.5 Decision Tree 26110.2.6 Regression Analysis 26310.2.6.1 Logistic Regression 26310.2.6.2 Ordinal Logistic Regression 26510.2.6.3 Estimating Parameters 26610.2.6.4 Multivariate Regression 26810.3 Data Sources 26910.4 Data Analysis 27210.5 Conclusion 27410.6 Future Scope 274References 274PART III: ADVANCED APPLICATIONS OF MACHINE LEARNING IN HEALTHCARE 27911 BEHAVIORAL MODELING USING DEEP NEURAL NETWORK FRAMEWORK FOR ASD DIAGNOSIS AND PROGNOSIS 281Tanu Wadhera, Deepti Kakkar and Rajneesh Rani11.1 Introduction 28211.2 Automated Diagnosis of ASD 28411.2.1 Deep Learning 28911.2.2 Deep Learning in ASD 29011.2.3 Transfer Learning Approach 29011.3 Purpose of the Chapter 29211.4 Proposed Diagnosis System 29311.5 Conclusion 294References 29512 RANDOM FOREST APPLICATION OF TWITTER DATA SENTIMENT ANALYSIS IN ONLINE SOCIAL NETWORK PREDICTION 299Arnav Munshi, M. Arvindhan and Thirunavukkarasu K.12.1 Introduction 30012.1.1 Motivation 30012.1.2 Domain Introduction 30012.2 Literature Survey 30212.3 Proposed Methodology 30412.4 Implementation 31112.5 Conclusion 311References 31113 REMEDY TO COVID-19: SOCIAL DISTANCING ANALYZER 315Sourabh Yadav13.1 Introduction 31513.2 Literature Review 31813.3 Proposed Methodology 32113.3.1 Person Detection 32113.3.1.1 Frame Creation 32413.3.1.2 Contour Detection 32513.3.1.3 Matching with COCO Model 32613.3.2 Distance Calculation 32613.3.2.1 Calculation of Centroid 32613.3.2.2 Distance Among Adjacent Centroids 32713.4 System Implementation 32813.5 Conclusion 333References 33414 IOT-ENABLED VEHICLE ASSISTANCE SYSTEM OF HIGHWAY RESOURCING FOR SMART HEALTHCARE AND SUSTAINABILITY 337Shubham Joshi and Radha Krishna Rambola14.1 Introduction 33814.2 Related Work 34014.2.1 Adoption of IoT in Vehicle to Ensure Driver Safety 34114.2.2 IoT in Healthcare System 34114.2.3 The Technology Used in Assistance Systems 34314.2.3.1 Adaptive Cruise Control (ACC) 34314.2.3.2 Lane Departure Warning 34314.2.3.3 Parking Assistance 34314.2.3.4 Collision Avoidance System 34314.2.3.5 Driver Drowsiness Detection 34414.2.3.6 Automotive Night Vision 34414.3 Objectives, Context, and Ethical Approval 34414.4 Technical Background 34514.4.1 IoT With Health 34514.4.2 Machine-to-Machine (M2M) Communication 34514.4.3 Device-to-Device (D2D) Communication 34514.4.4 Wireless Sensor Network 34614.4.5 Crowdsensing 34614.5 IoT Infrastructural Components for Vehicle Assistance System 34614.5.1 Communication Technology 34614.5.2 Sensor Network 34714.5.3 Infrastructural Component 34814.5.4 Human Health Detection by Sensors 34814.6 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability 34914.7 Challenges in Implementation 35314.8 Conclusion 353References 35415 AIDS OF MACHINE LEARNING FOR ADDITIVELY MANUFACTURED BONE SCAFFOLD 359Nimisha Rahul Shirbhate and Sanjay Bokade15.1 Introduction 36015.1.1 Bone Scaffold 36015.1.2 Bone Grafting 36215.1.3 Comparison Bone Grafting and Bone Scaffold 36315.2 Research Background 36415.3 Statement of Problem 36415.4 Research Gap 36515.5 Significance of Research 36615.6 Outline of Research Methodology 36615.6.1 Customized Design of Bone Scaffold 36615.6.2 Manufacturing Methods and Biocompatible Material 36715.6.2.1 Conventional Scaffold Fabrication 36815.6.2.2 Additive Manufacturing 36915.6.2.3 Application of Additive Manufacturing/3D Printing in Healthcare 37015.6.2.4 Automated Process Monitoring in 3D Printing Using Supervised Machine Learning 37615.7 Conclusion 377References 377Index 381
Introduction to Computational Thinking
Learn approaches of computational thinking and the art of designing algorithms. Most of the algorithms you will see in this book are used in almost all software that runs on your computer.Learning how to program can be very rewarding. It is a special feeling to seeing a computer translate your thoughts into actions and see it solve your problems for you. To get to that point, however, you must learn to think about computations in a new way—you must learn computational thinking.This book begins by discussing models of the world and how to formalize problems. This leads onto a definition of computational thinking and putting computational thinking in a broader context. The practical coding in the book is carried out in Python; you’ll get an introduction to Python programming, including how to set up your development environment.WHAT YOU WILL LEARN* Think in a computational way* Acquire general techniques for problem solving* See general and concrete algorithmic techniques* Program solutions that are both computationally efficient and maintainableWHO THIS BOOK IS FORThose new to programming and computer science who are interested in learning how to program algorithms and working with other computational aspects of programming.Thomas Mailund, PhD is an associate professor in bioinformatics at Aarhus University, Denmark. He has a background in math and computer science, including experience programming and teaching in the C, Python and R programming languages. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.1 Introduction 1Models of the world and formalising problems . . 4What is computational thinking? . . . . . . . . . 6Computational thinking in a broader context . . . 12What is to come . . . . . . . . . . . . . . . . . . 152 Introducing Python programming 19Obtaining Python . . . . . . . . . . . . . . . . . 20Running Python . . . . . . . . . . . . . . . . . . 22Expressions in Python . . . . . . . . . . . . . . . 22Logical (or boolean) expressions . . . . . . . . . . 26Variables . . . . . . . . . . . . . . . . . . . . . . 30Working with strings . . . . . . . . . . . . . . . . 32Lists . . . . . . . . . . . . . . . . . . . . . . . . 36Tuples . . . . . . . . . . . . . . . . . . . . . . . 41iiiSets and dictionaries . . . . . . . . . . . . . . . . 42Input and output . . . . . . . . . . . . . . . . . . 44Conditional statements (if statements) . . . . . . 47Loops (for and while) . . . . . . . . . . . . . . . 50Using modules . . . . . . . . . . . . . . . . . . . 543 Introduction to algorithms 57Designing algorithms . . . . . . . . . . . . . . . 62Exercises for sequential algorithms . . . . . . . . 81Exercises on lists . . . . . . . . . . . . . . . . . . 874 Algorithmic eciency 95The RAM model of a computer and its primitiveoperations . . . . . . . . . . . . . . . . . . 97Types of eciency . . . . . . . . . . . . . . . . . 107Asymptotic running time and big-Oh notation . . 116Empirically validating an algorithms running time 1355 Searching and sorting 141Searching . . . . . . . . . . . . . . . . . . . . . . 142Sorting . . . . . . . . . . . . . . . . . . . . . . . 147Generalising searching and sorting . . . . . . . . 182How computers represent numbers . . . . . . . . 1866 Functions 197Parameters and local and global variables . . . . . 203Side eects . . . . . . . . . . . . . . . . . . . . . 210Returning from a function . . . . . . . . . . . . . 215Higher order functions . . . . . . . . . . . . . . . 221Functions vs function instances . . . . . . . . . . 227Default parameters and keyword arguments . . . 230Generalising parameters . . . . . . . . . . . . . . 234Exceptions . . . . . . . . . . . . . . . . . . . . . 239Writing your own Python modules . . . . . . . . 2517 Inner functions 253A comparison function for a search algorithm . . 256Counter function . . . . . . . . . . . . . . . . . . 261Apply . . . . . . . . . . . . . . . . . . . . . . . . 265Currying functions . . . . . . . . . . . . . . . . . 269Function composition . . . . . . . . . . . . . . . 274Thunks and lazy evaluation . . . . . . . . . . . . 276Decorators . . . . . . . . . . . . . . . . . . . . . 281Eciency . . . . . . . . . . . . . . . . . . . . . . 2888 Recursion 291Denitions of recursion . . . . . . . . . . . . . . 291Recursive functions . . . . . . . . . . . . . . . . 293Recursion stacks . . . . . . . . . . . . . . . . . . 297Recursion and iteration . . . . . . . . . . . . . . 307Tail-calls . . . . . . . . . . . . . . . . . . . . . . 316Continuations . . . . . . . . . . . . . . . . . . . 324Continuations, thunks and trampolines . . . . . . 3359 Divide and conquer and dynamic programming 343Divide and conquer running times . . . . . . . . 355Dynamic programming . . . . . . . . . . . . . . 371Representing oating point numbers . . . . . . . 39210 Hidden Markov models 399Probabilities . . . . . . . . . . . . . . . . . . . . 399Conditional probabilities and dependency graphs . 410Markov models . . . . . . . . . . . . . . . . . . . 412Hidden Markov models . . . . . . . . . . . . . . 421Forward algorithm . . . . . . . . . . . . . . . . . 425Viterbi algorithm . . . . . . . . . . . . . . . . . . 43311 Data structures, objects and classes 439Classes . . . . . . . . . . . . . . . . . . . . . . . 441Exceptions and classes . . . . . . . . . . . . . . . 448Methods . . . . . . . . . . . . . . . . . . . . . . 453Magical methods . . . . . . . . . . . . . . . . . . 460Class variables . . . . . . . . . . . . . . . . . . . 464Objects, classes, meta-classes, and attributes . . . 471Return of the decorator . . . . . . . . . . . . . . 494Polymorphism . . . . . . . . . . . . . . . . . . . 500Abstract data structures . . . . . . . . . . . . . . 50412 Class hierarchies and inheritance 507Inheritance and code reuse . . . . . . . . . . . . 516Multiple inheritance . . . . . . . . . . . . . . . . 524Mixins . . . . . . . . . . . . . . . . . . . . . . . 53213 Sequences 537Sequences . . . . . . . . . . . . . . . . . . . . . 538Linked lists sequences . . . . . . . . . . . . . . . 540Doubly linked lists . . . . . . . . . . . . . . . . . 560A word on garbage collection . . . . . . . . . . . 579Iterators . . . . . . . . . . . . . . . . . . . . . . 587Python iterators and other interfaces . . . . . . . 590Generators . . . . . . . . . . . . . . . . . . . . . 59814 Sets 607Sets with builtin lists . . . . . . . . . . . . . . . . 612Linked lists sets . . . . . . . . . . . . . . . . . . . 618Search trees . . . . . . . . . . . . . . . . . . . . 620Hash table . . . . . . . . . . . . . . . . . . . . . 648Dictionaries . . . . . . . . . . . . . . . . . . . . 66315 Red-black search trees 669A persistent recursive solution . . . . . . . . . . . 670An iterative solution . . . . . . . . . . . . . . . . 71216 Stacks and queues 739Building stacks and queues from scratch . . . . . 745Expression stacks and stack machines . . . . . . . 748Quick-sort and the call stack . . . . . . . . . . . . 761Writing an iterator for a search tree . . . . . . . . 763Merge sort with an explicit stack . . . . . . . . . . 768Breadth-rst tree traversal and queues . . . . . . 77517 Priority queues 779A tree representation for a heap . . . . . . . . . . 782Leftist heaps . . . . . . . . . . . . . . . . . . . . 786Binomial heaps . . . . . . . . . . . . . . . . . . . 794Binary heaps . . . . . . . . . . . . . . . . . . . . 814Adding keys and values . . . . . . . . . . . . . . 825Comparisons . . . . . . . . . . . . . . . . . . . . 842Human encoding . . . . . . . . . . . . . . . . . 84618 Conclusions 853Where to go from here . . . . . . . . . . . . . . 855
Advanced Analytics with Transact-SQL
Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration.No analysis is good without data quality. ADVANCED ANALYTICS WITH TRANSACT-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well.Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining.Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented.T-SQL is supported in SQL Server,Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you.WHAT YOU WILL LEARN* Describe distribution of variables with statistical measures* Find associations between pairs of variables* Evaluate the quality of the data you are analyzing* Perform time-series analysis on your data* Forecast values of a continuous variable* Perform market-basket analysis to predict customer purchasing patterns* Predict target variable outcomes from one or more input variables* Categorize passages of text by extracting and analyzing keywordsWHO THIS BOOK IS FORDatabase developers and database administrators who want to translate their T-SQL skills into the world of business intelligence (BI) and data science. For readers who want to analyze large amounts of data efficiently by using their existing knowledge of T-SQL and Microsoft’s various database platforms such as SQL Server and Azure SQL Database. Also for readers who want to improve their querying by learning new and original optimization techniques.DEJAN SARKA, MCT and Data Platform MVP, is an independent trainer and consultant with more than 30 years of experience who focuses on development of database and business intelligence (BI) applications. He works on projects, and spends about half of his time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. Dejan Sarka is the main author or co-author of 19 books about databases and SQL Server, and has developed many courses and seminars for Microsoft, Radacad, SolidQ, and Pluralsight. PART I. STATISTICS.- 1. Descriptive Statistics.-2. Associations Between Pairs of Variables.- PART II. DATA PREPARATION AND QUALITY.- 3. Data Preparation.- 4. Data Quality and Information.- PART III. DEALING WITH TIME.- 5. Time-Oriented Data.- 6. Time-Oriented Analyses.- PART IV. DATA SCIENCE.- 7. Data Mining.- 8. Text Mining.
Machine Learning Approaches for Convergence of IoT and Blockchain
MACHINE LEARNING APPROACHES FOR CONVERGENCE OF IOT AND BLOCKCHAINTHE UNIQUE ASPECT OF THIS BOOK IS THAT ITS FOCUS IS THE CONVERGENCE OF MACHINE LEARNING, IOT, AND BLOCKCHAIN IN A SINGLE PUBLICATION.Blockchain technology and the Internet of Things (IoT) are two of the most impactful trends to have emerged in the field of machine learning. Although there are a number of books available solely on the subjects of machine learning, IoT and blockchain technology, no such book has been available which focuses on machine learning techniques for IoT and blockchain convergence until now. Thus, this book is unique in terms of the topics it covers. Designed as an essential guide for all academicians, researchers, and those in industry who are working in related fields, this book will provide insights into the convergence of blockchain technology and the IoT with machine learning. Highlights of the book include:* Examines many industries such as agriculture, manufacturing, food production, healthcare, the military, and IT* Security of the Internet of Things using blockchain and AI* Developing smart cities and transportation systems using machine learning and IoTAUDIENCEThe target audience of this book is professionals and researchers (artificial intelligence specialists, systems engineers, information technologists) in the fields of machine learning, IoT, and blockchain technology. KRISHNA KANT SINGH is an associate professor in the Faculty of Engineering & Technology, Jain (Deemed-to-be University), Bengaluru, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals.AKANSHA SINGH is an associate professor in the Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural networks and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes mobile computing, artificial intelligence, machine learning, digital image processing. SANJAY KUMAR SHARMA PhD is professor and Head in the Department of Electronics and Communication Engineering at KIET Group of Institutions. Dr. Sanjay Sharma has a total of 24 years of teaching and research experience. He has more than 45 publications in journals and international conferences. Preface xi1 BLOCKCHAIN AND INTERNET OF THINGS ACROSS INDUSTRIES 1Ananya Rakhra, Raghav Gupta and Akansha Singh1.1 Introduction 11.2 Insight About Industry 31.2.1 Agriculture Industry 51.2.2 Manufacturing Industry 51.2.3 Food Production Industry 61.2.4 Healthcare Industry 71.2.5 Military 71.2.6 IT Industry 81.3 What is Blockchain? 81.4 What is IoT? 111.5 Combining IoT and Blockchain 141.5.1 Agriculture Industry 151.5.2 Manufacturing Industry 171.5.3 Food Processing Industry 181.5.4 Healthcare Industry 201.5.5 Military 211.5.6 Information Technology Industry 241.6 Observing Economic Growth and Technology’s Impact 251.7 Applications of IoT and Blockchain Beyond Industries 281.8 Conclusion 32References 332 LAYERED SAFETY MODEL FOR IOT SERVICES THROUGH BLOCKCHAIN 35Anju Malik and Bharti Sharma2.1 Introduction 362.1.1 IoT Factors Impacting Security 382.2 IoT Applications 392.3 IoT Model With Communication Parameters 402.3.1 RFID (Radio Frequency Identification) 402.3.2 WSH (Wireless Sensor Network) 402.3.3 Middleware (Software and Hardware) 402.3.4 Computing Service (Cloud) 412.3.5 IoT Software 412.4 Security and Privacy in IoT Services 412.5 Blockchain Usages in IoT 442.6 Blockchain Model With Cryptography 442.6.1 Variations of Blockchain 452.7 Solution to IoT Through Blockchain 462.8 Conclusion 50References 513 INTERNET OF THINGS SECURITY USING AI AND BLOCKCHAIN 57Raghav Gupta, Ananya Rakhra and Akansha Singh3.1 Introduction 583.2 IoT and Its Application 593.3 Most Popular IoT and Their Uses 613.4 Use of IoT in Security 633.5 What is AI? 643.6 Applications of AI 653.7 AI and Security 663.8 Advantages of AI 683.9 Timeline of Blockchain 693.10 Types of Blockchain 703.11 Working of Blockchain 723.12 Advantages of Blockchain Technology 743.13 Using Blockchain Technology With IoT 743.14 IoT Security Using AI and Blockchain 763.15 AI Integrated IoT Home Monitoring System 783.16 Smart Homes With the Concept of Blockchain and AI 793.17 Smart Sensors 813.18 Authentication Using Blockchain 823.19 Banking Transactions Using Blockchain 833.20 Security Camera 843.21 Other Ways to Fight Cyber Attacks 853.22 Statistics on Cyber Attacks 883.23 Conclusion 90References 904 AMALGAMATION OF IOT, ML, AND BLOCKCHAIN IN THE HEALTHCARE REGIME 93Pratik Kumar, Piyush Yadav, Rajeev Agrawal and Krishna Kant Singh4.1 Introduction 934.2 What is Internet of Things? 954.2.1 Internet of Medical Things 974.2.2 Challenges of the IoMT 974.2.3 Use of IoT in Alzheimer Disease 994.3 Machine Learning 1004.3.1 Case 1: Multilayer Perceptron Network 1014.3.2 Case 2: Vector Support Machine 1024.3.3 Applications of the Deep Learning in the Healthcare Sector 1034.4 Role of the Blockchain in the Healthcare Field 1044.4.1 What is Blockchain Technology? 1044.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain 1054.5 Conclusion 106References 1065 APPLICATION OF MACHINE LEARNING AND IOT FOR SMART CITIES 109Nilanjana Pradhan, Ajay Shankar Singh, Shrddha Sagar, Akansha Singh and Ahmed A. Elngar5.1 Functionality of Image Analytics 1105.2 Issues Related to Security and Privacy in IoT 1125.3 Machine Learning Algorithms and Blockchain Methodologies 1145.3.1 Intrusion Detection System 1165.3.2 Deep Learning and Machine Learning Models 1185.3.3 Artificial Neural Networks 1185.3.4 Hybrid Approaches 1195.3.5 Review and Taxonomy of Machine Learning 1205.4 Machine Learning Open Source Tools for Big Data 1215.5 Approaches and Challenges of Machine Learning Algorithms in Big Data 1235.6 Conclusion 127References 1276 MACHINE LEARNING APPLICATIONS FOR IOT HEALTHCARE 129Neha Agarwal, Pushpa Singh, Narendra Singh, Krishna Kant Singh and Rohit Jain6.1 Introduction 1306.2 Machine Learning 1306.2.1 Types of Machine Learning Techniques 1316.2.1.1 Unsupervised Learning 1316.2.1.2 Supervised Learning 1316.2.1.3 Semi-Supervised Learning 1326.2.1.4 Reinforcement Learning 1326.2.2 Applications of Machine Learning 1326.2.2.1 Prognosis 1326.2.2.2 Diagnosis 1346.3 IoT in Healthcare 1356.3.1 IoT Architecture for Healthcare System 1356.3.1.1 Physical and Data Link Layer 1366.3.1.2 Network Layer 1376.3.1.3 Transport Layer 1376.3.1.4 Application Layer 1376.4 Machine Learning and IoT 1386.4.1 Application of ML and IoT in Healthcare 1386.4.1.1 Smart Diagnostic Care 1386.4.1.2 Medical Staff and Inventory Tracking 1396.4.1.3 Personal Care 1396.4.1.4 Healthcare Monitoring Device 1396.4.1.5 Chronic Disease Management 1396.5 Conclusion 140References 1407 BLOCKCHAIN FOR VEHICULAR AD HOC NETWORK AND INTELLIGENT TRANSPORTATION SYSTEM: A COMPREHENSIVE STUDY 145Raghav Sharma, Anirudhi Thanvi, Shatakshi Singh, Manish Kumar and Sunil Kumar Jangir7.1 Introduction 1467.2 Related Work 1497.3 Connected Vehicles and Intelligent Transportation System 1527.3.1 VANET 1537.3.2 Blockchain Technology and VANET 1537.4 An ITS-Oriented Blockchain Model 1557.5 Need of Blockchain 1567.5.1 Food Track and Trace 1597.5.2 Electric Vehicle Recharging 1607.5.3 Smart City and Smart Vehicles 1617.6 Implementation of Blockchain Supported Intelligent Vehicles 1647.7 Conclusion 1657.8 Future Scope 166References 1678 APPLICATIONS OF IMAGE PROCESSING IN TELERADIOLOGY FOR THE MEDICAL DATA ANALYSIS AND TRANSFER BASED ON IOT 175S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balázs Gulyás and Ajay Kumar H.8.1 Introduction 1768.2 Pre-Processing 1788.2.1 Principle of Diffusion Filtering 1788.3 Improved FCM Based on Crow Search Optimization 1838.4 Prediction-Based Lossless Compression Model 1848.5 Results and Discussion 1888.6 Conclusion 202Acknowledgment 202References 2039 INNOVATIVE IDEAS TO BUILD SMART CITIES WITH THE HELP OF MACHINE AND DEEP LEARNING AND IOT 205ShylajaVinaykumar Karatangi, Reshu Agarwal, Krishna Kant Singh and Ivan Izonin9.1 Introduction 2069.2 Related Work 2079.3 What Makes Smart Cities Smart? 2089.3.1 Intense Traffic Management 2089.3.2 Smart Parking 2099.3.3 Smart Waste Administration 2109.3.4 Smart Policing 2119.3.5 Shrewd Lighting 2119.3.6 Smart Power 2119.4 In Healthcare System 2129.5 In Homes 2139.6 In Aviation 2139.7 In Solving Social Problems 2139.8 Uses of AI-People 2149.8.1 Google Maps 2149.8.2 Ridesharing 2149.8.3 Voice-to-Text 2159.8.4 Individual Assistant 2159.9 Difficulties and Profit 2159.10 Innovations in Smart Cities 2169.11 Beyond Humans Focus 2179.12 Illustrative Arrangement 2179.13 Smart Cities with No Differentiation 2189.14 Smart City and AI 2199.15 Further Associated Technologies 2219.15.1 Model Identification 2219.15.2 Picture Recognition 2219.15.3 IoT 2229.15.4 Big Data 2239.15.5 Deep Learning 2239.16 Challenges and Issues 2249.16.1 Profound Learning Models 2249.16.2 Deep Learning Paradigms 2259.16.3 Confidentiality 2269.16.4 Information Synthesis 2269.16.5 Distributed Intelligence 2279.16.6 Restrictions of Deep Learning 2289.17 Conclusion and Future Scope 228References 229Index 233
Generating a New Reality
The emergence of artificial intelligence (AI) has brought us to the precipice of a new age where we struggle to understand what is real, from advanced CGI in movies to even faking the news. AI that was developed to understand our reality is now being used to create its own reality.In this book we look at the many AI techniques capable of generating new realities. We start with the basics of deep learning. Then we move on to autoencoders and generative adversarial networks (GANs). We explore variations of GAN to generate content. The book ends with an in-depth look at the most popular generator projects.By the end of this book you will understand the AI techniques used to generate different forms of content. You will be able to use these techniques for your own amusement or professional career to both impress and educate others around you and give you the ability to transform your own reality into something new.WHAT YOU WILL LEARN* Know the fundamentals of content generation from autoencoders to generative adversarial networks (GANs)* Explore variations of GAN* Understand the basics of other forms of content generation* Use advanced projects such as Faceswap, deepfakes, DeOldify, and StyleGAN2WHO THIS BOOK IS FORMachine learning developers and AI enthusiasts who want to understand AI content generation techniquesMICHEAL LANHAM is a proven software and tech innovator with more than 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence (AI), GIS, and machine learning (ML) applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He is an avid educator, has written more than eight books covering game development, extended reality, and AI, and teaches at meetups and other events. Micheal also likes to cook for his large family in his hometown of Calgary, Canada. Chapter 1: Deep Learning PerceptronChapter Goal: In this chapter we introduce the basics of deep learning from the perceptron to multi-layer perceptron.No of pages: 30Sub -Topics1. Understanding deep learning and supervised learning.1. Using the perceptron for supervised learning.2. Constructing a multilayer perceptron.3. Discover the basics of activation, loss, optimization and back propagation for problems of regression and classification.Chapter 2: Unleashing Autoencoders and Generative Adversarial NetworksChapter Goal: This chapter introduces the autoencoder and GAN for simple content generation. Along the way we also learn about using convolutional network layers for better feature extraction.No of pages: 30Sub - Topics1. Why we need autoencoders and how they function.2. Improving on the autoencoder with convolutional network layers.3. Generating content with the GAN.4. Explore methods for improving on the vanilla GAN.Chapter 3: Exploring the Latent SpaceChapter Goal: In this chapter we discover the latent space in AI. What it means to move through the AI latent space using variational autoencoders and conditional GANs.No of pages : 30Sub - Topics:1. Understanding variation and the variational autoencoder.2. Exploring the latent space with a VAE.3. Extending a GAN to be conditional.4. Generate interesting foods using a conditional GAN.Chapter 4: GANs, GANs and More GANsChapter Goal: In this chapter we begin uncovering the vast variations in GANs and their applications. We start with basics like the double convolution GAN and work up to the Stack and Progressive GANs.No of pages: 30Sub - Topics:1. Look at samples from the many variations of GANs.2. Setup and use a DCGAN.3. Understand how a StackGAN works.4. Work with and use a ProGAN.Chapter 5: Image to Image Translation with GANsCovers: Pix2Pix and DualGAN, side projects for understanding with ResNET and UNET, advanced network architectures for image classification/generationChapter 6: Translating Images with Cycle ConsistencyCovers: Cycle consistency loss and the CycleGAN, BiCycleGAN and StarGANChapter 7: Styling with GANsCovers: StyleGAN, Attention and the Self-attention GAN with a look at DeOldifyChapter 8: Developing DeepFakesChapter Goal: DeepFakes are taking the world by storm and in this chapter, we explore how to use a DeepFakes project. No of pages: 301. Learn how to isolate faces or other points of interest in images or video.2. Extract and replace faces from images or video.3. Use DeepFakes GAN to generate facial images based on input image.4. Put it all together and allow the user to generate their own DeepFake video.Chapter 9: Uncovering Adversarial Latent AutoencodersChapter Goal: GANs are not the only technique that allows for content manipulation and generations. In this chapter we look at the ALAE method for generating content.No of pages:1. Look at how to extend autoencoders for adversarial learning.2. Understanding how AE can be used to explore the latent space in data.3. Use ALAE to generate conditional content.4. Revisit our previous foods example and see what new foods we can generate.Chapter 10: Video Content with First Order Model MotionChapter Goal: In this chapter we explore a new technique for animating static images called First Order Model Motion. At the end of this chapter we will use this technique to create avatars for Skype or Zoom.No of pages: 301. Discover the basic of First Order Model Motion, what it is and how it works.2. Be able to apply FOMM to a number of static image datasets for various applications.3. Use the project Avatarify for generating real-time avatars from static avatars.4. Use Avatarify real-time in applications like Zoom or Skype.
How Algorithms Create and Prevent Fake News
"It's a joy to read a book by a mathematician who knows how to write. [...] There is no better guide to the strategies and stakes of this battle for the future." ---Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank. “By explaining the flaws and foibles of everything from Google search to QAnon—and by providing level-headed evaluations of efforts to fix them—Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.” —Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor of The Atlantic From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what’s real and what’s not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what’s at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics. How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information todayis filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias – which gets amplified in harmful data feedback loops. Don’t be afraid: with this book you’ll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope. What You Will Learn The ways that data labeling and storage impact machine learning and how feedback loops can occurThe history and inner-workings of YouTube’s recommendation algorithmThe state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so farThe algorithmic tools available to help with automated fact-checking and truth-detection Who This Book is For People who don’t have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work. 1. Perils of Pageview.- 2. Crafted by Computer.- 3. Deepfake Deception.- 4. Autoplay the Autocrats.- 5. Prevarication and the Polygraph.- 6. Gravitating to Google.- 7. Avarice of Advertising.- 8. Social Spread.- 9. Tools for Truth.
Machine Learning Approach for Cloud Data Analytics in IoT
Researchers and industry engineers in computer science and artificial intelligence, IT professionals, network administrators, cybersecurity experts. SACHI NANDAN MOHANTY received his PhD from IIT Kharagpur 2015 and he is now an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India. JYOTIR MOY CHATTERJEE is an assistant professor in the IT Department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. MONIKA MANGLA received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab in 2019, and is now an assistant professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India. SUNEETA SATPATHY received her PhD from Utkal University, Bhubaneswar, Odisha in 2015, and is now an associate professor in the Department of Computer Science & Engineering at College of Engineering Bhubaneswar (CoEB), Bhubaneswar, India. MS. SIRISHA POTLURI is an assistant professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India. Preface xixAcknowledgment xxiii1 MACHINE LEARNING–BASED DATA ANALYSIS 1M. Deepika and K. Kalaiselvi1.1 Introduction 11.2 Machine Learning for the Internet of Things Using Data Analysis 41.2.1 Computing Framework 61.2.2 Fog Computing 61.2.3 Edge Computing 61.2.4 Cloud Computing 71.2.5 Distributed Computing 71.3 Machine Learning Applied to Data Analysis 71.3.1 Supervised Learning Systems 81.3.2 Decision Trees 91.3.3 Decision Tree Types 91.3.4 Unsupervised Machine Learning 101.3.5 Association Rule Learning 101.3.6 Reinforcement Learning 101.4 Practical Issues in Machine Learning 111.5 Data Acquisition 121.6 Understanding the Data Formats Used in Data Analysis Applications 131.7 Data Cleaning 141.8 Data Visualization 151.9 Understanding the Data Analysis Problem-Solving Approach 151.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 161.11 Statistical Data Analysis Techniques 171.11.1 Hypothesis Testing 181.11.2 Regression Analysis 181.12 Text Analysis and Visual and Audio Analysis 181.13 Mathematical and Parallel Techniques for Data Analysis 191.13.1 Using Map-Reduce 201.13.2 Leaning Analysis 201.13.3 Market Basket Analysis 211.14 Conclusion 21References 222 MACHINE LEARNING FOR CYBER-IMMUNE IOT APPLICATIONS 25Suchismita Sahoo and Sushree Sangita Sahoo2.1 Introduction 252.2 Some Associated Impactful Terms 272.2.1 IoT 272.2.2 IoT Device 282.2.3 IoT Service 292.2.4 Internet Security 292.2.5 Data Security 302.2.6 Cyberthreats 312.2.7 Cyber Attack 312.2.8 Malware 322.2.9 Phishing 322.2.10 Ransomware 332.2.11 Spear-Phishing 332.2.12 Spyware 342.2.13 Cybercrime 342.2.14 IoT Cyber Security 352.2.15 IP Address 362.3 Cloud Rationality Representation 362.3.1 Cloud 362.3.2 Cloud Data 372.3.3 Cloud Security 382.3.4 Cloud Computing 382.4 Integration of IoT With Cloud 402.5 The Concepts That Rules Over 412.5.1 Artificial Intelligent 412.5.2 Overview of Machine Learning 412.5.2.1 Supervised Learning 412.5.2.2 Unsupervised Learning 422.5.3 Applications of Machine Learning in Cyber Security 432.5.4 Applications of Machine Learning in Cybercrime 432.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 432.5.6 Distributed Denial-of-Service 442.6 Related Work 452.7 Methodology 462.8 Discussions and Implications 482.9 Conclusion 49References 493 EMPLOYING MACHINE LEARNING APPROACHES FOR PREDICTIVE DATA ANALYTICS IN RETAIL INDUSTRY 53Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh3.1 Introduction 533.2 Related Work 553.3 Predictive Data Analytics in Retail 563.3.1 ML for Predictive Data Analytics 583.3.2 Use Cases 593.3.3 Limitations and Challenges 613.4 Proposed Model 613.4.1 Case Study 633.5 Conclusion and Future Scope 68References 694 EMERGING CLOUD COMPUTING TRENDS FOR BUSINESS TRANSFORMATION 71Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy4.1 Introduction 714.1.1 Computing Definition Cloud 724.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 734.1.3 Limitations of Cloud Computing 744.2 History of Cloud Computing 744.3 Core Attributes of Cloud Computing 754.4 Cloud Computing Models 774.4.1 Cloud Deployment Model 774.4.2 Cloud Service Model 794.5 Core Components of Cloud Computing Architecture: Hardware and Software 834.6 Factors Need to Consider for Cloud Adoption 844.6.1 Evaluating Cloud Infrastructure 844.6.2 Evaluating Cloud Provider 854.6.3 Evaluating Cloud Security 864.6.4 Evaluating Cloud Services 864.6.5 Evaluating Cloud Service Level Agreements (SLA) 874.6.6 Limitations to Cloud Adoption 874.7 Transforming Business Through Cloud 884.8 Key Emerging Trends in Cloud Computing 894.8.1 Technology Trends 904.8.2 Business Models 924.8.3 Product Transformation 924.8.4 Customer Engagement 924.8.5 Employee Empowerment 934.8.6 Data Management and Assurance 934.8.7 Digitalization 934.8.8 Building Intelligence Cloud System 934.8.9 Creating Hyper-Converged Infrastructure 944.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 944.10 Conclusion 95References 965 SECURITY OF SENSITIVE DATA IN CLOUD COMPUTING 99Kirti Wanjale, Monika Mangla and Paritosh Marathe5.1 Introduction 1005.1.1 Characteristics of Cloud Computing 1005.1.2 Deployment Models for Cloud Services 1015.1.3 Types of Cloud Delivery Models 1025.2 Data in Cloud 1025.2.1 Data Life Cycle 1035.3 Security Challenges in Cloud Computing for Data 1055.3.1 Security Challenges Related to Data at Rest 1065.3.2 Security Challenges Related to Data in Use 1075.3.3 Security Challenges Related to Data in Transit 1075.4 Cross-Cutting Issues Related to Network in Cloud 1085.5 Protection of Data 1095.6 Tighter IAM Controls 1145.7 Conclusion and Future Scope 117References 1176 CLOUD CRYPTOGRAPHY FOR CLOUD DATA ANALYTICS IN IOT 119N. Jayashri and K. Kalaiselvi6.1 Introduction 1206.2 Cloud Computing Software Security Fundamentals 1206.3 Security Management 1226.4 Cryptography Algorithms 1236.4.1 Types of Cryptography 1236.5 Secure Communications 1276.6 Identity Management and Access Control 1336.7 Autonomic Security 1376.8 Conclusion 139References 1397 ISSUES AND CHALLENGES OF CLASSICAL CRYPTOGRAPHY IN CLOUD COMPUTING 143Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul7.1 Introduction 1447.1.1 Problem Statement and Motivation 1457.1.2 Contribution 1467.2 Cryptography 1467.2.1 Cryptography Classification 1477.2.1.1 Classical Cryptography 1477.2.1.2 Homomorphic Encryption 1497.3 Security in Cloud Computing 1507.3.1 The Need for Security in Cloud Computing 1517.3.2 Challenges in Cloud Computing Security 1527.3.3 Benefits of Cloud Computing Security 1537.3.4 Literature Survey 1547.4 Classical Cryptography for Cloud Computing 1577.4.1 RSA 1577.4.2 AES 1577.4.3 DES 1587.4.4 Blowfish 1587.5 Homomorphic Cryptosystem 1587.5.1 Paillier Cryptosystem 1597.5.1.1 Additive Homomorphic Property 1597.5.2 RSA Homomorphic Cryptosystem 1607.5.2.1 Multiplicative Homomorphic Property 1607.6 Implementation 1607.7 Conclusion and Future Scope 162References 1628 CLOUD-BASED DATA ANALYTICS FOR MONITORING SMART ENVIRONMENTS 167D. Karthika8.1 Introduction 1678.2 Environmental Monitoring for Smart Buildings 1698.2.1 Smart Environments 1698.3 Smart Health 1718.3.1 Description of Solutions in General 1718.3.2 Detection of Distress 1728.3.3 Green Protection 1738.3.4 Medical Preventive/Help 1748.4 Digital Network 5G and Broadband Networks 1748.4.1 IoT-Based Smart Grid Technologies 1748.5 Emergent Smart Cities Communication Networks 1758.5.1 RFID Technologies 1778.5.2 Identifier Schemes 1778.6 Smart City IoT Platforms Analysis System 1778.7 Smart Management of Car Parking in Smart Cities 1788.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 1788.9 Virtual Integrated Storage System 1798.10 Convolutional Neural Network (CNN) 1818.10.1 IEEE 802.15.4 1828.10.2 BLE 1828.10.3 ITU-T G.9959 (Z-Wave) 1838.10.4 NFC 1838.10.5 LoRaWAN 1848.10.6 Sigfox 1848.10.7 NB-IoT 1848.10.8 PLC 1848.10.9 MS/TP 1848.11 Challenges and Issues 1858.11.1 Interoperability and Standardization 1858.11.2 Customization and Adaptation 1868.11.3 Entity Identification and Virtualization 1878.11.4 Big Data Issue in Smart Environments 1878.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 1888.13 Case Study 1898.14 Conclusion 191References 1919 PERFORMANCE METRICS FOR COMPARISON OF HEURISTICS TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING PLATFORM 195Nidhi Rajak and Ranjit Rajak9.1 Introduction 1959.2 Workflow Model 1979.3 System Computing Model 1989.4 Major Objective of Scheduling 1989.5 Task Computational Attributes for Scheduling 1989.6 Performance Metrics 2009.7 Heuristic Task Scheduling Algorithms 2019.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 2029.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 2089.7.3 As Late As Possible (ALAP) Algorithm 2139.7.4 Performance Effective Task Scheduling (PETS) Algorithm 2179.8 Performance Analysis and Results 2209.9 Conclusion 224References 22410 SMART ENVIRONMENT MONITORING MODELS USING CLOUD-BASED DATA ANALYTICS: A COMPREHENSIVE STUDY 227Pradnya S. Borkar and Reena Thakur10.1 Introduction 22810.1.1 Internet of Things 22910.1.2 Cloud Computing 23010.1.3 Environmental Monitoring 23210.2 Background and Motivation 23410.2.1 Challenges and Issues 23410.2.2 Technologies Used for Designing Cloud-Based Data Analytics 24010.2.2.1 Communication Technologies 24110.2.3 Cloud-Based Data Analysis Techniques and Models 24310.2.3.1 MapReduce for Data Analysis 24310.2.3.2 Data Analysis Workflows 24610.2.3.3 NoSQL Models 24710.2.4 Data Mining Techniques 24810.2.5 Machine Learning 25110.2.5.1 Significant Importance of Machine Learning and Its Algorithms 25310.2.6 Applications 25310.3 Conclusion 261References 26211 ADVANCEMENT OF MACHINE LEARNING AND CLOUD COMPUTING IN THE FIELD OF SMART HEALTH CARE 273Aradhana Behura, Shibani Sahu and Manas Ranjan Kabat11.1 Introduction 27411.2 Survey on Architectural WBAN 27811.3 Suggested Strategies 28011.3.1 System Overview 28011.3.2 Motivation 28111.3.3 DSCB Protocol 28111.3.3.1 Network Topology 28211.3.3.2 Starting Stage 28211.3.3.3 Cluster Evolution 28211.3.3.4 Sensed Information Stage 28311.3.3.5 Choice of Forwarder Stage 28311.3.3.6 Energy Consumption as Well as Routing Stage 28511.4 CNN-Based Image Segmentation (UNet Model) 28711.5 Emerging Trends in IoT Healthcare 29011.6 Tier Health IoT Model 29411.7 Role of IoT in Big Data Analytics 29411.8 Tier Wireless Body Area Network Architecture 29611.9 Conclusion 303References 30312 STUDY ON GREEN CLOUD COMPUTING—A REVIEW 307Meenal Agrawal and Ankita Jain12.1 Introduction 30712.2 Cloud Computing 30812.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 30812.3 Features of Cloud Computing 30912.4 Green Computing 30912.5 Green Cloud Computing 30912.6 Models of Cloud Computing 31012.7 Models of Cloud Services 31012.8 Cloud Deployment Models 31112.9 Green Cloud Architecture 31212.10 Cloud Service Providers 31212.11 Features of Green Cloud Computing 31312.12 Advantages of Green Cloud Computing 31312.13 Limitations of Green Cloud Computing 31412.14 Cloud and Sustainability Environmental 31512.15 Statistics Related to Cloud Data Centers 31512.16 The Impact of Data Centers on Environment 31512.17 Virtualization Technologies 31612.18 Literature Review 31612.19 The Main Objective 31812.20 Research Gap 31912.21 Research Methodology 31912.22 Conclusion and Suggestions 32012.23 Scope for Further Research 320References 32113 INTELLIGENT RECLAMATION OF PLANTAE AFFLICTION DISEASE 323Reshma Banu, G.F Ali Ahammed and Ayesha Taranum13.1 Introduction 32413.2 Existing System 32713.3 Proposed System 32713.4 Objectives of the Concept 32813.5 Operational Requirements 32813.6 Non-Operational Requirements 32913.7 Depiction Design Description 33013.8 System Architecture 33013.8.1 Module Characteristics 33113.8.2 Convolutional Neural System 33213.8.3 User Application 33213.9 Design Diagrams 33313.9.1 High-Level Design 33313.9.2 Low-Level Design 33313.9.3 Test Cases 33513.10 Comparison and Screenshot 33513.11 Conclusion 342References 34214 PREDICTION OF STOCK MARKET USING MACHINE LEARNING–BASED DATA ANALYTICS 347Maheswari P. and Jaya A.14.1 Introduction of Stock Market 34814.1.1 Impact of Stock Prices 34914.2 Related Works 35014.3 Financial Prediction Systems Framework 35214.3.1 Conceptual Financial Prediction Systems 35214.3.2 Framework of Financial Prediction Systems Using Machine Learning 35314.3.2.1 Algorithm to Predicting the Closing Price of the Given Stock Data Using Linear Regression 35514.3.3 Framework of Financial Prediction Systems Using Deep Learning 35514.3.3.1 Algorithm to Predict the Closing Price of the Given Stock Using Long Short-Term Memory 35614.4 Implementation and Discussion of Result 35714.4.1 Pharmaceutical Sector 35714.4.1.1 Cipla Limited 35714.4.1.2 Torrent Pharmaceuticals Limited 35914.4.2 Banking Sector 35914.4.2.1 ICICI Bank 35914.4.2.2 State Bank of India 35914.4.3 Fast-Moving Consumer Goods Sector 36214.4.3.1 ITC 36314.4.3.2 Hindustan Unilever Limited 36314.4.4 Power Sector 36314.4.4.1 Adani Power Limited 36314.4.4.2 Power Grid Corporation of India Limited 36414.4.5 Automobiles Sector 36814.4.5.1 Mahindra & Mahindra Limited 36814.4.5.2 Maruti Suzuki India Limited 36814.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 36814.5 Conclusion 37114.5.1 Future Enhancement 372References 372Web Citations 37315 PEHCHAAN: ANALYSIS OF THE ‘AADHAR DATASET’ TO FACILITATE A SMOOTH AND EFFICIENT CONDUCT OF THE UPCOMING NPR 375Soumyadev Mukherjee, Harshit Anand, Nishan Acharya, Subham Char, Pritam Ghosh and MinakhiRout15.1 Introduction 37615.2 Basic Concepts 37715.3 Study of Literature Survey and Technology 38015.4 Proposed Model 38115.5 Implementation and Results 38315.6 Conclusion 389References 38916 DEEP LEARNING APPROACH FOR RESOURCE OPTIMIZATION IN BLOCKCHAIN, CELLULAR NETWORKS, AND IOT: OPEN CHALLENGES AND CURRENT SOLUTIONS 391Upinder Kaur and Shalu16.1 Introduction 39216.1.1 Aim 39316.1.2 Research Contribution 39516.1.3 Organization 39616.2 Background 39616.2.1 Blockchain 39716.2.2 Internet of Things (IoT) 39816.2.3 5G Future Generation Cellular Networks 39816.2.4 Machine Learning and Deep Learning Techniques 39916.2.5 Deep Reinforcement Learning 39916.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 40116.3.1 Resource Management in Blockchain for 5G Cellular Networks 40216.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 40216.4 Future Research Challenges 41316.4.1 Blockchain Technology 41316.4.1.1 Scalability 41416.4.1.2 Efficient Consensus Protocols 41516.4.1.3 Lack of Skills and Experts 41516.4.2 IoT Networks 41616.4.2.1 Heterogeneity of IoT and 5G Data 41616.4.2.2 Scalability Issues 41616.4.2.3 Security and Privacy Issues 41616.4.3 5G Future Generation Networks 41616.4.3.1 Heterogeneity 41616.4.3.2 Security and Privacy 41716.4.3.3 Resource Utilization 41716.4.4 Machine Learning and Deep Learning 41716.4.4.1 Interpretability 41816.4.4.2 Training Cost for ML and DRL Techniques 41816.4.4.3 Lack of Availability of Data Sets 41816.4.4.4 Avalanche Effect for DRL Approach 41916.4.5 General Issues 41916.4.5.1 Security and Privacy Issues 41916.4.5.2 Storage 41916.4.5.3 Reliability 42016.4.5.4 Multitasking Approach 42016.5 Conclusion and Discussion 420References 42217 UNSUPERVISED LEARNING IN ACCORDANCE WITH NEW ASPECTS OF ARTIFICIAL INTELLIGENCE 429Riya Sharma, Komal Saxena and Ajay Rana17.1 Introduction 43017.2 Applications of Machine Learning in Data Management Possibilities 43117.2.1 Terminology of Basic Machine Learning 43217.2.2 Rules Based on Machine Learning 43417.2.3 Unsupervised vs. Supervised Methodology 43417.3 Solutions to Improve Unsupervised Learning Using Machine Learning 43617.3.1 Insufficiency of Labeled Data 43617.3.2 Overfitting 43717.3.3 A Closer Look Into Unsupervised Algorithms 43717.3.3.1 Reducing Dimensionally 43717.3.3.2 Principal Component Analysis 43817.3.4 Singular Value Decomposition (SVD) 43917.3.4.1 Random Projection 43917.3.4.2 Isomax 43917.3.5 Dictionary Learning 43917.3.6 The Latent Dirichlet Allocation 44017.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 44017.4.1 TensorFlow 44117.4.2 Keras 44117.4.3 Scikit-Learn 44117.4.4 Microsoft Cognitive Toolkit 44217.4.5 Theano 44217.4.6 Caffe 44217.4.7 Torch 44217.5 Applications of Unsupervised Learning 44317.5.1 Regulation of Digital Data 44317.5.2 Machine Learning in Voice Assistance 44317.5.3 For Effective Marketing 44417.5.4 Advancement of Cyber Security 44417.5.5 Faster Computing Power 44417.5.6 The Endnote 44517.6 Applications Using Machine Learning Algos 44517.6.1 Linear Regression 44517.6.2 Logistic Regression 44617.6.3 Decision Tree 44617.6.4 Support Vector Machine (SVM) 44617.6.5 Naive Bayes 44617.6.6 K-Nearest Neighbors 44717.6.7 K-Means 44717.6.8 Random Forest 44717.6.9 Dimensionality Reduction Algorithms 44817.6.10 Gradient Boosting Algorithms 448References 44918 PREDICTIVE MODELING OF ANTHROPOMORPHIC GAMIFYING BLOCKCHAIN-ENABLED TRANSITIONAL HEALTHCARE SYSTEM 461Deepa Kumari, B.S.A.S. Rajita, Medindrao Raja Sekhar, Ritika Garg and Subhrakanta Panda18.1 Introduction 46218.1.1 Transitional Healthcare Services and Their Challenges 46218.2 Gamification in Transitional Healthcare: A New Model 46318.2.1 Anthropomorphic Interface With Gamification 46418.2.2 Gamification in Blockchain 46518.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 46618.3 Existing Related Work 46818.4 The Framework 47818.4.1 Health Player 47918.4.2 Data Collection 48018.4.3 Anthropomorphic Gamification Layers 48018.4.4 Ethereum 48018.4.4.1 Ethereum-Based Smart Contracts for Healthcare 48118.4.4.2 Installation of Ethereum Smart Contract 48118.4.5 Reward Model 48218.4.6 Predictive Models 48218.5 Implementation 48318.5.1 Methodology 48318.5.2 Result Analysis 48418.5.3 Threats to the Validity 48618.6 Conclusion 487References 487Index 491
Raspberry Pi Kinderleicht
Das Buch "Raspberry Pi Kinderleicht" zeigt, wie der Einplatinencomputer Raspberry Pi 4 Mod. B eingerichtet und genutzt werden kann. Die neue Version des Raspberry Pi ist benutzerfreundlicher und leistungsfähiger als je zuvor. Das kleine Gerät lässt sich einfacher als bisher wie ein Desktop Computer zum Surfen im Internet, als Dateiserver im Netzwerk, sowie als Medienplayer einsetzen. Der für Einsteiger geeignete Technik Ratgeber erklärt anschaulich Grundlagen und zeigt mehrere Möglichkeiten auf.
Kostenoptimierte Anwendungsentwicklung
Das Buch soll keine wissenschaftliche Abhandlungen über Theorien der Informatik oder der Organisationslehre liefern, sondern einen praxiserprobten Weg aufzeigen, wie man durch eine stufenweise, kostenoptimierte und risikoarme Erneuerung der bisherigen Anwendungen eine reaktionsfähige und kontinuierlich erneuerbare Anwendungslandschaft aufbauen und dadurch zu einer hochflexiblen und effizienten Anwendungslandschaft kommen kann.Mit den beschriebenen Methoden können die Kosten der Anwendungsentwicklung um die Hälfte reduziert werden, wenn alle Datenverwaltungsfunktionen in anwendungsneutrale Datentabellen ausgelagert und die Fachfunktionen als selbstständig ablauffähige Geschäftsfall-Apps mit vorgefertigten Softwarekomponenten bedarfsgerecht konfiguriert werden.Dadurch kann eine flexible IT-Unterstützung für die Abarbeitung aller Arbeitsvorgänge entlang den bereichs- und unternehmensübergreifenden Geschäftsprozessen sichergestellt werden, die schnell an die kurzfristigen Veränderungen des dynamischen und komplexen Geschäftsumfelds angepasst werden können. HEINZ APPENZELLER wurde in Esslingen geboren. Nach der Ausbildung zum Industriekaufmann hat er praktische Erfahrungen bei der Prozess- und Systemgestaltung in allen Unternehmensbereichen eines Industrieunternehmens erworben. 1968 wurde er von einem Konzern für den Aufbau und die Leitung des Bereichs Organisation und Datenverarbeitung eines großen Industrieunternehmens mit einem breiten in- und ausländischen Vertriebsnetz und mehreren Tochterfirmen in Brasilien berufen. In dieser Zeit war er zusätzlich auch für den Aufbau und die Leitung einer IT-Tochterfirma mit zuletzt ca. 1000 Beschäftigten verantwortlich.
The Art of Attack
TAKE ON THE PERSPECTIVE OF AN ATTACKER WITH THIS INSIGHTFUL NEW RESOURCE FOR ETHICAL HACKERS, PENTESTERS, AND SOCIAL ENGINEERSIn The Art of Attack: Attacker Mindset for Security Professionals, experienced physical pentester and social engineer Maxie Reynolds untangles the threads of a useful, sometimes dangerous, mentality. The book shows ethical hackers, social engineers, and pentesters what an attacker mindset is and how to use it to their advantage. Adopting this mindset will result in the improvement of security, offensively and defensively, by allowing you to see your environment objectively through the eyes of an attacker.The book shows you the laws of the mindset and the techniques attackers use, from persistence to "start with the end" strategies and non-linear thinking, that make them so dangerous. You'll discover:* A variety of attacker strategies, including approaches, processes, reconnaissance, privilege escalation, redundant access, and escape techniques* The unique tells and signs of an attack and how to avoid becoming a victim of one* What the science of psychology tells us about amygdala hijacking and other tendencies that you need to protect againstPerfect for red teams, social engineers, pentesters, and ethical hackers seeking to fortify and harden their systems and the systems of their clients, The Art of Attack is an invaluable resource for anyone in the technology security space seeking a one-stop resource that puts them in the mind of an attacker.About the Author vAcknowledgments viiIntroduction xvPART I: THE ATTACKER MINDSET 1CHAPTER 1: WHAT IS THE ATTACKER MINDSET? 3Using the Mindset 6The Attacker and the Mindset 9AMs Is a Needed Set of Skills 11A Quick Note on Scope 13Summary 16Key Message 16CHAPTER 2: OFFENSIVE VS. DEFENSIVE ATTACKER MINDSET 17The Offensive Attacker Mindset 20Comfort and Risk 22Planning Pressure and Mental Agility 23Emergency Conditioning 26Defensive Attacker Mindset 31Consistency and Regulation 31Anxiety Control 32Recovery, Distraction, and Maintenance 34OAMs and DAMs Come Together 35Summary 35Key Message 36CHAPTER 3: THE ATTACKER MINDSET FRAMEWORK 37Development 39Phase 1 43Phase 2 47Application 48Preloading 51“Right Time, Right Place” Preload 51Ethics 52Intellectual Ethics 53Reactionary Ethics 53Social Engineering and Security 57Social Engineering vs. AMs 59Summary 60Key Message 60PART II: THE LAWS AND SKILLS 63CHAPTER 4: THE LAWS 65Law 1: Start with the End in Mind 65End to Start Questions 66Robbing a Bank 68Bringing It All together 70The Start of the End 71Clarity 71Efficiency 72The Objective 72How to Begin with the End in Mind 73Law 2: Gather, Weaponize, and Leverage Information 75Law 3: Never Break Pretext 77Law 4: Every Move Made Benefits the Objective 80Summary 81Key Message 82CHAPTER 5: CURIOSITY, PERSISTENCE, AND AGILITY 83Curiosity 86The Exercise: Part 1 87The Exercise: Part 2 89Persistence 92Skills and Common Sense 95Professional Common Sense 95Summary 98Key Message 98CHAPTER 6: INFORMATION PROCESSING: OBSERVATION AND THINKING TECHNIQUES 99Your Brain vs. Your Observation 102Observation vs. Heuristics 107Heuristics 107Behold Linda 108Observation vs. Intuition 109Using Reasoning and Logic 112Observing People 114Observation Exercise 116AMs and Observation 122Tying It All Together 123Critical and Nonlinear Thinking 124Vector vs. Arc 127Education and Critical Thinking 128Workplace Critical Thinking 128Critical Thinking and Other Psychological Constructs 129Critical Thinking Skills 130Nonlinear Thinking 131Tying Them Together 132Summary 133Key Message 134CHAPTER 7: INFORMATION PROCESSING IN PRACTICE 135Reconnaissance 136Recon: Passive 145Recon: Active 149Osint 150OSINT Over the Years 150Intel Types 153Alternative Data in OSINT 154Signal vs. Noise 155Weaponizing of Information 158Tying Back to the Objective 160Summary 170Key Message 170PART III: TOOLS AND ANATOMY 171CHAPTER 8: ATTACK STRATEGY 173Attacks in Action 175Strategic Environment 177The Necessity of Engagement and Winning 179The Attack Surface 183Vulnerabilities 183AMs Applied to the Attack Vectors 184Phishing 184Mass Phish 185Spearphish 186Whaling 187Vishing 190Smishing/Smshing 195Impersonation 196Physical 199Back to the Manhattan Bank 200Summary 203Key Message 203CHAPTER 9: PSYCHOLOGY IN ATTACKS 205Setting The Scene: Why Psychology Matters 205Ego Suspension, Humility & Asking for Help 210Humility 215Asking for Help 216Introducing the Target- Attacker Window Model 217Four TAWM Regions 218Target Psychology 221Optimism Bias 225Confirmation Bias and Motivated Reasoning 228Framing Effect 231Thin- Slice Assessments 233Default to Truth 236Summary 239Key Message 239PART IV: AFTER AMS 241CHAPTER 10: STAYING PROTECTED— THE INDIVIDUAL 243Attacker Mindset for Ordinary People 243Behavioral Security 246Amygdala Hijacking 250Analyze Your Attack Surface 252Summary 256Key Message 256CHAPTER 11: STAYING PROTECTED— THE BUSINESS 257Indicators of Attack 258Nontechnical Measures 258Testing and Red Teams 261Survivorship Bias 261The Complex Policy 263Protection 264Antifragile 264The Full Spectrum of Crises 266AMs on the Spectrum 268Final Thoughts 269Summary 270Key Message 271Index 273
Datenvisualisierung mit Tableau
* VISUELLE DATENANALYSE LEICHT GEMACHT: VON DEN ERSTEN BALKENDIAGRAMMEN ÜBER CLUSTER UND TRENDLINIEN BIS ZU GEOGRAFISCHEN ANALYSEN AUF LANDKARTEN* ERHALTEN SIE AUSSAGEFÄHIGE PROGNOSEN DURCH VORAUSSCHAUENDE ZUKUNFTSANALYSEN* ERSTELLEN UND TEILEN SIE INTERAKTIVE DASHBOARDS UND ÜBERSICHTLICHE INFOGRAFIKENAlexander Loth zeigt Ihnen in diesem Buch, wie Sie Ihre Daten ganz einfach visuell darstellen und analysieren. So können Sie selbst komplexe Datenstrukturen besser verstehen und daraus gewonnene Erkenntnisse effektiv kommunizieren.Der Autor erläutert Schritt für Schritt die grundlegenden Funktionen von Tableau. Anhand von Fallbeispielen lernen Sie praxisnah, welche Visualisierungsmöglichkeiten wann sinnvoll sind. Ferner zeigt er Anwendungen, die weit über gängige Standardanalysen hinausreichen, und geht auf Funktionen ein, die selbst erfahrenen Nutzern oft nicht hinlänglich bekannt sind. Sie erhalten außerdem zahlreiche Hinweise und Tipps, die Ihnen das Arbeiten mit Tableau merklich erleichtern. So können Sie zukünftig Ihre eigenen Daten bestmöglich visualisieren und analysieren.Das Buch richtet sich an:* alle, die Zugang zu Daten haben und diese verstehen möchten,* Führungskräfte, die Entscheidungen auf der Grundlage von Daten treffen,* Analysten und Entwickler, die Visualisierungen und Dashboards erstellen,* angehende Data ScientistsSie brauchen weder Tableau-Kenntnisse noch besondere mathematische Fähigkeiten oder Programmiererfahrung, um mit diesem Buch effektiv arbeiten zu können. Es eignet sich daher auch für Einsteiger und Anwender, die sich dem Thema Datenvisualisierung und -analyse praxisbezogen nähern möchten.AUS DEM INHALT:* Einführung und erste Schritte in Tableau* Datenquellen in Tableau anlegen* Visualisierungen erstellen* Aggregationen, Berechnungen und Parameter* Tabellenberechnungen und Detailgenauigkeitsausdrücke* Mit Karten zu weitreichenden Erkenntnissen* Tiefgehende Analysen mit Trends, Prognosen, Clustern und Verteilungen* Interaktive Dashboards* Teilen Sie Ihre Analysen mit Ihrem Unternehmen oder der ganzen Welt* Daten integrieren und vorbereiten mit Tableau Prep BuilderZUR NEUAUFLAGE:Die zweite Auflage wurde erheblich überarbeitet und erweitert. Sie enthält zusätzliche Unterkapitel (z.B. zum neuen Datenmodell mit logischer und physischer Ebene, zu Schaltflächen, Dashboard Starter und zu fortgeschrittenen Strategien zur Datenakquisition) sowie viele Erweiterungen, Tipps und Aktualisierungen. Viele Kapitel schließen nun zudem mit vertiefenden Links zu häufig gestellten Fragen ab. Die zugrunde liegende Version von Tableau Desktop ist 2021.2.Alexander Loth kommt aus der datenintensiven Kernforschung und arbeitet seit 2015 für Tableau. Er hat unter anderem einen MBA von der Frankfurt School of Finance & Management und war als Data Scientist am CERN, sowie in der Software-Entwicklung bei SAP tätig. In den letzten Jahren hat er sich auf die Bereiche Digital Transformation, Big Data, Machine Learning und Business Analytics im Umfeld Finanzen und Crypto Assets spezialisiert und berät Unternehmen beim Aufbau von datenzentrischen Strategien. Alexander Loth wurde von der BNN als 'der Daten-Verarbeiter' bezeichnet. Er gehört laut Brandwatch zu den einflussreichsten Autoren 'rund um das Thema Digitale Transformation'.
Decoupled Django
Apply decoupling patterns, properly test a decoupled project, and integrate a Django API with React, and Vue.js. This book covers decoupled architectures in Django, with Django REST framework and GraphQL. With practical and simple examples, you’ll see firsthand how, why, and when to decouple a Django project.Starting with an introduction to decoupled architectures versus monoliths, with a strong focus on the modern JavaScript scene, you’ll implement REST and GraphQL APIs with Django, add authentication to a decoupled project, and test the backend. You’ll then review functional testing for JavaScript frontends with Cypress. You will also learn how to integrate GraphQL in a Django project, with a focus on the benefits and drawbacks of this new query language.By the end of this book, you will be able to discern and apply all the different decoupling strategies to any Django project, regardless of its size.WHAT YOU'LL LEARN* Choose the right approach for decoupling a Django project* Build REST APIs with Django and a Django REST framework* Integrate Vue.js and GraphQL in a Django project* Consume a Django REST API with Next.js* Test decoupled Django projectsWHO THIS BOOK IS FORSoftware developers with basic Django skills keen to learn decoupled architectures with Django. JavaScript developers interested in learning full-stack development and decoupled architectures with Django.Valentino Gagliardi is a freelance consultant with a wealth of experience in the IT industry. He spent the last 8 years as a front-end consultant, providing advice and help, coaching and training on JavaScript and React. He worked as an instructor for multiple training agencies around the country, running in-person workshops and creating learning paths for aspiring developers. He loves to share his knowledge on his blog with tutorials reaching over 100,000 monthly visits. An avid Django user, he is active in the Python community as a speaker, and as a coach for Django Girls.Chapter 1: Introduction to the decoupled world.Chapter Goal: Introduce the reader to terminology and structure of a decoupled architecture.No of pages: 16Sub -Topics1. A review of the concept of monolithic applications vs decoupled applications.2. What is decoupling?3. Why and when to decouple?4. A brief introduction to REST.5. A brief introduction to GraphQL.Chapter 2: JavaScript meets django.Chapter Goal: Introduce the reader to the modern JavaScript scene, help the reader understand how modern frontend tools fit within Django.No of pages: 13Sub -Topics1. An overview of modern JavaScript.2. An overview of JavaScript and Django in production setups.3. A review of the most popular frontend libraries: Vue, React, Next.js, and the differences between them.Chapter 3: Modern Django and Django REST Framework.Chapter goal: Introduce the reader to intermediate Django concepts, and Django REST framework.No of pages: 11Sub -Topics1. A brief introduction to Django REST framework and how it fits within a Django project, compared to the basic Django building blocks (MVT architecture, forms, models, views).2. An introduction to ASGI and async Django.Chapter 4: Advantages and disadvantages of a decoupled architecture.Chapter Goal: Help the reader make an informed choice by outlining advantages and disadvantages of a decoupled architecture.No of pages: 12Sub -Topics1. Why and when to decouple?2. An overview of the various approaches for decoupling a Django project. How to choose between the various approaches.3. Advantages of decoupling a Django project.4. Disadvantages of decoupling a Django project.Chapter 5: Setting up a Django project.Chapter Goal: Help the reader to prepare a Django project.No of pages: 9Sub -Topics1. How to split setting files.2. How to configure Django to use environment variables.3. How to run Django under ASGI.Chapter 6: Decoupled Django with Django REST Framework.Chapter Goal: Help the reader understand how to decouple a Django project with Django REST framework.No of pages: 31Sub -Topics1. How to install and enable Django REST framework.2. Django REST framework serializers.3. How to create API endpoint with Django REST framework.4. Django REST relationships.5. Working with Vue.js in Django.Chapter 7: API security, and deploymentChapter Goal: Help the reader secure and deploy a decoupled Django project.No of pages: 23Sub -Topics1. Django and Django REST hardening2. Deploying a decoupled Django projectChapter 8: Django REST meets Next.js.Chapter Goal: Help the reader pair a Django REST project with Next.js, the React framework.No of pages: 24Sub -Topics1. Django as a headless CMS2. A reintroduction to React and its ecosystem3. Working with Next.js and Django RESTChapter 9: Testing in a Decoupled World.Chapter Goal: Teaches the reader how to test a decoupled Django REST project and a JavaScript frontend.No of pages: 22Sub -Topics1. A brief introduction to functional and unit testing.2. Testing the frontend with Cypress3. Testing Django REST framework and DjangoChapter 10: Authentication and authorization Django REST framework.Chapter Goal: Help the reader understand how to set up authentication and authorization in a decoupled Django project.No of pages: 21Sub -Topics1. A review of the most important authentication mechanisms in Django and Django REST framework2. What is token based authentication? What is JWT? JWT drawbacks3. Using session-based authentication for single-page apps4. How to handle authentication in the frontendChapter 11: GraphQL in Django with Ariadne.Chapter Goal: Help the reader understand what GraphQL is and how it fits into the Python/Django landscape.No of pages: 39Sub -Topics1. Creating a GraphQL schema in Ariadne2. Working with resolvers3. Implementing mutations4. Connecting React to a GraphQL backendChapter 12: GraphQL in Django with Strawberry.Chapter Goal: Help the reader understand in practice how to decouple a Django project with GraphQL and Strawberry.No of pages: 30 (estimated)Sub -Topics1. Creating a GraphQL schema in Straberry2. Working with resolvers3. Implementing mutations in the frontend