Computer und IT
Artificial Intelligence Programming with Python
A HANDS-ON ROADMAP TO USING PYTHON FOR ARTIFICIAL INTELLIGENCE PROGRAMMINGIn Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes:* Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning* Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning* Practical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development. PERRY XIAO, PHD, is Professor and Course Director of London South Bank University. He holds his doctorate in photophysics and is Director and co-Founder of Biox Systems Ltd., a university spin-out company that designs and manufactures the AquaFlux and Epsilon Permittivity Imaging system.Preface xxiiiPART I INTRODUCTIONCHAPTER 1 INTRODUCTION TO AI 31.1 What Is AI? 31.2 The History of AI 51.3 AI Hypes and AI Winters 91.4 The Types of AI 111.5 Edge AI and Cloud AI 121.6 Key Moments of AI 141.7 The State of AI 171.8 AI Resources 191.9 Summary 211.10 Chapter Review Questions 22CHAPTER 2 AI DEVELOPMENT TOOLS 232.1 AI Hardware Tools 232.2 AI Software Tools 242.3 Introduction to Python 272.4 Python Development Environments 302.4 Getting Started with Python 342.5 AI Datasets 452.6 Python AI Frameworks 472.7 Summary 492.8 Chapter Review Questions 50PART II MACHINE LEARNING AND DEEP LEARNINGCHAPTER 3 MACHINE LEARNING 533.1 Introduction 533.2 Supervised Learning: Classifications 55Scikit-Learn Datasets 56Support Vector Machines 56Naive Bayes 67Linear Discriminant Analysis 69Principal Component Analysis 70Decision Tree 73Random Forest 76K-Nearest Neighbors 77Neural Networks 783.3 Supervised Learning: Regressions 803.4 Unsupervised Learning 89K-means Clustering 893.5 Semi-supervised Learning 913.6 Reinforcement Learning 93Q-Learning 953.7 Ensemble Learning 1023.8 AutoML 1063.9 PyCaret 1093.10 LazyPredict 1113.11 Summary 1153.12 Chapter Review Questions 116CHAPTER 4 DEEP LEARNING 1174.1 Introduction 1174.2 Artificial Neural Networks 1204.3 Convolutional Neural Networks 1254.3.1 LeNet, AlexNet, GoogLeNet 1294.3.2 VGG, ResNet, DenseNet, MobileNet, EffecientNet, and YOLO 1404.3.3 U-Net 1524.3.4 AutoEncoder 1574.3.5 Siamese Neural Networks 1614.3.6 Capsule Networks 1634.3.7 CNN Layers Visualization 1654.4 Recurrent Neural Networks 1734.4.1 Vanilla RNNs 1754.4.2 Long-Short Term Memory 1764.4.3 Natural Language Processing and Python Natural Language Toolkit 1834.5 Transformers 1874.5.1 BERT and ALBERT 1874.5.2 GPT-3 1894.5.3 Switch Transformers 1904.6 Graph Neural Networks 1914.6.1 SuperGLUE 1924.7 Bayesian Neural Networks 1924.8 Meta Learning 1954.9 Summary 1974.10 Chapter Review Questions 197PART III AI APPLICATIONSCHAPTER 5 IMAGE CLASSIFICATION 2015.1 Introduction 2015.2 Classification with Pre-trained Models 2035.3 Classification with Custom Trained Models: Transfer Learning 2095.4 Cancer/Disease Detection 2275.4.1 Skin Cancer Image Classification 2275.4.2 Retinopathy Classification 2295.4.3 Chest X-Ray Classification 2305.4.5 Brain Tumor MRI Image Classification 2315.4.5 RSNA Intracranial Hemorrhage Detection 2315.5 Federated Learning for Image Classification 2325.6 Web-Based Image Classification 2335.6.1 Streamlit Image File Classification 2345.6.2 Streamlit Webcam Image Classification 2425.6.3 Streamlit from GitHub 2485.6.4 Streamlit Deployment 2495.7 Image Processing 2505.7.1 Image Stitching 2505.7.2 Image Inpainting 2535.7.3 Image Coloring 2555.7.4 Image Super Resolution 2565.7.5 Gabor Filter 2575.8 Summary 2625.9 Chapter Review Questions 263CHAPTER 6 FACE DETECTION AND FACE RECOGNITION 2656.1 Introduction 2656.2 Face Detection and Face Landmarks 2666.3 Face Recognition 2796.3.1 Face Recognition with Face_Recognition 2796.3.2 Face Recognition with OpenCV 2856.3.3 GUI-Based Face Recognition System 288Other GUI Development Libraries 3006.3.4 Google FaceNet 3016.4 Age, Gender, and Emotion Detection 3016.4.1 DeepFace 3026.4.2 TCS-HumAIn-2019 3056.5 Face Swap 3096.5.1 Face_Recognition and OpenCV 3106.5.2 Simple_Faceswap 3156.5.3 DeepFaceLab 3226.6 Face Detection Web Apps 3226.7 How to Defeat Face Recognition 3346.8 Summary 3356.9 Chapter Review Questions 336CHAPTER 7 OBJECT DETECTIONS AND IMAGE SEGMENTATIONS 3377.1 Introduction 337R-CNN Family 338YOLO 339SSD 3407.2 Object Detections with Pretrained Models 3417.2.1 Object Detection with OpenCV 3417.2.2 Object Detection with YOLO 3467.2.3 Object Detection with OpenCV and Deep Learning 3517.2.4 Object Detection with TensorFlow, ImageAI, Mask RNN, PixelLib, Gluon 354TensorFlow Object Detection 354ImageAI Object Detection 355MaskRCNN Object Detection 357Gluon Object Detection 3637.2.5 Object Detection with Colab OpenCV 3647.3 Object Detections with Custom Trained Models 3697.3.1 OpenCV 369Step 1 369Step 2 369Step 3 369Step 4 370Step 5 3717.3.2 YOLO 372Step 1 372Step 2 372Step 3 373Step 4 375Step 5 3757.3.3 TensorFlow, Gluon, and ImageAI 376TensorFlow 376Gluon 376ImageAI 3767.4 Object Tracking 3777.4.1 Object Size and Distance Detection 3777.4.2 Object Tracking with OpenCV 382Single Object Tracking with OpenCV 382Multiple Object Tracking with OpenCV 3847.4.2 Object Tracking with YOLOv4 and DeepSORT 3867.4.3 Object Tracking with Gluon 3897.5 Image Segmentation 3897.5.1 Image Semantic Segmentation and Image Instance Segmentation 390PexelLib 390Detectron2 394Gluon CV 3947.5.2 K-means Clustering Image Segmentation 3947.5.3 Watershed Image Segmentation 3967.6 Background Removal 4057.6.1 Background Removal with OpenCV 4057.6.2 Background Removal with PaddlePaddle 4237.6.3 Background Removal with PixelLib 4257.7 Depth Estimation 4267.7.1 Depth Estimation from a Single Image 4267.7.2 Depth Estimation from Stereo Images 4287.8 Augmented Reality 4307.9 Summary 4317.10 Chapter Review Questions 431CHAPTER 8 POSE DETECTION 4338.1 Introduction 4338.2 Hand Gesture Detection 4348.2.1 OpenCV 4348.2.2 TensorFlow.js 4528.3 Sign Language Detection 4538.4 Body Pose Detection 4548.4.1 OpenPose 4548.4.2 OpenCV 4558.4.3 Gluon 4558.4.4 PoseNet 4568.4.5 ML5JS 4578.4.6 MediaPipe 4598.5 Human Activity Recognition 461ActionAI 461Gluon Action Detection 461Accelerometer Data HAR 4618.6 Summary 4648.7 Chapter Review Questions 464CHAPTER 9 GAN AND NEURAL-STYLE TRANSFER 4659.1 Introduction 4659.2 Generative Adversarial Network 4669.2.1 CycleGAN 4679.2.2 StyleGAN 4699.2.3 Pix2Pix 4749.2.4 PULSE 4759.2.5 Image Super-Resolution 4759.2.6 2D to 3D 4789.3 Neural-Style Transfer 4799.4 Adversarial Machine Learning 4849.5 Music Generation 4869.6 Summary 4899.7 Chapter Review Questions 489CHAPTER 10 NATURAL LANGUAGE PROCESSING 49110.1 Introduction 49110.1.1 Natural Language Toolkit 49210.1.2 spaCy 49310.1.3 Gensim 49310.1.4 TextBlob 49410.2 Text Summarization 49410.3 Text Sentiment Analysis 50810.4 Text/Poem Generation 51010.5.1 Text to Speech 51510.5.2 Speech to Text 51710.6 Machine Translation 52210.7 Optical Character Recognition 52310.8 QR Code 52410.9 PDF and DOCX Files 52710.10 Chatbots and Question Answering 53010.10.1 ChatterBot 53010.10.2 Transformers 53210.10.3 J.A.R.V.I.S. 53410.10.4 Chatbot Resources and Examples 54010.11 Summary 54110.12 Chapter Review Questions 542CHAPTER 11 DATA ANALYSIS 54311.1 Introduction 54311.2 Regression 54411.2.1 Linear Regression 54511.2.2 Support Vector Regression 54711.2.3 Partial Least Squares Regression 55411.3 Time-Series Analysis 56311.3.1 Stock Price Data 56311.3.2 Stock Price Prediction 565Streamlit Stock Price Web App 56911.3.4 Seasonal Trend Analysis 57311.3.5 Sound Analysis 57611.4 Predictive Maintenance Analysis 58011.5 Anomaly Detection and Fraud Detection 58411.5.1 Numenta Anomaly Detection 58411.5.2 Textile Defect Detection 58411.5.3 Healthcare Fraud Detection 58411.5.4 Santander Customer Transaction Prediction 58411.6 COVID-19 Data Visualization and Analysis 58511.7 KerasClassifier and KerasRegressor 58811.7.1 KerasClassifier 58911.7.2 KerasRegressor 59311.8 SQL and NoSQL Databases 59911.9 Immutable Database 60811.9.1 Immudb 60811.9.2 Amazon Quantum Ledger Database 60911.10 Summary 61011.11 Chapter Review Questions 610CHAPTER 12 ADVANCED AI COMPUTING 61312.1 Introduction 61312.2 AI with Graphics Processing Unit 61412.3 AI with Tensor Processing Unit 61812.4 AI with Intelligence Processing Unit 62112.5 AI with Cloud Computing 62212.5.1 Amazon AWS 62312.5.2 Microsoft Azure 62412.5.3 Google Cloud Platform 62512.5.4 Comparison of AWS, Azure, and GCP 62512.6 Web-Based AI 62912.6.1 Django 62912.6.2 Flask 62912.6.3 Streamlit 63412.6.4 Other Libraries 63412.7 Packaging the Code 635Pyinstaller 635Nbconvert 635Py2Exe 636Py2app 636Auto-Py-To-Exe 636cx_Freeze 637Cython 638Kubernetes 639Docker 642PIP 64712.8 AI with Edge Computing 64712.8.1 Google Coral 64712.8.2 TinyML 64812.8.3 Raspberry Pi 64912.9 Create a Mobile AI App 65112.10 Quantum AI 65312.11 Summary 65712.12 Chapter Review Questions 657Index 659
Windows 11 Made Easy
Get started with Windows 11. This book shows you how to set up and personalize your PC in order to get the best experience from your documents, photos, and your time online. The book introduces you to the new desktop, start menu, and settings panel. It covers everything that’s been changed, added, or removed.Next, you will learn how to personalize and customize your PC, laptop, and tablet and how to make Windows 11 safer to use for your children and family. The book takes you through how to keep your personal information safe and secure, and how to make sure your precious documents and photos are backed-up with OneDrive.The book shows you how to use accessibility tools to make Windows 11 easier to use, see, hear, and touch, and how to have fun with Android apps and Xbox gaming. You will also learn how to become more productive, how to connect to your college or workplace, and how you can use multiple desktops and snap layouts to get stuff done.After reading this book, you will be able to install, manage, secure, and make the best of Windows 11 for your PC.What Will You Learn* Install and use the Android apps on your PC* Safely back up and safeguard your documents and photos* Maximize battery life on your laptop or tablet* Make Windows 11 easier to see, hear, touch, and useWHO THIS BOOK IS FORAnyone planning to install Windows 11 and customize their PC with the new updatesMIKE HALSEY is a recognized technical expert. He is the author of help and how-to books for Windows 7, 8, and 10, including accessibility, productivity, and troubleshooting. He is also the author of The Green IT Guide (Apress). Mike is well-versed in the problems and issues that PC users experience when setting up, using, and maintaining their PCs and knows how difficult and technical it can appear.He understands that some subjects can be intimidating, so he approaches each subject area in straightforward and easy-to-understand ways. Mike is originally from the UK, but now lives in the south of France with his rescue border collies, Evan and Robbie. You can contact Mike on Twitter @MikeHalsey.CHAPTER 1: FINDING YOUR WAY AROUND WINDOWS 11 (15 PAGES)Introducing Windows 11 and guiding you around what’s new, what’s moved, and what’s important, from the new desktop and Start Menu experience, to the Settings panel, the Microsoft Store now with Android apps, and the apps and tools you’ll want to use.1) Introducing the Windows 11 Desktop and Start Menu2) Configuring and Customizing Settings3) Introducing The Microsoft Store4) Accessing Documents and Photos5) Finding and Running Software and AppsCHAPTER 2: PERSONALIZING WINDOWS 11 (15 PAGES)Everybody wants to be able to personalize and customize their devices, and here we look at the many different ways you can do this with one of the most customizable and flexible operating systems available.1) Customizing How Windows 11 Looks and Feels2) Managing Multiple User-Accounts3) Setting Up Email and Other Accounts4) Managing Child Accounts in Windows 11CHAPTER 3: GETTING ONLINE AND USING THE INTERNET (15 PAGES)Everybody Needs to be online, and in this chapter we’ll look at how to connect to Wi-Fi networks safely and securely, and how to use Microsoft’s Edge web browser to browse the Internet safely and securely.1) Connecting to Wi-Fi Networks2) Getting Started with Microsoft’s Edge Browser3) Customizing and Configuring Edge4) Managing Internet DownloadsCHAPTER 4: USING WINDOWS AND ANDROID APPS (10 PAGES)There are several different ways and different types of apps that you can install in Windows 11, including many Android apps. In this chapter we’ll look at how you can install, manage, and get the best from them, in addition to seeing how you can play Xbox games on your PC.1) Installing and Managing Software on your PC2) Installing and Managing Apps from the Microsoft Store3) Install and Manage Android Apps in Windows 114) Connecting to Xbox Gaming Services and Playing GamesCHAPTER 5: MANAGING FILES, DOCUMENTS AND ONEDRIVE (10 PAGES)Managing and keeping your documents, photos and files safe and organized can be tricky, so here we’ll look at how to manage your files, keep them safely backed up, and how you can make sure they’ll always be secure and in-sync across your PCs.1) Managing Documents, Pictures, Videos, and Music2) Setting Up and Using OneDrive Cloud Storage3) Using Multiple Disks with Files and DocumentsCHAPTER 6: MAKING WINDOWS 11 EASIER TO USE (12 PAGES)There are many ways to make Windows 11 easier to use, and these can benefit anybody from children and older people, to those with color-blindness or dyslexia, shaky hands or a harder to manage disability. Here we look at all the ways to make your PC easier to use.1) Make Windows 11 Easier to Use2) Make Windows 11 Easier to See3) Make Windows 11 Easier to Hear4) Make Windows 11 Easier to TouchCHAPTER 7: BEING MORE PRODUCTIVE WITH WINDOWS 11 (15 PAGES)We all want to get stuff done on our PCs, so in this chapter we’ll examine all the top productivity tips including managing multiple windows, desktops and even monitors, how to print and share files and documents, and how to manage running apps.1) Switching Between Running Apps2) Managing Windows and Using Window Layouts3) Using Multiple Desktops in Windows 114) Searching for Files, Documents and More in Windows 115) Printing Files and Saving Files as PDFs6) Using Multiple Displays with Your PCCHAPTER 8: GETTING WORK DONE (10 PAGES)With more people working from home, you all need to be able to connect to your company or organization’s services and files. Here we show you how to get your home PC working with any business or school system safely and quickly.1) Connect to Your Company, Organization, or School2) Use OneDrive for Business3) Getting Started with Microsoft OfficeCHAPTER 9: MANAGING YOUR PRIVACY AND SECURITY (15 PAGES)We all need to be safe and secure online, and here we’ll examine how to prevent your PC becoming infected with malware, and how to help make sure you don’t fall victim to scammers. Additionally we’ll look at how you secure your own privacy on your PC with the websites and apps you like to use.1) Signing Into Your PC with Windows Hello2) The Windows Security Center3) Managing Privacy and Security Settings4) Top Tips for Security and Staying SafeCHAPTER 10: CONNECTING AND USING PERIPHERALS AND HARDWARE (10 PAGES)If you use any kind of device with your PC, from a printer to Bluetooth headphones or an Xbox controller, you’ll know they don’t always behave themselves. Here we’ll look at how you install and manage all types of devices in Windows 11.1) Adding and Managing Printers2) Adding and Managing Bluetooth Devices3) Connecting to Other Devices in Your Home or Workplace4) Fixing Problems with Hardware PeripheralsCHAPTER 11: KEEPING YOUR PC UPDATED AND RUNNING SMOOTHLY (10 PAGES)We need to keep our PCs up to date with security and stability patches, to keep ourselves and our files safe. Here we’ll look at managing Windows Updates, how to defer ones you don’t want yet, and how to quickly fix any problem that might be caused.1) Installing and Managing Windows Updates2) Deferring and Troubleshooting Updates3) What is the Windows Insider ProgramCHAPTER 12: TOP TIPS FOR GETTING THE VERY BEST FROM WINDOWS 11 (15 PAGES)There is so much you can do to make your experience using Windows 11 better, so here we share our top tips for getting the very best from your Windows 11 PCs.1) Using Keyboard Shortcuts with Windows 112) Getting the Best from Touch and Trackpad Gestures3) Maximize Battery Life on Your Laptop or Tablet4) Repurposing an Old PC To Sell or Donate5) Fixing Common PC Problems
Non-Smooth and Complementarity-Based Distributed Parameter Systems
Many of the most challenging problems in the applied sciences involve non-differentiable structures as well as partial differential operators, thus leading to non-smooth distributed parameter systems. This edited volume aims to establish a theoretical and numerical foundation and develop new algorithmic paradigms for the treatment of non-smooth phenomena and associated parameter influences. Other goals include the realization and further advancement of these concepts in the context of robust and hierarchical optimization, partial differential games, and nonlinear partial differential complementarity problems, as well as their validation in the context of complex applications. Areas for which applications are considered include optimal control of multiphase fluids and of superconductors, image processing, thermoforming, and the formation of rivers and networks. Chapters are written by leading researchers and present results obtained in the first funding phase of the DFG Special Priority Program on Nonsmooth and Complementarity Based Distributed Parameter Systems: Simulation and Hierarchical Optimization that ran from 2016 to 2019. S. Bartels, S. Hertzog, Error Bounds for Discretized Optimal Transport and its Reliable Efficient Numerical Solution.- H. G. Bock, E. Kostina, M. Sauter, J. P. Schlöder, M. Schlöder, Numerical Methods for Diagnosis and Therapy Design of Cerebral Palsy by Bilevel Optimal Control of Constrained Biomechanical Multi-Body Systems.- S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, S. Volkwein, ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation.- S. Dempe, F. Harder, P. Mehlitz, G. Wachsmuth, Analysis and Solution Methods for Bilevel Optimal Control Problems.- M. Herrmann, R. Herzog, S. Schmidt, J. Vidal-Núñez, A Calculus for Non-Smooth Shape Optimization with Applications to Geometric Inverse Problems.- R. Herzog, D. Knees, C. Meyer, M. Sievers, A. Stötzner, S. Thomas, Rate-Independent Systems and Their Viscous Regularizations: Analysis, Simulation, and Optimal Control.- D. Ganhururu, M. Hintermüller, S.-M. Stengl, T. M. Surowiec, Generalized Nash Equilibrium Problems with Partial Differential Operators: Theory, Algorithms, and Risk Aversion.- A. Alphonse, M. Hintermüller, C. N. Rautenberg, Stability and Sensitivity Analysis for Quasi-Variational Inequalities.- C. Gräßle, M. Hintermüller, M.Hinze, T. Keil, Simulation and Control of a Nonsmooth Cahn-Hilliard Navier-Stokes System with Variable Fluid Densities.- C. Kanzow, V. Karl, D.Steck, D. Wachsmuth, Safeguarded Augmented Lagrangian Methods in Banach Spaces.- M. Hahn, C. Kirches, P. Manns, S. Sager, C. Zeile, Decomposition and Approximation for PDE-Constrained Mixed-Integer Optimal Control.- C. Christof, C. Meyer, B. Schweizer, S. Turek, Strong Stationarity for Optimal Control of Variational Inequalities of the Second Kind.- A. Hehl, M. Mohammadi, I. Neitzel, W. Wollner, Optimizing Fracture Propagation Using a Phase-Field Approach.- A. Schiela, M. Stöcklein, Algorithms for Optimal Control of Elastic Contact Problems with Finite Strain.- O. Weiß, A. Walther, S.Schmidt, Algorithms based on Abs-Linearization for Nonsmooth Optimization with PDE Constraints.- V. Schulz, K.Welker, Shape Optimization for Variational Inequalities of Obstacle Type: Regularized and Unregularized Computational Approaches.- J. Becker, A.Schwartz, S.Steffensen, A. Thünen, Extensions of Nash Games in Finite and Infinite Dimensions with Applications.
Raspberry Pi 400 Schnelleinstieg
Der Raspberry Pi 400 ist ein minimalistischer „All-in-one“-PC zu einem unschlagbaren Preis. Die gesamte Technik ist in der Tastatur verbaut und die gesamte Software auf einer SD-Karte gespeichert. Mit diesem Buch erhalten Sie eine einfache und kompakte Einführung für den Einsatz des Raspberry Pi 400 und erfahren alles, was Sie brauchen, um mit dem Betriebssystem, der Arbeitsumgebung und der Software zu arbeiten. Herbert Hertramph zeigt Schritt für Schritt, wie Sie den Raspberry Pi 400 einrichten, um ihn im Alltag, Homeoffice oder für das Homeschooling optimal einzusetzen. Außerdem erhalten Sie jede Menge Tipps und Tricks für Streaming, Fotobearbeitung und vieles mehr. Alle notwendigen Linux-Grundlagen werden für Ein- und Umsteiger ganz einfach erläutert. Der Autor erklärt die Vorteile des Systems und geht besonders auf Sicherheit und Backups ein. Mit diesem Buch werden Sie die Möglichkeiten des Raspberry Pi 400 voll ausschöpfen und den Mini-PC optimal an die eigenen Bedürfnisse anpassen.Aus dem Inhalt:Linux-GrundlagenRaspberry Pi 400 einrichtenArbeiten mit LibreOffice, GoogleOffice, Microsoft und iWorksBackup und SynchronisationFernzugriff und SicherheitPi-AppsWeitere BetriebssystemeMit der Kommandozeile arbeiten Über den Autor:Herbert Hertramph ist am Institut für Psychologie und Pädagogik der Universität Ulm als Sozialwissenschaftler mit aktuellen Fragestellungen des digitalen Lehrens und Lernens befasst.
Smart City Infrastructure
SMART CITY INFRASTRUCTURETHE WIDE RANGE OF TOPICS PRESENTED IN THIS BOOK HAVE BEEN CHOSEN TO PROVIDE THE READER WITH A BETTER UNDERSTANDING OF SMART CITIES INTEGRATED WITH AI AND BLOCKCHAIN AND RELATED SECURITY ISSUES. The goal of this book is to provide detailed, in-depth information on the state-of-the-art architecture and infrastructure used to develop smart cities using the Internet of Things (IoT), artificial intelligence (AI), and blockchain security—the key technologies of the fourth industrial revolution. The book outlines the theoretical concepts, experimental studies, and various smart city applications that create value for inhabitants of urban areas. Several issues that have arisen with the advent of smart cities and novel solutions to resolve these issues are presented. The IoT along with the integration of blockchain and AI provides efficient, safe, secure, and transparent ways to solve different types of social, governmental, and demographic issues in the dynamic urban environment. A top-down strategy is adopted to introduce the architecture, infrastructure, features, and security. AUDIENCEThe core audience is researchers in artificial intelligence, information technology, electronic and electrical engineering, systems engineering, industrial engineering as well as government and city planners. VISHAL KUMAR, PHD is an assistant professor in the Department of Computer Science and Engineering at Bipin Tripathi Kumaon Institute of Technology, Dwarahat (an Autonomous Institute of Govt. of Uttarakhand), India.VISHAL JAIN, PHD is an associate professor at the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, UP India. He has more than 450 research citation indices with Google Scholar (h-index score 12 and i-10 index 15). BHARTI SHARMA, PHD is an assistant professor and academic head of the MCA department of DIT University, Dehradun, India. JYOTIR MOY CHATTERJEE is an assistant professor in the Information Technology Department at Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal. He has published more than 60 international research paper publications, three conference papers, three authored books, 10 edited books, 16 book chapters, two Master’s theses converted into books, and one patent. RAKESH SHRESTHA, PHD is a postdoctoral researcher at the Department of Information and Communication Engineering, Yeungnam University, South Korea. Preface xviiAcknowledgment xxi1 DEEP DIVE INTO BLOCKCHAIN TECHNOLOGY: CHARACTERISTICS, SECURITY AND PRIVACY ISSUES, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS 1Bhanu Chander1.1 Introduction 21.2 Blockchain Preliminaries 31.2.1 Functioning of Blockchain 31.2.2 Design of Blockchain 41.2.3 Blockchain Elements 51.3 Key Technologies of Blockchain 71.3.1 Distributed Ledger 71.3.2 Cryptography 81.3.3 Consensus 81.3.4 Smart Contracts 91.3.5 Benchmarks 91.4 Consensus Algorithms of Blockchain 91.4.1 Proof of Work (PoW) 101.4.2 Proof of Stake (PoS) 101.4.3 BFT-Based Consensus Algorithms 111.4.4 Practical Byzantine Fault Tolerance (PBFT) 121.4.5 Sleepy Consensus 121.4.6 Proof of Elapsed Time (PoET) 121.4.7 Proof of Authority (PoA) 131.4.8 Proof of Reputation (PoR) 131.4.9 Deputized Proof of Stake (DPoS) 131.4.10 SCP Design 131.5 Internet of Things and Blockchain 141.5.1 Internet of Things 141.5.2 IoT Blockchain 161.5.3 Up-to-Date Tendency in IoT Blockchain Progress 161.6 Applications of Blockchain in Smart City 181.6.1 Digital Identity 181.6.2 Security of Private Information 191.6.3 Data Storing, Energy Ingesting, Hybrid Development 191.6.4 Citizens Plus Government Frame 201.6.5 Vehicle-Oriented Blockchain Appliances in Smart Cities 201.6.6 Financial Applications 211.7 Security and Privacy Properties of Blockchain 211.7.1 Security and Privacy Necessities of Online Business Transaction 211.7.2 Secrecy of Connections and Data Privacy 231.8 Privacy and Security Practices Employed in Blockchain 241.8.1 Mixing 241.8.2 Anonymous Signatures 251.8.3 Homomorphic Encryption (HE) 251.8.4 Attribute-Based Encryption (ABE) 261.8.5 Secure Multi-Party Computation (MPC) 261.8.6 Non-Interactive Zero-Knowledge (NIZK) 261.8.7 The Trusted Execution Environment (TEE) 271.8.8 Game-Based Smart Contracts (GBSC) 271.9 Challenges of Blockchain 271.9.1 Scalability 271.9.2 Privacy Outflow 281.9.3 Selfish Mining 281.9.4 Security 281.10 Conclusion 29References 292 TOWARD SMART CITIES BASED ON THE INTERNET OF THINGS 33Djamel Saba, Youcef Sahli and Abdelkader Hadidi2.1 Introduction 342.2 Smart City Emergence 362.2.1 A Term Popularized by Private Foundations 362.2.2 Continuation of Ancient Reflections on the City of the Future 372.3 Smart and Sustainable City 382.4 Smart City Areas (Sub-Areas) 402.4.1 Technology and Data 402.4.2 Economy 402.4.3 Population 432.5 IoT 432.5.1 A New Dimension for the Internet and Objects 462.5.2 Issues Raised by the IoT 482.5.2.1 IoT Scale 482.5.2.2 IoT Heterogeneity 482.5.2.3 Physical World Influence on the IoT 512.5.2.4 Security and Privacy 522.5.3 Applications of the IoT That Revolutionize Society 522.5.3.1 IoT in the Field of Health 532.5.3.2 Digital Revolution in Response to Energy Imperatives 532.5.3.3 Home Automation (Connected Home) 542.5.3.4 Connected Industry 542.5.3.5 IoT in Agriculture 552.5.3.6 Smart Retail or Trendy Supermarkets 562.5.3.7 Smart and Connected Cities 572.5.3.8 IoT at the Service of Road Safety 572.5.3.9 Security Systems 592.5.3.10 Waste Management 602.6 Examples of Smart Cities 602.6.1 Barcelona, a Model Smart City 602.6.2 Vienna, the Smartest City in the World 612.7 Smart City Benefits 612.7.1 Security 612.7.2 Optimized Management of Drinking and Wastewater 622.7.3 Better Visibility of Traffic/Infrastructure Issues 642.7.4 Transport 642.8 Analysis and Discussion 652.9 Conclusion and Perspectives 67References 683 INTEGRATION OF BLOCKCHAIN AND ARTIFICIAL INTELLIGENCE IN SMART CITY PERSPECTIVES 77R. Krishnamoorthy, K. Kamala, I. D. Soubache, Mamidala Vijay Karthik and M. Amina Begum3.1 Introduction 783.2 Concept of Smart Cities, Blockchain Technology, and Artificial Intelligence 823.2.1 Concept and Definition of Smart Cities 823.2.1.1 Integration of Smart Cities with New Technologies 833.2.1.2 Development of Smart Cities by Integrated Technologies 853.2.2 Concept of Blockchain Technology 863.2.2.1 Features of Blockchain Technology 873.2.2.2 Framework and Working of Blockchain Technology 883.2.3 Concept and Definition of Artificial Intelligence 893.2.3.1 Classification of Artificial Intelligence– Machine Learning 903.3 Smart Cities Integrated with Blockchain Technology 913.3.1 Applications of Blockchain Technology in Smart City Development 933.3.1.1 Secured Data Transmission 933.3.1.2 Digital Transaction—Smart Contracts 943.3.1.3 Smart Energy Management 943.3.1.4 Modeling of Smart Assets 953.3.1.5 Smart Health System 963.3.1.6 Smart Citizen 963.3.1.7 Improved Safety 963.4 Smart Cities Integrated with Artificial Intelligence 973.4.1 Importance of AI for Developing Smart Cities 983.4.2 Applications of Artificial Intelligence in Smart City Development 993.4.2.1 Smart Transportation System 1003.4.2.2 Smart Surveillance and Monitoring System 1023.4.2.3 Smart Energy Management System 1033.4.2.4 Smart Disposal and Waste Management System 1063.5 Conclusion and Future Work 107References 1084 SMART CITY A CHANGE TO A NEW FUTURE WORLD 113Sonia Singla and Aman Choudhary4.1 Introduction 1134.2 Role in Education 1154.3 Impact of AI on Smart Cities 1164.3.1 Botler AI 1174.3.2 Spot 1174.3.3 Nimb 1174.3.4 Sawdhaan Application 1174.3.5 Basic Use Cases of Traffic AI 1184.4 AI and IoT Support in Agriculture 1194.5 Smart Meter Reading 1204.6 Conclusion 123References 1235 REGISTRATION OF VEHICLES WITH VALIDATION AND OBVIOUS MANNER THROUGH BLOCKCHAIN: SMART CITY APPROACH IN INDUSTRY 5.0 127Rohit Rastogi, Bhuvneshwar Prasad Sharma and Muskan Gupta5.1 Introduction 1285.1.1 Concept of Smart Cities 1285.1.2 Problem of Car Registration and Motivation 1295.1.2.1 Research Objectives 1295.1.2.2 Scope of the Research Work 1295.1.3 5G Technology and Its Implications 1305.1.4 IoT and Its Applications in Transportation 1305.1.5 Usage of AI and ML in IoT and Blockchain 1315.2 Related Work 1315.2.1 Carchain 1325.2.2 Fabcar IBM Blockchain 1325.2.3 Blockchain and Future of Automobiles 1325.2.4 Significance of 5G Technology 1345.3 Presented Methodology 1345.4 Software Requirement Specification 1355.4.1 Product Perspective 1355.4.1.1 Similarities Between Carchain and Our Application 1355.4.1.2 Differences Between Carchain and Our Application 1355.4.2 System Interfaces 1365.4.3 Interfaces (Hardware and Software and Communication) 1365.4.3.1 Hardware Interfaces 1375.4.3.2 Software Interfaces 1375.4.3.3 Communications Interfaces 1385.4.4 Operations (Product Functions, User Characteristics) 1385.4.4.1 Product Functions 1385.4.4.2 User Characteristics 1385.4.5 Use Case, Sequence Diagram 1395.4.5.1 Use Case 1395.4.5.2 Sequence Diagrams 1415.4.5.3 System Design 1425.4.5.4 Architecture Diagrams 1435.5 Software and Hardware Requirements 1505.5.1 Software Requirements 1505.5.2 Hardware Requirements 1515.6 Implementation Details 1515.7 Results and Discussions 1555.8 Novelty and Recommendations 1565.9 Future Research Directions 1575.10 Limitations 1575.11 Conclusions 158References 1596 DESIGNING OF FUZZY CONTROLLER FOR ADAPTIVE CHAIR AND DESK SYSTEM 163Puneet Kundra, Rashmi Vashisth and Ashwani Kumar Dubey6.1 Introduction 1636.2 Time Spent Sitting in Front of Computer Screen 1656.3 Posture 1666.3.1 Need for Correct Posture 1676.3.2 Causes of Sitting in the Wrong Posture 1676.4 Designing of Ergonomic Seat 1676.4.1 Considerate Factors of an Ergonomic Chair and Desk System 1686.5 Fuzzy Control Designing 1706.5.1 Fuzzy Logic Controller Algorithm 1716.5.2 Fuzzy Membership Functions 1726.5.3 Rule Base 1746.5.4 Why Fuzzy Controller? 1766.6 Result of Chair and Desk Control 1776.7 Conclusions and Further Improvements 177References 1817 BLOCKCHAIN TECHNOLOGY DISLOCATES TRADITIONAL PRACTICE THROUGH COST CUTTING IN INTERNATIONAL COMMODITY EXCHANGE 185Arya Kumar7.1 Introduction 1857.1.1 Maintenance of Documents of Supply Chain in Commodity Trading 1877.2 Blockchain Technology 1917.2.1 Smart Contracts 1917.3 Blockchain Solutions 1937.3.1 Monte Carlo Simulation in Blockchain Solution - An Illustration 1947.3.2 Supporting Blockchain Technology in the Food Industry Through Other Applications 1997.4 Conclusion 2007.5 Managerial Implication 2017.6 Future Scope of Study 201References 2028 INTERPLANETARY FILE SYSTEM PROTOCOL–BASED BLOCKCHAIN FRAMEWORK FOR ROUTINE DATA AND SECURITY MANAGEMENT IN SMART FARMING 205Sreethi Thangam M., Janeera D.A., Sherubha P., Sasirekha S.P., J. Geetha Ramani and Ruth Anita Shirley D.8.1 Introduction 2068.1.1 Blockchain Technology for Agriculture 2078.2 Data Management in Smart Farming 2088.2.1 Agricultural Information 2098.2.2 Supply Chain Efficiency 2098.2.3 Quality Management 2108.2.4 Nutritional Value 2108.2.5 Food Safety 2118.2.6 IoT Automation 2118.3 Proposed Smart Farming Framework 2128.3.1 Wireless Sensors 2128.3.2 Communication Channels 2138.3.3 IoT and Cloud Computing 2148.3.4 Blockchain and IPFS Integration 2158.4 Farmers Support System 2178.4.1 Sustainable Farming 2188.5 Results and Discussions 2198.5.1 Benefits and Challenges 2198.6 Conclusion 2218.7 Future Scope 221References 2219 A REVIEW ON BLOCKCHAIN TECHNOLOGY 225Er. Aarti9.1 Introduction 2269.1.1 Characteristics of Blockchain Technology 2279.1.1.1 Decentralization 2289.1.1.2 Transparency 2289.1.1.3 Immutability 2289.2 Related Work 2299.3 Architecture of Blockchain and Its Components 2299.4 Blockchain Taxonomy 2319.4.1 Public Blockchain 2319.4.2 Consortium Blockchain 2319.4.3 Private Blockchain 2329.5 Consensus Algorithms 2339.5.1 Functions of Blockchain Consensus Mechanisms 2339.5.2 Some Approaches to Consensus 2349.5.2.1 Proof of Work (PoW) 2349.5.2.2 Proof of Stake (PoS) 2359.5.2.3 Delegated Proof of Stake (DPoS) 2369.5.2.4 Leased Proof of Stake (LPoS) 2379.5.2.5 Practical Byzantine Fault Tolerance (PBFT) 2379.5.2.6 Proof of Burn (PoB) 2389.5.2.7 Proof of Elapsed Time (PoET) 2399.6 Challenges in Terms of Technologies 2399.7 Major Application Areas 2409.7.1 Finance 2409.7.2 Education 2409.7.3 Secured Connection 2409.7.4 Health 2409.7.5 Insurance 2419.7.6 E-Voting 2419.7.7 Smart Contracts 2419.7.8 Waste and Sanitation 2419.8 Conclusion 242References 24210 TECHNOLOGICAL DIMENSION OF A SMART CITY 247Laxmi Kumari Pathak, Shalini Mahato and Soni Sweta10.1 Introduction 24710.2 Major Advanced Technological Components of ICT in Smart City 24910.2.1 Internet of Things 24910.2.2 Big Data 25010.2.3 Artificial Intelligence 25010.3 Different Dimensions of Smart Cities 25010.4 Issues Related to Smart Cities 25010.5 Conclusion 265References 26611 BLOCKCHAIN—DOES IT UNLEASH THE HITCHED CHAINS OF CONTEMPORARY TECHNOLOGIES 269Abigail Christina Fernandez and Thamarai Selvi Rajukannu11.1 Introduction 27011.2 Historic Culmination of Blockchain 27111.3 The Hustle About Blockchain—Revealed 27211.3.1 How Does It Work? 27311.3.2 Consent in Accordance—Consensus Algorithm 27311.4 The Unique Upfront Statuesque of Blockchain 27511.4.1 Key Elements of Blockchain 27511.4.2 Adversaries Manoeuvred by Blockchain 27611.4.2.1 Double Spending Problem 27611.4.2.2 Selfish Mining and Eclipse Attacks 27611.4.2.3 Smart Contracts 27711.4.3 Breaking the Clutches of Centralized Operations 27711.5 Blockchain Compeers Complexity 27811.6 Paradigm Shift to Deciphering Technologies Adjoining Blockchain 27911.7 Convergence of Blockchain and AI Toward a Sustainable Smart City 28011.8 Business Manifestations of Blockchain 28211.9 Constraints to Adapt to the Resilient Blockchain 28711.10 Conclusion 287References 28812 AN OVERVIEW OF BLOCKCHAIN TECHNOLOGY: ARCHITECTURE AND CONSENSUS PROTOCOLS 293Himanshu Rastogi12.1 Introduction 29412.2 Blockchain Architecture 29512.2.1 Block Structure 29612.2.2 Hashing and Digital Signature 29712.3 Consensus Algorithm 29812.3.1 Compute-Intensive–Based Consensus (CIBC) Protocols 30012.3.1.1 Pure Proof of Work (PoW) 30012.3.1.2 Prime Number Proof of Work(Prime Number PoW) 30012.3.1.3 Delayed Proof of Work (DPoW) 30112.3.2 Capability-Based Consensus Protocols 30212.3.2.1 Proof of Stake (PoS) 30212.3.2.2 Delegated Proof of Stake (DPoS) 30312.3.2.3 Proof of Stake Velocity (PoSV) 30312.3.2.4 Proof of Burn (PoB) 30412.3.2.5 Proof of Space (PoSpace) 30412.3.2.6 Proof of History (PoH) 30512.3.2.7 Proof of Importance (PoI) 30512.3.2.8 Proof of Believability (PoBelievability) 30612.3.2.9 Proof of Authority (PoAuthority) 30712.3.2.10 Proof of Elapsed Time (PoET) 30712.3.2.11 Proof of Activity (PoA) 30812.3.3 Voting-Based Consensus Protocols 30812.3.3.1 Practical Byzantine Fault Tolerance (PBFT) 30912.3.3.2 Delegated Byzantine Fault Tolerance (DBFT) 31012.3.3.3 Federated Byzantine Arrangement (FBA) 31012.3.3.4 Combined Delegated Proof of Stake and Byzantine Fault Tolerance (DPoS+BFT) 31112.4 Conclusion 312References 31213 APPLICABILITY OF UTILIZING BLOCKCHAIN TECHNOLOGY IN SMART CITIES DEVELOPMENT 317Auwal Alhassan Musa, Shashivendra Dulawat, Kabeer Tijjani Saleh and Isyaku Auwalu Alhassan13.1 Introduction 31813.2 Smart Cities Concept 31913.3 Definition of Smart Cities 32013.4 Legal Framework by EU/AIOTI of Smart Cities 32113.5 The Characteristic of Smart Cities 32213.5.1 Climate and Environmentally Friendly 32213.5.2 Livability 32213.5.3 Sustainability 32313.5.4 Efficient Resources Management 32313.5.5 Resilient 32313.5.6 Dynamism 32313.5.7 Mobility 32313.6 Challenges Faced by Smart Cities 32413.6.1 Security Challenge 32413.6.2 Generation of Huge Data 32413.6.3 Concurrent Information Update 32513.6.4 Energy Consumption Challenge 32513.7 Blockchain Technology at Glance 32513.8 Key Drivers to the Implementation of Blockchain Technology for Smart Cities Development 32713.8.1 Internet of Things (IoT) 32813.8.2 Architectural Organization of the Internet of Things 32813.9 Challenges of Utilizing Blockchain in Smart City Development 32913.9.1 Security and Privacy as a Challenge to Blockchain Technology 33013.9.2 Lack of Cooperation 33113.9.3 Lack of Regulatory Clarity and Good Governance 33113.9.4 Energy Consumption and Environmental Cost 33213.10 Solution Offered by Blockchain to Smart Cities Challenges 33213.10.1 Secured Data 33313.10.2 Smart Contract 33313.10.3 Easing the Smart Citizen Involvement 33313.10.4 Ease of Doing Business 33313.10.5 Development of Sustainable Infrastructure 33313.10.6 Transparency in Protection and Security 33413.10.7 Consistency and Auditability of Data Record 33413.10.8 Effective, Efficient Automation Process 33413.10.9 Secure Authentication 33513.10.10 Reliability and Continuity of the Basic Services 33513.10.11 Crisis and Violence Management 33513.11 Conclusion 335References 336About the Editors 341Index 343
Introducing .NET 6
Welcome to .NET 6, Microsoft’s unified framework that converges the best of the modern and traditional .NET Framework. This book will introduce you to the new aspects of Microsoft’s fully supported .NET 6 Framework and will teach you how to get the most out of it. You will learn about the progress to one unified .NET, including MAUI and the revival of desktop development. You will dive into Roslyn, Blazor, CLI, Containers, Cloud, and much more, using a “framework first” learning approach. You will begin by learning what each tool is, its practical uses, and how to apply it and then you will try it out on your own for learning reinforcement. And, of course, there will be plenty of code samples using C# 10.INTRODUCING .NET 6 is aimed at .NET developers, both junior developers and those coming from the .NET framework, who want to understand everything the modern framework has to offer, besides the obvious programming languages. While you will still see a lot of fabulous C# 10 throughout the book, the focus of this learning is all about .NET and its tooling.WHAT YOU WILL LEARN* Become a more versatile developer by knowing the variety of options available to you in the .NET 6 framework and its powerful tooling* Know the different front-end frameworks .NET offers, such as UWP, WPF, and WinForms, and how they stack up to each other* Understand the different communication protocols, such as REST and gRPC, for your back-end services* Discover the secrets of cloud-native development, such as serverless computing with Azure Functions and deploying containers to Azure Container Services* Master the command line, take your skill set to the cloud, and containerize your .NET 6 appWHO THIS BOOK IS FORBoth students and more experienced developers, C# developers who want to learn more about the framework they use, developers who want to be more productive by diving deeper into the tooling that .NET 6 brings to the fold, developers who need to make technical decisions. A working knowledge of C# is recommended to follow the examples used in the book.NICO VERMEIR is an Microsoft MVP in the field of Windows development. He works as a Solution Architect at Inetum-Realdolmen Belgium and spends a lot of time keeping up with the rapidly changing world of technology. He loves talking about and using the newest and experimental technologies in the .NET stack. Nico founded MADN, a user group focusing on front end development in .NET. He regularly presents on the topic of .NET.CHAPTER 1: A TOUR OF.NET 6CHAPTER 2: RUNTIMES AND DESKTOP PACKSCHAPTER 3: COMMAND LINE INTERFACECHAPTER 4: DESKTOP DEVELOPMENTCHAPTER 5: BLAZORCHAPTER 6: MAUICHAPTER 7: ASP.NET CORECHAPTER 8: MICROSOFT AZURECHAPTER 9: APPLICATION ARCHITECTURECHAPTER 10: .NET COMPILER PLATFORMCHAPTER 11: ADVANCED .NET 6
Optimization and Machine Learning
Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated. RACHID CHELOUAH has a PhD and a Doctorate of Sciences (Habilitation) from CY Cergy Paris University, France. His main research interests are data science optimization and artificial intelligence methods and their applications in various fields of IT engineering, health, energy and security.PATRICK SIARRY is a Professor in automatics and informatics at Paris-East Creteil University, France. His main research interests are the design of stochastic global optimization heuristics and their applications in various engineering fields. He has coordinated several books in the field of optimization.Introduction xiRachid CHELOUAHPART 1 OPTIMIZATION 1CHAPTER 1 VEHICLE ROUTING PROBLEMS WITH LOADING CONSTRAINTS: AN OVERVIEW OF VARIANTS AND SOLUTION METHODS 3Ines SBAI and Saoussen KRICHEN1.1 Introduction 31.2 The capacitated vehicle routing problem with two-dimensional loading constraints 51.2.1 Solution methods 61.2.2 Problem description 81.2.3 The 2L-CVRP variants 91.2.4 Computational analysis 101.3 The capacitated vehicle routing problem with three-dimensional loading constraints 111.3.1 Solution methods 111.3.2 Problem description 131.3.3 3L-CVRP variants 141.3.4 Computational analysis 161.4 Perspectives on future research 181.5 References 18CHAPTER 2 MAS-AWARE APPROACH FOR QOS-BASED IOT WORKFLOW SCHEDULING IN FOG-CLOUD COMPUTING 25Marwa MOKNI and Sonia YASSA2.1 Introduction 262.2 Related works 272.3 Problem formulation 292.3.1 IoT-workflow modeling 312.3.2 Resources modeling 312.3.3 QoS-based workflow scheduling modeling 312.4 MAS-GA-based approach for IoT workflow scheduling 332.4.1 Architecture model 332.4.2 Multi-agent system model 342.4.3 MAS-based workflow scheduling process 352.5 GA-based workflow scheduling plan 382.5.1 Solution encoding 392.5.2 Fitness function 412.5.3 Mutation operator 412.6 Experimental study and analysis of the results 432.6.1 Experimental results 452.7 Conclusion 512.8 References 51CHAPTER 3 SOLVING FEATURE SELECTION PROBLEMS BUILT ON POPULATION-BASED METAHEURISTIC ALGORITHMS 55Mohamed SASSI3.1 Introduction 563.2 Algorithm inspiration 573.2.1 Wolf pack hierarchy 573.2.2 The four phases of pack hunting 583.3 Mathematical modeling 593.3.1 Pack hierarchy 593.3.2 Four phases of hunt modeling 613.3.3 Research phase – exploration 643.3.4 Attack phase – exploitation 653.3.5 Grey wolf optimization algorithm pseudocode 663.4 Theoretical fundamentals of feature selection 673.4.1 Feature selection definition 673.4.2 Feature selection methods 683.4.3 Filter method 683.4.4 Wrapper method 693.4.5 Binary feature selection movement 693.4.6 Benefits of feature selection for machine learning classification algorithms 703.5 Mathematical modeling of the feature selection optimization problem 703.5.1 Optimization problem definition 713.5.2 Binary discrete search space 713.5.3 Objective functions for the feature selection 723.6 Adaptation of metaheuristics for optimization in a binary search space 763.6.1 Module 𝑀1 773.6.2 Module 𝑀2 783.7 Adaptation of the grey wolf algorithm to feature selection in a binary search space 813.7.1 First algorithm bGWO1 813.7.2 Second algorithm bGWO2 833.7.3 Algorithm 2: first approach of the binary GWO 843.7.4 Algorithm 3: second approach of the binary GWO 853.8 Experimental implementation of bGWO1 and bGWO2 and discussion 863.9 Conclusion 873.10 References 88CHAPTER 4 SOLVING THE MIXED-MODEL ASSEMBLY LINE BALANCING PROBLEM BY USING A HYBRID REACTIVE GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE 91Belkharroubi LAKHDAR and Khadidja YAHYAOUI4.1 Introduction 924.2 Related works from the literature 954.3 Problem description and mathematical formulation 974.3.1 Problem description 974.3.2 Mathematical formulation 984.4 Basic greedy randomized adaptive search procedure 994.5 Reactive greedy randomized adaptive search procedure 1004.6 Hybrid reactive greedy randomized adaptive search procedure for the mixed model assembly line balancing problem type-2 1014.6.1 The proposed construction phase 1024.6.2 The local search phase 1064.7 Experimental examples 1074.7.1 Results and discussion 1114.8 Conclusion 1154.9 References 116PART 2 MACHINE LEARNING 119CHAPTER 5 AN INTERACTIVE ATTENTION NETWORK WITH STACKED ENSEMBLE MACHINE LEARNING MODELS FOR RECOMMENDATIONS 121Ahlem DRIF, SaadEddine SELMANI and Hocine CHERIFI5.1 Introduction 1225.2 Related work 1245.2.1 Attention network mechanism in recommender systems 1245.2.2 Stacked machine learning for optimization 1255.3 Interactive personalized recommender 1265.3.1 Notation 1285.3.2 The interactive attention network recommender 1295.3.3 The stacked content-based filtering recommender 1345.4 Experimental settings 1365.4.1 The datasets 1365.4.2 Evaluation metrics 1375.4.3 Baselines 1395.5 Experiments and discussion 1405.5.1 Hyperparameter analysis 1405.5.2 Performance comparison with the baselines 1435.6 Conclusion 1465.7 References 146CHAPTER 6 A COMPARISON OF MACHINE LEARNING AND DEEP LEARNING MODELS WITH ADVANCED WORD EMBEDDINGS: THE CASE OF INTERNAL AUDIT REPORTS 151Gustavo FLEURY SOARES and Induraj PUDHUPATTU RAMAMURTHY6.1 Introduction 1526.2 Related work 1546.2.1 Word embedding 1566.2.2 Deep learning models 1576.3 Experiments and evaluation 1586.4 Conclusion and future work 1636.5 References 165CHAPTER 7 HYBRID APPROACH BASED ON MULTI-AGENT SYSTEM AND FUZZY LOGIC FOR MOBILE ROBOT AUTONOMOUS NAVIGATION 169Khadidja YAHYAOUI7.1 Introduction 1707.2 Related works 1717.2.1 Classical approaches 1727.2.2 Advanced methods 1737.3 Problem position 1747.4 Developed control architecture 1767.4.1 Agents description 1777.5 Navigation principle by fuzzy logic 1837.5.1 Fuzzy logic overview 1837.5.2 Description of simulated robot 1847.5.3 Strategy of navigation 1857.5.4 Fuzzy controller agent 1867.6 Simulation and results 1947.7 Conclusion 1967.8 References 196CHAPTER 8 INTRUSION DETECTION WITH NEURAL NETWORKS: A TUTORIAL 201Alvise DE’ FAVERI TRON8.1 Introduction 2018.1.1 Intrusion detection systems 2018.1.2 Artificial neural networks 2028.1.3 The NSL-KDD dataset 2028.2 Dataset analysis 2038.2.1 Dataset summary 2038.2.2 Features 2038.2.3 Binary feature distribution 2048.2.4 Categorical features distribution 2078.2.5 Numerical data distribution 2118.2.6 Correlation matrix 2128.3 Data preparation 2138.3.1 Data cleaning 2138.3.2 Categorical columns encoding 2138.3.3 Normalization 2148.4 Feature selection 2178.4.1 Tree-based selection 2178.4.2 Univariate selection 2188.5 Model design 2198.5.1 Project environment 2198.5.2 Building the neural network 2208.5.3 Learning hyperparameters 2208.5.4 Epochs 2208.5.5 Batch size 2218.5.6 Dropout layers 2218.5.7 Activation functions 2228.6 Results comparison 2228.6.1 Evaluation metrics 2228.6.2 Preliminary models 2238.6.3 Adding dropout 2258.6.4 Adding more layers 2268.6.5 Adding feature selection 2278.7 Deployment in a network 2288.7.1 Sensors 2288.7.2 Model choice 2298.7.3 Model deployment 2298.7.4 Model adaptation 2318.8 Future work 2318.9 References 231List of Authors 233Index 235
Machine Learning Paradigm for Internet of Things Applications
MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONSAS COMPANIES GLOBALLY REALIZE THE REVOLUTIONARY POTENTIAL OF THE IOT, THEY HAVE STARTED FINDING A NUMBER OF OBSTACLES THEY NEED TO ADDRESS TO LEVERAGE IT EFFICIENTLY. MANY BUSINESSES AND INDUSTRIES USE MACHINE LEARNING TO EXPLOIT THE IOT’S POTENTIAL AND THIS BOOK BRINGS CLARITY TO THE ISSUE. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains. AUDIENCEScholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists. SHALLI RANI, PHD is an associate professor in the Department of CSE, Chitkara University, Punjab, India. R. MAHESWAR, PHD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India. G. R. KANAGACHIDAMBARESAN, PHD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India. SACHIN AHUJA, PHD is a professor in the Department of CSE, Chitkara University, Punjab, India. DEEPALI GUPTA, PHD is a professor, Department of CSE, Chitkara University, Punjab, India. Preface xiii1 MACHINE LEARNING CONCEPT–BASED IOT PLATFORMS FOR SMART CITIES’ IMPLEMENTATION AND REQUIREMENTS 1M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi1.1 Introduction 21.2 Smart City Structure in India 31.2.1 Bhubaneswar City 31.2.1.1 Specifications 31.2.1.2 Healthcare and Mobility Services 31.2.1.3 Productivity 41.2.2 Smart City in Pune 41.2.2.1 Specifications 51.2.2.2 Transport and Mobility 51.2.2.3 Water and Sewage Management 51.3 Status of Smart Cities in India 51.3.1 Funding Process by Government 61.4 Analysis of Smart City Setup 71.4.1 Physical Infrastructure-Based 71.4.2 Social Infrastructure-Based 71.4.3 Urban Mobility 81.4.4 Solid Waste Management System 81.4.5 Economical-Based Infrastructure 91.4.6 Infrastructure-Based Development 91.4.7 Water Supply System 101.4.8 Sewage Networking 101.5 Ideal Planning for the Sewage Networking Systems 101.5.1 Availability and Ideal Consumption of Resources 101.5.2 Anticipating Future Demand 111.5.3 Transporting Networks to Facilitate 111.5.4 Control Centers for Governing the City 121.5.5 Integrated Command and Control Center 121.6 Heritage of Culture Based on Modern Advancement 131.7 Funding and Business Models to Leverage 141.7.1 Fundings 151.8 Community-Based Development 161.8.1 Smart Medical Care 161.8.2 Smart Safety for The IT 161.8.3 IoT Communication Interface With ML 171.8.4 Machine Learning Algorithms 171.8.5 Smart Community 181.9 Revolutionary Impact With Other Locations 181.10 Finding Balanced City Development 201.11 E-Industry With Enhanced Resources 201.12 Strategy for Development of Smart Cities 211.12.1 Stakeholder Benefits 211.12.2 Urban Integration 221.12.3 Future Scope of City Innovations 221.12.4 Conclusion 23References 242 AN EMPIRICAL STUDY ON PADDY HARVEST AND RICE DEMAND PREDICTION FOR AN OPTIMAL DISTRIBUTION PLAN 27W. H. Rankothge2.1 Introduction 282.2 Background 292.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 292.2.2 Rice Distribution 312.3 Methodology 312.3.1 Requirements of the Proposed Platform 322.3.2 Data to Evaluate the ‘isRice” Platform 342.3.3 Implementation of Prediction Modules 342.3.3.1 Recurrent Neural Network 352.3.3.2 Long Short-Term Memory 362.3.3.3 Paddy Harvest Prediction Function 372.3.3.4 Rice Demand Prediction Function 392.3.4 Implementation of Rice Distribution Planning Module 402.3.4.1 Genetic Algorithm–Based Rice Distribution Planning 412.3.5 Front-End Implementation 442.4 Results and Discussion 452.4.1 Paddy Harvest Prediction Function 452.4.2 Rice Demand Prediction Function 462.4.3 Rice Distribution Planning Module 462.5 Conclusion 49References 493 A COLLABORATIVE DATA PUBLISHING MODEL WITH PRIVACY PRESERVATION USING GROUP-BASED CLASSIFICATION AND ANONYMITY 53Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni3.1 Introduction 543.2 Literature Survey 563.3 Proposed Model 583.4 Results 613.5 Conclusion 64References 644 PRODUCTION MONITORING AND DASHBOARD DESIGN FOR INDUSTRY 4.0 USING SINGLE-BOARD COMPUTER (SBC) 67Dineshbabu V., Arul Kumar V. P. and Gowtham M. S.4.1 Introduction 684.2 Related Works 694.3 Industry 4.0 Production and Dashboard Design 694.4 Results and Discussion 704.5 Conclusion 73References 735 GENERATION OF TWO-DIMENSIONAL TEXT-BASED CAPTCHA USING GRAPHICAL OPERATION 75S. Pradeep Kumar and G. Kalpana5.1 Introduction 755.2 Types of CAPTCHAs 785.2.1 Text-Based CAPTCHA 785.2.2 Image-Based CAPTCHA 805.2.3 Audio-Based CAPTCHA 805.2.4 Video-Based CAPTCHA 815.2.5 Puzzle-Based CAPTCHA 825.3 Related Work 825.4 Proposed Technique 825.5 Text-Based CAPTCHA Scheme 835.6 Breaking Text-Based CAPTCHA’s Scheme 855.6.1 Individual Character-Based Segmentation Method 855.6.2 Character Width-Based Segmentation Method 865.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 875.7.1 Graphical Operation 875.7.2 Two-Dimensional Composite Transformation Calculation 895.8 Graphical Text-Based CAPTCHA in Online Application 915.9 Conclusion and Future Enhancement 93References 946 SMART IOT-ENABLED TRAFFIC SIGN RECOGNITION WITH HIGH ACCURACY (TSR-HA) USING DEEP LEARNING 97Pradeep Kumar S., Jayanthi K. and Selvakumari S.6.1 Introduction 986.1.1 Internet of Things 986.1.2 Deep Learning 986.1.3 Detecting the Traffic Sign With the Mask R-CNN 996.1.3.1 Mask R-Convolutional Neural Network 996.1.3.2 Color Space Conversion 1006.2 Experimental Evaluation 1016.2.1 Implementation Details 1016.2.2 Traffic Sign Classification 1016.2.3 Traffic Sign Detection 1026.2.4 Sample Outputs 1036.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 1036.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 1036.2.6 Python Code 1086.3 Conclusion 109References 1107 OFFLINE AND ONLINE PERFORMANCE EVALUATION METRICS OF RECOMMENDER SYSTEM: A BIRD’S EYE VIEW 113R. Bhuvanya and M. Kavitha7.1 Introduction 1147.1.1 Modules of Recommender System 1147.1.2 Evaluation Structure 1157.1.3 Contribution of the Paper 1157.1.4 Organization of the Paper 1167.2 Evaluation Metrics 1167.2.1 Offline Analytics 1167.2.1.1 Prediction Accuracy Metrics 1167.2.1.2 Decision Support Metrics 1187.2.1.3 Rank Aware Top-N Metrics 1207.2.2 Item and List-Based Metrics 1227.2.2.1 Coverage 1227.2.2.2 Popularity 1237.2.2.3 Personalization 1237.2.2.4 Serendipity 1237.2.2.5 Diversity 1237.2.2.6 Churn 1247.2.2.7 Responsiveness 1247.2.3 User Studies and Online Evaluation 1257.2.3.1 Usage Log 1257.2.3.2 Polls 1267.2.3.3 Lab Experiments 1267.2.3.4 Online A/B Test 1267.3 Related Works 1277.3.1 Categories of Recommendation 1297.3.2 Data Mining Methods of Recommender System 1297.3.2.1 Data Pre-Processing 1297.3.2.2 Data Analysis 1317.4 Experimental Setup 1357.5 Summary and Conclusions 142References 1438 DEEP LEARNING–ENABLED SMART SAFETY PRECAUTIONS AND MEASURES IN PUBLIC GATHERING PLACES FOR COVID-19 USING IOT 147Pradeep Kumar S., Pushpakumar R. and Selvakumari S.8.1 Introduction 1488.2 Prelims 1488.2.1 Digital Image Processing 1488.2.2 Deep Learning 1498.2.3 WSN 1498.2.4 Raspberry Pi 1528.2.5 Thermal Sensor 1528.2.6 Relay 1528.2.7 TensorFlow 1538.2.8 Convolution Neural Network (CNN) 1538.3 Proposed System 1548.4 Math Model 1568.5 Results 1588.6 Conclusion 161References 1619 ROUTE OPTIMIZATION FOR PERISHABLE GOODS TRANSPORTATION SYSTEM 167Kowsalyadevi A. K., Megala M. and Manivannan C.9.1 Introduction 1679.2 Related Works 1689.2.1 Need for Route Optimization 1709.3 Proposed Methodology 1719.4 Proposed Work Implementation 1749.5 Conclusion 178References 17810 FAKE NEWS DETECTION USING MACHINE LEARNING ALGORITHMS 181M. Kavitha, R. Srinivasan and R. Bhuvanya10.1 Introduction 18110.2 Literature Survey 18310.3 Methodology 19310.3.1 Data Retrieval 19510.3.2 Data Pre-Processing 19510.3.3 Data Visualization 19610.3.4 Tokenization 19610.3.5 Feature Extraction 19610.3.6 Machine Learning Algorithms 19710.3.6.1 Logistic Regression 19710.3.6.2 Naïve Bayes 19810.3.6.3 Random Forest 20010.3.6.4 XGBoost 20010.4 Experimental Results 20210.5 Conclusion 203References 20311 OPPORTUNITIES AND CHALLENGES IN MACHINE LEARNING WITH IOT 209Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana11.1 Introduction 20911.2 Literature Review 21011.2.1 A Designed Architecture of ML on Big Data 21011.2.2 Machine Learning 21111.2.3 Types of Machine Learning 21211.2.3.1 Supervised Learning 21211.2.3.2 Unsupervised Learning 21511.3 Why Should We Care About Learning Representations? 21711.4 Big Data 21811.5 Data Processing Opportunities and Challenges 21911.5.1 Data Redundancy 21911.5.2 Data Noise 22011.5.3 Heterogeneity of Data 22011.5.4 Discretization of Data 22011.5.5 Data Labeling 22111.5.6 Imbalanced Data 22111.6 Learning Opportunities and Challenges 22111.7 Enabling Machine Learning With IoT 22311.8 Conclusion 224References 22512 MACHINE LEARNING EFFECTS ON UNDERWATER APPLICATIONS AND IOUT 229Mamta Nain, Nitin Goyal and Manni Kumar12.1 Introduction 22912.2 Characteristics of IoUT 23112.3 Architecture of IoUT 23212.3.1 Perceptron Layer 23312.3.2 Network Layer 23412.3.3 Application Layer 23412.4 Challenges in IoUT 23412.5 Applications of IoUT 23512.6 Machine Learning 24012.7 Simulation and Analysis 24112.8 Conclusion 242References 24213 INTERNET OF UNDERWATER THINGS: CHALLENGES, ROUTING PROTOCOLS, AND ML ALGORITHMS 247Monika Chaudhary, Nitin Goyal and Aadil Mushtaq13.1 Introduction 24813.2 Internet of Underwater Things 24813.2.1 Challenges in IoUT 24913.3 Routing Protocols of IoUT 25013.4 Machine Learning in IoUT 25513.4.1 Types of Machine Learning Algorithms 25813.5 Performance Evaluation 25913.6 Conclusion 260References 26014 CHEST X-RAY FOR PNEUMONIA DETECTION 265Sarang Sharma, Sheifali Gupta and Deepali Gupta14.1 Introduction 26614.2 Background 26714.3 Research Methodology 26814.4 Results and Discussion 27114.4.1 Results 27114.4.2 Discussion 27114.5 Conclusion 273Acknowledgment 273References 274Index 275
Exploring Careers in Cybersecurity and Digital Forensics
Exploring Careers in Cybersecurity and Digital Forensics is a one stop shop for students and advisors, providing information about education, certifications, and tools to guide them in making career decisions within the field.Cybersecurity is a fairly new academic discipline and with the continued rise in cyberattacks, the need for technological and non-technological skills in responding to criminal digital behavior, as well as the requirement to respond, investigate, gather and preserve evidence is growing. Exploring Careers in Cybersecurity and Digital Forensics is designed to help students and professionals navigate the unique opportunity that a career in digital forensics and cybersecurity provides. From undergraduate degrees, the job hunt and networking, to certifications and mid-career transitions, this book is a useful tool to students, advisors, and professionals alike. Lucy Tsado and Robert Osgood help students and school administrators understand the opportunity that exists in the cybersecurity and digital forensics field, provide guidance for students and professionals out there looking for alternatives through degrees, and offer solutions to close the cybersecurity skills gap through student recruiting and retention in the field.Lucy K. Tsado, PhD, is an assistant professor in the Department of Sociology, Social Work and Criminal Justice at Lamar University, where she teaches cybersecurity, digital forensics, cybercrime, corrections, criminal justice policy, planning and evaluation, class, race, gender and crime to criminal justice students.Robert Osgood is an engineer, CPA, and a 26-year veteran FBI Computer Forensics Examiner and Technically Trained Special Agent. His specialties include: digital forensics, data intercept, cyber-crime, enterprise criminal organizations, espionage, and counter-terrorism. In the course of his work, he has performed digital forensics research and development and created unique new software tools for digital forensic law enforcement. He also serves as a digital forensics consultant to Probity Inc. working with the Truxton development team. He formed the first FBI computer forensics squad in 2000, served as the Chief of the FBI’s Digital Media Exploitation Unit and was part of the team that executed the first court-authorized digital computer intercept at the FBI. Osgood managed and deployed the Washington, D.C. gunshot detection system.Chapter One: What Is Cybersecurity?Cybersecurity and The Criminal Justice ConnectionThe Evolution of Digital ForensicsChapter Two: The Cybersecurity Skills Gap: An Opportunity for Criminal Justice StudentsCriminal Justice Students and The Infusion of Cyber Forensic SkillsWhat Educators, Advisors, And Career Counselors Need to KnowHow Can A Student Attain A Successful Cybersecurity Career?Chapter Three: It’s All About SkillsDigital Forensics Swim LanesDigital Media ForensicsNetwork ForensicsCloud ForensicsMemory ForensicsMobile Device ForensicsReverse EngineeringWhat Baseline Skills Do I Need to Bring?ProgrammingOperating SystemsNetworkingSoft SkillsWritten Communications SkillsInterviewing SkillsLegal SkillsChain of CustodyOther Legal StuffNon-Examiner Based Analytical SkillsChapter Four: Education and CertificationsCyber Security ProgramsCertificate Programs (Certs)Formal (Academic) EducationUndergraduate ProgramsGraduate ProgramsComponents of An Effective Digital Forensics ProgramOnline ProgramsCostHow to Pick an Institution?The Centers for Academic Excellence (CAE) Designated InstitutionsChapter Five: Cybersecurity Career Opportunities in The Field of Criminal JusticeCurrent Opportunities and Jobs Needing Cybersecurity in Criminal JusticeJobs Within the Federal Government (Public Sector)Jobs Within State and Local Governments (Public Sector).Courts and CorrectionsJobs Within the Private SectorNational Initiative for Cybersecurity Education (NICE) CyberseekNational Institute of Standards and Technology (NIST) Workforce FrameworkOther Important RolesChapter Six: Planning Your Path into The Cybersecurity FieldA Proposed Model for A Successful Cybersecurity Education and Career.EducationTraining and CertificationsOther Activities That Are Important for Students’ SuccessNetworkingConferencesSchool Career Advancement ActivitiesInternshipsApprenticeshipClubs and Social OrganizationsCompetitionsThe Role of Colleges and Their CommunityChapter Seven: Getting the Job and Entering the Digital Forensics FieldSetting Up A Home Digital Forensics LabLooking for The Job Posting.Persistent but Not AnnoyanceThe ResumeConclusion: Career Advancement in CybersecurityRecap of Previous ChaptersQuestions Students Should Ask Themselves Before They Begin A Career/As They Progress Through Their CareerTips for Advancement in The Cybersecurity and Digital Forensics Field.After A Cybersecurity Career, What Next?Retirement: Was It All Worth It?Appendix 1: Complete List of Feeder Roles According to Cyberseek.OrgAppendix 2: Cybersecurity Roles Suitable for Criminal Justice Students Adapted from Cyberseek.OrgAppendix 3: Cybersecurity Roles for Criminal Justice Students. Adapted from The NIST SP 800 181
Mastering Azure API Management
Unsure of how or where to get started with Azure API Management, Microsoft’s managed service for securing, maintaining, and monitoring APIs? Then this guide is for you. Azure API Management integrates services like Azure Kubernetes Services (AKS), Function Apps, Logic Apps, and many others with the cloud and provides users with a single, unified, and well-structured façade in the cloud.MASTERING AZURE API MANAGEMENT is designed to help API developers and cloud engineers learn all aspects of Azure API Management, including security and compliance. It provides a pathway for getting started and learning valuable management and administration skills. You will learn what tools you need to publish a unified API façade towards backend services, independent of where and what they run on.You will begin with an overview of web APIs. You will learn about today’s challenges and how a unified API management approach can help you address them. From there you’ll dive into the key concepts of Azure API Management and be given a practical view and approach of API development in the context of Azure API Management. You'll then review different ways of integrating Azure API Management into your enterprise architecture. From there, you will learn how to optimally maintain and administer Azure API Management to secure your APIs, and learn from them, gaining valuable insights through logging and monitoring.WHAT YOU WILL LEARN* Discover the benefits of an enterprise API platform* Understand the basic concepts of API management in the Microsoft cloud* Develop and publish your APIs in the context of Azure API Management* Onboard users through the developer portal* Help your team or other developers to publish their APIs more efficiently* Integrate Azure API Management securely into your enterprise architecture* Manage and maintain to secure your APIs and gain insightsWHO THIS BOOK IS FORAPI developers, cloud engineers, and Microsoft Azure enthusiasts who want to deep dive into managing an API-centric enterprise architecture with Azure API Management. To get the most out of the book, the reader should have a good understanding of micro services and APIs. Basic coding skills, including some experience with PowerShell and Azure, are also beneficial.SVEN MALVIK is an experienced Azure expert. He specializes in compliance and digital transformation, most recently in the financial industry. He has decades of experience in software development, DevOps, and cloud engineering. Sven is a Microsoft MVP in Azure and a speaker, presenting sessions and tutorials at a number of global conferences, user group meetings, and international companies.PART I: GETTING STARTEDCHAPTER 1: QUICK STARTCHAPTER 2: OVERVIEWPART II: KEY CONCEPTSCHAPTER 3: APIS AND PRODUCTSCHAPTER 4: APIS AND PRODUCTSCHAPTER 5: VERSIONS AND REVISIONSCHAPTER 6: SUBSCRIPTIONSCHAPTER 7: POLICIES AND NAMED VALUESCHAPTER 8: DEVELOPER PORTALPART III: WORKFLOWCHAPTER 9: API DEVELOPMENT IN CONTEXTCHAPTER 10: DEVELOPING POLICIESCHAPTER 11: DEPLOYING APISCHAPTER 12: POWER APPSPART IV: ENTERPRISE INTEGRATIONCHAPTER 13: NETWORKINGCHAPTER 14: SELF-HOSTED API GATEWAYPART V: MAINTENANCECHAPTER 15: SECURITYCHAPTER 16: LOGGING & MONITORINGCHAPTER 17: ADMINISTRATION
The Internet of Medical Things (IoMT)
INTERNET OF MEDICAL THINGS (IOMT)PROVIDING AN ESSENTIAL ADDITION TO THE REFERENCE MATERIAL AVAILABLE IN THE FIELD OF IOMT, THIS TIMELY PUBLICATION COVERS A RANGE OF APPLIED RESEARCH ON HEALTHCARE, BIOMEDICAL DATA MINING, AND THE SECURITY AND PRIVACY OF HEALTH RECORDS.With their ability to collect, analyze and transmit health data, IoMT tools are rapidly changing healthcare delivery. For patients and clinicians, these applications are playing a central part in tracking and preventing chronic illnesses — and they are poised to evolve the future of care. In this book, the authors explore the potential applications of a wave of sensor-based tools—including wearables and stand-alone devices for remote patient monitoring—and the marriage of internet-connected medical devices with patient information that ultimately sets the IoMT ecosystem apart. This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facilities and in remote locations. AUDIENCEThis book will be suitable for a wide range of researchers who are interested in acquiring in-depth knowledge on the latest IoMT-based solutions for healthcare-related problems. The book is specifically for those in artificial intelligence, cyber-physical systems, robotics, information technology, safety-critical systems, digital forensics, and application domain communities such as critical infrastructures, smart healthcare, manufacturing, and smart cities. R.J. HEMALATHA, PHD in Electronics Engineering from Sathyabama University, India. She is currently the Head of the Department of Biomedical Engineering, in Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. She has published more than 50 research papers in various international journals. D. AKILA, PHD received his degree in Computer Science from Bharathiar University, Tamilnadu, India. She is an associate professor in the Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. She has published more than 25 research papers in various international journals. D. BALAGANESH, PHD is a Dean of Faculty Computer Science and Multimedia, Lincoln University College, Malaysia. ANAND PAUL, PHD is an associate professor in the School of Computer Science and Engineering, Kyungpook National University, South Korea. He received his PhD degree in Electrical Engineering from National Cheng Kung University, Taiwan, R.O.C. in 2010. Preface xv1 IN SILICO MOLECULAR MODELING AND DOCKING ANALYSIS IN LUNG CANCER CELL PROTEINS 1Manisha Sritharan and Asita Elengoe1.1 Introduction 21.2 Methodology 41.2.1 Sequence of Protein 41.2.2 Homology Modeling 41.2.3 Physiochemical Characterization 41.2.4 Determination of Secondary Models 41.2.5 Determination of Stability of Protein Structures 41.2.6 Identification of Active Site 41.2.7 Preparation of Ligand Model 51.2.8 Docking of Target Protein and Phytocompound 51.3 Results and Discussion 51.3.1 Determination of Physiochemical Characters 51.3.2 Prediction of Secondary Structures 71.3.3 Verification of Stability of Protein Structures 71.3.4 Identification of Active Sites 141.3.5 Target Protein-Ligand Docking 141.4 Conclusion 18References 182 MEDICAL DATA CLASSIFICATION IN CLOUD COMPUTING USING SOFT COMPUTING WITH VOTING CLASSIFIER: A REVIEW 23Saurabh Sharma, Harish K. Shakya and Ashish Mishra2.1 Introduction 242.1.1 Security in Medical Big Data Analytics 242.1.1.1 Capture 242.1.1.2 Cleaning 252.1.1.3 Storage 252.1.1.4 Security 262.1.1.5 Stewardship 262.2 Access Control–Based Security 272.2.1 Authentication 272.2.1.1 User Password Authentication 282.2.1.2 Windows-Based User Authentication 282.2.1.3 Directory-Based Authentication 282.2.1.4 Certificate-Based Authentication 282.2.1.5 Smart Card–Based Authentication 292.2.1.6 Biometrics 292.2.1.7 Grid-Based Authentication 292.2.1.8 Knowledge-Based Authentication 292.2.1.9 Machine Authentication 292.2.1.10 One-Time Password (OTP) 302.2.1.11 Authority 302.2.1.12 Global Authorization 302.3 System Model 302.3.1 Role and Purpose of Design 312.3.1.1 Patients 312.3.1.2 Cloud Server 312.3.1.3 Doctor 312.4 Data Classification 322.4.1 Access Control 322.4.2 Content 332.4.3 Storage 332.4.4 Soft Computing Techniques for Data Classification 342.5 Related Work 362.6 Conclusion 42References 433 RESEARCH CHALLENGES IN PRE-COPY VIRTUAL MACHINE MIGRATION IN CLOUD ENVIRONMENT 45Nirmala Devi N. and Vengatesh Kumar S.3.1 Introduction 463.1.1 Cloud Computing 463.1.1.1 Cloud Service Provider 473.1.1.2 Data Storage and Security 473.1.2 Virtualization 483.1.2.1 Virtualization Terminology 493.1.3 Approach to Virtualization 503.1.4 Processor Issues 513.1.5 Memory Management 513.1.6 Benefits of Virtualization 513.1.7 Virtual Machine Migration 513.1.7.1 Pre-Copy 523.1.7.2 Post-Copy 523.1.7.3 Stop and Copy 533.2 Existing Technology and Its Review 543.3 Research Design 563.3.1 Basic Overview of VM Pre-Copy Live Migration 573.3.2 Improved Pre-Copy Approach 583.3.3 Time Series–Based Pre-Copy Approach 603.3.4 Memory-Bound Pre-Copy Live Migration 623.3.5 Three-Phase Optimization Method (TPO) 623.3.6 Multiphase Pre-Copy Strategy 643.4 Results 653.4.1 Finding 653.5 Discussion 693.5.1 Limitation 693.5.2 Future Scope 703.6 Conclusion 70References 714 ESTIMATION AND ANALYSIS OF PREDICTION RATE OF PRE-TRAINED DEEP LEARNING NETWORK IN CLASSIFICATION OF BRAIN TUMOR MRI IMAGES 73Krishnamoorthy Raghavan Narasu, Anima Nanda, Marshiana D., Bestley Joe and Vinoth Kumar4.1 Introduction 744.2 Classes of Brain Tumors 754.3 Literature Survey 764.4 Methodology 784.5 Conclusion 93References 955 AN INTELLIGENT HEALTHCARE MONITORING SYSTEM FOR COMA PATIENTS 99Bethanney Janney J., T. Sudhakar, Sindu Divakaran, Chandana H. and Caroline Chriselda L.5.1 Introduction 1005.2 Related Works 1025.3 Materials and Methods 1045.3.1 Existing System 1045.3.2 Proposed System 1055.3.3 Working 1055.3.4 Module Description 1065.3.4.1 Pulse Sensor 1065.3.4.2 Temperature Sensor 1075.3.4.3 Spirometer 1075.3.4.4 OpenCV (Open Source Computer Vision) 1085.3.4.5 Raspberry Pi 1085.3.4.6 USB Camera 1095.3.4.7 AVR Module 1095.3.4.8 Power Supply 1095.3.4.9 USB to TTL Converter 1105.3.4.10 EEG of Comatose Patients 1105.4 Results and Discussion 1115.5 Conclusion 116References 1176 DEEP LEARNING INTERPRETATION OF BIOMEDICAL DATA 121T.R. Thamizhvani, R. Chandrasekaran and T.R. Ineyathendral6.1 Introduction 1226.2 Deep Learning Models 1256.2.1 Recurrent Neural Networks 1256.2.2 LSTM/GRU Networks 1276.2.3 Convolutional Neural Networks 1286.2.4 Deep Belief Networks 1306.2.5 Deep Stacking Networks 1316.3 Interpretation of Deep Learning With Biomedical Data 1326.4 Conclusion 139References 1407 EVOLUTION OF ELECTRONIC HEALTH RECORDS 143G. Umashankar, Abinaya P., J. Premkumar, T. Sudhakar and S. Krishnakumar7.1 Introduction 1437.2 Traditional Paper Method 1447.3 IoMT 1447.4 Telemedicine and IoMT 1457.4.1 Advantages of Telemedicine 1457.4.2 Drawbacks 1467.4.3 IoMT Advantages with Telemedicine 1467.4.4 Limitations of IoMT With Telemedicine 1477.5 Cyber Security 1477.6 Materials and Methods 1477.6.1 General Method 1477.6.2 Data Security 1487.7 Literature Review 1487.8 Applications of Electronic Health Records 1507.8.1 Clinical Research 1507.8.1.1 Introduction 1507.8.1.2 Data Significance and Evaluation 1517.8.1.3 Conclusion 1517.8.2 Diagnosis and Monitoring 1517.8.2.1 Introduction 1517.8.2.2 Contributions 1527.8.2.3 Applications 1527.8.3 Track Medical Progression 1537.8.3.1 Introduction 1537.8.3.2 Method Used 1537.8.3.3 Conclusion 1547.8.4 Wearable Devices 1547.8.4.1 Introduction 1547.8.4.2 Proposed Method 1557.8.4.3 Conclusion 1557.9 Results and Discussion 1557.10 Challenges Ahead 1577.11 Conclusion 158References 1588 ARCHITECTURE OF IOMT IN HEALTHCARE 161A. Josephin Arockia Dhiyya8.1 Introduction 1618.1.1 On-Body Segment 1628.1.2 In-Home Segment 1628.1.3 Network Segment Layer 1638.1.4 In-Clinic Segment 1638.1.5 In-Hospital Segment 1638.1.6 Future of IoMT? 1648.2 Preferences of the Internet of Things 1658.2.1 Cost Decrease 1658.2.2 Proficiency and Efficiency 1658.2.3 Business Openings 1658.2.4 Client Experience 1668.2.5 Portability and Nimbleness 1668.3 loMT Progress in COVID-19 Situations: Presentation 1678.3.1 The IoMT Environment 1688.3.2 IoMT Pandemic Alleviation Design 1698.3.3 Man-Made Consciousness and Large Information Innovation in IoMT 1708.4 Major Applications of IoMT 171References 1729 PERFORMANCE ASSESSMENT OF IOMT SERVICES AND PROTOCOLS 173A. Keerthana and Karthiga9.1 Introduction 1749.2 IoMT Architecture and Platform 1759.2.1 Architecture 1769.2.2 Devices Integration Layer 1779.3 Types of Protocols 1779.3.1 Internet Protocol for Medical IoT Smart Devices 1779.3.1.1 HTTP 1789.3.1.2 Message Queue Telemetry Transport (MQTT) 1799.3.1.3 Constrained Application Protocol (CoAP) 1809.3.1.4 AMQP: Advanced Message Queuing Protocol (AMQP) 1819.3.1.5 Extensible Message and Presence Protocol (XMPP) 1819.3.1.6 DDS 1839.4 Testing Process in IoMT 1839.5 Issues and Challenges 1859.6 Conclusion 185References 18510 PERFORMANCE EVALUATION OF WEARABLE IOT-ENABLED MESH NETWORK FOR RURAL HEALTH MONITORING 187G. Merlin Sheeba and Y. Bevish Jinila10.1 Introduction 18810.2 Proposed System Framework 19010.2.1 System Description 19010.2.2 Health Monitoring Center 19210.2.2.1 Body Sensor 19210.2.2.2 Wireless Sensor Coordinator/Transceiver 19210.2.2.3 Ontology Information Center 19510.2.2.4 Mesh Backbone-Placement and Routing 19610.3 Experimental Evaluation 20010.4 Performance Evaluation 20110.4.1 Energy Consumption 20110.4.2 Survival Rate 20110.4.3 End-to-End Delay 20210.5 Conclusion 204References 20411 MANAGEMENT OF DIABETES MELLITUS (DM) FOR CHILDREN AND ADULTS BASED ON INTERNET OF THINGS (IOT) 207Krishnakumar S., Umashankar G., Lumen Christy V., Vikas and Hemalatha R.J.11.1 Introduction 20811.1.1 Prevalence 20911.1.2 Management of Diabetes 20911.1.3 Blood Glucose Monitoring 21011.1.4 Continuous Glucose Monitors 21111.1.5 Minimally Invasive Glucose Monitors 21111.1.6 Non-Invasive Glucose Monitors 21111.1.7 Existing System 21111.2 Materials and Methods 21211.2.1 Artificial Neural Network 21211.2.2 Data Acquisition 21311.2.3 Histogram Calculation 21311.2.4 IoT Cloud Computing 21411.2.5 Proposed System 21511.2.6 Advantages 21511.2.7 Disadvantages 21511.2.8 Applications 21611.2.9 Arduino Pro Mini 21611.2.10 LM78XX 21711.2.11 MAX30100 21811.2.12 LM35 Temperature Sensors 21811.3 Results and Discussion 21911.4 Summary 22211.5 Conclusion 222References 22312 WEARABLE HEALTH MONITORING SYSTEMS USING IOMT 225Jaya Rubi and A. Josephin Arockia Dhivya12.1 Introduction 22512.2 IoMT in Developing Wearable Health Surveillance System 22612.2.1 A Wearable Health Monitoring System with Multi-Parameters 22712.2.2 Wearable Input Device for Smart Glasses Based on a Wristband-Type Motion-Aware Touch Panel 22812.2.3 Smart Belt: A Wearable Device for Managing Abdominal Obesity 22812.2.4 Smart Bracelets: Automating the Personal Safety Using Wearable Smart Jewelry 22812.3 Vital Parameters That Can Be Monitored Using Wearable Devices 22912.3.1 Electrocardiogram 23012.3.2 Heart Rate 23112.3.3 Blood Pressure 23212.3.4 Respiration Rate 23212.3.5 Blood Oxygen Saturation 23412.3.6 Blood Glucose 23512.3.7 Skin Perspiration 23612.3.8 Capnography 23812.3.9 Body Temperature 23912.4 Challenges Faced in Customizing Wearable Devices 24012.4.1 Data Privacy 24012.4.2 Data Exchange 24012.4.3 Availability of Resources 24112.4.4 Storage Capacity 24112.4.5 Modeling the Relationship Between Acquired Measurement and Diseases 24212.4.6 Real-Time Processing 24212.4.7 Intelligence in Medical Care 24212.5 Conclusion 243References 24413 FUTURE OF HEALTHCARE: BIOMEDICAL BIG DATA ANALYSIS AND IOMT 247Tamiziniyan G. and Keerthana A.13.1 Introduction 24813.2 Big Data and IoT in Healthcare Industry 25013.3 Biomedical Big Data Types 25113.3.1 Electronic Health Records 25213.3.2 Administrative and Claims Data 25213.3.3 International Patient Disease Registries 25213.3.4 National Health Surveys 25313.3.5 Clinical Research and Trials Data 25413.4 Biomedical Data Acquisition Using IoT 25413.4.1 Wearable Sensor Suit 25413.4.2 Smartphones 25513.4.3 Smart Watches 25513.5 Biomedical Data Management Using IoT 25613.5.1 Apache Spark Framework 25713.5.2 MapReduce 25813.5.3 Apache Hadoop 25813.5.4 Clustering Algorithms 25913.5.5 K-Means Clustering 25913.5.6 Fuzzy C-Means Clustering 26013.5.7 DBSCAN 26113.6 Impact of Big Data and IoMT in Healthcare 26213.7 Discussions and Conclusions 263References 26414 MEDICAL DATA SECURITY USING BLOCKCHAIN WITH SOFT COMPUTING TECHNIQUES: A REVIEW 269Saurabh Sharma, Harish K. Shakya and Ashish Mishra14.1 Introduction 27014.2 Blockchain 27214.2.1 Blockchain Architecture 27214.2.2 Types of Blockchain Architecture 27314.2.3 Blockchain Applications 27414.2.4 General Applications of the Blockchain 27614.3 Blockchain as a Decentralized Security Framework 27714.3.1 Characteristics of Blockchain 27814.3.2 Limitations of Blockchain Technology 28014.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science 28114.4.1 Data Science in Healthcare 28114.5 Literature Review: Medical Data Security in Cloud Storage 28114.6 Conclusion 286References 28715 ELECTRONIC HEALTH RECORDS: A TRANSITIONAL VIEW 289Srividhya G.15.1 Introduction 28915.2 Ancient Medical Record, 1600 BC 29015.3 Greek Medical Record 29115.4 Islamic Medical Record 29115.5 European Civilization 29215.6 Swedish Health Record System 29215.7 French and German Contributions 29315.8 American Descriptions 29315.9 Beginning of Electronic Health Recording 29715.10 Conclusion 298References 298Index 301
Multimedia Security 1
Today, more than 80% of the data transmitted over networks and archived on our computers, tablets, cell phones or clouds is multimedia data - images, videos, audio, 3D data. The applications of this data range from video games to healthcare, and include computer-aided design, video surveillance and biometrics.It is becoming increasingly urgent to secure this data, not only during transmission and archiving, but also during its retrieval and use. Indeed, in today’s "all-digital" world, it is becoming ever-easier to copy data, view it unrightfully, steal it or falsify it.Multimedia Security 1 analyzes the issues of the authentication of multimedia data, code and the embedding of hidden data, both from the point of view of defense and attack. Regarding the embedding of hidden data, it also covers invisibility, color, tracing and 3D data, as well as the detection of hidden messages in an image by steganalysis. WILLIAM PUECH is Professor of Computer Science at Université de Montpellier, France. His research focuses on image processing and multimedia security in particular, from its theories to its applications.Foreword by Gildas Avoine xiForeword by Cédric Richard xiiiPreface xvilliam PUECHCHAPTER 1 HOW TO RECONSTRUCT THE HISTORY OF A DIGITAL IMAGE, AND OF ITS ALTERATIONS 1Quentin BAMMEY, Miguel COLOM, Thibaud EHRET, Marina GARDELLA, Rafael GROMPONE, Jean-Michel MOREL, Tina NIKOUKHAH and Denis PERRAUD1.1 Introduction 21.1.1 General context 21.1.2 Criminal background 31.1.3 Issues for law enforcement 41.1.4 Current methods and tools of law enforcement 51.1.5 Outline of this chapter 51.2 Describing the image processing chain 81.2.1 Raw image acquisition 81.2.2 Demosaicing 81.2.3 Color correction 101.2.4 JPEG compression 111.3 Traces left on noise by image manipulation 111.3.1 Non-parametric estimation of noise in images 111.3.2 Transformation of noise in the processing chain 131.3.3 Forgery detection through noise analysis 151.4 Demosaicing and its traces 181.4.1 Forgery detection through demosaicing analysis 191.4.2 Detecting the position of the Bayer matrix 201.4.3 Limits of detection demosaicing 231.5 JPEG compression, its traces and the detection of its alterations 231.5.1 The JPEG compression algorithm 231.5.2 Grid detection 251.5.3 Detecting the quantization matrix 271.5.4 Beyond indicators, making decisions with a statistical model 281.6 Internal similarities and manipulations 311.7 Direct detection of image manipulation 331.8 Conclusion 341.9 References 35CHAPTER 2 DEEP NEURAL NETWORK ATTACKS AND DEFENSE: THE CASE OF IMAGE CLASSIFICATION 41Hanwei ZHANG, Teddy FURON, Laurent AMSALEG and Yannis AVRITHIS2.1 Introduction 412.1.1 A bit of history and vocabulary 422.1.2 Machine learning 442.1.3 The classification of images by deep neural networks 462.1.4 Deep Dreams 482.2 Adversarial images: definition 492.3 Attacks: making adversarial images 512.3.1 About white box 522.3.2 Black or gray box 622.4 Defenses 642.4.1 Reactive defenses 642.4.2 Proactive defenses 662.4.3 Obfuscation technique 672.4.4 Defenses: conclusion 682.5 Conclusion 682.6 References 69CHAPTER 3 CODES AND WATERMARKS 77Pascal LEFEVRE, Philippe CARRE and Philippe GABORIT3.1 Introduction 773.2 Study framework: robust watermarking 783.3 Index modulation 813.3.1 LQIM: insertion 813.3.2 LQIM: detection 823.4 Error-correcting codes approach 823.4.1 Generalities 843.4.2 Codes by concatenation 863.4.3 Hamming codes 883.4.4 BCH codes 903.4.5 RS codes 933.5 Contradictory objectives of watermarking: the impact of codes 963.6 Latest developments in the use of correction codes for watermarking 983.7 Illustration of the influence of the type of code, according to the attacks 1023.7.1 JPEG compression 1033.7.2 Additive Gaussian noise 1063.7.3 Saturation 1063.8 Using the rank metric 1083.8.1 Rank metric correcting codes 1093.8.2 Code by rank metric: a robust watermarking method for image cropping 1133.9 Conclusion 1213.10 References 121CHAPTER 4 INVISIBILITY 129Pascal LEFEVRE, Philippe CARRE and David ALLEYSSON4.1 Introduction 1294.2 Color watermarking: an approach history? 1314.2.1 Vector quantization in the RGB space 1324.2.2 Choosing a color direction 1334.3 Quaternionic context for watermarking color images 1354.3.1 Quaternions and color images 1354.3.2 Quaternionic Fourier transforms 1374.4 Psychovisual approach to color watermarking 1394.4.1 Neurogeometry and perception 1394.4.2 Photoreceptor model and trichromatic vision 1414.4.3 Model approximation 1444.4.4 Parameters of the model 1454.4.5 Application to watermarking color images 1464.4.6 Conversions 1474.4.7 Psychovisual algorithm for color images 1484.4.8 Experimental validation of the psychovisual approach for color watermarking 1514.5 Conclusion 1554.6 References 157CHAPTER 5 STEGANOGRAPHY: EMBEDDING DATA INTO MULTIMEDIA CONTENT 161Patrick BAS, Remi COGRANNE and Marc CHAUMONT5.1 Introduction and theoretical foundations 1625.2 Fundamental principles 1635.2.1 Maximization of the size of the embedded message 1635.2.2 Message encoding 1655.2.3 Detectability minimization 1665.3 Digital image steganography: basic methods 1685.3.1 LSB substitution and matching 1685.3.2 Adaptive embedding methods 1695.4 Advanced principles in steganography 1725.4.1 Synchronization of modifications 1735.4.2 Batch steganography 1755.4.3 Steganography of color images 1775.4.4 Use of side information 1785.4.5 Steganography mimicking a statistical model 1805.4.6 Adversarial steganography 1825.5 Conclusion 1865.6 References 186CHAPTER 6 TRAITOR TRACING 189Teddy FURON6.1 Introduction 1896.1.1 The contribution of the cryptography community 1906.1.2 Multimedia content 1916.1.3 Error probabilities 1926.1.4 Collusion strategy 1926.2 The original Tardos code 1946.2.1 Constructing the code 1956.2.2 The collusion strategy and its impact on the pirated series 1956.2.3 Accusation with a simple decoder 1976.2.4 Study of the Tardos code-Škori´c original 1996.2.5 Advantages 2026.2.6 The problems 2046.3 Tardos and his successors 2056.3.1 Length of the code 2056.3.2 Other criteria 2056.3.3 Extensions 2076.4 Research of better score functions 2086.4.1 The optimal score function 2086.4.2 The theory of the compound communication channel 2096.4.3 Adaptive score functions 2116.4.4 Comparison 2136.5 How to find a better threshold 2136.6 Conclusion 2156.7 References 216CHAPTER 7 3D WATERMARKING 219Sebastien BEUGNON, Vincent ITIER and William PUECH7.1 Introduction 2207.2 Preliminaries 2217.2.1 Digital watermarking 2217.2.2 3D objects 2227.3 Synchronization 2247.3.1 Traversal scheduling 2247.3.2 Patch scheduling 2247.3.3 Scheduling based on graphs 2257.4 3D data hiding 2307.4.1 Transformed domains 2317.4.2 Spatial domain 2317.4.3 Other domains 2327.5 Presentation of a high-capacity data hiding method 2337.5.1 Embedding of the message 2347.5.2 Causality issue 2357.6 Improvements 2367.6.1 Error-correcting codes 2367.6.2 Statistical arithmetic coding 2367.6.3 Partitioning and acceleration structures 2377.7 Experimental results 2387.8 Trends in high-capacity 3D data hiding 2407.8.1 Steganalysis 2407.8.2 Security analysis 2417.8.3 3D printing 2427.9 Conclusion 2427.10 References 243CHAPTER 8 STEGANALYSIS: DETECTION OF HIDDEN DATA IN MULTIMEDIA CONTENT 247Remi COGRANNE, Marc CHAUMONT and Patrick BAS8.1 Introduction, challenges and constraints 2478.1.1 The different aims of steganalysis 2488.1.2 Different methods to carry out steganalysis 2498.2 Incompatible signature detection 2508.3 Detection using statistical methods 2528.3.1 Statistical test of χ2 2528.3.2 Likelihood-ratio test 2568.3.3 LSB match detection 2618.4 Supervised learning detection 2638.4.1 Extraction of characteristics in the spatial domain 2648.4.2 Learning how to detect with features 2698.5 Detection by deep neural networks 2708.5.1 Foundation of a deep neural network 2718.5.2 The preprocessing module 2728.6 Current avenues of research 2798.6.1 The problem of Cover-Source mismatch 2798.6.2 The problem with steganalysis in real life 2798.6.3 Reliable steganalysis 2808.6.4 Steganalysis of color images 2808.6.5 Taking into account the adaptivity of steganography 2818.6.6 Grouped steganalysis (batch steganalysis) 2818.6.7 Universal steganalysis 2828.7 Conclusion 2838.8 References 283List of Authors 289Index 293
Industrial Internet of Things (IIoT)
INDUSTRIAL INTERNET OF THINGS (IIOT)THIS BOOK DISCUSSES HOW THE INDUSTRIAL INTERNET WILL BE AUGMENTED THROUGH INCREASED NETWORK AGILITY, INTEGRATED ARTIFICIAL INTELLIGENCE (AI) AND THE CAPACITY TO DEPLOY, AUTOMATE, ORCHESTRATE, AND SECURE DIVERSE USER CASES AT HYPERSCALE.Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines. The volume will help industry leaders by* Advancing hands-on experience working with industrial architecture* Demonstrating the potential of cloud-based Industrial IoT platforms, analytics, and protocols* Putting forward business models revitalizing the workforce with Industry 4.0.AUDIENCEResearchers and scholars in industrial engineering and manufacturing, artificial intelligence, cyber-physical systems, robotics, safety engineering, safety-critical systems, and application domain communities such as aerospace, agriculture, automotive, critical infrastructures, healthcare, manufacturing, retail, smart transports, smart cities, and smart healthcare. R. ANANDAN, PHD completed his degree in Computer Science and Engineering, is an IBMS/390 Mainframe professional, is recognized as a Chartered Engineer from the Institution of Engineers in India, and received a fellowship from Bose Science Society, India. He is a professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He has published more than 110 research papers in various international journals, authored 9 books in the computer science and engineering disciplines, and has received 13 awards. G. SUSEENDRAN, PHD received his degree in Information Technology-Mathematics from Presidency College, University of Madras, Tamil Nadu, India. He passed away during the production of this book. SOUVIK PAL, PHD is an associate professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. Dr. Pal received his PhD in the field of computer science and engineering. He is the editor/author of 12 books and has been granted 3 patents. He is the recipient of a Lifetime Achievement Award in 2018. NOOR ZAMAN, PHD completed his degree in IT from University Technology Petronas (UTP) Malaysia. He has authored many research papers in WoS/ISI indexed and impact factor research journals and edited 12 books in computer science. Preface xvii1 A LOOK AT IIOT: THE PERSPECTIVE OF IOT TECHNOLOGY APPLIED IN THE INDUSTRIAL FIELD 1Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur, Yuzo Iano, Andrea Coimbra Segatti, Giulliano Paes Carnielli, Julio Cesar Pereira, Henri Alves de Godoy and Elder Carlos Fernandes1.1 Introduction 21.2 Relationship Between Artificial Intelligence and IoT 51.2.1 AI Concept 61.2.2 IoT Concept 101.3 IoT Ecosystem 151.3.1 Industry 4.0 Concept 181.3.2 Industrial Internet of Things 191.4 Discussion 211.5 Trends 231.6 Conclusions 24References 262 ANALYSIS ON SECURITY IN IOT DEVICES—AN OVERVIEW 31T. Nalini and T. Murali Krishna2.1 Introduction 322.2 Security Properties 332.3 Security Challenges of IoT 342.3.1 Classification of Security Levels 352.3.1.1 At Information Level 362.3.1.2 At Access Level 362.3.1.3 At Functional Level 362.3.2 Classification of IoT Layered Architecture 372.3.2.1 Edge Layer 372.3.2.2 Access Layer 372.3.2.3 Application Layer 372.4 IoT Security Threats 382.4.1 Physical Device Threats 392.4.1.1 Device-Threats 392.4.1.2 Resource Led Constraints 392.4.2 Network-Oriented Communication Assaults 392.4.2.1 Structure 402.4.2.2 Protocol 402.4.3 Data-Based Threats 412.4.3.1 Confidentiality 412.4.3.2 Availability 412.4.3.3 Integrity 422.5 Assaults in IoT Devices 432.5.1 Devices of IoT 432.5.2 Gateways and Networking Devices 442.5.3 Cloud Servers and Control Devices 452.6 Security Analysis of IoT Platforms 462.6.1 ARTIK 462.6.2 GiGA IoT Makers 472.6.3 AWS IoT 472.6.4 Azure IoT 472.6.5 Google Cloud IoT (GC IoT) 482.7 Future Research Approaches 492.7.1 Blockchain Technology 512.7.2 5G Technology 522.7.3 Fog Computing (FC) and Edge Computing (EC) 52References 543 SMART AUTOMATION, SMART ENERGY, AND GRID MANAGEMENT CHALLENGES 59J. Gayathri Monicka and C. Amuthadevi3.1 Introduction 603.2 Internet of Things and Smart Grids 623.2.1 Smart Grid in IoT 633.2.2 IoT Application 643.2.3 Trials and Imminent Investigation Guidelines 663.3 Conceptual Model of Smart Grid 673.4 Building Computerization 713.4.1 Smart Lighting 733.4.2 Smart Parking 733.4.3 Smart Buildings 743.4.4 Smart Grid 753.4.5 Integration IoT in SG 773.5 Challenges and Solutions 813.6 Conclusions 83References 834 INDUSTRIAL AUTOMATION (IIOT) 4.0: AN INSIGHT INTO SAFETY MANAGEMENT 89C. Amuthadevi and J. Gayathri Monicka4.1 Introduction 894.1.1 Fundamental Terms in IIoT 914.1.1.1 Cloud Computing 924.1.1.2 Big Data Analytics 924.1.1.3 Fog/Edge Computing 924.1.1.4 Internet of Things 934.1.1.5 Cyber-Physical-System 944.1.1.6 Artificial Intelligence 954.1.1.7 Machine Learning 954.1.1.8 Machine-to-Machine Communication 994.1.2 Intelligent Analytics 994.1.3 Predictive Maintenance 1004.1.4 Disaster Predication and Safety Management 1014.1.4.1 Natural Disasters 1014.1.4.2 Disaster Lifecycle 1024.1.4.3 Disaster Predication 1034.1.4.4 Safety Management 1044.1.5 Optimization 1054.2 Existing Technology and Its Review 1064.2.1 Survey on Predictive Analysis in Natural Disasters 1064.2.2 Survey on Safety Management and Recovery 1084.2.3 Survey on Optimizing Solutions in Natural Disasters 1094.3 Research Limitation 1104.3.1 Forward-Looking Strategic Vision (FVS) 1104.3.2 Availability of Data 1114.3.3 Load Balancing 1114.3.4 Energy Saving and Optimization 1114.3.5 Cost Benefit Analysis 1124.3.6 Misguidance of Analysis 1124.4 Finding 1134.4.1 Data Driven Reasoning 1134.4.2 Cognitive Ability 1134.4.3 Edge Intelligence 1134.4.4 Effect of ML Algorithms and Optimization 1144.4.5 Security 1144.5 Conclusion and Future Research 1144.5.1 Conclusion 1144.5.2 Future Research 114References 1155 AN INDUSTRIAL PERSPECTIVE ON RESTRUCTURED POWER SYSTEMS USING SOFT COMPUTING TECHNIQUES 119Kuntal Bhattacharjee, Akhilesh Arvind Nimje, Shanker D. Godwal and Sudeep Tanwar5.1 Introduction 1205.2 Fuzzy Logic 1215.2.1 Fuzzy Sets 1215.2.2 Fuzzy Logic Basics 1225.2.3 Fuzzy Logic and Power System 1225.2.4 Fuzzy Logic—Automatic Generation Control 1235.2.5 Fuzzy Microgrid Wind 1235.3 Genetic Algorithm 1235.3.1 Important Aspects of Genetic Algorithm 1245.3.2 Standard Genetic Algorithm 1265.3.3 Genetic Algorithm and Its Application 1275.3.4 Power System and Genetic Algorithm 1275.3.5 Economic Dispatch Using Genetic Algorithm 1285.4 Artificial Neural Network 1285.4.1 The Biological Neuron 1295.4.2 A Formal Definition of Neural Network 1305.4.3 Neural Network Models 1315.4.4 Rosenblatt’s Perceptron 1315.4.5 Feedforward and Recurrent Networks 1325.4.6 Back Propagation Algorithm 1335.4.7 Forward Propagation 1335.4.8 Algorithm 1345.4.9 Recurrent Network 1355.4.10 Examples of Neural Networks 1365.4.10.1 AND Operation 1365.4.10.2 OR Operation 1375.4.10.3 XOR Operation 1375.4.11 Key Components of an Artificial Neuron Network 1385.4.12 Neural Network Training 1415.4.13 Training Types 1425.4.13.1 Supervised Training 1425.4.13.2 Unsupervised Training 1425.4.14 Learning Rates 1425.4.15 Learning Laws 1435.4.16 Restructured Power System 1445.4.17 Advantages of Precise Forecasting of the Price 1455.5 Conclusion 145References 1466 RECENT ADVANCES IN WEARABLE ANTENNAS: A SURVEY 149Harvinder Kaur and Paras Chawla6.1 Introduction 1506.2 Types of Antennas 1536.2.1 Description of Wearable Antennas 1536.2.1.1 Microstrip Patch Antenna 1536.2.1.2 Substrate Integrated Waveguide Antenna 1536.2.1.3 Planar Inverted-F Antenna 1536.2.1.4 Monopole Antenna 1536.2.1.5 Metasurface Loaded Antenna 1546.3 Design of Wearable Antennas 1546.3.1 Effect of Substrate and Ground Geometries on Antenna Design 1546.3.1.1 Conducting Coating on Substrate 1546.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure 1576.3.1.3 Partial Ground Plane 1586.3.2 Logo Antennas 1596.3.3 Embroidered Antenna 1596.3.4 Wearable Antenna Based on Electromagnetic Band Gap 1606.3.5 Wearable Reconfigurable Antenna 1616.4 Textile Antennas 1626.5 Comparison of Wearable Antenna Designs 1686.6 Fractal Antennas 1686.6.1 Minkowski Fractal Geometries Using Wearable Electro-Textile Antennas 1716.6.2 Antenna Design With Defected Semi-Elliptical Ground Plane 1726.6.3 Double-Fractal Layer Wearable Antenna 1726.6.4 Development of Embroidered Sierpinski Carpet Antenna 1726.7 Future Challenges of Wearable Antenna Designs 1746.8 Conclusion 174References 1757 AN OVERVIEW OF IOT AND ITS APPLICATION WITH MACHINE LEARNING IN DATA CENTER 181Manikandan Ramanathan and Kumar Narayanan7.1 Introduction 1817.1.1 6LoWPAN 1837.1.2 Data Protocols 1857.1.2.1 CoAP 1857.1.2.2 MQTT 1877.1.2.3 Rest APIs 1877.1.3 IoT Components 1897.1.3.1 Hardware 1907.1.3.2 Middleware 1907.1.3.3 Visualization 1917.2 Data Center and Internet of Things 1917.2.1 Modern Data Centers 1917.2.2 Data Storage 1917.2.3 Computing Process 1927.2.3.1 Fog Computing 1927.2.3.2 Edge Computing 1947.2.3.3 Cloud Computing 1947.2.3.4 Distributed Computing 1957.2.3.5 Comparison of Cloud Computing and Fog Computing 1967.3 Machine Learning Models and IoT 1967.3.1 Classifications of Machine Learning Supported in IoT 1977.3.1.1 Supervised Learning 1977.3.1.2 Unsupervised Learning 1987.3.1.3 Reinforcement Learning 1987.3.1.4 Ensemble Learning 1997.3.1.5 Neural Network 1997.4 Challenges in Data Center and IoT 1997.4.1 Major Challenges 1997.5 Conclusion 201References 2018 IMPACT OF IOT TO MEET CHALLENGES IN DRONE DELIVERY SYSTEM 203J. Ranjani, P. Kalaichelvi, V.K.G Kalaiselvi, D. Deepika Sree and K. Swathi8.1 Introduction 2048.1.1 IoT Components 2048.1.2 Main Division to Apply IoT in Aviation 2058.1.3 Required Field of IoT in Aviation 2068.1.3.1 Airports as Smart Cities or Airports as Platforms 2078.1.3.2 Architecture of Multidrone 2088.1.3.3 The Multidrone Design has the Accompanying Prerequisites 2088.2 Literature Survey 2098.3 Smart Airport Architecture 2118.4 Barriers to IoT Implementation 2158.4.1 How is the Internet of Things Converting the Aviation Enterprise? 2168.5 Current Technologies in Aviation Industry 2168.5.1 Methodology or Research Design 2178.6 IoT Adoption Challenges 2188.6.1 Deployment of IoT Applications on BroadScale Includes the Underlying Challenges 2188.7 Transforming Airline Industry With Internet of Things 2198.7.1 How the IoT Is Improving the Aviation Industry 2198.7.1.1 IoT: Game Changer for Aviation Industry 2208.7.2 Applications of AI in the Aviation Industry 2208.7.2.1 Ticketing Systems 2208.7.2.2 Flight Maintenance 2218.7.2.3 Fuel Efficiency 2218.7.2.4 Crew Management 2218.7.2.5 Flight Health Checks and Maintenance 2218.7.2.6 In-Flight Experience Management 2228.7.2.7 Luggage Tracking 2228.7.2.8 Airport Management 2228.7.2.9 Just the Beginning 2228.8 Revolution of Change (Paradigm Shift) 2228.9 The Following Diagram Shows the Design of the Application 2238.10 Discussion, Limitations, Future Research, and Conclusion 2248.10.1 Growth of Aviation IoT Industry 2248.10.2 IoT Applications—Benefits 2258.10.3 Operational Efficiency 2258.10.4 Strategic Differentiation 2258.10.5 New Revenue 2268.11 Present and Future Scopes 2268.11.1 Improving Passenger Experience 2268.11.2 Safety 2278.11.3 Management of Goods and Luggage 2278.11.4 Saving 2278.12 Conclusion 227References 2279 IOT-BASED WATER MANAGEMENT SYSTEM FOR A HEALTHY LIFE 229N. Meenakshi, V. Pandimurugan and S. Rajasoundaran9.1 Introduction 2309.1.1 Human Activities as a Source of Pollutants 2309.2 Water Management Using IoT 2319.2.1 Water Quality Management Based on IoT Framework 2329.3 IoT Characteristics and Measurement Parameters 2339.4 Platforms and Configurations 2359.5 Water Quality Measuring Sensors and Data Analysis 2399.6 Wastewater and Storm Water Monitoring Using IoT 2419.6.1 System Initialization 2419.6.2 Capture and Storage of Information 2419.6.3 Information Modeling 2419.6.4 Visualization and Management of the Information 2439.7 Sensing and Sampling of Water Treatment Using IoT 244References 24610 FUEL COST OPTIMIZATION USING IOT IN AIR TRAVEL 249P. Kalaichelvi, V. Akila, J. Ranjani, S. Sowmiya and C. Divya10.1 Introduction 25010.1.1 Introduction to IoT 25010.1.2 Processing IoT Data 25010.1.3 Advantages of IoT 25110.1.4 Disadvantages of IoT 25110.1.5 IoT Standards 25110.1.6 Lite Operating System (Lite OS) 25110.1.7 Low Range Wide Area Network (LoRaWAN) 25210.2 Emerging Frameworks in IoT 25210.2.1 Amazon Web Service (AWS) 25210.2.2 Azure 25210.2.3 Brillo/Weave Statement 25210.2.4 Calvin 25210.3 Applications of IoT 25310.3.1 Healthcare in IoT 25310.3.2 Smart Construction and Smart Vehicles 25410.3.3 IoT in Agriculture 25410.3.4 IoT in Baggage Tracking 25410.3.5 Luggage Logbook 25410.3.6 Electrical Airline Logbook 25410.4 IoT for Smart Airports 25510.4.1 IoT in Smart Operation in Airline Industries 25710.4.2 Fuel Emissions on Fly 25810.4.3 Important Things in Findings 25810.5 Related Work 25810.6 Existing System and Analysis 26410.6.1 Technology Used in the System 26510.7 Proposed System 26810.8 Components in Fuel Reduction 27610.9 Conclusion 27610.10 Future Enhancements 277References 27711 OBJECT DETECTION IN IOT-BASED SMART REFRIGERATORS USING CNN 281Ashwathan R., Asnath Victy Phamila Y., Geetha S. and Kalaivani K.11.1 Introduction 28211.2 Literature Survey 28311.3 Materials and Methods 28711.3.1 Image Processing 29211.3.2 Product Sensing 29211.3.3 Quality Detection 29311.3.4 Android Application 29311.4 Results and Discussion 29411.5 Conclusion 299References 29912 EFFECTIVE METHODOLOGIES IN PHARMACOVIGILANCE FOR IDENTIFYING ADVERSE DRUG REACTIONS USING IOT 301Latha Parthiban, Maithili Devi Reddy and A. Kumaravel12.1 Introduction 30212.2 Literature Review 30212.3 Data Mining Tasks 30412.3.1 Classification 30512.3.2 Regression 30612.3.3 Clustering 30612.3.4 Summarization 30612.3.5 Dependency Modeling 30612.3.6 Association Rule Discovery 30712.3.7 Outlier Detection 30712.3.8 Prediction 30712.4 Feature Selection Techniques in Data Mining 30812.4.1 GAs for Feature Selection 30812.4.2 GP for Feature Selection 30912.4.3 PSO for Feature Selection 31012.4.4 ACO for Feature Selection 31112.5 Classification With Neural Predictive Classifier 31212.5.1 Neural Predictive Classifier 31312.5.2 MapReduce Function on Neural Class 31712.6 Conclusions 319References 31913 IMPACT OF COVID-19 ON IIOT 321K. Priyadarsini, S. Karthik, K. Malathi and M.V.V Rama Rao13.1 Introduction 32113.1.1 The Use of IoT During COVID-19 32113.1.2 Consumer IoT 32213.1.3 Commercial IoT 32213.1.4 Industrial Internet of Things (IIoT) 32213.1.5 Infrastructure IoT 32213.1.6 Role of IoT in COVID-19 Response 32313.1.7 Telehealth Consultations 32313.1.8 Digital Diagnostics 32313.1.9 Remote Monitoring 32313.1.10 Robot Assistance 32313.2 The Benefits of Industrial IoT 32613.2.1 How IIoT is Being Used 32713.2.2 Remote Monitoring 32713.2.3 Predictive Maintenance 32813.3 The Challenges of Wide-Spread IIoT Implementation 32913.3.1 Health and Safety Monitoring Will Accelerate Automation and Remote Monitoring 33013.3.2 Integrating Sensor and Camera Data Improves Safety and Efficiency 33013.3.3 IIoT-Supported Safety for Customers Reduces Liability for Businesses 33113.3.4 Predictive Maintenance Will Deliver for Organizations That Do the Work 33213.3.5 Building on the Lessons of 2020 33213.4 Effects of COVID-19 on Industrial Manufacturing 33213.4.1 New Challenges for Industrial Manufacturing 33313.4.2 Smarter Manufacturing for Actionable Insights 33313.4.3 A Promising Future for IIoT Adoption 33413.5 Winners and Losers—The Impact on IoT/Connected Applications and Digital Transformation due toCOVID-19 Impact 33513.6 The Impact of COVID-19 on IoT Applications 33713.6.1 Decreased Interest in Consumer IoT Devices 33813.6.2 Remote Asset Access Becomes Important 33813.6.3 Digital Twins Help With Scenario Planning 33913.6.4 New Uses for Drones 33913.6.5 Specific IoT Health Applications Surge 34013.6.6 Track and Trace Solutions Get Used More Extensively 34013.6.7 Smart City Data Platforms Become Key 34013.7 The Impact of COVID-19 on Technology in General 34113.7.1 Ongoing Projects Are Paused 34113.7.2 Some Enterprise Technologies Take Off 34113.7.3 Declining Demand for New Projects/Devices/ Services 34213.7.4 Many Digitalization Initiatives Get Accelerated or Intensified 34213.7.5 The Digital Divide Widens 34313.8 The Impact of COVID-19 on Specific IoT Technologies 34313.8.1 IoT Networks Largely Unaffected 34313.8.2 Technology Roadmaps Get Delayed 34413.9 Coronavirus With IoT, Can Coronavirus Be Restrained? 34413.10 The Potential of IoT in Coronavirus Like Disease Control 34513.11 Conclusion 346References 34614 A COMPREHENSIVE COMPOSITE OF SMART AMBULANCE BOOKING AND TRACKING SYSTEMS USING IOT FOR DIGITAL SERVICES 349Sumanta Chatterjee, Pabitra Kumar Bhunia, Poulami Mondal, Aishwarya Sadhu and Anusua Biswas14.1 Introduction 35014.2 Literature Review 35314.3 Design of Smart Ambulance Booking System Through App 35614.4 Smart Ambulance Booking 35914.4.1 Welcome Page 36014.4.2 Sign Up 36014.4.3 Home Page 36114.4.4 Ambulance Section 36114.4.5 Ambulance Selection Page 36214.4.6 Confirmation of Booking and Tracking 36314.5 Result and Discussion 36314.5.1 How It Works? 36514.6 Conclusion 36514.7 Future Scope 366References 36615 AN EFFICIENT ELDERLY DISEASE PREDICTION AND PRIVACY PRESERVATION USING INTERNET OF THINGS 369Resmi G. Nair and N. Kumar15.1 Introduction 37015.2 Literature Survey 37115.3 Problem Statement 37215.4 Proposed Methodology 37315.4.1 Design a Smart Wearable Device 37315.4.2 Normalization 37415.4.3 Feature Extraction 37715.4.4 Classification 37815.4.5 Polynomial HMAC Algorithm 37915.5 Result and Discussion 38215.5.1 Accuracy 38215.5.2 Positive Predictive Value 38215.5.3 Sensitivity 38315.5.4 Specificity 38315.5.5 False Out 38315.5.6 False Discovery Rate 38315.5.7 Miss Rate 38315.5.8 F-Score 38315.6 Conclusion 390References 390Index 393
Automatisierter Test numerischer Fehler in Softwaresystemen mit physikbasierten Berechnungen für eingebettete Systeme
Philipp Göttlich stellt ein Konzept zum automatisierten Testen von numerischen Fehlern in Softwaresystemen mit physikbasierten Berechnungen vor und gibt dabei einen weitreichenden Überblick der Arten und Auswirkungen numerischer Fehler. Die wesentlichen Neuerungen des Konzepts spiegeln sich in der optimierungsbasierten Erzeugung geeigneter Testsignale und den Back-to-Back Tests einzelner Entwicklungsartefakte zur präzisen Fehlerlokalisierung wider. Am Beispiel von drei Softwaresystemen eines aktuellen Forschungsprojektes und dem Vergleich mit Referenztests wird die hohe Effizienz des Ansatzes bei der Analyse nachgewiesen. Auch die Erweiterbarkeit des Ansatzes wird im Verlauf der Arbeit demonstriert und dient als Ausgangspunkt für weitere Studien. Grundlagen von Softwaresystemen mit physikbasierten Berechnungen.- Beschreibung und Klassifizierung von Fehlerquellen.- Konzept eines automatisierten Test- und Testsignalgenerierungsansatzes.- Exemplarische Untersuchung des Lösungsansatzes.
Robot Operating System (ROS) for Absolute Beginners
Start programming your own robots using Robot Operation System (ROS). Targeted for absolute beginners in ROS, Linux, and Python, this guide lets you build your own robotics projects.You'll learn the basic foundation of Ubuntu Linux. Begin with the fundamentals. Installation and useful commands will give you the basic tools you need while programming a robot. Then add useful software applications that can be used while making robots. Programming robots can be done using any of the programming languages. Most popular programming languages are Python and C++. You will incorporate the fundamentals of C++ by learning object oriented programing concepts from example and building C++ projects.Finally, tackle an ROS hands-on project to apply all the concepts of ROS you've learned. The aim of the project is to perform a dead-reckoning using a cheap mobile robot. You can command your robot's position on Rviz and your robot will move to that position! Not only will you learn to program, you'll gain hands-on experience working with hardware to create a real robot.WHAT YOU’LL LEARN* Install Ubuntu 20* Install ROS Noetic* Use ROS Programming with roscpp and rospy * Build a mobile robot from scratch using ROSWHO THIS BOOK IS FORRobotics enthusiast with little or no prior programming experience.LENTIN JOSEPH is an author, roboticist and robotics entrepreneur from India. He runs a robotics software company called Qbotics Labs in Kochi/Kerala. He has 10 years of experience in the robotics domain primarily in Robot Operating System, OpenCV, and PCL.He has authored 8 books in ROS, namely, Learning Robotics using Python first and second edition, Mastering ROS for Robotics Programming first and second edition, ROS Robotics Projects first and second edition, ROS Learning Path and Robot Operating System for Absolute Beginners.He has his Masters in Robotics and Automation from India and has also worked at Robotics Institute, CMU, USA. He is a TEDx speaker.ALEENA JOHNY is a robotics software engineer currently working at Qbotics Labs from India. She completed her M. Tech and B. Tech from Rajagiri School of Engineering and Technology (RSET), Kerala. After her post-graduation, she worked as an Asst. Professor in computer science for 1 year. After that, she started working in Qbotics Labs. She has experience with robotics software platforms such as Robot Operating System (ROS), Open-CV, and Gazebo. She has published a research paper in International Journal and National Conferences.ROBOT OPERATING SYSTEM FOR ABSOLUTE BEGINNERSCHAPTER 1: GETTING STARTED WITH UBUNTU/LINUX FOR ROBOTICSThis chapter will give the basic foundation of Ubuntu Linux. Starting from the fundamentals, installation and useful commands which are using while building and programming a robot, we will also see useful software applications which can be used while making robots.CHAPTER 2: FUNDAMENTALS OF PYTHON AND C++ FOR ROBOTIC PROGRAMMINGProgramming robots can be done using any of the programming languages. Most popular programming languages are Python and C++. In this chapter, we will see fundamentals of Python which are mainly used for programming robots. This chapter will also cover C++ and the important topics needed to program a robot.CHAPTER 3: KICK STARTING ROBOT OPERATING SYSTEMThis chapter starts discussing Robot Operating System, Fundamentals, Installing, ROS tools and ROS commands it on Ubuntu.CHAPTER 4: PROGRAMMING WITH ROSThis chapter is fully focused on programming ROS using C++ and Python. We will see some example code in this chapter.CHAPTER 5: BUILDING AND PROGRAMMING MOBILE ROBOT USING ROSThis is a hands-on project which is used to apply all concepts of ROS that we discussed in the above chapters. We will see some more concepts which will explain in the appropriate sections. The aim of the project is to perform a dead-reckoning using a cheap mobile robot. We can command robot position on Rviz and robot will move to that position.CHAPTER 6. ROBOTICS PROJECT USING ROS
Smarte Services mit künstlicher Intelligenz
In diesem Buch erfährt der Leser, wie smarte Services mit künstlicher Intelligenz realisierbar sind und wie eine digitale Transformation gelingt, mit der sich die Kundenorientierung, Wettbewerbsfähigkeit, Widerstandsfähigkeit, Agilität und Nachhaltigkeit von Unternehmen verbessern lässt. Was sind smarte Services und wie sehen sie in der Praxis aus? Was beinhalten die dafür erforderlichen Komponenten Internet of Things, Data Lake und Advanced Analytics? Wofür lässt sich die künstliche Intelligenz einsetzen und wie erfolgt das in der Praxis? Wie entsteht Digital Trust? Wie lässt sich der digitale Reifegrad von Unternehmen ermitteln? Welches Vorgehen hat sich für die digitale Transformation in der Praxis bewährt? Wofür wird ein digitales Ecosystem benötigt und wie kann es aussehen? Was wird unter „New Work“ verstanden? Wie arbeiten datengetriebene Unternehmen und welche Vorteile hat das? Was ist ein Digital Use Case? Wie läuft ein Use-Case-Entwicklungs-Workshop ab? Wie lässt sich ein Digital Use Case strukturiert beschreiben? Welche interessanten, innovativen Beispiele für Digital Use Cases gibt es? Wie erfolgt ein Proof of Concept? Wie lassen sich die Kernprozesse Order to Cash (O2C), Procure to Pay (P2P), Design to Operate (D2O), Recruit to Retire (R2R) und Awareness to Advocacy (A2A) digitalisieren? Welche neuen digitalen Technologien und in ihrem Zusammenhang angewandte Verfahren existieren?DER AUTORDR.-ING. EGMONT FOTH war nach dem Studium der Informationstechnik und einer Promotion in der Nachrichtentechnik in zahlreichen Führungsfunktionen in der Industrie tätig. Seit 2017 hat er bei SPIE, dem unabhängigen europäischen Marktführer für Multitechnik-Dienstleistungen in den Bereichen Energie und Kommunikation, als Mitglied der Geschäftsleitung sowie CIO & CTO für Deutschland und Zentraleuropa den Einkauf, die Informationstechnologie, das Geschäftsprozessmanagement und die Digitalisierung verantwortet. Er ist Autor mehrerer Fachbücher und mehrfacher Preisträger der von Computerwoche und CIO-Magazin organisierten Wahl zum CIO des Jahres. 2017 gewann er mit seinem Team für SPIE den Digital Leader Award in der Kategorie "Spark Collaboration" und 2019 erhielt SPIE für die mit einem umfassenden Digital Ecosystem implementierte Digitalisierungsstrategie als Zweiter in der Kategorie "Strategy" erneut den Digital Leader Award. Eine Kontaktaufnahme mit ihm ist über seine Website www.changeprojekte.de möglich.Einleitung - Smarte Services - Digitale Transformation von Unternehmen - Digital Use Cases - Digitalisierung von Kernprozessen - Neue digitale Technologien und angewandte Verfahren - Schlusswort
Scrum Master Kompagnon
Mit agilen Teams starten, wachsen und Wirkung entfalten Scrum Master zu sein, ist nicht nur einer der herausforderndsten Jobs der Welt, sondern gleichzeitig einer der spannendsten und interessantesten. Dabei gibt es nicht den einen Tätigkeitsbereich des Scrum Masters, sondern es existieren – je nach Unternehmen und Kontext – viele verschiedene: Aufgaben als Trainer, als Coach, als Moderator, als Teammitglied und als Veränderungskraft in der Organisation. Der Scrum Master Kompagnon setzt den Fokus auf die Kernkompetenz des Scrum Masters: die Begleitung und Unterstützung eines Scrum-Teams. Dabei orientiert sich die Struktur des Buches an den typischen Entwicklungsphasen des Teams und dem Lebenszyklus der Zusammenarbeit zwischen Scrum Master und Team sowie Product Owner und Stakeholdern. Es werden relevante theoretische Modelle und Konzepte vorgestellt, die in den jeweiligen Prozessphasen hilfreich sein können, sowie ganz praktische und durchführbare Interventionen präsentiert.Die Themen im Einzelnen: Verantwortlichkeiten und Wirksamkeit als Scrum MasterGute Rahmenbedingungen für TeamarbeitTeams kennenlernen und startenTeams begleitenTeams verabschiedenOrganisationsstrukturen und -kulturPersönliche WeiterentwicklungZahlreiche Praxisbeispiele und Erfahrungsberichte sowie mehr als 20 konkrete Workshop-Designs machen das Buch zu einem unverzichtbaren Begleiter jedes Scrum Masters. Autor: Martin Heider hat über 10 Jahre Erfahrung in agiler Produktentwicklung in verschiedensten Branchen und Rollen. Er ist Co-Creator verschiedener Community-Intitiativen, wie Agile Coach Camp, Play4Agile, Coach Reflection Day sowie Agile Monday in Nürnberg. Als selbständiger Agile Coach und Trainer begleitet er Organisationen, Teams und Einzelpersonen. Ein besonderes Anliegen ist ihm die Aus- und Weiterbildung von wirkungsvollen Scrum Mastern. So war er bereits 2014 Mitbegründer der ersten berufsbegleitenden Scrum-Master-Ausbildung in Deutschland. Fabian Schiller hat über 10 Jahre Erfahrung in agiler Produktentwicklung in verschiedensten Branchen und Rollen. Derzeit arbeitet er selbständig als Coach und Trainer und berät vom 30 Mann Startup bis zum Großkonzern seine Kunden bei der Weiterentwicklung der Organisation und agiler Methoden. Er ist Sprecher auf nationalen und internationalen Konferenzen und einer der Gründer der CoReDay- (Coach Reflection Day-)Bewegung zur kontinuierlichen Weiterentwicklung von Scrum Mastern und Agile Coaches.Zielgruppe: Scrum MasterAgile CoachesTrainer*innenWorkshop-Leiter*innen
CISSP For Dummies
GET CISSP CERTIFIED, WITH THIS COMPREHENSIVE STUDY PLAN!Revised for the updated 2021 exam, CISSP For Dummies is packed with everything you need to succeed on test day. With deep content review on every domain, plenty of practice questions, and online study tools, this book helps aspiring security professionals unlock the door to success on this high-stakes exam. This book, written by CISSP experts, goes beyond the exam material and includes tips on setting up a 60-day study plan, exam-day advice, and access to an online test bank of questions.Make your test day stress-free with CISSP For Dummies!* Review every last detail you need to pass the CISSP certification exam * Master all 8 test domains, from Security and Risk Management through Software Development Security * Get familiar with the 2021 test outline * Boost your performance with an online test bank, digital flash cards, and test-day tips If you’re a security professional seeking your CISSP certification, this book is your secret weapon as you prepare for the exam.LAWRENCE C. MILLER, CISSP, is a veteran information security professional. He has served as a consultant for multinational corporations and holds many networking certifications.PETER H. GREGORY, CISSP, is a security, risk, and technology director with experience in SAAS, retail, telecommunications, non-profit, manufacturing, healthcare, and beyond. Larry and Peter have been coauthors of CISSP For Dummies for more than 20 years. INTRODUCTION 1About This Book 2Foolish Assumptions 3Icons Used in This Book 3Beyond the Book 4Where to Go from Here 5PART 1: GETTING STARTED WITH CISSP CERTIFICATION 7CHAPTER 1: (ISC)2 AND THE CISSP CERTIFICATION 9About (ISC)2 and the CISSP Certification 9You Must Be This Tall to Ride This Ride (And Other Requirements) 10Preparing for the Exam 12Studying on your own 13Getting hands-on experience 14Getting official (ISC)2 CISSP training 14Attending other training courses or study groups 15Taking practice exams 15Are you ready for the exam? 16Registering for the Exam 16About the CISSP Examination 17After the Examination 20CHAPTER 2: PUTTING YOUR CERTIFICATION TO GOOD USE 23Networking with Other Security Professionals 24Being an Active (ISC)2 Member 25Considering (ISC)2 Volunteer Opportunities 26Writing certification exam questions 27Speaking at events 27Helping at (ISC)2 conferences 27Reading and contributing to (ISC)2 publications 27Supporting the (ISC)2 Center for Cyber Safety and Education 28Participating in bug-bounty programs 28Participating in (ISC)2 focus groups 28Joining the (ISC)2 community 28Getting involved with a CISSP study group 28Helping others learn more about data security 29Becoming an Active Member of Your Local Security Chapter 30Spreading the Good Word about CISSP Certification 31Leading by example 32Using Your CISSP Certification to Be an Agent of Change 32Earning Other Certifications 33Other (ISC)2 certifications 33CISSP concentrations 34Non-(ISC)2 certifications 34Choosing the right certifications 38Finding a mentor, being a mentor 39Building your professional brand 39Pursuing Security Excellence 40PART 2: CERTIFICATION DOMAINS 43CHAPTER 3: SECURITY AND RISK MANAGEMENT 45Understand, Adhere to, and Promote Professional Ethics 45(ISC)2 Code of Professional Ethics 46Organizational code of ethics 47Understand and Apply Security Concepts 49Confidentiality 50Integrity 51Availability 51Authenticity 52Nonrepudiation 52Evaluate and Apply Security Governance Principles 53Alignment of security function to business strategy, goals, mission, and objectives 53Organizational processes 54Organizational roles and responsibilities 56Security control frameworks 57Due care and due diligence 60Determine Compliance and Other Requirements 61Contractual, legal, industry standards, and regulatory requirements 61Privacy requirements 66Understand Legal and Regulatory Issues That Pertain to Information Security 67Cybercrimes and data breaches 67Licensing and intellectual property requirements 82Import/export controls 85Transborder data flow 85Privacy 86Understand Requirements for Investigation Types 93Develop, Document, and Implement Security Policies, Standards, Procedures, and Guidelines 94Policies 95Standards (and baselines) 95Procedures 96Guidelines 96Identify, Analyze, and Prioritize Business Continuity (BC) Requirements 96Business impact analysis 99Develop and document the scope and the plan 107Contribute to and Enforce Personnel Security Policies and Procedures 120Candidate screening and hiring 120Employment agreements and policies 123Onboarding, transfers, and termination processes 123Vendor, consultant, and contractor agreements and controls 124Compliance policy requirements 125Privacy policy requirements 125Understand and Apply Risk Management Concepts 125Identify threats and vulnerabilities 126Risk assessment/analysis 126Risk appetite and risk tolerance 132Risk treatment 133Countermeasure selection and implementation 133Applicable types of controls 135Control assessments (security and privacy) 137Monitoring and measurement 139Reporting 140Continuous improvement 141Risk frameworks 141Understand and Apply Threat Modeling Concepts and Methodologies 143Identifying threats 143Determining and diagramming potential attacks 144Performing reduction analysis 145Remediating threats 145Apply Supply Chain Risk Management (SCRM) Concepts 146Risks associated with hardware, software, and services 147Third-party assessment and monitoring 147Fourth-party risk 147Minimum security requirements 147Service-level agreement requirements 147Establish and Maintain a Security Awareness, Education, and Training Program 148Methods and techniques to present awareness and training 148Periodic content reviews 151Program effectiveness evaluation 151CHAPTER 4: ASSET SECURITY 153Identify and Classify Information and Assets 153Data classification 157Asset classification 161Establish Information and Asset Handling Requirements 162Provision Resources Securely 164Information and asset ownership 164Asset inventory 165Asset management 166Manage Data Life Cycle 167Data roles 168Data collection 168Data location 169Data maintenance 169Data retention 169Data remanence 170Data destruction 171Ensure Appropriate Asset Retention 171End of life 171End of support 172Determine Data Security Controls and Compliance Requirements 172Data states 173Scoping and tailoring 174Standards selection 175Data protection methods 176CHAPTER 5: SECURITY ARCHITECTURE AND ENGINEERING 179Research, Implement, and Manage Engineering Processes Using Secure Design Principles 180Threat modeling 182Least privilege (and need to know) 186Defense in depth 187Secure defaults 188Fail securely 188Separation of duties 189Keep it simple 189Zero trust 189Privacy by design 191Trust but verify 192Shared responsibility 194Understand the Fundamental Concepts of Security Models 196Select Controls Based Upon Systems Security Requirements 199Evaluation criteria 200System certification and accreditation 205Understand Security Capabilities of Information Systems 208Trusted Computing Base 208Trusted Platform Module 209Secure modes of operation 209Open and closed systems 210Memory protection 210Encryption and decryption 210Protection rings 211Security modes 211Recovery procedures 212Assess and Mitigate the Vulnerabilities of Security Architectures, Designs, and Solution Elements 213Client-based systems 214Server-based systems 215Database systems 215Cryptographic systems 216Industrial control systems 217Cloud-based systems 218Distributed systems 220Internet of Things 221Microservices 221Containerization 222Serverless 223Embedded systems 224High-performance computing systems 225Edge computing systems 225Virtualized systems 226Web-based systems 226Mobile systems 228Select and Determine Cryptographic Solutions 228Plaintext and ciphertext 230Encryption and decryption 230End-to-end encryption 230Link encryption 231Putting it all together: The cryptosystem 232Classes of ciphers 233Types of ciphers 234Cryptographic life cycle 237Cryptographic methods 238Public key infrastructure 248Key management practices 248Digital signatures and digital certificates 250Nonrepudiation 250Integrity (hashing) 251Understand Methods of Cryptanalytic Attacks 253Brute force 254Ciphertext only 254Known plaintext 255Frequency analysis 255Chosen ciphertext 255Implementation attacks 255Side channel 255Fault injection 256Timing 256Man in the middle 256Pass the hash 257Kerberos exploitation 257Ransomware 257Apply Security Principles to Site and Facility Design 259Design Site and Facility Security Controls 261Wiring closets, server rooms, and more 264Restricted and work area security 265Utilities and heating, ventilation, and air conditioning 266Environmental issues 267Fire prevention, detection, and suppression 268Power 272CHAPTER 6: COMMUNICATION AND NETWORK SECURITY 275Assess and Implement Secure Design Principles in Network Architectures 275OSI and TCP/IP models 277The OSI Reference Model 278The TCP/IP Model 315Secure Network Components 316Operation of hardware 316Transmission media 317Network access control devices 318Endpoint security 328Implement Secure Communication Channels According to Design 331Voice 331Multimedia collaboration 332Remote access 332Data communications 336Virtualized networks 336Third-party connectivity 338CHAPTER 7: IDENTITY AND ACCESS MANAGEMENT 339Control Physical and Logical Access to Assets 340Information 340Systems and devices 340Facilities 342Applications 342Manage Identification and Authentication of People, Devices, and Services 343Identity management implementation 343Single-/multifactor authentication 343Accountability 358Session management 359Registration, proofing, and establishment of identity 360Federated identity management 361Credential management systems 361Single sign-on 362Just-in-Time 363Federated Identity with a Third-Party Service 363On-premises 365Cloud 365Hybrid 365Implement and Manage Authorization Mechanisms 365Role-based access control 366Rule-based access control 367Mandatory access control 367Discretionary access control 368Attribute-based access control 369Risk-based access control 370Manage the Identity and Access Provisioning Life Cycle 370Implement Authentication Systems 372OpenID Connect/Open Authorization 372Security Assertion Markup Language 372Kerberos 373Radius and Tacacs+ 376CHAPTER 8: SECURITY ASSESSMENT AND TESTING 379Design and Validate Assessment, Test, and Audit Strategies 379Conduct Security Control Testing 381Vulnerability assessment 381Penetration testing 383Log reviews 388Synthetic transactions 389Code review and testing 390Misuse case testing 391Test coverage analysis 392Interface testing 392Breach attack simulations 393Compliance checks 393Collect Security Process Data 393Account management 395Management review and approval 395Key performance and risk indicators 396Backup verification data 397Training and awareness 399Disaster recovery and business continuity 400Analyze Test Output and Generate Reports 400Remediation 401Exception handling 402Ethical disclosure 403Conduct or Facilitate Security Audits 404CHAPTER 9: SECURITY OPERATIONS 407Understand and Comply with Investigations 408Evidence collection and handling 408Reporting and documentation 415Investigative techniques 416Digital forensics tools, tactics, and procedures 418Artifacts 419Conduct Logging and Monitoring Activities 419Intrusion detection and prevention 419Security information and event management 421Security orchestration, automation, and response 421Continuous monitoring 422Egress monitoring 422Log management 423Threat intelligence 423User and entity behavior analysis 424Perform Configuration Management 424Apply Foundational Security Operations Concepts 426Need-to-know and least privilege 427Separation of duties and responsibilities 428Privileged account management 429Job rotation 431Service-level agreements 433Apply Resource Protection 436Media management 436Media protection techniques 438Conduct Incident Management 438Operate and Maintain Detective and Preventative Measures 440Implement and Support Patch and Vulnerability Management 442Understand and Participate in Change Management Processes 443Implement Recovery Strategies 444Backup storage strategies 444Recovery site strategies 445Multiple processing sites 445System resilience, high availability, quality of service, and fault tolerance 445Implement Disaster Recovery Processes 448Response 451Personnel 453Communications 454Assessment 455Restoration 455Training and awareness 456Lessons learned 456Test Disaster Recovery Plans 456Read-through or tabletop 457Walkthrough 457Simulation 458Parallel 459Full interruption (or cutover) 459Participate in Business Continuity Planning and Exercises 460Implement and Manage Physical Security 460Address Personnel Safety and Security Concerns 461CHAPTER 10: SOFTWARE DEVELOPMENT SECURITY 463Understand and Integrate Security in the SoftwareDevelopment Life Cycle 464Development methodologies 464Maturity models 473Operation and maintenance 474Change management 475Integrated product team 476Identify and Apply Security Controls in Software Development Ecosystems 476Programming languages 477Libraries 478Tool sets 478Integrated development environment 480Runtime 480Continuous integration/continuous delivery 481Security orchestration, automation, and response 481Software configuration management 482Code repositories 483Application security testing 484Assess the Effectiveness of Software Security 486Auditing and logging of changes 486Risk analysis and mitigation 487Assess Security Impact of Acquired Software 489Define and Apply Secure Coding Guidelines and Standards 490Security weaknesses and vulnerabilities at the source-code level 491Security of application programming interfaces 492Secure coding practices 493Software-defined security 495PART 3: THE PART OF TENS 497CHAPTER 11: TEN WAYS TO PREPARE FOR THE EXAM 499Know Your Learning Style 499Get a Networking Certification First 500Register Now 500Make a 60-Day Study Plan 500Get Organized and Read 501Join a Study Group 501Take Practice Exams 502Take a CISSP Training Seminar 502Adopt an Exam-Taking Strategy 502Take a Breather 503CHAPTER 12: TEN TEST-DAY TIPS 505Get a Good Night’s Rest 505Dress Comfortably 506Eat a Good Meal 506Arrive Early 506Bring Approved Identification 506Bring Snacks and Drinks 507Bring Prescription and Over-the-Counter Medications 507Leave Your Mobile Devices Behind 507Take Frequent Breaks 507Guess — As a Last Resort 508Glossary 509Index 565
C# 10 Quick Syntax Reference
Discover what's new in C# and .NET for Windows programming. This book is a condensed code and syntax reference to the C# programming language, updated with the latest features of version 10 for .NET 6.You'll review the essential C# 10 and earlier syntax, not previously covered, in a well-organized format that can be used as a handy reference. Specifically, unions, generic attributes, CallerArgumentExpression, params span, Records, Init only setters, Top-level statements, Pattern matching enhancements, Native sized integers, Function pointers and more.You'll find a concise reference to the C# language syntax: short, simple, and focused code examples; a well laid out table of contents; and a comprehensive index allowing easy review. You won’t find any technical jargon, bloated samples, drawn-out history lessons, or witty stories. What you will find is a language reference that is to the point and highly accessible.The book is a must-have for any C# programmer.WHAT YOU WILL LEARN* Employ nullable reference types * Work with ranges and indices * Apply recursive patterns to your applications* Use switch expressions WHO THIS BOOK IS FORThose with some experience in programming, looking for a quick, handy reference. Some C# or .NET recommended but not necessary.Mikael Olsson is a professional web entrepreneur, programmer, and author. He works for an R&D company in Finland, where he specializes in software development. In his spare time he writes books and creates websites that summarize various fields of interest. The books he writes are focused on teaching their subjects in the most efficient way possible, by explaining only what is relevant and practical without any unnecessary repetition or theory. The portal to his online businesses and other websites is siforia.com.1. Hello World2. Compile and Run3. Variables4. Operators5. Strings6. Arrays7. Conditionals8. Loops9. Methods10. Class11. Inheritance12. Redefining Members13. Access Levels14. Static15. Properties16. Indexers17. Interfaces18. Abstract19. Namespaces20. Enum21. Exception Handling22. Operator Overloading23. Custom Conversions24. Struct25. Preprocessors26. Delegates27. Events28. Generics29. Constants30. Asynchronous Methods
Datenrendite
Gegenwärtig gibt es einen starken Hype um die Themen künstliche Intelligenz, Machine Learning und Data Science. Doch wie lassen sich datengetriebene Methoden und Technologien nutzen und in Unternehmen gewinnbringend einsetzen? Dieses Buch zeigt praxisnah und anschaulich, wie mit der richtigen Datenbasis und Datenmodellen der Wert von Daten für Unternehmen erschlossen werden kann. Dabei geht es nicht um technische Details zu Algorithmen und Technologien, sondern um Instrumente und sofort anwendbare Lösungen zur erfolgreichen Projektumsetzung. Jedes Kapitel stellt die jeweilige Zielsetzung vor, vermittelt anschließend alle Inhalte und fasst am Ende – neben einer Checkliste der wichtigsten Maßnahmen – die Aussagen für verschiedene Organisationsformen zusammen. Anhand zahlreicher Beispiele wird gezeigt, wie Struktur und Vorgehen den Anforderungen der komplexen Werkzeuge gerecht werden können.
Roboter- und KI-Ethik
Was ist die Ethik der Roboter? Was ist KI-Ethik? Was sind „moralische Maschinen“? Welchen Gesetzen sollen sie folgen?Haben wir die Roboter, die wir brauchen, und brauchen wir die Roboter, die wir haben?In vorliegendem Buch werden Grundlagen der Ethik im Umgang mit Robotern, Drohnen und KI allgemeinverständlich dargestellt. Hierzu zählt die Unterscheidung von Moral, Ethik und Ethos sowie deren Anwendung auf Menschen und Maschinen. Kriterien, Fehlschlüsse und Robotergesetze werden vorgestellt, wie auch in die umfassende Gegenwartsdebatte übersichtlich eingeführt. Grafiken und Beispiele bieten Orientierung in einem hochaktuellen und komplexen Feld.Als methodische Einführung richtet sich vorliegendes Buch an Ingenieurwissenschaftler*innen, Informatiker*innen und Geisteswissenschaftler*innen im Berufsalltag, aber auch an interessierte Lai*innen, die Grundlagen der Ethik kennen lernen wollen. Es bildet den ersten, in sich abgerundeten Teil der Buchreihe Grundlagen der Technikethik.Mit einem Geleitwort von Yvonne Hofstetter.MICHAEL FUNK forscht und lehrt an der Universität Wien in den Bereichen Medien- und Technikphilosophie (Institut für Philosophie) sowie Cooperative Systems (Fakultät für Informatik). Einleitung: Das Nichttechnische zwischen Künstlicher Intelligenz und (Post-)Digitalisierung - Roboter- und KI-Ethik als philosophische Disziplin (Bedetung 1) - Können und dürfen Maschinen moralisch handeln? (Bedeutng 2) - Können und dürfen Maschinen ethisch argumentieren? (Bedeutung 3) - Welchen Regeln und Gesetzen müssen Maschinen folgen? (Bedetung 4) - Anwendungen
Fundamentals and Methods of Machine and Deep Learning
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNINGTHE BOOK PROVIDES A PRACTICAL APPROACH BY EXPLAINING THE CONCEPTS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS, EVALUATION OF METHODOLOGY ADVANCES, AND ALGORITHM DEMONSTRATIONS WITH APPLICATIONS.Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. AUDIENCEResearchers and engineers in artificial intelligence, computer scientists as well as software developers. PRADEEP SINGH PHD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology, Raipur, India. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models. He has more than 15 years of teaching experience with many publications in reputed international journals, conferences, and book chapters.Preface xix1 SUPERVISED MACHINE LEARNING: ALGORITHMS AND APPLICATIONS 1Shruthi H. Shetty, Sumiksha Shetty, Chandra Singh and Ashwath Rao1.1 History 21.2 Introduction 21.3 Supervised Learning 41.4 Linear Regression (LR) 51.4.1 Learning Model 61.4.2 Predictions With Linear Regression 71.5 Logistic Regression 81.6 Support Vector Machine (SVM) 91.7 Decision Tree 111.8 Machine Learning Applications in Daily Life 121.8.1 Traffic Alerts (Maps) 121.8.2 Social Media (Facebook) 131.8.3 Transportation and Commuting (Uber) 131.8.4 Products Recommendations 131.8.5 Virtual Personal Assistants 131.8.6 Self-Driving Cars 141.8.7 Google Translate 141.8.8 Online Video Streaming (Netflix) 141.8.9 Fraud Detection 141.9 Conclusion 15References 152 ZONOTIC DISEASES DETECTION USING ENSEMBLE MACHINE LEARNING ALGORITHMS 17Bhargavi K.2.1 Introduction 182.2 Bayes Optimal Classifier 192.3 Bootstrap Aggregating (Bagging) 212.4 Bayesian Model Averaging (BMA) 222.5 Bayesian Classifier Combination (BCC) 242.6 Bucket of Models 262.7 Stacking 272.8 Efficiency Analysis 292.9 Conclusion 30References 303 MODEL EVALUATION 33Ravi Shekhar Tiwari3.1 Introduction 343.2 Model Evaluation 343.2.1 Assumptions 363.2.2 Residual 363.2.3 Error Sum of Squares (Sse) 373.2.4 Regression Sum of Squares (Ssr) 373.2.5 Total Sum of Squares (Ssto) 373.3 Metric Used in Regression Model 383.3.1 Mean Absolute Error (Mae) 383.3.2 Mean Square Error (Mse) 393.3.3 Root Mean Square Error (Rmse) 413.3.4 Root Mean Square Logarithm Error (Rmsle) 423.3.5 R-Square (R2) 453.3.5.1 Problem With R-Square (R2) 463.3.6 Adjusted R-Square (R2) 463.3.7 Variance 473.3.8 AIC 483.3.9 BIC 493.3.10 ACP, Press, and R2-Predicted 493.3.11 Solved Examples 513.4 Confusion Metrics 523.4.1 How to Interpret the Confusion Metric? 533.4.2 Accuracy 553.4.2.1 Why Do We Need the Other Metric Along With Accuracy? 563.4.3 True Positive Rate (TPR) 563.4.4 False Negative Rate (FNR) 573.4.5 True Negative Rate (TNR) 573.4.6 False Positive Rate (FPR) 583.4.7 Precision 583.4.8 Recall 593.4.9 Recall-Precision Trade-Off 603.4.10 F1-Score 613.4.11 F-Beta Sore 613.4.12 Thresholding 633.4.13 AUC - ROC 643.4.14 AUC - PRC 653.4.15 Derived Metric From Recall, Precision, and F1-Score 673.4.16 Solved Examples 683.5 Correlation 703.5.1 Pearson Correlation 703.5.2 Spearman Correlation 713.5.3 Kendall’s Rank Correlation 733.5.4 Distance Correlation 743.5.5 Biweight Mid-Correlation 753.5.6 Gamma Correlation 763.5.7 Point Biserial Correlation 773.5.8 Biserial Correlation 783.5.9 Partial Correlation 783.6 Natural Language Processing (NLP) 783.6.1 N-Gram 793.6.2 BELU Score 793.6.2.1 BELU Score With N-Gram 803.6.3 Cosine Similarity 813.6.4 Jaccard Index 833.6.5 ROUGE 843.6.6 NIST 853.6.7 SQUAD 853.6.8 MACRO 863.7 Additional Metrics 863.7.1 Mean Reciprocal Rank (MRR) 863.7.2 Cohen Kappa 873.7.3 Gini Coefficient 873.7.4 Scale-Dependent Errors 873.7.5 Percentage Errors 883.7.6 Scale-Free Errors 883.8 Summary of Metric Derived from Confusion Metric 893.9 Metric Usage 903.10 Pro and Cons of Metrics 943.11 Conclusion 95References 964 ANALYSIS OF M-SEIR AND LSTM MODELS FOR THE PREDICTION OF COVID-19 USING RMSLE 101Archith S., Yukta C., Archana H.R. and Surendra H.H.4.1 Introduction 1014.2 Survey of Models 1034.2.1 SEIR Model 1034.2.2 Modified SEIR Model 1034.2.3 Long Short-Term Memory (LSTM) 1044.3 Methodology 1064.3.1 Modified SEIR 1064.3.2 LSTM Model 1084.3.2.1 Data Pre-Processing 1084.3.2.2 Data Shaping 1094.3.2.3 Model Design 1094.4 Experimental Results 1114.4.1 Modified SEIR Model 1114.4.2 LSTM Model 1134.5 Conclusion 1164.6 Future Work 116References 1185 THE SIGNIFICANCE OF FEATURE SELECTION TECHNIQUES IN MACHINE LEARNING 121N. Bharathi, B.S. Rishiikeshwer, T. Aswin Shriram, B. Santhi and G.R. Brindha5.1 Introduction 1225.2 Significance of Pre-Processing 1225.3 Machine Learning System 1235.3.1 Missing Values 1235.3.2 Outliers 1235.3.3 Model Selection 1245.4 Feature Extraction Methods 1245.4.1 Dimension Reduction 1255.4.1.1 Attribute Subset Selection 1265.4.2 Wavelet Transforms 1275.4.3 Principal Components Analysis 1275.4.4 Clustering 1285.5 Feature Selection 1285.5.1 Filter Methods 1295.5.2 Wrapper Methods 1295.5.3 Embedded Methods 1305.6 Merits and Demerits of Feature Selection 1315.7 Conclusion 131References 1326 USE OF MACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE—A REVIEW ON DISEASE PREDICTION SYSTEM 135Radha R. and Gopalakrishnan R.6.1 Introduction to Healthcare System 1366.2 Causes for the Failure of the Healthcare System 1376.3 Artificial Intelligence and Healthcare System for Predicting Diseases 1386.3.1 Monitoring and Collection of Data 1406.3.2 Storing, Retrieval, and Processing of Data 1416.4 Facts Responsible for Delay in Predicting the Defects 1426.5 Pre-Treatment Analysis and Monitoring 1436.6 Post-Treatment Analysis and Monitoring 1456.7 Application of ML and DL 1456.7.1 ML and DL for Active Aid 1456.7.1.1 Bladder Volume Prediction 1476.7.1.2 Epileptic Seizure Prediction 1486.8 Challenges and Future of Healthcare Systems Based on ML and DL 1486.9 Conclusion 149References 1507 DETECTION OF DIABETIC RETINOPATHY USING ENSEMBLE LEARNING TECHNIQUES 153Anirban Dutta, Parul Agarwal, Anushka Mittal, Shishir Khandelwal and Shikha Mehta7.1 Introduction 1537.2 Related Work 1557.3 Methodology 1557.3.1 Data Pre-Processing 1557.3.2 Feature Extraction 1617.3.2.1 Exudates 1617.3.2.2 Blood Vessels 1617.3.2.3 Microaneurysms 1627.3.2.4 Hemorrhages 1627.3.3 Learning 1637.3.3.1 Support Vector Machines 1637.3.3.2 K-Nearest Neighbors 1637.3.3.3 Random Forest 1647.3.3.4 AdaBoost 1647.3.3.5 Voting Technique 1647.4 Proposed Models 1657.4.1 AdaNaive 1657.4.2 AdaSVM 1667.4.3 AdaForest 1667.5 Experimental Results and Analysis 1677.5.1 Dataset 1677.5.2 Software and Hardware 1677.5.3 Results 1687.6 Conclusion 173References 1748 MACHINE LEARNING AND DEEP LEARNING FOR MEDICAL ANALYSIS—A CASE STUDY ON HEART DISEASE DATA 177Swetha A.M., Santhi B. and Brindha G.R.8.1 Introduction 1788.2 Related Works 1798.3 Data Pre-Processing 1818.3.1 Data Imbalance 1818.4 Feature Selection 1828.4.1 Extra Tree Classifier 1828.4.2 Pearson Correlation 1838.4.3 Forward Stepwise Selection 1838.4.4 Chi-Square Test 1848.5 ML Classifiers Techniques 1848.5.1 Supervised Machine Learning Models 1858.5.1.1 Logistic Regression 1858.5.1.2 SVM 1868.5.1.3 Naive Bayes 1868.5.1.4 Decision Tree 1868.5.1.5 K-Nearest Neighbors (KNN) 1878.5.2 Ensemble Machine Learning Model 1878.5.2.1 Random Forest 1878.5.2.2 AdaBoost 1888.5.2.3 Bagging 1888.5.3 Neural Network Models 1898.5.3.1 Artificial Neural Network (ANN) 1898.5.3.2 Convolutional Neural Network (CNN) 1898.6 Hyperparameter Tuning 1908.6.1 Cross-Validation 1908.7 Dataset Description 1908.7.1 Data Pre-Processing 1938.7.2 Feature Selection 1958.7.3 Model Selection 1968.7.4 Model Evaluation 1978.8 Experiments and Results 1978.8.1 Study 1: Survival Prediction Using All Clinical Features 1988.8.2 Study 2: Survival Prediction Using Age, Ejection Fraction and Serum Creatinine 1988.8.3 Study 3: Survival Prediction Using Time, Ejection Fraction, and Serum Creatinine 1998.8.4 Comparison Between Study 1, Study 2, and Study 3 2038.8.5 Comparative Study on Different Sizes of Data 2048.9 Analysis 2068.10 Conclusion 206References 2079 A NOVEL CONVOLUTIONAL NEURAL NETWORK MODEL TO PREDICT SOFTWARE DEFECTS 211Kumar Rajnish, Vandana Bhattacharjee and Mansi Gupta9.1 Introduction 2129.2 Related Works 2139.2.1 Software Defect Prediction Based on Deep Learning 2139.2.2 Software Defect Prediction Based on Deep Features 2149.2.3 Deep Learning in Software Engineering 2149.3 Theoretical Background 2159.3.1 Software Defect Prediction 2159.3.2 Convolutional Neural Network 2169.4 Experimental Setup 2189.4.1 Data Set Description 2189.4.2 Building Novel Convolutional Neural Network (NCNN) Model 2199.4.3 Evaluation Parameters 2229.4.4 Results and Analysis 2249.5 Conclusion and Future Scope 230References 23310 PREDICTIVE ANALYSIS ON ONLINE TELEVISION VIDEOS USING MACHINE LEARNING ALGORITHMS 237Rebecca Jeyavadhanam B., Ramalingam V.V., Sugumaran V. and Rajkumar D.10.1 Introduction 23810.1.1 Overview of Video Analytics 24110.1.2 Machine Learning Algorithms 24210.1.2.1 Decision Tree C4.5 24310.1.2.2 J48 Graft 24310.1.2.3 Logistic Model Tree 24410.1.2.4 Best First Tree 24410.1.2.5 Reduced Error Pruning Tree 24410.1.2.6 Random Forest 24410.2 Proposed Framework 24510.2.1 Data Collection 24610.2.2 Feature Extraction 24610.2.2.1 Block Intensity Comparison Code 24710.2.2.2 Key Frame Rate 24810.3 Feature Selection 24910.4 Classification 25010.5 Online Incremental Learning 25110.6 Results and Discussion 25310.7 Conclusion 255References 25611 A COMBINATIONAL DEEP LEARNING APPROACH TO VISUALLY EVOKED EEG-BASED IMAGE CLASSIFICATION 259Nandini Kumari, Shamama Anwar and Vandana Bhattacharjee11.1 Introduction 26011.2 Literature Review 26211.3 Methodology 26411.3.1 Dataset Acquisition 26411.3.2 Pre-Processing and Spectrogram Generation 26511.3.3 Classification of EEG Spectrogram Images With Proposed CNN Model 26611.3.4 Classification of EEG Spectrogram Images With Proposed Combinational CNN+LSTM Model 26811.4 Result and Discussion 27011.5 Conclusion 272References 27312 APPLICATION OF MACHINE LEARNING ALGORITHMS WITH BALANCING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION: A COMPARATIVE ANALYSIS 277Shiksha12.1 Introduction 27812.2 Methods and Techniques 28012.2.1 Research Approach 28012.2.2 Dataset Description 28212.2.3 Data Preparation 28312.2.4 Correlation Between Features 28412.2.5 Splitting the Dataset 28512.2.6 Balancing Data 28512.2.6.1 Oversampling of Minority Class 28612.2.6.2 Under-Sampling of Majority Class 28612.2.6.3 Synthetic Minority Over Sampling Technique 28612.2.6.4 Class Weight 28712.2.7 Machine Learning Algorithms (Models) 28812.2.7.1 Logistic Regression 28812.2.7.2 Support Vector Machine 28812.2.7.3 Decision Tree 29012.2.7.4 Random Forest 29212.2.8 Tuning of Hyperparameters 29412.2.9 Performance Evaluation of the Models 29412.3 Results and Discussion 29812.3.1 Results Using Balancing Techniques 29912.3.2 Result Summary 29912.4 Conclusions 30512.4.1 Future Recommendations 305References 30613 CRACK DETECTION IN CIVIL STRUCTURES USING DEEP LEARNING 311Bijimalla Shiva Vamshi Krishna, Rishiikeshwer B.S., J. Sanjay Raju, N. Bharathi, C. Venkatasubramanian and G.R. Brindha13.1 Introduction 31213.2 Related Work 31213.3 Infrared Thermal Imaging Detection Method 31413.4 Crack Detection Using CNN 31413.4.1 Model Creation 31613.4.2 Activation Functions (AF) 31713.4.3 Optimizers 32213.4.4 Transfer Learning 32213.5 Results and Discussion 32213.6 Conclusion 323References 32314 MEASURING URBAN SPRAWL USING MACHINE LEARNING 327Keerti Kulkarni and P. A. Vijaya14.1 Introduction 32714.2 Literature Survey 32814.3 Remotely Sensed Images 32914.4 Feature Selection 33114.4.1 Distance-Based Metric 33114.5 Classification Using Machine Learning Algorithms 33214.5.1 Parametric vs. Non-Parametric Algorithms 33214.5.2 Maximum Likelihood Classifier 33214.5.3 k-Nearest Neighbor Classifiers 33414.5.4 Evaluation of the Classifiers 33414.5.4.1 Precision 33414.5.4.2 Recall 33514.5.4.3 Accuracy 33514.5.4.4 F1-Score 33514.6 Results 33514.7 Discussion and Conclusion 338Acknowledgements 338References 33815 APPLICATION OF DEEP LEARNING ALGORITHMS IN MEDICAL IMAGE PROCESSING: A SURVEY 341Santhi B., Swetha A.M. and Ashutosh A.M.15.1 Introduction 34215.2 Overview of Deep Learning Algorithms 34315.2.1 Supervised Deep Neural Networks 34315.2.1.1 Convolutional Neural Network 34315.2.1.2 Transfer Learning 34415.2.1.3 Recurrent Neural Network 34415.2.2 Unsupervised Learning 34515.2.2.1 Autoencoders 34515.2.2.2 GANs 34515.3 Overview of Medical Images 34615.3.1 MRI Scans 34615.3.2 CT Scans 34715.3.3 X-Ray Scans 34715.3.4 PET Scans 34715.4 Scheme of Medical Image Processing 34815.4.1 Formation of Image 34815.4.2 Image Enhancement 34915.4.3 Image Analysis 34915.4.4 Image Visualization 34915.5 Anatomy-Wise Medical Image Processing With Deep Learning 34915.5.1 Brain Tumor 35215.5.2 Lung Nodule Cancer Detection 35715.5.3 Breast Cancer Segmentation and Detection 36215.5.4 Heart Disease Prediction 36415.5.5 COVID-19 Prediction 37015.6 Conclusion 372References 37216 SIMULATION OF SELF-DRIVING CARS USING DEEP LEARNING 379Rahul M. K., Praveen L. Uppunda, Vinayaka Raju S., Sumukh B. and C. Gururaj16.1 Introduction 38016.2 Methodology 38016.2.1 Behavioral Cloning 38016.2.2 End-to-End Learning 38016.3 Hardware Platform 38116.4 Related Work 38216.5 Pre-Processing 38216.5.1 Lane Feature Extraction 38216.5.1.1 Canny Edge Detector 38316.5.1.2 Hough Transform 38316.5.1.3 Raw Image Without Pre-Processing 38416.6 Model 38416.6.1 CNN Architecture 38516.6.2 Multilayer Perceptron Model 38516.6.3 Regression vs. Classification 38516.6.3.1 Regression 38616.6.3.2 Classification 38616.7 Experiments 38716.8 Results 38716.9 Conclusion 394References 39417 ASSISTIVE TECHNOLOGIES FOR VISUAL, HEARING, AND SPEECH IMPAIRMENTS: MACHINE LEARNING AND DEEP LEARNING SOLUTIONS 397Shahira K. C., Sruthi C. J. and Lijiya A.17.1 Introduction 39717.2 Visual Impairment 39817.2.1 Conventional Assistive Technology for the VIP 39917.2.1.1 Way Finding 39917.2.1.2 Reading Assistance 40217.2.2 The Significance of Computer Vision and Deep Learning in AT of VIP 40317.2.2.1 Navigational Aids 40317.2.2.2 Scene Understanding 40517.2.2.3 Reading Assistance 40617.2.2.4 Wearables 40817.3 Verbal and Hearing Impairment 41017.3.1 Assistive Listening Devices 41017.3.2 Alerting Devices 41117.3.3 Augmentative and Alternative Communication Devices 41117.3.3.1 Sign Language Recognition 41217.3.4 Significance of Machine Learning and Deep Learning in Assistive Communication Technology 41717.4 Conclusion and Future Scope 418References 41818 CASE STUDIES: DEEP LEARNING IN REMOTE SENSING 425Emily Jenifer A. and Sudha N.18.1 Introduction 42618.2 Need for Deep Learning in Remote Sensing 42718.3 Deep Neural Networks for Interpreting Earth Observation Data 42718.3.1 Convolutional Neural Network 42718.3.2 Autoencoder 42818.3.3 Restricted Boltzmann Machine and Deep Belief Network 42918.3.4 Generative Adversarial Network 43018.3.5 Recurrent Neural Network 43118.4 Hybrid Architectures for Multi-Sensor Data Processing 43218.5 Conclusion 434References 434Index 439
Advanced Healthcare Systems
ADVANCED HEALTHCARE SYSTEMSTHIS BOOK OFFERS A COMPLETE PACKAGE INVOLVING THE INCUBATION OF MACHINE LEARNING, AI, AND IOT IN HEALTHCARE THAT IS BENEFICIAL FOR RESEARCHERS, HEALTHCARE PROFESSIONALS, SCIENTISTS, AND TECHNOLOGISTS.The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. AUDIENCEThis book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector. ROHIT TANWAR, PHD (Kurukshetra University, Kurukshetra, India) is an assistant professor in the School of Computer Science at UPES Dehradun, India.S. BALAMURUGAN, PHD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. R. K. SAINI, PHD (DIT University, Dehradun, India) is an assistant professor in the Department of Computer Science & Applications at DIT University, Dehradun (Uttarakhand). VISHAL BHARTI, PHD is a professor in the Department of Computer Science and Engineering, Chandigarh University, India. He has published more than 75 research papers in both national & international journals. PREMKUMAR CHITHALURU, PHD is an assistant professor in the Department of SCS at the University of Petroleum and Energy Studies (UPES), Dehradun, India. Preface xvii1 INTERNET OF MEDICAL THINGS—STATE-OF-THE-ART 1Kishor Joshi and Ruchi Mehrotra1.1 Introduction 21.2 Historical Evolution of IoT to IoMT 21.2.1 IoT and IoMT—Market Size 41.3 Smart Wearable Technology 41.3.1 Consumer Fitness Smart Wearables 41.3.2 Clinical-Grade Wearables 51.4 Smart Pills 71.5 Reduction of Hospital-Acquired Infections 81.5.1 Navigation Apps for Hospitals 81.6 In-Home Segment 81.7 Community Segment 91.8 Telehealth and Remote Patient Monitoring 91.9 IoMT in Healthcare Logistics and Asset Management 121.10 IoMT Use in Monitoring During COVID-19 131.11 Conclusion 14References 152 ISSUES AND CHALLENGES RELATED TO PRIVACY AND SECURITY IN HEALTHCARE USING IOT, FOG, AND CLOUD COMPUTING 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma2.1 Introduction 222.2 Related Works 232.3 Architecture 252.3.1 Device Layer 252.3.2 Fog Layer 262.3.3 Cloud Layer 262.4 Issues and Challenges 262.5 Conclusion 29References 303 STUDY OF THYROID DISEASE USING MACHINE LEARNING 33Shanu Verma, Rashmi Popli and Harish Kumar3.1 Introduction 343.2 Related Works 343.3 Thyroid Functioning 353.4 Category of Thyroid Cancer 363.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 373.5.1 Decision Tree Algorithm 383.5.2 Support Vector Machines 393.5.3 Random Forest 393.5.4 Logistic Regression 393.5.5 Naïve Bayes 403.6 Conclusion 41References 414 A REVIEW OF VARIOUS SECURITY AND PRIVACY INNOVATIONS FOR IOT APPLICATIONS IN HEALTHCARE 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi4.1 Introduction 444.1.1 Introduction to IoT 444.1.2 Introduction to Vulnerability, Attack, and Threat 454.2 IoT in Healthcare 464.2.1 Confidentiality 464.2.2 Integrity 464.2.3 Authorization 464.2.4 Availability 474.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 484.4 Conclusion 54References 545 METHODS OF LUNG SEGMENTATION BASED ON CT IMAGES 59Amit Verma and Thipendra P. Singh5.1 Introduction 595.2 Semi-Automated Algorithm for Lung Segmentation 605.2.1 Algorithm for Tracking to Lung Edge 605.2.2 Outlining the Region of Interest in CT Images 625.2.2.1 Locating the Region of Interest 625.2.2.2 Seed Pixels and Searching Outline 625.3 Automated Method for Lung Segmentation 635.3.1 Knowledge-Based Automatic Model for Segmentation 635.3.2 Automatic Method for Segmenting the Lung CT Image 645.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 645.5 Conclusion 65References 656 HANDLING UNBALANCED DATA IN CLINICAL IMAGES 69Amit Verma6.1 Introduction 706.2 Handling Imbalance Data 716.2.1 Cluster-Based Under-Sampling Technique 726.2.2 Bootstrap Aggregation (Bagging) 756.3 Conclusion 76References 767 IOT-BASED HEALTH MONITORING SYSTEM FOR SPEECH-IMPAIRED PEOPLE USING ASSISTIVE WEARABLE ACCELEROMETER 81Ishita Banerjee and Madhumathy P.7.1 Introduction 827.2 Literature Survey 847.3 Procedure 867.4 Results 937.5 Conclusion 97References 978 SMART IOT DEVICES FOR THE ELDERLY AND PEOPLE WITH DISABILITIES 101K. N. D. Saile and Kolisetti Navatha8.1 Introduction 1018.2 Need for IoT Devices 1028.3 Where Are the IoT Devices Used? 1038.3.1 Home Automation 1038.3.2 Smart Appliances 1048.3.3 Healthcare 1048.4 Devices in Home Automation 1048.4.1 Automatic Lights Control 1048.4.2 Automated Home Safety and Security 1048.5 Smart Appliances 1058.5.1 Smart Oven 1058.5.2 Smart Assistant 1058.5.3 Smart Washers and Dryers 1068.5.4 Smart Coffee Machines 1068.5.5 Smart Refrigerator 1068.6 Healthcare 1068.6.1 Smart Watches 1078.6.2 Smart Thermometer 1078.6.3 Smart Blood Pressure Monitor 1078.6.4 Smart Glucose Monitors 1078.6.5 Smart Insulin Pump 1088.6.6 Smart Wearable Asthma Monitor 1088.6.7 Assisted Vision Smart Glasses 1098.6.8 Finger Reader 1098.6.9 Braille Smart Watch 1098.6.10 Smart Wand 1098.6.11 Taptilo Braille Device 1108.6.12 Smart Hearing Aid 1108.6.13 E-Alarm 1108.6.14 Spoon Feeding Robot 1108.6.15 Automated Wheel Chair 1108.7 Conclusion 112References 1129 IOT-BASED HEALTH MONITORING AND TRACKING SYSTEM FOR SOLDIERS 115Kavitha N. and Madhumathy P.9.1 Introduction 1169.2 Literature Survey 1179.3 System Requirements 1189.3.1 Software Requirement Specification 1199.3.2 Functional Requirements 1199.4 System Design 1199.4.1 Features 1219.4.1.1 On-Chip Flash Memory 1229.4.1.2 On-Chip Static RAM 1229.4.2 Pin Control Block 1229.4.3 UARTs 1239.4.3.1 Features 1239.4.4 System Control 1239.4.4.1 Crystal Oscillator 1239.4.4.2 Phase-Locked Loop 1249.4.4.3 Reset and Wake-Up Timer 1249.4.4.4 Brown Out Detector 1259.4.4.5 Code Security 1259.4.4.6 External Interrupt Inputs 1259.4.4.7 Memory Mapping Control 1259.4.4.8 Power Control 1269.4.5 Real Monitor 1269.4.5.1 GPS Module 1269.4.6 Temperature Sensor 1279.4.7 Power Supply 1289.4.8 Regulator 1289.4.9 LCD 1289.4.10 Heart Rate Sensor 1299.5 Implementation 1299.5.1 Algorithm 1309.5.2 Hardware Implementation 1309.5.3 Software Implementation 1319.6 Results and Discussions 1339.6.1 Heart Rate 1339.6.2 Temperature Sensor 1359.6.3 Panic Button 1359.6.4 GPS Receiver 1359.7 Conclusion 136References 13610 CLOUD-IOT SECURED PREDICTION SYSTEM FOR PROCESSING AND ANALYSIS OF HEALTHCARE DATA USING MACHINE LEARNING TECHNIQUES 137G. K. Kamalam and S. Anitha10.1 Introduction 13810.2 Literature Survey 13910.3 Medical Data Classification 14110.3.1 Structured Data 14210.3.2 Semi-Structured Data 14210.4 Data Analysis 14210.4.1 Descriptive Analysis 14210.4.2 Diagnostic Analysis 14310.4.3 Predictive Analysis 14310.4.4 Prescriptive Analysis 14310.5 ML Methods Used in Healthcare 14410.5.1 Supervised Learning Technique 14410.5.2 Unsupervised Learning 14510.5.3 Semi-Supervised Learning 14510.5.4 Reinforcement Learning 14510.6 Probability Distributions 14510.6.1 Discrete Probability Distributions 14610.6.1.1 Bernoulli Distribution 14610.6.1.2 Uniform Distribution 14710.6.1.3 Binomial Distribution 14710.6.1.4 Normal Distribution 14810.6.1.5 Poisson Distribution 14810.6.1.6 Exponential Distribution 14910.7 Evaluation Metrics 15010.7.1 Classification Accuracy 15010.7.2 Confusion Matrix 15010.7.3 Logarithmic Loss 15110.7.4 Receiver Operating Characteristic Curve, or ROC Curve 15210.7.5 Area Under Curve (AUC) 15210.7.6 Precision 15310.7.7 Recall 15310.7.8 F1 Score 15310.7.9 Mean Absolute Error 15410.7.10 Mean Squared Error 15410.7.11 Root Mean Squared Error 15510.7.12 Root Mean Squared Logarithmic Error 15510.7.13 R-Squared/Adjusted R-Squared 15610.7.14 Adjusted R-Squared 15610.8 Proposed Methodology 15610.8.1 Neural Network 15810.8.2 Triangular Membership Function 15810.8.3 Data Collection 15910.8.4 Secured Data Storage 15910.8.5 Data Retrieval and Merging 16110.8.6 Data Aggregation 16210.8.7 Data Partition 16210.8.8 Fuzzy Rules for Prediction of Heart Disease 16310.8.9 Fuzzy Rules for Prediction of Diabetes 16410.8.10 Disease Prediction With Severity and Diagnosis 16510.9 Experimental Results 16610.10 Conclusion 169References 16911 CLOUDIOT-DRIVEN HEALTHCARE: REVIEW, ARCHITECTURE, SECURITY IMPLICATIONS, AND OPEN RESEARCH ISSUES 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan11.1 Introduction 17411.2 Background Elements 18011.2.1 Security Comparison Between Traditional and IoT Networks 18511.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 18711.3.1 Security Protocols 18711.3.2 Enabling Technologies 18811.4 CloudIoT Health System Framework 19111.4.1 Data Perception/Acquisition 19211.4.2 Data Transmission/Communication 19311.4.3 Cloud Storage and Warehouse 19411.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 19411.4.5 Design Considerations 19711.5 Security Challenges and Vulnerabilities 19911.5.1 Security Characteristics and Objectives 20011.5.1.1 Confidentiality 20211.5.1.2 Integrity 20211.5.1.3 Availability 20211.5.1.4 Identification and Authentication 20211.5.1.5 Privacy 20311.5.1.6 Light Weight Solutions 20311.5.1.7 Heterogeneity 20311.5.1.8 Policies 20311.5.2 Security Vulnerabilities 20311.5.2.1 IoT Threats and Vulnerabilities 20511.5.2.2 Cloud-Based Threats 20811.6 Security Countermeasures and Considerations 21411.6.1 Security Countermeasures 21411.6.1.1 Security Awareness and Survey 21411.6.1.2 Security Architecture and Framework 21511.6.1.3 Key Management 21611.6.1.4 Authentication 21711.6.1.5 Trust 21811.6.1.6 Cryptography 21911.6.1.7 Device Security 21911.6.1.8 Identity Management 22011.6.1.9 Risk-Based Security/Risk Assessment 22011.6.1.10 Block Chain–Based Security 22011.6.1.11 Automata-Based Security 22011.6.2 Security Considerations 23411.7 Open Research Issues and Security Challenges 23711.7.1 Security Architecture 23711.7.2 Resource Constraints 23811.7.3 Heterogeneous Data and Devices 23811.7.4 Protocol Interoperability 23811.7.5 Trust Management and Governance 23911.7.6 Fault Tolerance 23911.7.7 Next-Generation 5G Protocol 24011.8 Discussion and Analysis 24011.9 Conclusion 241References 24212 A NOVEL USAGE OF ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS IN REMOTE-BASED HEALTHCARE APPLICATIONS 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan12.1 Introduction Machine Learning 25612.2 Importance of Machine Learning 25612.2.1 ML vs. Classical Algorithms 25812.2.2 Learning Supervised 25912.2.3 Unsupervised Learning 26112.2.4 Network for Neuralism 26312.2.4.1 Definition of the Neural Network 26312.2.4.2 Neural Network Elements 26312.3 Procedure 26512.3.1 Dataset and Seizure Identification 26512.3.2 System 26512.4 Feature Extraction 26612.5 Experimental Methods 26612.5.1 Stepwise Feature Optimization 26612.5.2 Post-Classification Validation 26812.5.3 Fusion of Classification Methods 26812.6 Experiments 26912.7 Framework for EEG Signal Classification 26912.8 Detection of the Preictal State 27012.9 Determination of the Seizure Prediction Horizon 27112.10 Dynamic Classification Over Time 27212.11 Conclusion 273References 27313 USE OF MACHINE LEARNING IN HEALTHCARE 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi13.1 Introduction 27613.2 Uses of Machine Learning in Pharma and Medicine 27613.2.1 Distinguish Illnesses and Examination 27713.2.2 Drug Discovery and Manufacturing 27713.2.3 Scientific Imaging Analysis 27813.2.4 Twisted Therapy 27813.2.5 AI to Know-Based Social Change 27813.2.6 Perception Wellness Realisms 27913.2.7 Logical Preliminary and Exploration 27913.2.8 Publicly Supported Perceptions Collection 27913.2.9 Better Radiotherapy 28013.2.10 Incidence Forecast 28013.3 The Ongoing Preferences of ML in Human Services 28113.4 The Morals of the Use of Calculations in Medicinal Services 28413.5 Opportunities in Healthcare Quality Improvement 28813.5.1 Variation in Care 28813.5.2 Inappropriate Care 28913.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 28913.5.4 The Fact That People Are Unable to do What They Know Works 28913.5.5 A Waste 29013.6 A Team-Based Care Approach Reduces Waste 29013.7 Conclusion 291References 29214 METHODS OF MRI BRAIN TUMOR SEGMENTATION 295Amit Verma14.1 Introduction 29514.2 Generative and Descriptive Models 29614.2.1 Region-Based Segmentation 30014.2.2 Generative Model With Weighted Aggregation 30014.3 Conclusion 302References 30315 EARLY DETECTION OF TYPE 2 DIABETES MELLITUS USING DEEP NEURAL NETWORK–BASED MODEL 305Varun Sapra and Luxmi Sapra15.1 Introduction 30615.2 Data Set 30715.2.1 Data Insights 30815.3 Feature Engineering 31015.4 Framework for Early Detection of Disease 31215.4.1 Deep Neural Network 31315.5 Result 31415.6 Conclusion 315References 31516 A COMPREHENSIVE ANALYSIS ON MASKED FACE DETECTION ALGORITHMS 319Pranjali Singh, Amitesh Garg and Amritpal Singh16.1 Introduction 32016.2 Literature Review 32116.3 Implementation Approach 32516.3.1 Feature Extraction 32516.3.2 Image Processing 32516.3.3 Image Acquisition 32516.3.4 Classification 32516.3.5 MobileNetV2 32616.3.6 Deep Learning Architecture 32616.3.7 LeNet-5, AlexNet, and ResNet-50 32616.3.8 Data Collection 32616.3.9 Development of Model 32716.3.10 Training of Model 32816.3.11 Model Testing 32816.4 Observation and Analysis 32816.4.1 CNN Algorithm 32816.4.2 SSDNETV2 Algorithm 33016.4.3 SVM 33116.5 Conclusion 332References 33317 IOT-BASED AUTOMATED HEALTHCARE SYSTEM 335Darpan Anand and Aashish Kumar17.1 Introduction 33517.1.1 Software-Defined Network 33617.1.2 Network Function Virtualization 33717.1.3 Sensor Used in IoT Devices 33817.2 SDN-Based IoT Framework 34117.3 Literature Survey 34317.4 Architecture of SDN-IoT for Healthcare System 34417.5 Challenges 34517.6 Conclusion 347References 347Index 351
Python for Cybersecurity
DISCOVER AN UP-TO-DATE AND AUTHORITATIVE EXPLORATION OF PYTHON CYBERSECURITY STRATEGIESPython For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today.Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including:* Reconnaissance, resource development, initial access, and execution* Persistence, privilege escalation, defense evasion, and credential access* Discovery, lateral movement, collection, and command and control* Exfiltration and impactEach chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.HOWARD E. POSTON III is a freelance consultant and content creator with a professional focus on blockchain and cybersecurity. He has over ten years’ experience in programming with Python and has developed and taught over a dozen courses teaching cybersecurity. He is a sought-after speaker on blockchain and cybersecurity at international security conferences. Introduction xviiCHAPTER 1 FULFILLING PRE- ATT&CK OBJECTIVES 1Active Scanning 2Scanning Networks with scapy 2Implementing a SYN Scan in scapy 4Performing a DNS Scan in scapy 5Running the Code 5Network Scanning for Defenders 6Monitoring Traffic with scapy 7Building Deceptive Responses 8Running the Code 9Search Open Technical Databases 9Offensive DNS Exploration 10Searching DNS Records 11Performing a DNS Lookup 12Reverse DNS Lookup 12Running the Code 13DNS Exploration for Defenders 13Handling DNS Requests 15Building a DNS Response 15Running the Code 16Summary 17Suggested Exercises 17CHAPTER 2 GAINING INITIAL ACCESS 19Valid Accounts 20Discovering Default Accounts 20Accessing a List of Default Credentials 21Starting SSH Connections in Python 22Performing Telnet Queries in Python 23Running the Code 24Account Monitoring for Defenders 24INTRODUCTION TO WINDOWS EVENT LOGS 25Accessing Event Logs in Python 28Detecting Failed Logon Attempts 28Identifying Unauthorized Access to Default Accounts 30Running the Code 30Replication Through Removable Media 31Exploiting Autorun 31Converting Python Scripts to Windows Executables 32Generating an Autorun File 33Setting Up the Removable Media 34Running the Code 34Detecting Autorun Scripts 34Identifying Removable Drives 35Finding Autorun Scripts 36Detecting Autorun Processes 36Running the Code 36Summary 37Suggested Exercises 37CHAPTER 3 ACHIEVING CODE EXECUTION 39Windows Management Instrumentation 40Executing Code with WMI 40Creating Processes with WMI 41Launching Processes with PowerShell 41Running the Code 42WMI Event Monitoring for Defenders 42WMI in Windows Event Logs 43Accessing WMI Event Logs in Python 45Processing Event Log XML Data 45Running the Code 46Scheduled Task/Job 47Scheduling Malicious Tasks 47Checking for Scheduled Tasks 48Scheduling a Malicious Task 48Running the Code 49Task Scheduling for Defenders 50Querying Scheduled Tasks 51Identifying Suspicious Tasks 52Running the Code 52Summary 53Suggested Exercises 53CHAPTER 4 MAINTAINING PERSISTENCE 55Boot or Logon Autostart Execution 56Exploiting Registry Autorun 56The Windows Registry and Autorun Keys 57Modifying Autorun Keys with Python 60Running the Code 61Registry Monitoring for Defenders 62Querying Windows Registry Keys 63Searching the HKU Hive 64Running the Code 64Hijack Execution Flow 65Modifying the Windows Path 65Accessing the Windows Path 66Modifying the Path 67Running the Code 68Path Management for Defenders 69Detecting Path Modification via Timestamps 69Enabling Audit Events 71Monitoring Audit Logs 73Running the Code 75Summary 76Suggested Exercises 76CHAPTER 5 PERFORMING PRIVILEGE ESCALATION 77Boot or Logon Initialization Scripts 78Creating Malicious Logon Scripts 78Achieving Privilege Escalation with Logon Scripts 79Creating a Logon Script 79Running the Code 79Searching for Logon Scripts 80Identifying Autorun Keys 81Running the Code 81Hijack Execution Flow 81Injecting Malicious Python Libraries 82How Python Finds Libraries 82Creating a Python Library 83Running the Code 83Detecting Suspicious Python Libraries 83Identifying Imports 85Detecting Duplicates 85Running the Code 86Summary 86Suggested Exercises 87CHAPTER 6 EVADING DEFENSES 89Impair Defenses 90Disabling Antivirus 90Disabling Antivirus Autorun 90Terminating Processes 93Creating Decoy Antivirus Processes 94Catching Signals 95Running the Code 95Hide Artifacts 95Concealing Files in Alternate Data Streams 96Exploring Alternate Data Streams 96Alternate Data Streams in Python 97Running the Code 98Detecting Alternate Data Streams 98Walking a Directory with Python 99Using PowerShell to Detect ADS 100Parsing PowerShell Output 101Running the Code 102Summary 102Suggested Exercises 103CHAPTER 7 ACCESSING CREDENTIALS 105Credentials from Password Stores 106Dumping Credentials from Web Browsers 106Accessing the Chrome Master Key 108Querying the Chrome Login Data Database 108Parsing Output and Decrypting Passwords 109Running the Code 109Monitoring Chrome Passwords 110Enabling File Auditing 110Detecting Local State Access Attempts 111Running the Code 113Network Sniffing 114Sniffing Passwords with scapy 114Port- Based Protocol Identification 116Sniffing FTP Passwords 116Extracting SMTP Passwords 117Tracking Telnet Authentication State 119Running the Code 121Creating Deceptive Network Connections 121Creating Decoy Connections 122Running the Code 122Summary 123Suggested Exercises 123CHAPTER 8 PERFORMING DISCOVERY 125Account Discovery 126Collecting User Account Data 126Identifying Administrator Accounts 127Collecting User Account Information 128Accessing Windows Password Policies 128Running the Code 129Monitoring User Accounts 130Monitoring Last Login Times 130Monitoring Administrator Login Attempts 131Running the Code 132File and Directory Discovery 133Identifying Valuable Files and Folders 133Regular Expressions for Data Discovery 135Parsing Different File Formats 135Running the Code 136Creating Honeypot Files and Folders 136Monitoring Decoy Content 136Creating the Decoy Content 137Running the Code 138Summary 138Suggested Exercises 139CHAPTER 9 MOVING LATERALLY 141Remote Services 142Exploiting Windows Admin Shares 142Enabling Full Access to Administrative Shares 143Transferring Files via Administrative Shares 144Executing Commands on Administrative Shares 144Running the Code 144Admin Share Management for Defenders 145Monitoring File Operations 146Detecting Authentication Attempts 147Running the Code 148Use Alternative Authentication Material 148Collecting Web Session Cookies 149Accessing Web Session Cookies 150Running the Code 150Creating Deceptive Web Session Cookies 151Creating Decoy Cookies 151Monitoring Decoy Cookie Usage 153Running the Code 153Summary 154Suggested Exercises 155CHAPTER 10 COLLECTING INTELLIGENCE 157Clipboard Data 158Collecting Data from the Clipboard 158Accessing the Windows Clipboard 159Replacing Clipboard Data 159Running the Code 160Clipboard Management for Defenders 160Monitoring the Clipboard 161Processing Clipboard Messages 161Identifying the Clipboard Owner 161Running the Code 162Email Collection 162Collecting Local Email Data 162Accessing Local Email Caches 163Running the Code 163Protecting Against Email Collection 164Identifying Email Caches 165Searching Archive Files 165Running the Code 166Summary 166Suggested Exercises 166CHAPTER 11 IMPLEMENTING COMMAND AND CONTROL 169Encrypted Channel 170Command and Control Over Encrypted Channels 170Encrypted Channel Client 171Encrypted Channel Server 172Running the Code 173Detecting Encrypted C2 Channels 174Performing Entropy Calculations 175Detecting Encrypted Traffic 175Running the Code 176Protocol Tunneling 176Command and Control via Protocol Tunneling 176Protocol Tunneling Client 177Protocol Tunneling Server 177Running the Code 179Detecting Protocol Tunneling 179Extracting Field Data 181Identifying Encoded Data 181Running the Code 181Summary 182Suggested Exercises 182CHAPTER 12 EXFILTRATING DATA 183Alternative Protocols 184Data Exfiltration Over Alternative Protocols 184Alternative Protocol Client 185Alternative Protocol Server 186Running the Code 188Detecting Alternative Protocols 189Detecting Embedded Data 190Running the Code 191Non- Application Layer Protocols 191Data Exfiltration via Non- Application Layer Protocols 192Non- Application Layer Client 193Non- Application Layer Server 193Running the Code 194Detecting Non- Application Layer Exfiltration 195Identifying Anomalous Type and Code Values 196Running the Code 196Summary 197Suggested Exercises 197CHAPTER 13 ACHIEVING IMPACT 199Data Encrypted for Impact 200Encrypting Data for Impact 200Identifying Files to Encrypt 201Encrypting and Decrypting Files 202Running the Code 202Detecting File Encryption 203Finding Files of Interest 204Calculating File Entropies 204Running the Code 205Account Access Removal 205Removing Access to User Accounts 205Changing Windows Passwords 207Changing Linux Passwords 207Running the Code 207Detecting Account Access Removal 208Detecting Password Changes in Windows 209Detecting Password Changes in Linux 210Running the Code 211Summary 211Suggested Exercises 212Index 213