Allgemein
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.WHAT YOU'LL LEARN* Review data structures in NumPy and Pandas * Demonstrate machine learning techniques and algorithm* Understand supervised learning and unsupervised learning * Examine convolutional neural networks and Recurrent neural networks* Get acquainted with scikit-learn and PyTorch* Predict sequences in recurrent neural networks and long short term memoryWHO THIS BOOK IS FORData scientists, machine learning engineers, and software professionals with basic skills in Python programming.Ashwin Pajankar holds a Master of Technology from IIIT Hyderabad, and has over 25 years of programming experience. He started his journey in programming and electronics with BASIC programming language and is now proficient in Assembly programming, C, C++, Java, Shell Scripting, and Python. Other technical experience includes single board computers such as Raspberry Pi and Banana Pro, and Arduino. He is currently a freelance online instructor teaching programming bootcamps to more than 60,000 students from tech companies and colleges. His Youtube channel has an audience of 10000 subscribers and he has published more than 15 books on programming and electronics with many international publications.Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move to the field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter NotebookChapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics:1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypesChapter 3 : Introduction to Data VisualizationChapter goal – In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics:1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizationsChapter 4 : Introduction to PandasChapter goal – Get started with Pandas data structuresNo of pages: 10Sub - Topics:1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) ClipboardChapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal – Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasetsChapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and imagesChapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision treesChapter 8: Tuning Supervised LearnersChapter Goal: Analyzing and improving the performance of supervised learning modelsNo of Pages: 201. Training methodology, evaluation methodology2. Hyperparameter tuning3. Regularization in linear regression4. Regularization in logistic regression5. Regularization in decision trees6. Crossvalidation, K-fold cross validation7. ROC CurveChapter 9: Supervised Learning Methods - 2Chapter Goal: Learn more algorithmsNo of Pages: 151. Naive bayes2. Support vector machines3. Visualization of decision boundariesChapter 10: Ensemble Learning MethodsChapter Goal: Learn the in-depth background of ensemble learning methodsNo of Pages: 101. Bagging vs boosting2. Random forest3. Adaboost4. Gradient boostingChapter 11: Unsupervised Learning MethodsChapter Goal: Detailed theory and practically oriented introduction to dimensionality reduction and clustering algorithmsNo of Pages: 201. Dimensionality reduction2. Principle components analysis3. Clustering4. K-Means method5. Density-based methodChapter 12: Neural Networks and Pytorch BasicsChapter Goal: Understand the basics of neural networks, deep learning, and PytorchNo of Pages: 101. Introduction to Pytorch, tensors2. Tensor operations3. ExercisesChapter 13: Feedforward Neural NetworksChapter Goal: In-depth introduction to basic dense neural networks along with necessary mathematical background and implementation. (chapter might split into two while writing)No of Pages: 201. Perceptron model2. Neural network and activation functions3. Multiclass classification4. Cost functions and gradient descent5. Backpropagation6. Pytorch gradients7. Linear regression with PyTorch8. Basic dense network with PyTorch for regression9. Basic dense network with Pytorch for classificationChapter 14: Convolutional Neural NetworkChapter Goal: Explore details behind CNNs and implement two solutions for image classificationNo of Pages: 201. Dense network for digits classification2. Image filters and kernels3. Convolutional layers4. Pooling layers5. CNN for digits classification6. CNN for image classificationChapter 15: Recurrent Neural NetworkChapter Goal: Understand sequence networks and implement them for forecasting values (or text classification)No of Pages: 151. Introduction to recurrent neural networks2. Vanishing gradient problem3. LSTM4. RNN batches, LSTM5. Text classification Problem (or forecasting problem)Chapter 16: Bringing It All TogetherChapter Goal: Discuss, conceptualize, design, and develop end to endNo of Pages: 201. Project 12. Project 2
Wireshark Fundamentals
Understand the fundamentals of the Wireshark tool that is key for network engineers and network security analysts. This book explains how the Wireshark tool can be used to analyze network traffic and teaches you network protocols and features.Author Vinit Jain walks you through the use of Wireshark to analyze network traffic by expanding each section of a header and examining its value. Performing packet capture and analyzing network traffic can be a complex, time-consuming, and tedious task. With the help of this book, you will use the Wireshark tool to its full potential. You will be able to build a strong foundation and know how Layer 2, 3, and 4 traffic behave, how various routing protocols and the Overlay Protocol function, and you will become familiar with their packet structure.Troubleshooting engineers will learn how to analyze traffic and identify issues in the network related to packet loss, bursty traffic, voice quality issues, etc. The book will help you understand the challenges faced in any network environment and how packet capture tools can be used to identify and isolate those issues.This hands-on guide teaches you how to perform various lab tasks. By the end of the book, you will have in-depth knowledge of the Wireshark tool and its features, including filtering and traffic analysis through graphs. You will know how to analyze traffic, find patterns of offending traffic, and secure your network.WHAT YOU WILL LEARN* Understand the architecture of Wireshark on different operating systems* Analyze Layer 2 and 3 traffic frames* Analyze routing protocol traffic* Troubleshoot using Wireshark GraphsWHO THIS BOOK IS FORNetwork engineers, security specialists, technical support engineers, consultants, and cyber security engineersVINIT JAIN, CCIE No. 22854 (R&S, SP, Security & DC), is a Sr. Technical Leader for Network Engineering at Cisco focusing on architecting network infrastructure for edge computing solutions. Prior to that, he worked as a Network Development Engineer at Amazon as part of Amazon’s backbone network operations team and as a technical leader at Cisco Technical Assistance Center (TAC), providing escalation support in enterprise, service provider, and data center technologies.Vinit is a speaker at various networking forums, including Cisco Live events, NANOG, and CHINOG. He has co-authored several Cisco Press books and video courses with Cisco Press. Vinit holds a Bachelor of Arts degree in Mathematics from Delhi University and also holds a Master of Science in Information Technology. Apart from CCIE, he also holds multiple certifications in programming, database, and system administration and is also a Certified Ethical Hacker. Vinit can be found on twitter @vinugenie.Chapter 1: Introduction to WiresharkCHAPTER GOAL: THE GOAL OF THE CHAPTER IS TO HELP THE READERS UNDERSTAND THE NEED FOR WIRESHARK TOOL AND WHAT ARE THE VARIOUS WAYS TO INSTALL THE TOOL ON DIFFERENT OPERATING SYSTEMS.NO OF PAGES 20-30SUB -TOPICS1. Introduction to Network Traffic Analysisa. Network Sniffing2. Wiresharka. Installing Wireshark3. Setting up Port Mirroringa. SPAN on Cisco IOS/IOS-XEb. SPAN on Cisco Nexusc. Enabling Port Mirroring on Arista EOSd. Enabling Port Mirroring on JunOSChapter 2: Getting Familiar with WiresharkCHAPTER GOAL: THE GOAL OF THIS CHAPTER IS TO FAMILIARIZE THE READERS WITH THE WIRESHARK TOOLS, ITS CAPABILITIES AND HOW IT CAN BE USED IN DIFFERENT SCENARIOS.NO OF PAGES: 40-50Sub - Topics1. Overview of Wireshark Toola. Wireshark Preferences2. Performing Packet Capturea. Dissectorsb. Configuration Profilesc. Filtering with Wireshark3. Wireshark Capture Filesa. PCAP vs. PCAPngb. Splitting Packet Captures into multiple filesc. Merging multiple capture files4. Analyzing packets in Wiresharka. OSI Modelb. Analyzing packetsChapter 3: Analyzing Layer-2 and Layer-3 TrafficCHAPTER GOAL: THE GOAL OF THIS CHAPTER IS TO FAMILIARIZE THE READERS HOW TO ANALYZE LAYER-2 AND LAYER-3 TRAFFIC AND THE VARIOUS FIELDS THAT ONE NEEDS TO LOOK AT WHEN ANALYZING NETWORK TRAFFIC.NO OF PAGES: 60-70SUB - TOPICS1. Layer-2 Framesa. Ethernet Frames2. Layer-3 Packetsa. Address Resolution Protocolb. IPv4 Packetsc. IPv6 Packets3. Analyzing QoS MarkingsChapter 4: Analyzing Layer-4 TrafficCHAPTER GOAL: GOAL OF THIS CHAPTER IS TO HELP THE READERS HOW TO ANALYZE TCP AND UDP TRAFFIC STREAMS AND HOW TO IDENTIFY PACKET LOSS ISSUESNO OF PAGES : 40-50SUB - TOPICS:1. Understanding TCP/IP Modela. Problem of Ownership2. Transmission Control Protocola. TCP Flagsb. TCP 3-way Handshakec. Port Scanningd. Investigating Packet Losse. Troubleshooting with Wireshark Graphsf. TCP Expert3. User Datagram ProtocolChapter 5: Analyzing Routing Protocol TrafficCHAPTER GOAL: GOAL OF THIS CHAPTER IS TO HELP THE READERS GET FAMILIAR WITH VARIOUS ROUTING PROTOCOL PACKET FORMATS AND TO IDENTIFY ANY POSSIBLE ISSUES WITH THOSE PROTOCOLSNO OF PAGES : 40-50SUB - TOPICS:1. Routing Protocols1. OSPF2. EIGRP3. BGP4. PIM2. Analyzing Overlay Traffic1. GRE2. IPSEC3. LISP4. VXLAN
Snowflake Access Control
Understand the different access control paradigms available in the Snowflake Data Cloud and learn how to implement access control in support of data privacy and compliance with regulations such as GDPR, APPI, CCPA, and SOX. The information in this book will help you and your organization adhere to privacy requirements that are important to consumers and becoming codified in the law. You will learn to protect your valuable data from those who should not see it while making it accessible to the analysts whom you trust to mine the data and create business value for your organization.Snowflake is increasingly the choice for companies looking to move to a data warehousing solution, and security is an increasing concern due to recent high-profile attacks. This book shows how to use Snowflake's wide range of features that support access control, making it easier to protect data access from the data origination point all the way to the presentation and visualization layer. Reading this book helps you embrace the benefits of securing data and provide valuable support for data analysis while also protecting the rights and privacy of the consumers and customers with whom you do business.WHAT YOU WILL LEARN* Identify data that is sensitive and should be restricted* Implement access control in the Snowflake Data Cloud* Choose the right access control paradigm for your organization* Comply with CCPA, GDPR, SOX, APPI, and similar privacy regulations* Take advantage of recognized best practices for role-based access control* Prevent upstream and downstream services from subverting your access control* Benefit from access control features unique to the Snowflake Data CloudWHO THIS BOOK IS FORData engineers, database administrators, and engineering managers who want to improve their access control model; those whose access control model is not meeting privacy and regulatory requirements; those new to Snowflake who want to benefit from access control features that are unique to the platform; technology leaders in organizations that have just gone public and are now required to conform to SOX reporting requirementsJESSICA MEGAN LARSON was born and raised in a small town across the Puget Sound from Seattle, but now calls Oakland, California home. She studied cognitive science with a minor in computer science at University of California Berkeley. She thrives on mentorship, solving data puzzles, and equipping colleagues with new technical skills. Jessica is passionate about helping women and non-binary people find their place in the technology industry. She was the first engineer within the Enterprise Data Warehouse team at Pinterest, and additionally helps to develop fantastic women through Built By Girls. Previously, she wrangled data at Eaze and Flexport. Outside of work, Jessica spends her time soaking up the California sun playing volleyball on the beach or at the park. PART I. BACKGROUND1. What is Access Control?2. Data Types Requiring Access Control3. Data Privacy Laws and Regulatory Drivers4. Permission typesPART II. CREATING ROLES5. Functional Roles - What A Person Does6. Team Roles - Who A Person Is7. Assuming A Primary Role8. Secondary RolesPART III. GRANTING PERMISSIONS TO ROLES9. Role Inheritance10. Account and Database Level Privileges11. Schema-Level Privileges12. Table and View Level Privileges13. Row-Level Permissioning and Fine-Grained Access Control14. Column-Level Permissioning and Data MaskingPART IV. OPERATIONALLY MANAGING ACCESS CONTROL15. Secure Data Sharing16. Separating Production from Development17. Upstream & Downstream Services18. Managing Access Requests
Artificial Intelligent Techniques for Wireless Communication and Networking
ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKINGTHE 20 CHAPTERS ADDRESS AI PRINCIPLES AND TECHNIQUES USED IN WIRELESS COMMUNICATION AND NETWORKING AND OUTLINE THEIR BENEFIT, FUNCTION, AND FUTURE ROLE IN THE FIELD. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. AUDIENCEResearchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies. R. KANTHAVEL, PhD is a Professor in the Department of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.K. ANANTHAJOTHI, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on "Theory of Computation and Python Programming" and holds 2 patents.S. BALAMURUGAN, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.R. KARTHIK GANESH, PhD is an associate professor in the Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.Preface xvii1 COMPREHENSIVE AND SELF-CONTAINED INTRODUCTION TO DEEP REINFORCEMENT LEARNING 1P. Anbalagan, S. Saravanan and R. Saminathan1.1 Introduction 21.2 Comprehensive Study 31.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 71.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 91.5 Conclusion 122 IMPACT OF AI IN 5G WIRELESS TECHNOLOGIES AND COMMUNICATION SYSTEMS 15A. Sivasundari and K. Ananthajothi2.1 Introduction 162.2 Integrated Services of AI in 5G and 5G in AI 182.3 Artificial Intelligence and 5G in the Industrial Space 232.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 252.5 Conclusion 283 ARTIFICIAL INTELLIGENCE REVOLUTION IN LOGISTICS AND SUPPLY CHAIN MANAGEMENT 31P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy3.1 Introduction 323.2 Theory--AI in Logistics and Supply Chain Market 353.3 Factors to Propel Business Into the Future Harnessing Automation 403.4 Conclusion 434 AN EMPIRICAL STUDY OF CROP YIELD PREDICTION USING REINFORCEMENT LEARNING 47M. P. Vaishnnave and R. Manivannan4.1 Introduction 474.2 An Overview of Reinforcement Learning in Agriculture 494.3 Reinforcement Learning Startups for Crop Prediction 524.4 Conclusion 575 COST OPTIMIZATION FOR INVENTORY MANAGEMENT IN BLOCKCHAIN AND CLOUD 59C. Govindasamy, A. Antonidoss and A. Pandiaraj5.1 Introduction 605.2 Blockchain: The Future of Inventory Management 625.3 Cost Optimization for Blockchain Inventory Management in Cloud 665.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 715.5 Conclusion 726 REVIEW OF DEEP LEARNING ARCHITECTURES USED FOR IDENTIFICATION AND CLASSIFICATION OF PLANT LEAF DISEASES 75G. Gangadevi and C. Jayakumar6.1 Introduction 756.2 Literature Review 766.3 Proposed Idea 826.4 Reference Gap 866.5 Conclusion 877 GENERATING ART AND MUSIC USING DEEP NEURAL NETWORKS 91A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna7.1 Introduction 917.2 Related Works 927.3 System Architecture 947.4 System Development 967.5 Algorithm-LSTM 1007.6 Result 1007.7 Conclusions 1018 DEEP LEARNING ERA FOR FUTURE 6G WIRELESS COMMUNICATIONS--THEORY, APPLICATIONS, AND CHALLENGES 105S.K.B. Sangeetha and R. Dhaya8.1 Introduction 1068.2 Study of Wireless Technology 1088.3 Deep Learning Enabled 6G Wireless Communication 1138.4 Applications and Future Research Directions 1179 ROBUST COOPERATIVE SPECTRUM SENSING TECHNIQUES FOR A PRACTICAL FRAMEWORK EMPLOYING COGNITIVE RADIOS IN 5G NETWORKS 121J. Banumathi, S.K.B. Sangeetha and R. Dhaya9.1 Introduction 1229.2 Spectrum Sensing in Cognitive Radio Networks 1229.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 1249.4 Cooperative Sensing Among Cognitive Radios 1259.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 1289.6 Spectrum Agile Radios: Utilization and Sensing Architectures 1289.7 Some Fundamental Limits on Cognitive Radio 1309.8 Cooperative Strategies and Capacity Theorems for Relay Networks 1319.9 Research Challenges in Cooperative Communication 1339.10 Conclusion 13510 NATURAL LANGUAGE PROCESSING 139S. Meera and S. Geerthik10.1 Introduction 13910.2 Conclusions 152References 15211 CLASS LEVEL MULTI-FEATURE SEMANTIC SIMILARITY-BASED EFFICIENT MULTIMEDIA BIG DATA RETRIEVAL 155D. Sujatha, M. Subramaniam and A. Kathirvel11.1 Introduction 15611.2 Literature Review 15811.3 Class Level Semantic Similarity-Based Retrieval 15911.4 Results and Discussion 16412 SUPERVISED LEARNING APPROACHES FOR UNDERWATER SCALAR SENSORY DATA MODELING WITH DIURNAL CHANGES 175J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya12.1 Introduction 17612.2 Literature Survey 17612.3 Proposed Work 17712.4 Results 18012.5 Conclusion and Future Work 19013 MULTI-LAYER UAV AD HOC NETWORK ARCHITECTURE, PROTOCOL AND SIMULATION 193Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna13.1 Introduction 19413.2 Background 19613.3 Issues and Gap Identified 19713.4 Main Focus of the Chapter 19813.5 Mobility 19913.6 Routing Protocol 20113.7 High Altitude Platforms (HAPs) 20213.8 Connectivity Graph Metrics 20413.9 Aerial Vehicle Network Simulator (AVENs) 20613.10 Conclusion 20714 ARTIFICIAL INTELLIGENCE IN LOGISTICS AND SUPPLY CHAIN 211Jeyaraju Jayaprakash14.1 Introduction to Logistics and Supply Chain 21214.2 Recent Research Avenues in Supply Chain 21714.3 Importance and Impact of AI 22214.4 Research Gap of AI-Based Supply Chain 22415 HEREDITARY FACTOR-BASED MULTI-FEATURED ALGORITHM FOR EARLY DIABETES DETECTION USING MACHINE LEARNING 235S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan15.1 Introduction 23615.2 Literature Review 23715.3 Objectives of the Proposed System 24415.4 Proposed System 24515.5 HIVE and R as Evaluation Tools 24615.6 Decision Trees 24715.7 Results and Discussions 25015.8 Conclusion 25216 ADAPTIVE AND INTELLIGENT OPPORTUNISTIC ROUTING USING ENHANCED FEEDBACK MECHANISM 255V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai16.1 Introduction 25516.2 Related Study 25816.3 System Model 25916.4 Experiments and Results 26416.5 Conclusion 26717 ENABLING ARTIFICIAL INTELLIGENCE AND CYBER SECURITY IN SMART MANUFACTURING 269R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumarand R. Mahaveerakannan17.1 Introduction 27017.2 New Development of Artificial Intelligence 27117.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 27117.4 Current Status and Problems of Green Manufacturing 27217.5 Artificial Intelligence for Green Manufacturing 27617.6 Detailed Description of Common Encryption Algorithms 28017.7 Current and Future Works 28217.8 Conclusion 28318 DEEP LEARNING IN 5G NETWORKS 287G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani18.1 5G Networks 28718.2 Artificial Intelligence and 5G Networks 29118.3 Deep Learning in 5G Networks 29319 EIDR UMPIRING SECURITY MODELS FOR WIRELESS SENSOR NETWORKS 299A. Kathirvel, S. Navaneethan and M. Subramaniam19.1 Introduction 29919.2 A Review of Various Routing Protocols 30219.3 Scope of Chapter 30719.4 Conclusions and Future Work 31120 ARTIFICIAL INTELLIGENCE IN WIRELESS COMMUNICATION 317Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta20.1 Introduction 31820.2 Artificial Intelligence: A Grand Jewel Mine 31820.3 Wireless Communication: An Overview 32020.4 Wireless Revolution 32020.5 The Present Times 32120.6 Artificial Intelligence in Wireless Communication 32120.7 Artificial Neural Network 32420.8 The Deployment of 5G 32620.9 Looking Into the Features of 5G 32720.10 AI and the Internet of Things (IoT) 32820.11 Artificial Intelligence in Software-Defined Networks (SDN) 32920.12 Artificial Intelligence in Network Function Virtualization 33120.13 Conclusion 332References 332Index 335
Patterns of Software Construction
Master how to implement a repeatable software construction system. This book closely examines how a system is designed to tie a series of activities together that are needed when building software-intensive systems.Software construction and operations don't get enough attention as a repeatable system. The world is stuck in agile backlog grooming sessions, and quality is not increasing. Companies' budgets are shrinking, and teams need a way to get more done with less, consistently. This topic is very relevant to our current economic conditions and continuing globalization trends. A reason we constantly need more hands-on-the-keyboards is because of all the waste created in development cycles. We need more literature on how to "do software" not just write software.These goals are accomplished using the concept of evolutions, much like the Navy SEALS train their team members. For LIFT, the evolutions are: Plan, Build, Test, Release, Operate and Manage. The entire purpose of the book is instructing professionals how to use these distinct evolutions while remaining agile. And then, inside of each evolution, to explicitly break down the inputs to the evolution, outputs and series of activities taking place. Patterns of Software Construction clearly outlines how together this becomes the system.WHAT YOU WILL LEARN* Optimize each evolution of a software delivery cycle* Review best practices of planning, highest return in the build cycle, and ignored practices in test, release, and operate * Apply the highest return techniques during the software build evolutionWHO THIS BOOK IS FORManagers, developers, tech lead, team lead, aspiring engineer, department leaders in corporations, executives, small business owner, IT DirectorStephen Rylander is currently SVP, Global Head of Engineering Company at Donnelley Financial Solutions. He is a software engineer turned technical executive who has seen a variety of industries from music, to ecommerce, to finance and more. He is invested in improving the practice of software delivery, operational platforms and all the people involved in making this happen. He has worked on platforms handling millions of daily transactions and developed digital transformation programs driving financial platforms. He has also had the opportunity to construct platforms with digital investing advice engines and has a history of dealing with scale and delivering results leading local and distributed teams.For fun he used to also run the API Craft Chicago Meetup, help organize Morningstar Tech Talks and has been a member mentor at 1871 - Chicago's Technology & Entrepreneurship Center.Chapter 1: Not a Processo 1.1 Systemo 1.2 The Problemo 1.3 Realityo 1.4 The Solutiono 1.5.The EvolutionsChapter 2 LIFT System EvolutionsChapter 3 Plano 3.1 Plano 3.1 Targeto 3.1 Map it outo 3.1 Development StrategyChapter 4 Buildo 4.1 Anatomy of a Sprinto 4.2 Most Software Looks like this.o 4.3 Non-functional Requirements Pay the Billso 4.4 …Chapter 5 TestChapter 6 ReleaseChapter 7 OperateChapter 8 ManageChapter 9 The Long GameChapter 10 - Summary
Introducing Software Verification with Dafny Language
Get introduced to software verification and proving correctness using the Microsoft Research-backed programming language, Dafny. While some other books on this topic are quite mathematically rigorous, this book will use as little mathematical symbols and rigor as possible, and explain every concept using plain English. It's the perfect primer for software programmers and developers with C# and other programming language skills.Writing correct software can be hard, so you'll learn the concept of computation and software verification. Then, apply these concepts and techniques to confidently write bug-free code that is easy to understand. Source code will be available throughout the book and freely available via GitHub.After reading and using this book you'll be able write correct, big free software source code applicable no matter which platform and programming language you use.WHAT YOU WILL LEARN* Discover the Microsoft Research-backed Dafny programming language* Explore Hoare logic, imperative and functional programs* Work with pre- and post-conditions* Use data types, pattern matching, and classes* Dive into verification examples for potential re-use for your own projectsWHO THIS BOOK IS FORSoftware developers and programmers with at least prior, basic programming experience. No specific language needed. It is also for those with very basic mathematical experience (function, variables).BORO SITNIKOVSKI has over ten years of experience working professionally as a software engineer. He started programming with assembly on an Intel x86 at the age of ten. While in high school, he won several prizes in competitive programming, varying from 4th, 3rd, and 1st place. He is an informatics graduate - his bachelor’s thesis was titled “Programming in Haskell using algebraic data structures”, and his master’s thesis was titled “Formal verification of Instruction Sets in Virtual Machines”. He has also published a few papers on software verification. Other research interests of his include programming languages, mathematics, logic, algorithms, and writing correct software. He is a strong believer in the open-source philosophy and contributes to various open-source projects. In his spare time, he enjoys some time off with his family.Introduction: Languages and SystemsChapter 1: Our First ProgramChapter 2: LogicChapter 3: ComputationChapter 4: Mathematical FoundationsChapter 5: ProofsChapter 6: SpecificationsChapter 7: Mathematical InductionChapter 8: Verification ExercisesChapter 9: Implementing a Formal SystemConclusionBibliographyAppendix A: Gödel’s Theorems
Modellselektion
Die Modellselektion ist der Bereich der Statistik, welcher Wissenschaftlern eine Möglichkeit bietet ein Modell für die Analyse von Rohdaten zu geben. Dabei ist die Wahl eins geeigneten Modells entscheidend, da mit der Wahl eines geeigneten Modells die jeweilige Theorie einer wissenschaftlichen Forschung unterstützt werden kann. In der wissenschaftlichen Praxis stehen hierfür diverse Ansätze zur Verfügung. Die Modellselektion bietet, mit diversen Ansätzen, einen Anhaltspunkt, wie Modelle selektiert werden können, um die vorhandenen Daten zu analysieren und in der Folge die Theorie zu verifizieren bzw. falsifizieren.Hierbei stehen Wissenschaftlern diverse Ansätze und Selektionskriterien zur Verfügung, welche die Wissenschaftler dabei unterstützen können, ein geeignetes Modell für die Analyse der Daten zu selektieren. Die Selektion kann dabei mittels Tests und der Richtung der Modellselektion, mittels diversen mittels Shrinkageansätzen oder auf Basis eines Informationskriteriums erfolgen. Die Wahl eines Informationskriteriums findet in der Folge Anwendung in einer Regressionsanalyse. Dabei stehen dem Wissenschaftler diverse univariate und multivariate Regressionsmodelle zur Verfügung. Falls die Daten von Kollinearität gekennzeichnet sind, sollten Verfahren, wie die Ridge Regression oder die LASSO Regression den linearen Regressionsmodellen bevorzugt werden.
Mastering Snowflake Solutions
Design for large-scale, high-performance queries using Snowflake’s query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.MASTERING SNOWFLAKE SOLUTIONS starts with a refresher on Snowflake’s unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater operational agility in responding to the needs of modern enterprises, for example in supporting agile development techniques via database cloning. The practical examples and in-depth background on theory in this book help you unleash the power of Snowflake in building a high-performance system with little to no administrative overhead. Your result from reading will be a deep understanding of Snowflake that enables taking full advantage of Snowflake’s architecture to deliver value analytics insight to your business.WHAT YOU WILL LEARN* Optimize performance and costs associated with your use of the Snowflake data platform* Enable data security to help in complying with consumer privacy regulations such as CCPA and GDPR* Share data securely both inside your organization and with external partners* Gain visibility to each interaction with your customers using continuous data feeds from Snowpipe* Break down data silos to gain complete visibility your business-critical processes* Transform customer experience and product quality through real-time analyticsWHO THIS BOOK IS FORData engineers, scientists, and architects who have had some exposure to the Snowflake data platform or bring some experience from working with another relational database. This book is for those beginning to struggle with new challenges as their Snowflake environment begins to mature, becoming more complex with ever increasing amounts of data, users, and requirements. New problems require a new approach and this book aims to arm you with the practical knowledge required to take advantage of Snowflake’s unique architecture to get the results you need.ADAM MORTON is a senior data and analytics professional with almost two decades of experience. He has architected, designed, and led the implementation of numerous data warehouse and business intelligence solutions. Adam has extensive experience and certifications across several data analytics platforms ranging from Microsoft SQL Server, Teradata, and Hortonworks, to modern cloud-based tools such as AWS Redshift, Google Big Query, and Snowflake.Having successfully combined his experience with traditional technologies with his knowledge of modern platforms, Adam has accumulated substantial practical expertise in data warehousing and analytics in Snowflake, which he has captured and distilled into this book. Today, Adam runs his own data and analytics consultancy which focuses on helping companies solve problems with data, along with designing and executing modern data strategies to deliver tangible business value. Adam currently lives in Sydney, Australia and is the proud recipient of a Global Talent Visa. 1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake
Analytics Optimization with Columnstore Indexes in Microsoft SQL Server
Meet the challenge of storing and accessing analytic data in SQL Server in a fast and performant manner. This book illustrates how columnstore indexes can provide an ideal solution for storing analytic data that leads to faster performing analytic queries and the ability to ask and answer business intelligence questions with alacrity. The book provides a complete walk through of columnstore indexing that encompasses an introduction, best practices, hands-on demonstrations, explanations of common mistakes, and presents a detailed architecture that is suitable for professionals of all skill levels.With little or no knowledge of columnstore indexing you can become proficient with columnstore indexes as used in SQL Server, and apply that knowledge in development, test, and production environments. This book serves as a comprehensive guide to the use of columnstore indexes and provides definitive guidelines. You will learn when columnstore indexes should be used, and the performance gains that you can expect. You will also become familiar with best practices around architecture, implementation, and maintenance. Finally, you will know the limitations and common pitfalls to be aware of and avoid.As analytic data can become quite large, the expense to manage it or migrate it can be high. This book shows that columnstore indexing represents an effective storage solution that saves time, money, and improves performance for any applications that use it. You will see that columnstore indexes are an effective performance solution that is included in all versions of SQL Server, with no additional costs or licensing required.WHAT YOU WILL LEARN* Implement columnstore indexes in SQL Server* Know best practices for the use and maintenance of analytic data in SQL Server* Use metadata to fully understand the size and shape of data stored in columnstore indexes* Employ optimal ways to load, maintain, and delete data from large analytic tables* Know how columnstore compression saves storage, memory, and time* Understand when a columnstore index should be used instead of a rowstore index* Be familiar with advanced features and analyticsWHO THIS BOOK IS FORDatabase developers, administrators, and architects who are responsible for analytic data, especially for those working with very large data sets who are looking for new ways to achieve high performance in their queries, and those with immediate or future challenges to analytic data and query performance who want a methodical and effective solutionEdward Pollack has over 20 years of experience in database and systems administration, architecture, and development, becoming an advocate for designing efficient data structures that can withstand the test of time. He has spoken at many events, such as SQL Saturdays, PASS Community Summit, Dativerse, and at many user groups and is the organizer of SQL Saturday Albany. Edward has authored many articles, as well as the book Dynamic SQL: Applications, Performance, and Security, and a chapter in Expert T-SQL Window Functions in SQL Server.In his free time, Ed enjoys video games, sci-fi & fantasy, traveling and baking. He lives in the sometimes-frozen icescape of Albany, NY with his wife Theresa and sons Nolan and Oliver, and a mountain of (his) video game plushies that help break the fall when tripping on (their) kids’ toys.1. Introduction to Analytic Data in a Transactional Database2. Transactional vs. Analytic Workloads3. What are Columnstore Indexes?4. Columnstore Index Architecture5. Columnstore Compression6. Columnstore Metadata7. Batch Execution8. Bulk Loading Data9. Delete and Update Operations10. Segment and Rowgroup Elimination11. Partitioning12. Non-Clustered Columnstore Indexes on Rowstore Tables13. Non-Clustered Rowstore Indexes on Columnstore Tables14. Columnstore Index Maintenance15. Columnstore Index Performance
Machine Learning for Auditors
Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings.MACHINE LEARNING FOR AUDITORS provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization.WHAT YOU WILL LEARN* Understand the role of auditors as trusted advisors* Perform exploratory data analysis to gain a deeper understanding of your organization* Build machine learning predictive models that detect fraudulent vendor payments and expenses* Integrate data analytics with existing and new technologies* Leverage storytelling to communicate and validate your findings effectively* Apply practical implementation use cases within your organizationWHO THIS BOOK IS FORAI AUDITING is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes.MARIS SEKAR is a professional computer engineer, Certified Information Systems Auditor (ISACA), and Senior Data Scientist (Data Science Council of America). He has a passion for using storytelling to communicate on high-risk items within an organization to enable better decision making and drive operational efficiencies. He has cross-functional work experience in various domains such as risk management, data analysis and strategy, and has functioned as a subject matter expert in organizations such as PricewaterhouseCoopers LLP, Shell Canada Ltd., and TC Energy. Maris’ love for data has motivated him to win awards, write LinkedIn articles, and publish two papers with IEEE on applied machine learning and data science.PART I. TRUSTED ADVISORS1. Three Lines of Defense2. Common Audit Challenges3. Existing Solutions4. Data Analytics5. Analytics Structure & EnvironmentPART II. UNDERSTANDING ARTIFICIAL INTELLIGENCE6. Introduction to AI, Data Science, and Machine Learning7. Myths and Misconceptions8. Trust, but Verify9. Machine Learning Fundamentals10. Data Lakes11. Leveraging the Cloud12. SCADA and Operational TechnologyPART III. STORYTELLING13. What is Storytelling?14. Why Storytelling?15. When to Use Storytelling16. Types of Visualizations17. Effective Stories18. Storytelling Tools19. Storytelling in AuditingPART IV. IMPLEMENTATION RECIPES20. How to Use the Recipes21. Fraud and Anomaly Detection22. Access Management23. Project Management24. Data Exploration25. Vendor Duplicate Payments26. CAATs 2.027. Log Analysis28. Concluding Remarks
Data Science
Dieses Buch entstand aus der Motivation heraus, eines der ersten deutschsprachigen Nachschlagewerke zu entwickeln, in welchem relativ simple Quellcode-Beispiele enthalten sind, um so Lösungsansätze für die (wiederkehrenden) Programmierprobleme in der Datenanalyse weiterzugeben. Dabei ist dieses Werk nicht uneigennützig verfasst worden. Es enthält Lösungswege für immer wiederkehrende Problemstellungen die ich über meinen täglichen Umgang entwickelt habe Zweifellos gehört das Nachschlagen von Lösungsansätzen in Büchern oder im Internet zur normalen Arbeit eines Programmierers. Allerdings ist diese Suche in der Regel ein unstrukturierter und damit, zumindest teilweise, ein zeitaufwendiger Prozess.Unabhängig davon, ob Sie das Buch als Student, Mitarbeiter oder Gründer lesen, hoffe ich, dass Ihnen dieses Nachschlagewerk ein wertvoller Helfer für die ersten Anfänge sein wird. Ich gehe davon aus, dass jede Person die Grundlagen der Datenanalyse mit Hilfe moderner Programmiersprachen erlernen kann.Seit März 2018 forscht und promoviert Herr BENJAMIN M. ABDEL-KARIM im Bereich der künstlichen Intelligenz im Kontext der Wissensextraktion. Das spezielle Augenmerk seiner Forschung sind künstliche neuronale Netze, beispielsweise zur Modellierung komplexer Finanzmarktstrukturen. Zuvor hat er eine klassische Bankausbildung sowie ein Bachelor- und Masterstudium in der Wirtschaftsinformatik absolviert. Seit März 2021 bringt Herr Benjamin M. Abdel-Karim als Berater sein Fachwissen aus Forschung und Entwicklung bei der Unternehmungsberatung Capgemini im Bereich Financial Services mit ein.Data Science - Datenanalyse - Python - Quellcode-Beispiele - Datenauswertung - Datentypen - Datenstrukturen - Kontrollstrukturen - Funktionen -Anwendungsbeispiele Data Science.
Pro ASP.NET Core 6
Professional developers will produce leaner applications for the ASP.NET Core platform using the guidance in this best-selling book, now in its 9th edition and updated for ASP.NET Core for .NET 6. It contains detailed explanations of the ASP.NET Core platform and the application frameworks it supports. This cornerstone guide puts ASP.NET Core for .NET 6 into context and dives deep into the tools and techniques required to build modern, extensible web applications. New features and capabilities such as MVC, Razor Pages, Blazor Server, and Blazor WebAssembly are covered, along with demonstrations of how they are applied.ASP.NET Core for .NET 6 is the latest evolution of Microsoft’s ASP.NET web platform and provides a "host-agnostic" framework and a high-productivity programming model that promotes cleaner code architecture, test-driven development, and powerful extensibility.Author Adam Freeman has thoroughly revised this market-leading book and explains how to get the most from ASP.NET Core for .NET 6. He starts with the nuts-and-bolts topics, teaching you about middleware components, built-in services, request model binding, and more. As you gain knowledge and confidence, he introduces increasingly more complex topics and advanced features, including endpoint routing and dependency injection. He goes in depth to give you the knowledge you need.This book follows the same format and style as the popular previous editions but brings everything up to date for the new ASP.NET Core for .NET 6 release and broadens the focus to include all of the ASP.NET Core platform. You will appreciate the fully worked case study of a functioning ASP.NET Core application that you can use as a template for your own projects.Source code for this book can be found at https://github.com/Apress/pro-asp.net-core-6.WHAT YOU WILL LEARN* Explore the entire ASP.NET Core platform* Apply the new ASP.NET Core for .NET 6 features in your developer environment* See how to create RESTful web services, web applications, and client-side applications* Build on your existing knowledge to get up and running with new programming models quickly and effectivelyWHO THIS BOOK IS FORWeb developers with a basic knowledge of web development and C# who want to incorporate the latest improvements and functionality in ASP.NET Core for .NET 6 into their own projects.ADAM FREEMAN is an experienced IT professional who has held senior positions in a range of companies, most recently serving as chief technology officer and chief operating officer of a global bank. Now retired, he spends his time writing and long-distance running.Part 11. Putting ASP.NET Core into Context2. Getting Started3. Your First ASP.NET Core Application4. Using the Development Tools5. Essential C# Features6. Unit Testing ASP.NET Core Applications7. SportsStore8. SportsStore: Navigation & Cart9. SportsStore: Completing the Cart10. SportsStore: Adminstration11. SportsStore: Security & DeploymentPart 212. Understanding the ASP.NET Core Platform13. Using URL Routing14. Using Dependency Injection15. Using the Platform Features, Part 116. Using the Platform Features, Part 217. Working with DataPart 318. Creating the Example Project19. Creating RESTFul Web Services20. Advanced Web Service Features21. Using Controllers with Views22. Using Controllers with Views, Part 223. Using Razor Pages24. Using View Components25. Using Tag Helpers26. Using the Built-In Tag Helpers27. Using the Forms Tag Helpers28. Using Model Binding29. Using Model Validation30. Using Filters31. Creating Form ApplicationsPart 432. Creating the Example Application33. Using Blazor Server, Part 134. Using Blazor Server Part 235. Advanced Blazor Features36. Blazor Forms and Data37. Blazor Web Assembly38. Using ASP.NET Core Identity39. Applying ASP.NET Core Identity
Java 17 Recipes
Quickly find solutions to dozens of common programming problems encountered while building Java applications, with recipes presented in the popular problem-solution format. Look up the programming problem that you want to resolve. Read the solution. Apply the solution directly in your own code. Problem solved!Java 17 Recipes is updated to reflect changes in specification and implementation since the Java 9 edition of this book. Java 17 is the next long-term support release (LTS) of the core Java Standard Edition (SE) version 17 which also includes some of the features from previous short term support (STS) releases of Java 16 and previous versions.This new edition covers of some of the newest features, APIs, and more such as pattern matching for switch, Restore Always-Strict-Floating-Point-Semantics, enhanced pseudo-random number generators, the vector API, sealed classes, and enhancements in the use of String. Source code for all recipes is available in a dedicated GitHub repository.This must-have reference belongs in your library.WHAT YOU WILL LEARN* Look up solutions to everyday problems involving Java SE 17 LTS and other recent releases* Develop Java SE applications using the latest in Java SE technology* Incorporate Java major features introduced in versions 17, 16, and 15 into your codeWHO THIS BOOK IS FORProgrammers and developers with some prior Java experience.JOSH JUNEAU has been developing software and enterprise applications since the early days of Java EE. Application and database development have been his focus since the start of his career. He became an Oracle database administrator and adopted the PL/SQL language for performing administrative tasks and developing applications for the Oracle database. In an effort to build more complex solutions, he began to incorporate Java into his PL/SQL applications and later developed standalone and web applications with Java. Josh wrote his early Java web applications utilizing JDBC and servlets or JSP to work with backend databases. Later, he began to incorporate frameworks into his enterprise solutions, such as Java EE and JBoss Seam. Today, he primarily develops enterprise web solutions utilizing Java EE and other technologies. He also includes the use of alternative languages, such as Jython and Groovy, for some of his projects. Over the years, Josh has dabbled in many different programming languages, including alternative languages for the JVM, in particular. In 2006, Josh began devoting time to the Jython Project as editor and publisher of the Jython Monthly newsletter. In late 2008, he began a podcast dedicated to the Jython programming language. Josh was the lead author for The Definitive Guide to Jython, Oracle PL/SQL Recipes, and Java 7 Recipes, and a solo author of Java EE 7 Recipes and Introducing Java EE 7, which were all published by Apress. He works as an application developer and system analyst at Fermi National Accelerator Laboratory, and he also writes technical articles for Oracle and OTN. He was a member of the JSR 372 and JSR 378 expert groups, and is an active member of the Java Community, helping to lead the Chicago Java User Group’s Adopt-a-JSR effort. When not coding or writing, Josh enjoys spending time with his wonderful wife and five children, especially swimming, fishing, playing ball, and watching movies. To hear more from Josh, follow him on Twitter at @javajuneau.LUCIANO MANELLI earned a PhD in computer science from the IT department, University of Bari-Aldo Moro. His PhD focused on grid computing and formal methods, and he published the results in international publications. Luciano obtained several certificates in the IT sector, and, in 2014, began working for the Port Network Authority of the Ionian Sea–Port of Taranto, after working for 13 years for InfoCamere SCpA. He has worked mainly in the design, analysis, and development of large software systems; research and development; testing; and production with roles of increasing responsibility in several areas over the years. Luciano has developed a great capability to make decisions in a technical and business context and is mainly interested in project management and business process management. In his current position, he deals with port community systems and software innovation. Additionally, he has written several IT books and is a contract professor at the Polytechnic of Bari (foundations of computer science), and at the University of Bari-Aldo Moro (programming for web, computer science, and computer lab).1. Getting Started with Java 172. Java 17 Enhancements3. Strings4. Numbers and Dates5. Object-Oriented Java6. Lambda Expressions7. Data Structures and Collections8. Input and Output9. Exceptions and Logging10. Concurrency11. Debugging and Unit Testing12. Unicode, Internationalization, and Currency Codes13. Working with Databases14. JavaFX Fundamentals15. Graphics with JavaFX16. Media with JavaFX17. Java Web Applications18. Nashorn and Scripting19. E-mail20. JSON and XML Processing21. Networking22. Java Modularity
Introducing Blockchain with Java
Create your own crypto currency by implementing blockchain technology using Java. This step-by-step guide will teach you how to create a user interface using Java FX and implement SQLite DB using JDBC Driver for the blockchain.INTRODUCING BLOCKCHAIN WITH JAVA includes numerous exercises and test questions to help you solidify what you have learned as you progress through the book, and provides ideas on expanding the codebase to make it your own. You will have access to a fully-functioning repository with Java code.Upon completing this book, you will have the knowledge necessary to program your own blockchains with Java and you will have a completed project for your portfolio.WHAT YOU WILL LEARN* Know the most important theoretical concepts of the blockchain* Code the blockchain in Java* Create a user interface with JavaFX* Implement SQLite DB using JDBC Driver* Create a P2P multi-threaded app * Create your own cryptocurrency app with full functionality* Implement blockchain technology on a P2P network from scratch using Java, JavaFX, and SQLWHO THIS BOOK IS FORAnyone with a basic level knowledge of: Java or similar object-oriented programming language, FXML or HTML or similar markup language, and SQLSPIRO BUZHAROVSKI is a full-stack software developer in the IT sector. He has a degree in mechanical engineering and has worked as an engineer in the oil and gas sector for more than six years. His interests include Java frameworks, blockchain, and the latest high-tech trends. Inspiration for this book came while working as a technical reviewer on the Apress book by Boro Sitnikovski, Introducing Blockchain with Lisp: Implement and Extend Blockchains with the Racket Language.1. Introduction to Blockchain . . . . . . . .1.1. Motivation and basic definitions . .1.2. Encryption . . . . . . . . . . . . . . .1.2.1. Functions . . . . . . . . . . . .1.2.2. Symmetric-key algorithm . .1.2.3. Asymmetric-key algorithm .1.3. Hashing . . . . . . . . . . . . . . . . .1.4. Smart contracts . . . . . . . . . . . .1.5. Bitcoin . . . . . . . . . . . . . . . . . .1.6. Example workflows . . . . . . . . . .Summary . . . . . . . . . . . . . . . . . . .2. Blockchain Core - Model . . . . . . . .2.1 Block.java . .2.2. Transaction.java . . . . . . . . . . . . . . .2.3. Wallet.java . . . . . . . . . . . . . . . . .Summary . . . . . . . . . . . . . . . . .3. Database Setup. . . . . . . .3.1. SQLite Database Browser Quick Setup .3.2. Blockchain.db3.3. Wallet.db . . . . . . . . . . . . . . . . . .3.4 JDBC Driver for SQLite setup3.5 Writing your App init() method.Summary . . . . . . . . . . . . . . . . . . .4. Service Layer Implementation. . . . . .4.1. BlockData.java44.2. WalletData.javaSummary . . . . . . . . . . . . . . . . .5. UI – View Layer. . . . . .5.1. SceneBuilder Quick Setup5.2. Creating Your Views3.2.1. MainWindow.fxml . . . . . . . . . . . . . . . .3.2.2. AddNewTransactionWindow.fxml . . . . . . . . . . . . . . . .5.3. Creating Your View Controllers5.3.1 MainWindowController.java5.3.1 AddNewTransactionController.javaSummary . . . . . . . . . . . . . . . . .6. Network Handlers – Networking Layer. . . . . .6.1. UI Thread6.2. Peer Client Thread6.3. Peer Server Handler – Multithreading requests 6.3.1 Peer Request Thread6.4. Mining ThreadSummary . . . . . . . . . . . . . . . . .
Azure Virtual Desktop Specialist
Enhance your knowledge and become certified with the Azure Virtual Desktop technology. This book provides the theory, lab exercises, and knowledge checks you need to prepare for the AZ-140 exam.The book starts with an introduction to Azure Virtual Desktop and AZ-140 exam objectives. You will learn the architecture behind Azure Virtual Desktop, including compute, identity, and storage. And you will learn how to implement all of the services that make up the Azure Virtual Desktop platform. Each chapter includes exam and practice questions. The book takes you through the access and security of Azure Virtual Desktop along with its user environment and application. And it teaches you how to monitor and maintain an Azure Virtual Desktop infrastructure.After reading this book, you will be prepared to take the AZ-140 exam.WHAT YOU WILL LEARN* Plan an Azure Virtual Desktop architecture* Install and configure apps on a session host* Plan and implement business continuity and disaster recovery* Understand user environment and applications in Azure Virtual DesktopWHO THIS BOOK IS FORAzure administrators who wish to increase their knowledge and become certified with the Azure Virtual Desktop technologySHABAZ DARR has more than 15 years of experience in the IT industry and more than eight years working with cloud technologies. Currently, he is working as a Senior Infrastructure Specialist for Netcompany. He is a certified Microsoft MVP in Enterprise Mobility, a certified Microsoft trainer with certifications in Azure Virtual Desktop Administrator, Office 365 Identity and Services, Modern Workplace Administrator Associate, and Azure Administrator Associate.CHAPTER 1: EXAM OVERVIEW & INTRODUCTION TO AZURE VIRTUAL DESKTOPCHAPTER GOAL: Introduce Microsoft Certification exams and Azure Virtual DesktopNO OF PAGES: 15SUB -TOPICS1. Prepare for your Microsoft exam and AZ-140 objectives2. Introduction to Azure Virtual DesktopCHAPTER 2: PLAN AN AZURE VIRTUAL DESKTOP ARCHITECTURECHAPTER GOAL: Outline the architecture behind Azure Virtual Desktop, including compute, identity and storage.NO OF PAGES: 35SUB - TOPICS1. Design the Azure Virtual Desktop architecture2. Design for User identities and profiles3. Knowledge CheckCHAPTER 3: IMPLEMENT AN AZURE VIRTUAL DESKTOP INFRASTRUCTURECHAPTER GOAL: Learn how to implement all services that make up the Azure Virtual Desktop platformNO OF PAGES : 45SUB - TOPICS:1. Implement and manage networking for Azure Virtual Desktop2. Implement and manage storage for Azure Virtual Desktop3. Create and configure host pools and session hosts.4. Create and manage session host images5. Knowledge CheckCHAPTER 4: MANAGE ACCESS AND SECURITY TO AZURE VIRTUAL DESKTOPCHAPTER GOAL: Learn how to secure user access and implement additional security within Azure for AVDNO OF PAGES: 35SUB - TOPICS:1. Manage Access to Azure Virtual Desktop2. Manage Security for Azure Virtual Desktop3. Knowledge CheckCHAPTER 5: MANAGE USER ENVIRONMENT AND APPLICATIONS FOR AZURE VIRTUAL DESKTOPCHAPTER GOAL: Learn how to implement and Manage the user experience and deploy applications within Azure Virtual Desktop.NO OF PAGES: 40SUB-TOPICS:1. Implement and manage FSLogix2. Configure user experience settings3. Install and configure apps on a session host4. Knowledge CheckCHAPTER 6: MONITOR AND MAINTAIN AN AZURE VIRTUAL DESKTOP INFRASTRUCTURECHAPTER GOALS: Learn how to monitor and keep an Azure Virtual Desktop Infrastructure fully up-to-dateNO OF PAGES: 45SUB-TOPICS:1. Plan and implement business continuity and disaster recovery2. Automate Azure Virtual desktop management tasks3. Monitor and manage performance tasks4. Knowledge check
Linux System Administration for the 2020s
Build and manage large estates, and use the latest OpenSource management tools to breakdown a problems. This book is divided into 4 parts all focusing on the distinct aspects of Linux system administration.The book begins by reviewing the foundational blocks of Linux and can be used as a brief summary for new users to Linux and the OpenSource world. Moving on to Part 2 you'll start by delving into how practices have changed and how management tooling has evolved over the last decade. You’ll explore new tools to improve the administration experience, estate management and its tools, along with automation and containers of Linux.Part 3 explains how to keep your platform healthy through monitoring, logging, and security. You'll also review advanced tooling and techniques designed to resolve technical issues. The final part explains troubleshooting and advanced administration techniques, and less known methods for resolving stubborn problems.With Linux System Administration for the 2020s you'll learn how to spend less time doing sysadmin work and more time on tasks that push the boundaries of your knowledge.WHAT YOU'LL LEARN* Explore a shift in culture and redeploy rather than fix* Improve administration skills by adopting modern tooling* Avoid bad practices and rethink troubleshooting* Create a platform that requires less human interventionWHO THIS BOOK IS FOREveryone from sysadmins, consultants, architects or hobbyists.Ken Hitchcock currently is a Principal Consultant working for Red Hat, with over twenty years of experience in IT. He has spent the last eleven years predominately focused on Red Hat products, certificating himself as a Red Hat Architect along the way. The last eleven years have been paramount in his understanding of how large Linux estates should be managed and in the spirit of openness, was inspired to share his knowledge and experiences in this book. Originally from Durban South Africa, he now lives in the south of England where he hopes to not only continue inspiring all he meets but also to continue improving himself and the industry he works in.PART ONE: Laying the foundation.- CHAPTER 1: Linux at a Glance.- PART TWO : Strengthening core skills.- CHAPTER 2: New tools to improve the administration experience.- CHAPTER 3: Estate management.- CHAPTER 4: Estate Management Tools.- CHAPTER 5: Automation.- CHAPTER 6: Containers.-PART THREE: Day two practices and keeping the lights on.-CHAPTER 7: Monitoring.-CHAPTER 8: Logging.-CHAPTER 9: Security.-CHAPTER 10: Maintenance tasks and planning.- PART FOUR: See, analyze and act.-CHAPTER 11: Troubleshooting.-CHAPTER 12: Advanced Administration
Azure Arc-enabled Data Services Revealed
Get introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more.This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can’t move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports.WHAT YOU WILL LEARN* Understand the fundamentals and architecture of Azure Arc-enabled data services* Build a multi-cloud strategy based on Azure Data Services* Deploy Azure Arc-enabled data services on premises or in any cloud* Deploy Azure Arc-enabled SQL Managed Instance on premises or in any cloud* Deploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloud* Backup and Restore your data that is managed by Azure Arc-enabled data services* Manage Azure-enabled data services running outside of Azure* Monitor Azure-enabled data services through Grafana and Kibana* Monitor Azure-enabled data services running outside of Azure through Azure MonitorWHO THIS BOOK IS FORDatabase administrators and architects who want to manage on-premises or hybrid cloud data resources from the Microsoft Azure cloud. Especially for those wishing to take advantage of cloud technologies while keeping sensitive data on premises and under physical control.BEN WEISSMAN is the owner and founder of Solisyon, a consulting firm based in Germany and focused on business intelligence, business analytics, and data warehousing. He is a Microsoft Data Platform MVP, the first German BimlHero, and has been working with SQL Server since SQL Server 6.5. Ben is also an MCSE, Charter Member of the Microsoft Professional Program for Big Data, Artificial Intelligence, and Data Science, and he is a Certified Data Vault Data Modeler. If he is not currently working with data, he is probably travelling to explore the world.ANTHONY E. NOCENTINO is the Founder and President of Centino Systems as well as a Pluralsight author, a Microsoft Data Platform MVP, and an industry recognized Kubernetes, SQL Server, and Linux expert. In his consulting practice, Anthony designs solutions, deploys the technology, and provides expertise on system performance, architecture, and security. He has bachelor's and master's degrees in computer science, with research publications in machine virtualization, high performance/low latency data access algorithms, and spatial database systems. 1. A Kubernetes Primer2. Azure Arc-Enabled Data Services3. Getting Ready for Deployment4. Installing Kubernetes5. Deploying a Data Controller in Indirect Mode6. Deploying a Data Controller in Direct Mode7. Deploying an Azure Arc-Enabled SQL Managed Instance8. Deploying Azure Arc-Enabled PostgreSQL Hyperscale9. Monitoring and Management
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.
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