Understanding Data and AI

AI at war : how big data, artificial intelligence, and machine learning are changing naval warfare


"AI at War is intended to provide a balance and practical understanding for both national security professionals and the interested public of the application of AI to war fighting. Although the themes and findings of the chapters are generally applicable across the U.S. Department of Defense (DoD), to include all Services, Joint Staff and defense agencies, as well as allied and partner ministries of defense/defence, it is a "case study" of war fighting functions in the Naval Services-the United States Navy and United States Marine Corps"--
Date Published:2021

"AI at War is intended to provide a balance and practical understanding for both national security professionals and the interested public of the application of AI to war fighting. Although the themes and findings of the chapters are generally applicable across the U.S. Department of Defense (DoD), to include all Services, Joint Staff and defense agencies, as well as allied and partner ministries of defense/defence, it is a "case study" of war fighting functions in the Naval Services-the United States Navy and United States Marine Corps"--

Date Published:2021

AI for Digital Warfare

Hageback, Niklas.


Description based upon print version of record. Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Authors -- Introduction -- 1 Principles of War : Clausewitz and Beyond -- 2 Welcome to the Murky World of Psychological Warfare -- 3 What Is Digital Warfare? -- 4 Weaponising Artificial Intelligence -- 5 Blitzkrieg in the Digital Age -- References -- Index "AI for Digital Warfare explores how the weaponising of artificial intelligence can and will change how warfare is being conducted, and what impact it will have on the corporate world. With artificial intelligence tools becoming increasingly advanced, and in many cases more humanlike, their potential in psychological warfare is being recognised, which means digital warfare can move beyond just shutting down IT systems into more all-encompassing hybrid war strategies." --
Date Published:2021

Description based upon print version of record. Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Authors -- Introduction -- 1 Principles of War : Clausewitz and Beyond -- 2 Welcome to the Murky World of Psychological Warfare -- 3 What Is Digital Warfare? -- 4 Weaponising Artificial Intelligence -- 5 Blitzkrieg in the Digital Age -- References -- Index "AI for Digital Warfare explores how the weaponising of artificial intelligence can and will change how warfare is being conducted, and what impact it will have on the corporate world. With artificial intelligence tools becoming increasingly advanced, and in many cases more humanlike, their potential in psychological warfare is being recognised, which means digital warfare can move beyond just shutting down IT systems into more all-encompassing hybrid war strategies." --

Date Published:2021

Algorithmic culture : how big data and artificial intelligence are transforming everyday life


"This book explores the complex ways in which algorithms and big data are reshaping everyday culture, while at the same time perpetuating inequality and intersectional discrimination. It situates issues of humanity, identity, and culture in relation to free will, surveillance, capitalism, neoliberalism, consumerism, solipsism, and creativity"--
Date Published:2021

All in on AI : how smart companies win big with artificial intelligence

Davenport, Thomas H., 1954-


A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice. Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures. Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business models, make better decisions, have better relationships with their customers, offer better products and services, and command higher prices. Written by bestselling author Tom Davenport and Deloitte's Nitin Mittal, All-In on AI looks at artificial intelligence at its cutting edge from the viewpoint of established companies like Anthem, Ping An, Airbus, and Capital One. Filled with insights, strategies, and best practices, All-In on AI also provides leaders and their teams with the information they need to help their own companies take AI to the next level. If you're curious about the next phase in the implementation of artificial intelligence within companies, or if you're looking to adopt this powerful technology in a more robust way yourself, All-In on AI will give you a rare inside look at what the leading adopters are doing, while providing you with the tools to put AI at the core of everything you do.
Date Published:2023

A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice. Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures. Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business models, make better decisions, have better relationships with their customers, offer better products and services, and command higher prices. Written by bestselling author Tom Davenport and Deloitte's Nitin Mittal, All-In on AI looks at artificial intelligence at its cutting edge from the viewpoint of established companies like Anthem, Ping An, Airbus, and Capital One. Filled with insights, strategies, and best practices, All-In on AI also provides leaders and their teams with the information they need to help their own companies take AI to the next level. If you're curious about the next phase in the implementation of artificial intelligence within companies, or if you're looking to adopt this powerful technology in a more robust way yourself, All-In on AI will give you a rare inside look at what the leading adopters are doing, while providing you with the tools to put AI at the core of everything you do.

Date Published:2023

Applications of machine learning in big-data analytics and cloud computing


Preface xv List of Contributors xxi List of Figures xxv List of Tables xxix List of Abbreviations xxxi 1 Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm 1 1.1 Introduction 2 1.2 Problem Description 3 1.2.1 Greedy Approach: Bicluster Size Maximization Based Fitness Function 4 1.2.2 Data Description 5 1.3 Proposed Work: COVID 19 Pattern Identification Using Greedy Biclustering 7 1.4 Results and Discussions 8 1.5 Conclusion 18 1.6 Acknowledgements 18 References 18 2 Artificial Fish Swarm Optimization Algorithm with Hill Climbing Based Clustering Technique for Throughput Maximization in Wireless Multimedia Sensor Network 23 2.1 Introduction 24 2.2 The Proposed AFSA-HC Technique 27 2.2.1 AFSA-HC Based Clustering Phase 28 2.2.2 Deflate-Based Data Aggregation Phase 33 2.2.3 Hybrid Data Transmission Phase 34 2.3 Performance Validation 34 2.4 Conclusion 40 References 40 3 Analysis of Machine Learning Techniques for Spam Detection 43 3.1 Introduction 44 3.1.1 Ham Messages 44 3.1.2 Spam Messages 44 3.2 Types of Spam Attack 45 3.2.1 Email Phishing 45 3.2.2 Spear Phishing 45 3.2.3 Whaling 46 3.3 Spammer Methods 46 3.4 Some Prevention Methods From User End 46 3.4.1 Protect Email Addresses 46 3.4.2 Preventing Spam from Being Sent 47 3.4.3 Block Spam to be Delivered 48 3.4.4 Identify and Separate Spam After Delivery 48 3.4.4.1 Targeted Link Analysis 48 3.4.4.2 Bayesian Filters 48 3.4.5 Report Spam 48 3.5 Machine Learning Algorithms 48 3.5.1 Naïve Bayes (NB) 48 3.5.2 Random Forests (RF) 49 3.5.3 Support Vector Machine (SVM) 49 3.5.4 Logistic Regression (LR) 50 3.6 Methodology 51 3.6.1 Database Used 51 3.6.2 Work Flow 51 3.7 Results and Analysis 52 3.7.1 Performance Metric 52 3.7.2 Experimental Results 52 3.7.2.1 Cleaning Data by Removing Punctuations, White Spaces, and Stop Words 54 3.7.2.2 Stemming the Messages 55 3.7.2.3 Analyzing the Common Words from the Spam and Ham Messages 55 3.7.3 Analyses of Machine Learning Algorithms 55 3.7.3.1 Accuracy Score Before Stemming 55 3.7.3.2 Accuracy Score After Stemming 55 3.7.3.3 Splitting Dataset into Train and Test Data 56 3.7.3.4 Mapping Confusion Matrix 58 3.7.3.5 Accuracy 58 3.8 Conclusion and Future Work 59 References 59 4 Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques 63 4.1 Introduction 64 4.2 Literature Survey 65 4.3 Proposed Method 67 4.4 Data Collection in IoT 67 4.4.1 Fetching Data from Sensors 68 4.4.2 K-Nearest Neighbor Classifier 69 4.4.3 Random Forest Classifier 70 4.4.4 Decision Tree Classifier 70 4.4.5 Extreme Gradient Boost Classifier 71 4.5 Results and Discussions 72 4.6 Conclusion 78 4.7 Acknowledgements 78 References 78 5 Assimilate Machine Learning Algorithms in Big Data Analytics: Review 81 5.1 Introduction 82 5.2 Literature Survey 86 5.3 Big Data 89 5.4 Machine Learning 92 5.5 File Categories 95 5.6 Storage And Expenses 95 5.7 The Device Learning Anatomy 96 5.8 Machine Learning Technology Methods in Big Data Analytics 97 5.9 Structure Mapreduce 97 5.10 Associated Investigations 98 5.11 Multivariate Data Coterie in Machine Learning 99 5.12 Machine Learning Algorithm 99 5.12.1 Machine Learning Framework 99 5.12.2 Parametric and Non-Parametric Techniques in Machine Learning 99 5.12.2.1 Bias 100 5.12.2.2 Variance 100 5.12.3 Parametric Techniques 101 5.12.3.1 Linear Regression 101 5.12.3.2 Decision Tree 101 5.12.3.3 Naive Bayes 102 5.12.3.4 Support Vector Machine 102 5.12.3.5 Random Forest 102 5.12.3.6 K-Nearest Neighbor 103 5.12.3.7 Deep Learning 104 5.12.3.8 Linear Vector Quantization (LVQ) 104 5.12.3.9 Transfer Learning 104 5.12.4 Non-Parametric Techniques 105 5.12.4.1 K-Means Clustering 105 5.12.4.2 Principal Component Analysis 105 5.12.4.3 A Priori Algorithm 105 5.12.4.4 Reinforcement Learning (RL) 105 5.12.4.5 Semi-Supervised Learning 106 5.13 Machine Learning Technology Assessment Parameters 106 5.13.1 Ranking Performance 106 5.13.2 Loss in Logarithmic Form 106 5.13.3 Assessment Measures 107 5.13.3.1 Accuracy 107 5.13.3.2 Precision/Specificity 107 5.13.3.3 Recall 107 5.13.3.4 F-Measure 108 5.13.4 Mean Definite Error (MAE) 108 5.13.5 Mean Quadruple Error (MSE) 108 5.14 Correlation of Outcomes of ML Algorithms 109 5.15 Applications 109 5.15.1 Economical Facilities 109 5.15.2 Business and Endorsement 110 5.15.3 Government Bodies 110 5.15.4 Hygiene 110 5.15.5 Transport 110 5.15.6 Fuel and Energy 111 5.15.7 Spoken Validation 111 5.15.8 Perception of the Device 111 5.15.9 Bio-Surveillance 111 5.15.10 Mechanization or Realigning 111 5.15.11 Mining Text 112 5.16 Conclusion 112 References 113 6 Resource Allocation Methodologies in Cloud Computing: A Review and Analysis 115 6.1 Introduction 116 6.1.1 Cloud Services Models 116 6.1.1.1 Infrastructure as a Service 117 6.1.1.2 Platform as a Service 118 6.1.1.3 Software as a Service 118 6.1.2 Types of Cloud Computing 118 6.1.2.1 Public Cloud 119 6.1.2.2 Private Cloud 119 6.1.2.3 Community Cloud 120 6.1.2.4 Hybrid Cloud 121 6.2 Resource Allocations in Cloud Computing 121 6.2.1 Static Allocation 122 6.2.2 Dynamic Allocation 122 6.3 Dynamic Resource Allocation Models in Cloud Computing 123 6.3.1 Service-Level Agreement Based Dynamic Resource Allocation Models 124 6.3.2 Market-Based Dynamic Resource Allocation Models 125 6.3.3 Utilization-Based Dynamic Resource Allocation Models 126 6.3.4 Task Scheduling in Cloud Computing 127 6.4 Research Challenges 130 6.5 Future Research Paths 131 6.6 Advantages and Disadvantages 131 6.7 Conclusion 135 References 135 7 Role of Machine Learning in Big Data 139 7.1 Introduction 140 7.2 Related Work 141 7.3 Tools in Big Data 142 7.3.1 Batch Analysis Big Data Tools 142 7.3.2 Stream Analysis Big Data Tools 143 7.3.3 Interactive Analysis Big Data Tools 144 7.4 Machine Learning Algorithms in Big Data 145 7.5 Applications of Machine Learning in Big Data 151 7.6 Challenges of Machine Learning in Big Data 154 7.6.1 Volume 154 7.6.2 Variety 156 7.6.3 Velocity 157 7.6.4 Veracity 159 7.7 Conclusion 160 References 161 8 Healthcare System for COVID-19: Challenges and Developments 165 8.1 Introduction 166 8.2 Related Work 167 8.3 IoT with Architecture 169 8.4 IoHT Security Requirements and Challenges 170 8.5 COVID-19 (Coronavirus Disease 2019) 172 8.6 The Potential of IoHT in COVID-19 Like Disease Control 173 8.7 The Current Applications of IoHT During COVID-19 175 8.7.1 Using IoHT to Dissect an Outbreak 175 8.7.2 Using IoHT to Ensure Compliance to Quarantine 176 8.7.3 Using IoHT to Manage Patient Care 176 8.8 IoHT Development for COVID-19 177 8.8.1 Smart Home 178 8.8.2 Smart Office 178 8.8.3 Smart Hotel 178 8.8.4 Smart Hospitals 178 8.9 Conclusion 179 References 179 9 An Integrated Approach of Blockchain & Big Data in Health Care Sector 183 9.1 Introduction 184 9.2 Blockchain for Health care 185 9.2.1 Healthcare data sharing through gem Network 186 9.2.2 OmniPHR 187 9.2.3 Medrec 188 9.2.4 PSN (Pervasive Social Network) System 189 9.2.5 Healthcare Data Gateway 190 9.2.6 Resources that are virtual 190 9.3 Overview of Blockchain & Big data in health care 191 9.3.1 Big Data in Healthcare 191 9.3.2 Blockchain in Health Care 192 9.3.3 Benefits of Blockchain in Healthcare 193 9.3.3.1 Master patient indices 193 9.3.3.2 Supply chain management 193 9.3.3.3 Claims adjudication 193 9.3.3.4 Interoperability 194 9.3.3.5 Single, longitudinal patient records 194 9.4 Application of Big Data for Blockchain 194 9.4.1 Smart Ecosystem 194 9.4.2 Digital Trust 195 9.4.3 Cybersecurity 195 9.4.4 Fighting Drugs 195 9.4.5 Online Accessing of Patient's Data 196 9.4.6 Research as well as Development 196 9.4.7 Management of Data 196 9.4.8 Due to privacy storing of off-chain data 196 9.4.9 Collaboration of patient data 197 9.5 Solutions of Blockchain For Big Data in Health Care 197 9.6 Conclusion and Future Scope 198 References 199 10 Cloud Resource Management for Network Cameras 207 10.1 Introduction 207 10.2 Resource Analysis 210 10.2.1 Network Cameras 210 10.2.2 Resource Management on Cloud Environment 210 10.2.3 Image and Video Analysis 213 10.3 Cloud Resource Management Problems 214 10.4 Cloud Resource Manager 216 10.4.1 Evaluation of Performance 217 10.4.2 View of Resource Requirements 217 10.5 Bin Packing 218 10.5.1 Analysis of Dynamic Bin Packing 219 10.5.2 MinTotal DBP Problem 220 10.6 Resource Monitoring and Scaling 222 10.7 Conclusion 224 References 225 11 Software-Defined Networking for Healthcare Internet of Things 231 11.1 Introduction 231 11.2 Healthcare Internet of Things 233 11.2.1 Challenges in H-IoT 238 11.3 Software-Defined Networking 239 11.4 Opportunities, challenges, and possible solutions 243 11.5 Conclusion 245 References 246 12 Cloud Computing in the Public Sector: A Study 249 12.1 Introduction 250 12.2 History and Evolution of Cloud Computing 251 12.3 Application of Cloud Computing 252 12.4 Advantages of Cloud Computing 258 12.5 Challenges 263 12.6 Conclusion 269 13 Big Data Analytics: An overview 271 13.1 Introduction 271 13.2 Related Work 272 13.2.1 Big Data: What Is It? 275 13.2.1.1 Characteristics of Big Data 276 13.2.2 Big Data Analytics: What Is It? 277 13.3 Hadoop and Big Data 278 13.4 Big Data Analytics Framework 279 13.5 Big Data Analytics Techniques 280 13.5.1 Partitioning on Big Data 280 13.5.2 Sampling on Big Data 281 13.5.3 Sampling-Based Approximation 281 13.6 Big Social Data Analytics 281 13.7 Applications 282 13.7.1 Manufacturing Production 282 13.7.2 Smart Grid 283 13.7.3 Outbreak of Flu Prediction from Social Site 283 13.7.4 Sentiment Analysis of Twitter Data 283 13.8 Electricity Price Forecasting 284 13.9 Security Situational Analysis for Smart Grid 285 13.10 Future Scope 285 13.11 Challenges 285 13.12 Conclusion 286 References 286 14 Video Usefulness Detection in Big Surveillance Syst ... Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.
Date Published:2021

Preface xv List of Contributors xxi List of Figures xxv List of Tables xxix List of Abbreviations xxxi 1 Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm 1 1.1 Introduction 2 1.2 Problem Description 3 1.2.1 Greedy Approach: Bicluster Size Maximization Based Fitness Function 4 1.2.2 Data Description 5 1.3 Proposed Work: COVID 19 Pattern Identification Using Greedy Biclustering 7 1.4 Results and Discussions 8 1.5 Conclusion 18 1.6 Acknowledgements 18 References 18 2 Artificial Fish Swarm Optimization Algorithm with Hill Climbing Based Clustering Technique for Throughput Maximization in Wireless Multimedia Sensor Network 23 2.1 Introduction 24 2.2 The Proposed AFSA-HC Technique 27 2.2.1 AFSA-HC Based Clustering Phase 28 2.2.2 Deflate-Based Data Aggregation Phase 33 2.2.3 Hybrid Data Transmission Phase 34 2.3 Performance Validation 34 2.4 Conclusion 40 References 40 3 Analysis of Machine Learning Techniques for Spam Detection 43 3.1 Introduction 44 3.1.1 Ham Messages 44 3.1.2 Spam Messages 44 3.2 Types of Spam Attack 45 3.2.1 Email Phishing 45 3.2.2 Spear Phishing 45 3.2.3 Whaling 46 3.3 Spammer Methods 46 3.4 Some Prevention Methods From User End 46 3.4.1 Protect Email Addresses 46 3.4.2 Preventing Spam from Being Sent 47 3.4.3 Block Spam to be Delivered 48 3.4.4 Identify and Separate Spam After Delivery 48 3.4.4.1 Targeted Link Analysis 48 3.4.4.2 Bayesian Filters 48 3.4.5 Report Spam 48 3.5 Machine Learning Algorithms 48 3.5.1 Naïve Bayes (NB) 48 3.5.2 Random Forests (RF) 49 3.5.3 Support Vector Machine (SVM) 49 3.5.4 Logistic Regression (LR) 50 3.6 Methodology 51 3.6.1 Database Used 51 3.6.2 Work Flow 51 3.7 Results and Analysis 52 3.7.1 Performance Metric 52 3.7.2 Experimental Results 52 3.7.2.1 Cleaning Data by Removing Punctuations, White Spaces, and Stop Words 54 3.7.2.2 Stemming the Messages 55 3.7.2.3 Analyzing the Common Words from the Spam and Ham Messages 55 3.7.3 Analyses of Machine Learning Algorithms 55 3.7.3.1 Accuracy Score Before Stemming 55 3.7.3.2 Accuracy Score After Stemming 55 3.7.3.3 Splitting Dataset into Train and Test Data 56 3.7.3.4 Mapping Confusion Matrix 58 3.7.3.5 Accuracy 58 3.8 Conclusion and Future Work 59 References 59 4 Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques 63 4.1 Introduction 64 4.2 Literature Survey 65 4.3 Proposed Method 67 4.4 Data Collection in IoT 67 4.4.1 Fetching Data from Sensors 68 4.4.2 K-Nearest Neighbor Classifier 69 4.4.3 Random Forest Classifier 70 4.4.4 Decision Tree Classifier 70 4.4.5 Extreme Gradient Boost Classifier 71 4.5 Results and Discussions 72 4.6 Conclusion 78 4.7 Acknowledgements 78 References 78 5 Assimilate Machine Learning Algorithms in Big Data Analytics: Review 81 5.1 Introduction 82 5.2 Literature Survey 86 5.3 Big Data 89 5.4 Machine Learning 92 5.5 File Categories 95 5.6 Storage And Expenses 95 5.7 The Device Learning Anatomy 96 5.8 Machine Learning Technology Methods in Big Data Analytics 97 5.9 Structure Mapreduce 97 5.10 Associated Investigations 98 5.11 Multivariate Data Coterie in Machine Learning 99 5.12 Machine Learning Algorithm 99 5.12.1 Machine Learning Framework 99 5.12.2 Parametric and Non-Parametric Techniques in Machine Learning 99 5.12.2.1 Bias 100 5.12.2.2 Variance 100 5.12.3 Parametric Techniques 101 5.12.3.1 Linear Regression 101 5.12.3.2 Decision Tree 101 5.12.3.3 Naive Bayes 102 5.12.3.4 Support Vector Machine 102 5.12.3.5 Random Forest 102 5.12.3.6 K-Nearest Neighbor 103 5.12.3.7 Deep Learning 104 5.12.3.8 Linear Vector Quantization (LVQ) 104 5.12.3.9 Transfer Learning 104 5.12.4 Non-Parametric Techniques 105 5.12.4.1 K-Means Clustering 105 5.12.4.2 Principal Component Analysis 105 5.12.4.3 A Priori Algorithm 105 5.12.4.4 Reinforcement Learning (RL) 105 5.12.4.5 Semi-Supervised Learning 106 5.13 Machine Learning Technology Assessment Parameters 106 5.13.1 Ranking Performance 106 5.13.2 Loss in Logarithmic Form 106 5.13.3 Assessment Measures 107 5.13.3.1 Accuracy 107 5.13.3.2 Precision/Specificity 107 5.13.3.3 Recall 107 5.13.3.4 F-Measure 108 5.13.4 Mean Definite Error (MAE) 108 5.13.5 Mean Quadruple Error (MSE) 108 5.14 Correlation of Outcomes of ML Algorithms 109 5.15 Applications 109 5.15.1 Economical Facilities 109 5.15.2 Business and Endorsement 110 5.15.3 Government Bodies 110 5.15.4 Hygiene 110 5.15.5 Transport 110 5.15.6 Fuel and Energy 111 5.15.7 Spoken Validation 111 5.15.8 Perception of the Device 111 5.15.9 Bio-Surveillance 111 5.15.10 Mechanization or Realigning 111 5.15.11 Mining Text 112 5.16 Conclusion 112 References 113 6 Resource Allocation Methodologies in Cloud Computing: A Review and Analysis 115 6.1 Introduction 116 6.1.1 Cloud Services Models 116 6.1.1.1 Infrastructure as a Service 117 6.1.1.2 Platform as a Service 118 6.1.1.3 Software as a Service 118 6.1.2 Types of Cloud Computing 118 6.1.2.1 Public Cloud 119 6.1.2.2 Private Cloud 119 6.1.2.3 Community Cloud 120 6.1.2.4 Hybrid Cloud 121 6.2 Resource Allocations in Cloud Computing 121 6.2.1 Static Allocation 122 6.2.2 Dynamic Allocation 122 6.3 Dynamic Resource Allocation Models in Cloud Computing 123 6.3.1 Service-Level Agreement Based Dynamic Resource Allocation Models 124 6.3.2 Market-Based Dynamic Resource Allocation Models 125 6.3.3 Utilization-Based Dynamic Resource Allocation Models 126 6.3.4 Task Scheduling in Cloud Computing 127 6.4 Research Challenges 130 6.5 Future Research Paths 131 6.6 Advantages and Disadvantages 131 6.7 Conclusion 135 References 135 7 Role of Machine Learning in Big Data 139 7.1 Introduction 140 7.2 Related Work 141 7.3 Tools in Big Data 142 7.3.1 Batch Analysis Big Data Tools 142 7.3.2 Stream Analysis Big Data Tools 143 7.3.3 Interactive Analysis Big Data Tools 144 7.4 Machine Learning Algorithms in Big Data 145 7.5 Applications of Machine Learning in Big Data 151 7.6 Challenges of Machine Learning in Big Data 154 7.6.1 Volume 154 7.6.2 Variety 156 7.6.3 Velocity 157 7.6.4 Veracity 159 7.7 Conclusion 160 References 161 8 Healthcare System for COVID-19: Challenges and Developments 165 8.1 Introduction 166 8.2 Related Work 167 8.3 IoT with Architecture 169 8.4 IoHT Security Requirements and Challenges 170 8.5 COVID-19 (Coronavirus Disease 2019) 172 8.6 The Potential of IoHT in COVID-19 Like Disease Control 173 8.7 The Current Applications of IoHT During COVID-19 175 8.7.1 Using IoHT to Dissect an Outbreak 175 8.7.2 Using IoHT to Ensure Compliance to Quarantine 176 8.7.3 Using IoHT to Manage Patient Care 176 8.8 IoHT Development for COVID-19 177 8.8.1 Smart Home 178 8.8.2 Smart Office 178 8.8.3 Smart Hotel 178 8.8.4 Smart Hospitals 178 8.9 Conclusion 179 References 179 9 An Integrated Approach of Blockchain & Big Data in Health Care Sector 183 9.1 Introduction 184 9.2 Blockchain for Health care 185 9.2.1 Healthcare data sharing through gem Network 186 9.2.2 OmniPHR 187 9.2.3 Medrec 188 9.2.4 PSN (Pervasive Social Network) System 189 9.2.5 Healthcare Data Gateway 190 9.2.6 Resources that are virtual 190 9.3 Overview of Blockchain & Big data in health care 191 9.3.1 Big Data in Healthcare 191 9.3.2 Blockchain in Health Care 192 9.3.3 Benefits of Blockchain in Healthcare 193 9.3.3.1 Master patient indices 193 9.3.3.2 Supply chain management 193 9.3.3.3 Claims adjudication 193 9.3.3.4 Interoperability 194 9.3.3.5 Single, longitudinal patient records 194 9.4 Application of Big Data for Blockchain 194 9.4.1 Smart Ecosystem 194 9.4.2 Digital Trust 195 9.4.3 Cybersecurity 195 9.4.4 Fighting Drugs 195 9.4.5 Online Accessing of Patient's Data 196 9.4.6 Research as well as Development 196 9.4.7 Management of Data 196 9.4.8 Due to privacy storing of off-chain data 196 9.4.9 Collaboration of patient data 197 9.5 Solutions of Blockchain For Big Data in Health Care 197 9.6 Conclusion and Future Scope 198 References 199 10 Cloud Resource Management for Network Cameras 207 10.1 Introduction 207 10.2 Resource Analysis 210 10.2.1 Network Cameras 210 10.2.2 Resource Management on Cloud Environment 210 10.2.3 Image and Video Analysis 213 10.3 Cloud Resource Management Problems 214 10.4 Cloud Resource Manager 216 10.4.1 Evaluation of Performance 217 10.4.2 View of Resource Requirements 217 10.5 Bin Packing 218 10.5.1 Analysis of Dynamic Bin Packing 219 10.5.2 MinTotal DBP Problem 220 10.6 Resource Monitoring and Scaling 222 10.7 Conclusion 224 References 225 11 Software-Defined Networking for Healthcare Internet of Things 231 11.1 Introduction 231 11.2 Healthcare Internet of Things 233 11.2.1 Challenges in H-IoT 238 11.3 Software-Defined Networking 239 11.4 Opportunities, challenges, and possible solutions 243 11.5 Conclusion 245 References 246 12 Cloud Computing in the Public Sector: A Study 249 12.1 Introduction 250 12.2 History and Evolution of Cloud Computing 251 12.3 Application of Cloud Computing 252 12.4 Advantages of Cloud Computing 258 12.5 Challenges 263 12.6 Conclusion 269 13 Big Data Analytics: An overview 271 13.1 Introduction 271 13.2 Related Work 272 13.2.1 Big Data: What Is It? 275 13.2.1.1 Characteristics of Big Data 276 13.2.2 Big Data Analytics: What Is It? 277 13.3 Hadoop and Big Data 278 13.4 Big Data Analytics Framework 279 13.5 Big Data Analytics Techniques 280 13.5.1 Partitioning on Big Data 280 13.5.2 Sampling on Big Data 281 13.5.3 Sampling-Based Approximation 281 13.6 Big Social Data Analytics 281 13.7 Applications 282 13.7.1 Manufacturing Production 282 13.7.2 Smart Grid 283 13.7.3 Outbreak of Flu Prediction from Social Site 283 13.7.4 Sentiment Analysis of Twitter Data 283 13.8 Electricity Price Forecasting 284 13.9 Security Situational Analysis for Smart Grid 285 13.10 Future Scope 285 13.11 Challenges 285 13.12 Conclusion 286 References 286 14 Video Usefulness Detection in Big Surveillance Syst ... Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to opportunities and transformation in various areas such as healthcare, enterprises, industrial manufacturing and transportation. New Cloud Computing and Big Data tools endow researchers and analysts with novel techniques and opportunities to collect, manage and analyze the vast quantities of data. In Cloud and Big Data Analytics, the two areas of Swarm Intelligence and Deep Learning are a developing type of Machine Learning techniques that show enormous potential for solving complex business problems. Deep Learning enables computers to analyze large quantities of unstructured and binary data and to deduce relationships without requiring specific models or programming instructions. This book introduces the state-of-the-art trends and advances in the use of Machine Learning in Cloud and Big Data Analytics. The book will serve as a reference for Data Scientists, systems architects, developers, new researchers and graduate level students in Computer and Data science. The book will describe the concepts necessary to understand current Machine Learning issues, challenges and possible solutions as well as upcoming trends in Big Data Analytics.

Date Published:2021

Arguing with numbers : the intersections of rhetoric and mathematics


From division to multiplication : uncovering the relationship between mathematics and rhetoric through transdisciplinary scholarship / James Wynn and G. Mitchell Reyes -- In what ways shall we describe mathematics as rhetorical? / Edward Schiappa -- The mathematization of the invisible hand : rhetorical energy and the crafting of economic spontaneity / Catherine Chaput and Crystal Broch Colombini -- The horizons of judgment in mathematical discourse : copulas, economics, and subprime mortgages / G. Mitchell Reyes -- The Ourang-Outang in the Rue Morgue : Charles Peirce, Edgar Allan Poe, and the rhetoric of diagrams in detective fiction / Andrew C. Jones and Nathan Crick -- Rhetoric and mathematics in the Saturnian account of atomic spectra / Joseph Little -- The new mathematical arts of argument : naturalistic images and geometric diagrams / Jeanne Fahnestock -- Accommodating young women : addressing the gender gap in mathematics with female-centered epideictic / James Wynn -- Turning principles of action into practice : examining the National Council of Teachers of Mathematics' reform rhetoric / Michael Dreher. "A collection of essays which deploy rhetorical lenses to explore how mathematics influences the values and beliefs with which we assess the world and make decisions, as well as how our values and beliefs influence the kinds of mathematical instruments we construct and accept"--
Date Published:2021

From division to multiplication : uncovering the relationship between mathematics and rhetoric through transdisciplinary scholarship / James Wynn and G. Mitchell Reyes -- In what ways shall we describe mathematics as rhetorical? / Edward Schiappa -- The mathematization of the invisible hand : rhetorical energy and the crafting of economic spontaneity / Catherine Chaput and Crystal Broch Colombini -- The horizons of judgment in mathematical discourse : copulas, economics, and subprime mortgages / G. Mitchell Reyes -- The Ourang-Outang in the Rue Morgue : Charles Peirce, Edgar Allan Poe, and the rhetoric of diagrams in detective fiction / Andrew C. Jones and Nathan Crick -- Rhetoric and mathematics in the Saturnian account of atomic spectra / Joseph Little -- The new mathematical arts of argument : naturalistic images and geometric diagrams / Jeanne Fahnestock -- Accommodating young women : addressing the gender gap in mathematics with female-centered epideictic / James Wynn -- Turning principles of action into practice : examining the National Council of Teachers of Mathematics' reform rhetoric / Michael Dreher. "A collection of essays which deploy rhetorical lenses to explore how mathematics influences the values and beliefs with which we assess the world and make decisions, as well as how our values and beliefs influence the kinds of mathematical instruments we construct and accept"--

Date Published:2021

Artificial intelligence and global security : future trends, threats and considerations


Artificial Intelligence and Global Security: Future Trends, Threats and Considerations brings a much-needed perspective on the impact of the integration of Artificial Intelligence (AI) technologies in military affairs. Experts forecast that AI will shape future military operations in ways that will revolutionize warfare. That is why there is an urgent need to consider the potential ethical and moral consequences related to enabling AI to make decisions that will shape the future world. This book masterfully presents a vision of a future that is replete with integrated networks of Artificial Intelligence that are designed to both defend and attack nations. Artificial Intelligence and Global Security: Future Trends, Threats and Considerations has rendered a major service to those interested in the impact of Artificial Intelligence technologies and its contribution to the evolution and revolution in military warfare. It will also explore questions such as: What are the implications of AI for the individual, for personal identity, for society, and for global security? And examine the impact of AI on Just War Theory, as well as the perspectives and consequences for the integration of AI in our daily lives and society.
Date Published:2020

Artificial Intelligence and Global Security: Future Trends, Threats and Considerations brings a much-needed perspective on the impact of the integration of Artificial Intelligence (AI) technologies in military affairs. Experts forecast that AI will shape future military operations in ways that will revolutionize warfare. That is why there is an urgent need to consider the potential ethical and moral consequences related to enabling AI to make decisions that will shape the future world. This book masterfully presents a vision of a future that is replete with integrated networks of Artificial Intelligence that are designed to both defend and attack nations. Artificial Intelligence and Global Security: Future Trends, Threats and Considerations has rendered a major service to those interested in the impact of Artificial Intelligence technologies and its contribution to the evolution and revolution in military warfare. It will also explore questions such as: What are the implications of AI for the individual, for personal identity, for society, and for global security? And examine the impact of AI on Just War Theory, as well as the perspectives and consequences for the integration of AI in our daily lives and society.

Date Published:2020

Bad data : why we measure the wrong things and often miss the metrics that matter

Schryvers, Peter, 1983-


Introduction -- Teaching to the test : Goodhart's Law and the paradox of metrics -- The ins and outs : the logic model and program evaluation -- The long and short of it : intertemporal problems and undervaluing time -- The problem of per : denominator errors -- The forest and the trees : simplifying complex systems -- Apples and oranges : ignoring differing qualities -- Not everything that can be counted counts : the lamppost problem -- Not everything that counts can be counted : measuring what matters -- The measure of metrics -- Gateways not yardsticks. "Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts."--
Date Published:2020

Introduction -- Teaching to the test : Goodhart's Law and the paradox of metrics -- The ins and outs : the logic model and program evaluation -- The long and short of it : intertemporal problems and undervaluing time -- The problem of per : denominator errors -- The forest and the trees : simplifying complex systems -- Apples and oranges : ignoring differing qualities -- Not everything that can be counted counts : the lamppost problem -- Not everything that counts can be counted : measuring what matters -- The measure of metrics -- Gateways not yardsticks. "Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it. The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts."--

Date Published:2020

Be data literate : the data literacy skills everyone needs to succeed

Morrow, Jordan,


The world of data -- The four levels of analytics -- Defining data literacy -- The data literacy umbrella -- Reading and speaking the language of data -- Combining data literacy and the four levels of analytics -- The steps of data literacy learning -- The three Cs of data literacy -- Data informed decision-making -- Data literacy and data and analytical strategy -- Begin your data and analytics journey. "In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best technology if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With exercises to show development and real-world examples from industries implementing data literacy skills, this book explains how to confidently read and speak the 'language of data' in the modern business environment and everyday life. Be Data Literate is a practical guide to understanding the four levels of analytics, how to analyze data and the key steps to making smarter, data-informed decisions. Written by a founding pioneer and worldwide leading expert on data literacy, this book empowers professionals with the skills they need to succeed in the digital world"--
Date Published:2021

The world of data -- The four levels of analytics -- Defining data literacy -- The data literacy umbrella -- Reading and speaking the language of data -- Combining data literacy and the four levels of analytics -- The steps of data literacy learning -- The three Cs of data literacy -- Data informed decision-making -- Data literacy and data and analytical strategy -- Begin your data and analytics journey. "In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best technology if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With exercises to show development and real-world examples from industries implementing data literacy skills, this book explains how to confidently read and speak the 'language of data' in the modern business environment and everyday life. Be Data Literate is a practical guide to understanding the four levels of analytics, how to analyze data and the key steps to making smarter, data-informed decisions. Written by a founding pioneer and worldwide leading expert on data literacy, this book empowers professionals with the skills they need to succeed in the digital world"--

Date Published:2021

Becoming a Data Head : How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

Gutman, Alex J.


PART ONE: Thinking like a data head. What is the problem? ; What is data? ; Prepare to think statistically -- PART TWO: Speaking like a data head: Argue with the data ; Explore the data ; Examine the probabilities ; Challange the statistics -- PART THREE: Understanding the data scientist's toolbox. Search for hidden groups ; Understand the regression model ; Understand the classification model ; Understand the text analytics ; Conceptualize deep learning -- PART FOUR: Ensuring success. Watch out for pitfalls ; Know the people and personalities ; What's next? “In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making. Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace. Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence. Avoid common pitfalls when working with and interpreting data. Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head - an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you”--
Date Published:2021

PART ONE: Thinking like a data head. What is the problem? ; What is data? ; Prepare to think statistically -- PART TWO: Speaking like a data head: Argue with the data ; Explore the data ; Examine the probabilities ; Challange the statistics -- PART THREE: Understanding the data scientist's toolbox. Search for hidden groups ; Understand the regression model ; Understand the classification model ; Understand the text analytics ; Conceptualize deep learning -- PART FOUR: Ensuring success. Watch out for pitfalls ; Know the people and personalities ; What's next? “In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making. Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace. Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence. Avoid common pitfalls when working with and interpreting data. Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head - an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you”--

Date Published:2021

Beginner's guide to code algorithms : experiments to enhance productivity and solve problems

Maitra, Deepankar,


1. What is an Algorithm? 2. Build your own game with a simple algorithm -- Tic Tac Toe. 3. Explore your deductive logic -- solve a Sudoku for ever. 4. Introduction to multi-platform integration -- build your own Remote control. 5. The Organizer -- Build your own virtual filing cabinet. 6. Merging Sheets -- Combine multiple workbooks of the same format into one workbook automaticall. 7. Introduction to Graphs -- create your own interface diagram instantly. 8. Shaping up -- Analyze a picture and document its components in text. 9. Real Time currency conversion -- An Introduction to simple Web Scraping techniques. 10. The genius of collaboration- Build a burglar alarm using a free webcam application. 11. Advanced Graphics -- Complex visualizations and more -- The Final Word. -- References -- A little bit of Computer Science. "This book takes you on a problem-solving journey to expand your mind and increase your willingness to experiment with code"--
Date Published:2022

1. What is an Algorithm? 2. Build your own game with a simple algorithm -- Tic Tac Toe. 3. Explore your deductive logic -- solve a Sudoku for ever. 4. Introduction to multi-platform integration -- build your own Remote control. 5. The Organizer -- Build your own virtual filing cabinet. 6. Merging Sheets -- Combine multiple workbooks of the same format into one workbook automaticall. 7. Introduction to Graphs -- create your own interface diagram instantly. 8. Shaping up -- Analyze a picture and document its components in text. 9. Real Time currency conversion -- An Introduction to simple Web Scraping techniques. 10. The genius of collaboration- Build a burglar alarm using a free webcam application. 11. Advanced Graphics -- Complex visualizations and more -- The Final Word. -- References -- A little bit of Computer Science. "This book takes you on a problem-solving journey to expand your mind and increase your willingness to experiment with code"--

Date Published:2022

Competing in the age of AI : strategy and leadership when algorithms and networks run the world

Iansiti, Marco, 1961-


The age of AI: artificial intelligence is transforming the way firms function and restructuring the economy -- Rethinking the firm: how software, networks, and AI are changing the fundamental nature of companies-the way they operate and compete -- The AI factory: the core of the new firm is a scalable decision factory, powered by software, data, and algorithms -- Rearchitecting the firm: to use the full power of digital networks and AI, firms need a fundamentally different operating architecture -- Becoming an AI company: how to transform and rearchitect the firm to leverage the power of data, networks, and artificial intelligence -- Strategy for a new age: digital firms enable and require a new approach to strategy -- Strategic collisions: what happens when digital firms compete ("collide") with traditional firms -- The ethics of digital scale, scope and learning: the ethical challenges generated by the transformation of the nature of firms -- The new meta: how the age of AI is changing the "rules of the game," with fundamental implications for all of us -- A leadership mandate: the age of AI is defining a new set of challenges for leaders of digital firms, traditional organizations, startups, regulatory institution, and communities. "In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have constrained business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, drive massive scope increase, enabling companies to straddle industry boundaries, and enable powerful opportunities for learning--to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition and altering the structure of our economy Show how these collisions force traditional companies to change their operating models to drive scale, scope, and learning Explain the risks involved in operating model transformation and how to overcome them Describe the new challenges and responsibilities for the leaders of these firms Packed with examples--including the most powerful and innovative global, AI-driven competitors--and based on research in hundreds of firms across many sectors, this is the essential guide for rethinking how your firm competes and operates in the era of AI"--
Date Published:2020

The age of AI: artificial intelligence is transforming the way firms function and restructuring the economy -- Rethinking the firm: how software, networks, and AI are changing the fundamental nature of companies-the way they operate and compete -- The AI factory: the core of the new firm is a scalable decision factory, powered by software, data, and algorithms -- Rearchitecting the firm: to use the full power of digital networks and AI, firms need a fundamentally different operating architecture -- Becoming an AI company: how to transform and rearchitect the firm to leverage the power of data, networks, and artificial intelligence -- Strategy for a new age: digital firms enable and require a new approach to strategy -- Strategic collisions: what happens when digital firms compete ("collide") with traditional firms -- The ethics of digital scale, scope and learning: the ethical challenges generated by the transformation of the nature of firms -- The new meta: how the age of AI is changing the "rules of the game," with fundamental implications for all of us -- A leadership mandate: the age of AI is defining a new set of challenges for leaders of digital firms, traditional organizations, startups, regulatory institution, and communities. "In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have constrained business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, drive massive scope increase, enabling companies to straddle industry boundaries, and enable powerful opportunities for learning--to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition and altering the structure of our economy Show how these collisions force traditional companies to change their operating models to drive scale, scope, and learning Explain the risks involved in operating model transformation and how to overcome them Describe the new challenges and responsibilities for the leaders of these firms Packed with examples--including the most powerful and innovative global, AI-driven competitors--and based on research in hundreds of firms across many sectors, this is the essential guide for rethinking how your firm competes and operates in the era of AI"--

Date Published:2020

Data analytics made easy : use machine learning and data storytelling in your work without writing any code

Mauro, Andrea de,


Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own. Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Date Published:2021

Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own. Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.

Date Published:2021

Data science for dummies

Pierson, Lillian,


Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one's talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.
Date Published:2021

Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one's talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.

Date Published:2021

Deception and Delay in Organized Conflict : Essays on the Mathematical Theory of Maskirovka.

Wallace, Rodrick.


Intro -- Preface -- References -- Contents -- About the Author -- 1 Failure of Control -- 1.1 Introduction -- 1.2 The Data Rate Theorem -- 1.3 Cognition Rate and Stability -- 1.4 Multiple Delays -- 1.5 Multiple Theaters -- 1.6 Control System Thrashing -- 1.7 Discussion -- References -- 2 Failure of Institutional Cognition I -- 2.1 Introduction -- 2.2 The Data Rate Theorem -- 2.3 Scalarizing Resources and Their Interactions -- 2.4 Fog, Friction, and Delay -- 2.5 Institutional Cognition Under Delay -- 2.6 Discussion -- References -- 3 Failure of Institutional Cognition II -- 3.1 Introduction -- 3.2 Institutional Cognition Reconsidered -- 3.3 Iterated `Free Energy' and System Dynamics -- 3.4 Some Examples -- 3.5 Some Remarks on Workgroup Topology -- 3.6 Discussion -- 3.7 Mathematical Appendix -- References -- 4 The `Self-Maskirovka' of Military Scientism -- 4.1 Introduction -- 4.2 The Data Rate Theorem -- 4.3 Scalarizing Resources and Their Interactions -- 4.4 A First Failure Model -- 4.5 A Second Failure Model -- 4.6 Other `Regression Models' -- 4.7 Discussion -- References -- 5 Extending the Models -- References. "This book explores the role of deception, delay, and self-deception in the dynamics of organized conflict, taking a formal approach that hews closely to the asymptotic limit theorems of information and control theories. The resulting probability models can, with some effort—and some confidence—be converted to statistical tools for the analysis of real-time observational and ‘experimental’ data on institutionalized confrontation across both traditional and emerging ‘Clausewitz Landscapes’."
Date Published:2022

Intro -- Preface -- References -- Contents -- About the Author -- 1 Failure of Control -- 1.1 Introduction -- 1.2 The Data Rate Theorem -- 1.3 Cognition Rate and Stability -- 1.4 Multiple Delays -- 1.5 Multiple Theaters -- 1.6 Control System Thrashing -- 1.7 Discussion -- References -- 2 Failure of Institutional Cognition I -- 2.1 Introduction -- 2.2 The Data Rate Theorem -- 2.3 Scalarizing Resources and Their Interactions -- 2.4 Fog, Friction, and Delay -- 2.5 Institutional Cognition Under Delay -- 2.6 Discussion -- References -- 3 Failure of Institutional Cognition II -- 3.1 Introduction -- 3.2 Institutional Cognition Reconsidered -- 3.3 Iterated `Free Energy' and System Dynamics -- 3.4 Some Examples -- 3.5 Some Remarks on Workgroup Topology -- 3.6 Discussion -- 3.7 Mathematical Appendix -- References -- 4 The `Self-Maskirovka' of Military Scientism -- 4.1 Introduction -- 4.2 The Data Rate Theorem -- 4.3 Scalarizing Resources and Their Interactions -- 4.4 A First Failure Model -- 4.5 A Second Failure Model -- 4.6 Other `Regression Models' -- 4.7 Discussion -- References -- 5 Extending the Models -- References. "This book explores the role of deception, delay, and self-deception in the dynamics of organized conflict, taking a formal approach that hews closely to the asymptotic limit theorems of information and control theories. The resulting probability models can, with some effort—and some confidence—be converted to statistical tools for the analysis of real-time observational and ‘experimental’ data on institutionalized confrontation across both traditional and emerging ‘Clausewitz Landscapes’."

Date Published:2022

The digital mindset : what it really takes to thrive in the age of data, algorithms, and AI

Leonardi, Paul M., 1979-


Description based upon print version of record. Intro -- Contents -- Introduction: The 30 Percent Rule -- Part 1: Collaboration -- Ch. 1: Working with Machines -- Ch. 2: Cultivating Your Digital Presence -- Part 2: Computation -- Ch. 3: Data and Analytics -- Ch. 4: Drunks and Lampposts -- Part 3: Change -- Ch. 5: Cybersecurity and Privacy -- Ch. 6: The Experimentation Imperative -- Ch. 7: The Only Constant -- Conclusion -- Appendix -- Glossary -- Notes -- Index -- Acknowledgments -- About the Authors
Date Published:2022

Description based upon print version of record. Intro -- Contents -- Introduction: The 30 Percent Rule -- Part 1: Collaboration -- Ch. 1: Working with Machines -- Ch. 2: Cultivating Your Digital Presence -- Part 2: Computation -- Ch. 3: Data and Analytics -- Ch. 4: Drunks and Lampposts -- Part 3: Change -- Ch. 5: Cybersecurity and Privacy -- Ch. 6: The Experimentation Imperative -- Ch. 7: The Only Constant -- Conclusion -- Appendix -- Glossary -- Notes -- Index -- Acknowledgments -- About the Authors

Date Published:2022

Good charts workbook : tips, tools, and exercises for making better data visualizations

Berinato, Scott.


How do I start? -- Build skills. Controlling color -- Crafting for clarity -- Choosing chart types -- Practicing persuasion -- Capturing concepts -- Make good charts. Talk, sketch, prototype -- The monthly report -- The plastic problem presentation -- Appendix A: Glossary of chart types -- Appendix B: Chart type guide -- Appendix C: Keywords for chart types Talk. Sketch. Prototype. Repeat. You know right away when you see an effective chart or graphic. It hits you with an immediate sense of its meaning and impact. But what actually makes it clearer, sharper, and more effective? If you're ready to create your own "good charts"--data visualizations that powerfully communicate your ideas and research and that advance your career--the Good Charts Workbook is the hands-on guide you've been looking for. The original Good Charts changed the landscape by helping readers understand how to think visually and by laying out a process for creating powerful data visualizations. Now, the Good Charts Workbook provides tools, exercises, and practical insights to help people in all kinds of enterprises gain the skills they need to get started. Harvard Business Review Senior Editor and dataviz expert Scott Berinato leads you, step-by-step, through the key challenges in creating good charts--controlling color, crafting for clarity, choosing chart types, practicing persuasion, capturing concepts--with warm-up exercises and mini-challenges for each. The Workbook includes helpful prompts and reminders throughout, as well as white space for users to practice the Good Charts talk-sketch-prototype process. Good Charts Workbook is the must-have manual for better understanding the dataviz around you and for creating better charts to make your case more effectively
Date Published:2019

How do I start? -- Build skills. Controlling color -- Crafting for clarity -- Choosing chart types -- Practicing persuasion -- Capturing concepts -- Make good charts. Talk, sketch, prototype -- The monthly report -- The plastic problem presentation -- Appendix A: Glossary of chart types -- Appendix B: Chart type guide -- Appendix C: Keywords for chart types Talk. Sketch. Prototype. Repeat. You know right away when you see an effective chart or graphic. It hits you with an immediate sense of its meaning and impact. But what actually makes it clearer, sharper, and more effective? If you're ready to create your own "good charts"--data visualizations that powerfully communicate your ideas and research and that advance your career--the Good Charts Workbook is the hands-on guide you've been looking for. The original Good Charts changed the landscape by helping readers understand how to think visually and by laying out a process for creating powerful data visualizations. Now, the Good Charts Workbook provides tools, exercises, and practical insights to help people in all kinds of enterprises gain the skills they need to get started. Harvard Business Review Senior Editor and dataviz expert Scott Berinato leads you, step-by-step, through the key challenges in creating good charts--controlling color, crafting for clarity, choosing chart types, practicing persuasion, capturing concepts--with warm-up exercises and mini-challenges for each. The Workbook includes helpful prompts and reminders throughout, as well as white space for users to practice the Good Charts talk-sketch-prototype process. Good Charts Workbook is the must-have manual for better understanding the dataviz around you and for creating better charts to make your case more effectively

Date Published:2019

Handbook of dynamics and probability

Müller, Peter K.,


Introduction -- I Dynamics -- Deterministic dynamical systems -- Linear and integrable systems -- Stability and long-time behavior -- Ergodic theory -- Numerical Algorithms -- II Probability -- Probability Theory -- Discrete-time stochastic processes -- Continuous-time stochastic processes -- Information entropy -- III Probability in dynamical evolution -- Time evolution of broadened initial states -- Stochastic processes generated by observables -- Stochastic dynamical systems -- IV Probability in scientific reasoning -- Interpretations of probability -- Parameter estimation and hypothesis testing -- Bayesian inferences -- V Probability in physics -- Equilibrium statistical mechanics -- Non-equilibrium statistical mechanics -- Foundational issues of statistical mechanics -- Quantum Mechanics -- A Classical Mechanics -- B Thermostatics -- C Fluid dynamics -- D Some proofs and explicit computations -- E Mathematical tools, conventions and notation -- References -- Index. Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Müller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.
Date Published:2022

Introduction -- I Dynamics -- Deterministic dynamical systems -- Linear and integrable systems -- Stability and long-time behavior -- Ergodic theory -- Numerical Algorithms -- II Probability -- Probability Theory -- Discrete-time stochastic processes -- Continuous-time stochastic processes -- Information entropy -- III Probability in dynamical evolution -- Time evolution of broadened initial states -- Stochastic processes generated by observables -- Stochastic dynamical systems -- IV Probability in scientific reasoning -- Interpretations of probability -- Parameter estimation and hypothesis testing -- Bayesian inferences -- V Probability in physics -- Equilibrium statistical mechanics -- Non-equilibrium statistical mechanics -- Foundational issues of statistical mechanics -- Quantum Mechanics -- A Classical Mechanics -- B Thermostatics -- C Fluid dynamics -- D Some proofs and explicit computations -- E Mathematical tools, conventions and notation -- References -- Index. Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Müller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.

Date Published:2022

Handbook of research on modern educational technologies, applications, and management


Title Page -- Copyright Page -- Dedication -- Editorial Advisory Board -- List of Contributors -- Table of Contents -- Detailed Table of Contents -- Preface -- Acknowledgment -- Section 1: Administrative Technologies, Data Management, and Performance Assessments -- Chapter 1: Multi-Perspectives of Cloud Computing Service Adoption Quality and Risks in Higher Education -- Chapter 2: A Comparative Evaluation of ERP Implementation Factors in Higher Education -- Chapter 3: Automated Essay Scoring Using Deep Learning Algorithms Chapter 4: A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques -- Chapter 5: Sequence Clustering Techniques in Educational Data Mining -- Chapter 6: Estimating Levels of Learning Outcomes Acquirement Based on Fuzzy Sets, Relations, and Their Compositions -- Chapter 7: Development of the Internet Literacy Indicator for Students (ILAS) and Longitudinal Analysis of Scores -- Chapter 8: Quality Videos and Integrated Performance Assessments Are Essential in the World Language edTPA Chapter 9: Implementation Imperilment and Imperatives of Integrated eIQA of HEI -- Chapter 10: Identifying and Evaluating Language-Learning Technology Tools -- Section 2: Educational and Assistive Technologies -- Chapter 11: Educational Games as Software Through the Lens of Designing Process -- Chapter 12: Usability Analysis of a Mobile Learning Application -- Chapter 13: Main Features and Types of Educational Use of Wiki Technology -- Chapter 14: A Survey of Recent Approaches Integrating Blogs in School Education -- Chapter 15: Essential Features and Critical Issues With Educational Chatbots Chapter 16: Reflecting on the Results of the Initiative ETiE for Using Tablets in Primary Schools -- Chapter 17: FeedForward With Screencasts -- Chapter 18: Developing Computational Thinking Using Lego Education WeDo at 4th Grade of Primary Education -- Chapter 19: Assessment of the Use of Social Media by Students of the National Open University of Nigeria, Abeokuta Study Centre -- Chapter 20: Multimedia-Enabled Dot Codes as Communication Aids -- Chapter 21: An Overview of the Technological Options for Promoting Communication Skills of Children With Cerebral Palsy Section 3: Knowledge Sharing, Collaboration, and Professional Development -- Chapter 22: Designing Schools as Learning Centers -- Chapter 23: Generalizable Models for Online Professional Learning Communities for America's K-12 Teachers -- Chapter 24: Professional Development and Training Needs for Administrators in an Islamic University Malaysia -- Chapter 25: Supporting Community Engagement Through Real-World Instructional Learning -- Chapter 26: Design, New Media, and Human-Computer Interactions -- Chapter 27: The University Challenge in the Collaboration Relationship With the Industry "This book provides cutting-edge, multidisciplinary research and expert insights on advancing technologies used in educational settings as well as current strategies for administrative and leadership roles in education. Covering a wide range of topics including but not limited to community engagement, educational games, data management, and mobile learning, this publication provides insights into technological advancements with educational applications and examines forthcoming implementation strategies"--
Date Published:2020

Title Page -- Copyright Page -- Dedication -- Editorial Advisory Board -- List of Contributors -- Table of Contents -- Detailed Table of Contents -- Preface -- Acknowledgment -- Section 1: Administrative Technologies, Data Management, and Performance Assessments -- Chapter 1: Multi-Perspectives of Cloud Computing Service Adoption Quality and Risks in Higher Education -- Chapter 2: A Comparative Evaluation of ERP Implementation Factors in Higher Education -- Chapter 3: Automated Essay Scoring Using Deep Learning Algorithms Chapter 4: A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques -- Chapter 5: Sequence Clustering Techniques in Educational Data Mining -- Chapter 6: Estimating Levels of Learning Outcomes Acquirement Based on Fuzzy Sets, Relations, and Their Compositions -- Chapter 7: Development of the Internet Literacy Indicator for Students (ILAS) and Longitudinal Analysis of Scores -- Chapter 8: Quality Videos and Integrated Performance Assessments Are Essential in the World Language edTPA Chapter 9: Implementation Imperilment and Imperatives of Integrated eIQA of HEI -- Chapter 10: Identifying and Evaluating Language-Learning Technology Tools -- Section 2: Educational and Assistive Technologies -- Chapter 11: Educational Games as Software Through the Lens of Designing Process -- Chapter 12: Usability Analysis of a Mobile Learning Application -- Chapter 13: Main Features and Types of Educational Use of Wiki Technology -- Chapter 14: A Survey of Recent Approaches Integrating Blogs in School Education -- Chapter 15: Essential Features and Critical Issues With Educational Chatbots Chapter 16: Reflecting on the Results of the Initiative ETiE for Using Tablets in Primary Schools -- Chapter 17: FeedForward With Screencasts -- Chapter 18: Developing Computational Thinking Using Lego Education WeDo at 4th Grade of Primary Education -- Chapter 19: Assessment of the Use of Social Media by Students of the National Open University of Nigeria, Abeokuta Study Centre -- Chapter 20: Multimedia-Enabled Dot Codes as Communication Aids -- Chapter 21: An Overview of the Technological Options for Promoting Communication Skills of Children With Cerebral Palsy Section 3: Knowledge Sharing, Collaboration, and Professional Development -- Chapter 22: Designing Schools as Learning Centers -- Chapter 23: Generalizable Models for Online Professional Learning Communities for America's K-12 Teachers -- Chapter 24: Professional Development and Training Needs for Administrators in an Islamic University Malaysia -- Chapter 25: Supporting Community Engagement Through Real-World Instructional Learning -- Chapter 26: Design, New Media, and Human-Computer Interactions -- Chapter 27: The University Challenge in the Collaboration Relationship With the Industry "This book provides cutting-edge, multidisciplinary research and expert insights on advancing technologies used in educational settings as well as current strategies for administrative and leadership roles in education. Covering a wide range of topics including but not limited to community engagement, educational games, data management, and mobile learning, this publication provides insights into technological advancements with educational applications and examines forthcoming implementation strategies"--

Date Published:2020

HBR guide to AI basics for managers


Includes index. "From product design and financial modeling to performance management and hiring decisions-artificial intelligence and machine learning are becoming everyday tools for managers at businesses of all sizes. But the rewards of every AI system come with risks-and if you don't understand how to make sense of them, you're not going to make the right decisions. Whether you want to get up to speed quickly, could just use a refresher, or are working with an AI expert for the first time, HBR Guide to AI Basics for Managers will give you the information and skills you need. You'll learn how to: understand key terms and concepts; identify which of your projects and processes would benefit from an AI approach; deal with ethical issues before they come up; hire the best AI vendors; run small experiments; work better with your AI experts and data scientists"--
Date Published:2023

Includes index. "From product design and financial modeling to performance management and hiring decisions-artificial intelligence and machine learning are becoming everyday tools for managers at businesses of all sizes. But the rewards of every AI system come with risks-and if you don't understand how to make sense of them, you're not going to make the right decisions. Whether you want to get up to speed quickly, could just use a refresher, or are working with an AI expert for the first time, HBR Guide to AI Basics for Managers will give you the information and skills you need. You'll learn how to: understand key terms and concepts; identify which of your projects and processes would benefit from an AI approach; deal with ethical issues before they come up; hire the best AI vendors; run small experiments; work better with your AI experts and data scientists"--

Date Published:2023