Allgemein
Fundamentals and Methods of Machine and Deep Learning
FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNINGTHE BOOK PROVIDES A PRACTICAL APPROACH BY EXPLAINING THE CONCEPTS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS, EVALUATION OF METHODOLOGY ADVANCES, AND ALGORITHM DEMONSTRATIONS WITH APPLICATIONS.Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. AUDIENCEResearchers and engineers in artificial intelligence, computer scientists as well as software developers. PRADEEP SINGH PHD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology, Raipur, India. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models. He has more than 15 years of teaching experience with many publications in reputed international journals, conferences, and book chapters.Preface xix1 SUPERVISED MACHINE LEARNING: ALGORITHMS AND APPLICATIONS 1Shruthi H. Shetty, Sumiksha Shetty, Chandra Singh and Ashwath Rao1.1 History 21.2 Introduction 21.3 Supervised Learning 41.4 Linear Regression (LR) 51.4.1 Learning Model 61.4.2 Predictions With Linear Regression 71.5 Logistic Regression 81.6 Support Vector Machine (SVM) 91.7 Decision Tree 111.8 Machine Learning Applications in Daily Life 121.8.1 Traffic Alerts (Maps) 121.8.2 Social Media (Facebook) 131.8.3 Transportation and Commuting (Uber) 131.8.4 Products Recommendations 131.8.5 Virtual Personal Assistants 131.8.6 Self-Driving Cars 141.8.7 Google Translate 141.8.8 Online Video Streaming (Netflix) 141.8.9 Fraud Detection 141.9 Conclusion 15References 152 ZONOTIC DISEASES DETECTION USING ENSEMBLE MACHINE LEARNING ALGORITHMS 17Bhargavi K.2.1 Introduction 182.2 Bayes Optimal Classifier 192.3 Bootstrap Aggregating (Bagging) 212.4 Bayesian Model Averaging (BMA) 222.5 Bayesian Classifier Combination (BCC) 242.6 Bucket of Models 262.7 Stacking 272.8 Efficiency Analysis 292.9 Conclusion 30References 303 MODEL EVALUATION 33Ravi Shekhar Tiwari3.1 Introduction 343.2 Model Evaluation 343.2.1 Assumptions 363.2.2 Residual 363.2.3 Error Sum of Squares (Sse) 373.2.4 Regression Sum of Squares (Ssr) 373.2.5 Total Sum of Squares (Ssto) 373.3 Metric Used in Regression Model 383.3.1 Mean Absolute Error (Mae) 383.3.2 Mean Square Error (Mse) 393.3.3 Root Mean Square Error (Rmse) 413.3.4 Root Mean Square Logarithm Error (Rmsle) 423.3.5 R-Square (R2) 453.3.5.1 Problem With R-Square (R2) 463.3.6 Adjusted R-Square (R2) 463.3.7 Variance 473.3.8 AIC 483.3.9 BIC 493.3.10 ACP, Press, and R2-Predicted 493.3.11 Solved Examples 513.4 Confusion Metrics 523.4.1 How to Interpret the Confusion Metric? 533.4.2 Accuracy 553.4.2.1 Why Do We Need the Other Metric Along With Accuracy? 563.4.3 True Positive Rate (TPR) 563.4.4 False Negative Rate (FNR) 573.4.5 True Negative Rate (TNR) 573.4.6 False Positive Rate (FPR) 583.4.7 Precision 583.4.8 Recall 593.4.9 Recall-Precision Trade-Off 603.4.10 F1-Score 613.4.11 F-Beta Sore 613.4.12 Thresholding 633.4.13 AUC - ROC 643.4.14 AUC - PRC 653.4.15 Derived Metric From Recall, Precision, and F1-Score 673.4.16 Solved Examples 683.5 Correlation 703.5.1 Pearson Correlation 703.5.2 Spearman Correlation 713.5.3 Kendall’s Rank Correlation 733.5.4 Distance Correlation 743.5.5 Biweight Mid-Correlation 753.5.6 Gamma Correlation 763.5.7 Point Biserial Correlation 773.5.8 Biserial Correlation 783.5.9 Partial Correlation 783.6 Natural Language Processing (NLP) 783.6.1 N-Gram 793.6.2 BELU Score 793.6.2.1 BELU Score With N-Gram 803.6.3 Cosine Similarity 813.6.4 Jaccard Index 833.6.5 ROUGE 843.6.6 NIST 853.6.7 SQUAD 853.6.8 MACRO 863.7 Additional Metrics 863.7.1 Mean Reciprocal Rank (MRR) 863.7.2 Cohen Kappa 873.7.3 Gini Coefficient 873.7.4 Scale-Dependent Errors 873.7.5 Percentage Errors 883.7.6 Scale-Free Errors 883.8 Summary of Metric Derived from Confusion Metric 893.9 Metric Usage 903.10 Pro and Cons of Metrics 943.11 Conclusion 95References 964 ANALYSIS OF M-SEIR AND LSTM MODELS FOR THE PREDICTION OF COVID-19 USING RMSLE 101Archith S., Yukta C., Archana H.R. and Surendra H.H.4.1 Introduction 1014.2 Survey of Models 1034.2.1 SEIR Model 1034.2.2 Modified SEIR Model 1034.2.3 Long Short-Term Memory (LSTM) 1044.3 Methodology 1064.3.1 Modified SEIR 1064.3.2 LSTM Model 1084.3.2.1 Data Pre-Processing 1084.3.2.2 Data Shaping 1094.3.2.3 Model Design 1094.4 Experimental Results 1114.4.1 Modified SEIR Model 1114.4.2 LSTM Model 1134.5 Conclusion 1164.6 Future Work 116References 1185 THE SIGNIFICANCE OF FEATURE SELECTION TECHNIQUES IN MACHINE LEARNING 121N. Bharathi, B.S. Rishiikeshwer, T. Aswin Shriram, B. Santhi and G.R. Brindha5.1 Introduction 1225.2 Significance of Pre-Processing 1225.3 Machine Learning System 1235.3.1 Missing Values 1235.3.2 Outliers 1235.3.3 Model Selection 1245.4 Feature Extraction Methods 1245.4.1 Dimension Reduction 1255.4.1.1 Attribute Subset Selection 1265.4.2 Wavelet Transforms 1275.4.3 Principal Components Analysis 1275.4.4 Clustering 1285.5 Feature Selection 1285.5.1 Filter Methods 1295.5.2 Wrapper Methods 1295.5.3 Embedded Methods 1305.6 Merits and Demerits of Feature Selection 1315.7 Conclusion 131References 1326 USE OF MACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE—A REVIEW ON DISEASE PREDICTION SYSTEM 135Radha R. and Gopalakrishnan R.6.1 Introduction to Healthcare System 1366.2 Causes for the Failure of the Healthcare System 1376.3 Artificial Intelligence and Healthcare System for Predicting Diseases 1386.3.1 Monitoring and Collection of Data 1406.3.2 Storing, Retrieval, and Processing of Data 1416.4 Facts Responsible for Delay in Predicting the Defects 1426.5 Pre-Treatment Analysis and Monitoring 1436.6 Post-Treatment Analysis and Monitoring 1456.7 Application of ML and DL 1456.7.1 ML and DL for Active Aid 1456.7.1.1 Bladder Volume Prediction 1476.7.1.2 Epileptic Seizure Prediction 1486.8 Challenges and Future of Healthcare Systems Based on ML and DL 1486.9 Conclusion 149References 1507 DETECTION OF DIABETIC RETINOPATHY USING ENSEMBLE LEARNING TECHNIQUES 153Anirban Dutta, Parul Agarwal, Anushka Mittal, Shishir Khandelwal and Shikha Mehta7.1 Introduction 1537.2 Related Work 1557.3 Methodology 1557.3.1 Data Pre-Processing 1557.3.2 Feature Extraction 1617.3.2.1 Exudates 1617.3.2.2 Blood Vessels 1617.3.2.3 Microaneurysms 1627.3.2.4 Hemorrhages 1627.3.3 Learning 1637.3.3.1 Support Vector Machines 1637.3.3.2 K-Nearest Neighbors 1637.3.3.3 Random Forest 1647.3.3.4 AdaBoost 1647.3.3.5 Voting Technique 1647.4 Proposed Models 1657.4.1 AdaNaive 1657.4.2 AdaSVM 1667.4.3 AdaForest 1667.5 Experimental Results and Analysis 1677.5.1 Dataset 1677.5.2 Software and Hardware 1677.5.3 Results 1687.6 Conclusion 173References 1748 MACHINE LEARNING AND DEEP LEARNING FOR MEDICAL ANALYSIS—A CASE STUDY ON HEART DISEASE DATA 177Swetha A.M., Santhi B. and Brindha G.R.8.1 Introduction 1788.2 Related Works 1798.3 Data Pre-Processing 1818.3.1 Data Imbalance 1818.4 Feature Selection 1828.4.1 Extra Tree Classifier 1828.4.2 Pearson Correlation 1838.4.3 Forward Stepwise Selection 1838.4.4 Chi-Square Test 1848.5 ML Classifiers Techniques 1848.5.1 Supervised Machine Learning Models 1858.5.1.1 Logistic Regression 1858.5.1.2 SVM 1868.5.1.3 Naive Bayes 1868.5.1.4 Decision Tree 1868.5.1.5 K-Nearest Neighbors (KNN) 1878.5.2 Ensemble Machine Learning Model 1878.5.2.1 Random Forest 1878.5.2.2 AdaBoost 1888.5.2.3 Bagging 1888.5.3 Neural Network Models 1898.5.3.1 Artificial Neural Network (ANN) 1898.5.3.2 Convolutional Neural Network (CNN) 1898.6 Hyperparameter Tuning 1908.6.1 Cross-Validation 1908.7 Dataset Description 1908.7.1 Data Pre-Processing 1938.7.2 Feature Selection 1958.7.3 Model Selection 1968.7.4 Model Evaluation 1978.8 Experiments and Results 1978.8.1 Study 1: Survival Prediction Using All Clinical Features 1988.8.2 Study 2: Survival Prediction Using Age, Ejection Fraction and Serum Creatinine 1988.8.3 Study 3: Survival Prediction Using Time, Ejection Fraction, and Serum Creatinine 1998.8.4 Comparison Between Study 1, Study 2, and Study 3 2038.8.5 Comparative Study on Different Sizes of Data 2048.9 Analysis 2068.10 Conclusion 206References 2079 A NOVEL CONVOLUTIONAL NEURAL NETWORK MODEL TO PREDICT SOFTWARE DEFECTS 211Kumar Rajnish, Vandana Bhattacharjee and Mansi Gupta9.1 Introduction 2129.2 Related Works 2139.2.1 Software Defect Prediction Based on Deep Learning 2139.2.2 Software Defect Prediction Based on Deep Features 2149.2.3 Deep Learning in Software Engineering 2149.3 Theoretical Background 2159.3.1 Software Defect Prediction 2159.3.2 Convolutional Neural Network 2169.4 Experimental Setup 2189.4.1 Data Set Description 2189.4.2 Building Novel Convolutional Neural Network (NCNN) Model 2199.4.3 Evaluation Parameters 2229.4.4 Results and Analysis 2249.5 Conclusion and Future Scope 230References 23310 PREDICTIVE ANALYSIS ON ONLINE TELEVISION VIDEOS USING MACHINE LEARNING ALGORITHMS 237Rebecca Jeyavadhanam B., Ramalingam V.V., Sugumaran V. and Rajkumar D.10.1 Introduction 23810.1.1 Overview of Video Analytics 24110.1.2 Machine Learning Algorithms 24210.1.2.1 Decision Tree C4.5 24310.1.2.2 J48 Graft 24310.1.2.3 Logistic Model Tree 24410.1.2.4 Best First Tree 24410.1.2.5 Reduced Error Pruning Tree 24410.1.2.6 Random Forest 24410.2 Proposed Framework 24510.2.1 Data Collection 24610.2.2 Feature Extraction 24610.2.2.1 Block Intensity Comparison Code 24710.2.2.2 Key Frame Rate 24810.3 Feature Selection 24910.4 Classification 25010.5 Online Incremental Learning 25110.6 Results and Discussion 25310.7 Conclusion 255References 25611 A COMBINATIONAL DEEP LEARNING APPROACH TO VISUALLY EVOKED EEG-BASED IMAGE CLASSIFICATION 259Nandini Kumari, Shamama Anwar and Vandana Bhattacharjee11.1 Introduction 26011.2 Literature Review 26211.3 Methodology 26411.3.1 Dataset Acquisition 26411.3.2 Pre-Processing and Spectrogram Generation 26511.3.3 Classification of EEG Spectrogram Images With Proposed CNN Model 26611.3.4 Classification of EEG Spectrogram Images With Proposed Combinational CNN+LSTM Model 26811.4 Result and Discussion 27011.5 Conclusion 272References 27312 APPLICATION OF MACHINE LEARNING ALGORITHMS WITH BALANCING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION: A COMPARATIVE ANALYSIS 277Shiksha12.1 Introduction 27812.2 Methods and Techniques 28012.2.1 Research Approach 28012.2.2 Dataset Description 28212.2.3 Data Preparation 28312.2.4 Correlation Between Features 28412.2.5 Splitting the Dataset 28512.2.6 Balancing Data 28512.2.6.1 Oversampling of Minority Class 28612.2.6.2 Under-Sampling of Majority Class 28612.2.6.3 Synthetic Minority Over Sampling Technique 28612.2.6.4 Class Weight 28712.2.7 Machine Learning Algorithms (Models) 28812.2.7.1 Logistic Regression 28812.2.7.2 Support Vector Machine 28812.2.7.3 Decision Tree 29012.2.7.4 Random Forest 29212.2.8 Tuning of Hyperparameters 29412.2.9 Performance Evaluation of the Models 29412.3 Results and Discussion 29812.3.1 Results Using Balancing Techniques 29912.3.2 Result Summary 29912.4 Conclusions 30512.4.1 Future Recommendations 305References 30613 CRACK DETECTION IN CIVIL STRUCTURES USING DEEP LEARNING 311Bijimalla Shiva Vamshi Krishna, Rishiikeshwer B.S., J. Sanjay Raju, N. Bharathi, C. Venkatasubramanian and G.R. Brindha13.1 Introduction 31213.2 Related Work 31213.3 Infrared Thermal Imaging Detection Method 31413.4 Crack Detection Using CNN 31413.4.1 Model Creation 31613.4.2 Activation Functions (AF) 31713.4.3 Optimizers 32213.4.4 Transfer Learning 32213.5 Results and Discussion 32213.6 Conclusion 323References 32314 MEASURING URBAN SPRAWL USING MACHINE LEARNING 327Keerti Kulkarni and P. A. Vijaya14.1 Introduction 32714.2 Literature Survey 32814.3 Remotely Sensed Images 32914.4 Feature Selection 33114.4.1 Distance-Based Metric 33114.5 Classification Using Machine Learning Algorithms 33214.5.1 Parametric vs. Non-Parametric Algorithms 33214.5.2 Maximum Likelihood Classifier 33214.5.3 k-Nearest Neighbor Classifiers 33414.5.4 Evaluation of the Classifiers 33414.5.4.1 Precision 33414.5.4.2 Recall 33514.5.4.3 Accuracy 33514.5.4.4 F1-Score 33514.6 Results 33514.7 Discussion and Conclusion 338Acknowledgements 338References 33815 APPLICATION OF DEEP LEARNING ALGORITHMS IN MEDICAL IMAGE PROCESSING: A SURVEY 341Santhi B., Swetha A.M. and Ashutosh A.M.15.1 Introduction 34215.2 Overview of Deep Learning Algorithms 34315.2.1 Supervised Deep Neural Networks 34315.2.1.1 Convolutional Neural Network 34315.2.1.2 Transfer Learning 34415.2.1.3 Recurrent Neural Network 34415.2.2 Unsupervised Learning 34515.2.2.1 Autoencoders 34515.2.2.2 GANs 34515.3 Overview of Medical Images 34615.3.1 MRI Scans 34615.3.2 CT Scans 34715.3.3 X-Ray Scans 34715.3.4 PET Scans 34715.4 Scheme of Medical Image Processing 34815.4.1 Formation of Image 34815.4.2 Image Enhancement 34915.4.3 Image Analysis 34915.4.4 Image Visualization 34915.5 Anatomy-Wise Medical Image Processing With Deep Learning 34915.5.1 Brain Tumor 35215.5.2 Lung Nodule Cancer Detection 35715.5.3 Breast Cancer Segmentation and Detection 36215.5.4 Heart Disease Prediction 36415.5.5 COVID-19 Prediction 37015.6 Conclusion 372References 37216 SIMULATION OF SELF-DRIVING CARS USING DEEP LEARNING 379Rahul M. K., Praveen L. Uppunda, Vinayaka Raju S., Sumukh B. and C. Gururaj16.1 Introduction 38016.2 Methodology 38016.2.1 Behavioral Cloning 38016.2.2 End-to-End Learning 38016.3 Hardware Platform 38116.4 Related Work 38216.5 Pre-Processing 38216.5.1 Lane Feature Extraction 38216.5.1.1 Canny Edge Detector 38316.5.1.2 Hough Transform 38316.5.1.3 Raw Image Without Pre-Processing 38416.6 Model 38416.6.1 CNN Architecture 38516.6.2 Multilayer Perceptron Model 38516.6.3 Regression vs. Classification 38516.6.3.1 Regression 38616.6.3.2 Classification 38616.7 Experiments 38716.8 Results 38716.9 Conclusion 394References 39417 ASSISTIVE TECHNOLOGIES FOR VISUAL, HEARING, AND SPEECH IMPAIRMENTS: MACHINE LEARNING AND DEEP LEARNING SOLUTIONS 397Shahira K. C., Sruthi C. J. and Lijiya A.17.1 Introduction 39717.2 Visual Impairment 39817.2.1 Conventional Assistive Technology for the VIP 39917.2.1.1 Way Finding 39917.2.1.2 Reading Assistance 40217.2.2 The Significance of Computer Vision and Deep Learning in AT of VIP 40317.2.2.1 Navigational Aids 40317.2.2.2 Scene Understanding 40517.2.2.3 Reading Assistance 40617.2.2.4 Wearables 40817.3 Verbal and Hearing Impairment 41017.3.1 Assistive Listening Devices 41017.3.2 Alerting Devices 41117.3.3 Augmentative and Alternative Communication Devices 41117.3.3.1 Sign Language Recognition 41217.3.4 Significance of Machine Learning and Deep Learning in Assistive Communication Technology 41717.4 Conclusion and Future Scope 418References 41818 CASE STUDIES: DEEP LEARNING IN REMOTE SENSING 425Emily Jenifer A. and Sudha N.18.1 Introduction 42618.2 Need for Deep Learning in Remote Sensing 42718.3 Deep Neural Networks for Interpreting Earth Observation Data 42718.3.1 Convolutional Neural Network 42718.3.2 Autoencoder 42818.3.3 Restricted Boltzmann Machine and Deep Belief Network 42918.3.4 Generative Adversarial Network 43018.3.5 Recurrent Neural Network 43118.4 Hybrid Architectures for Multi-Sensor Data Processing 43218.5 Conclusion 434References 434Index 439
Advanced Healthcare Systems
ADVANCED HEALTHCARE SYSTEMSTHIS BOOK OFFERS A COMPLETE PACKAGE INVOLVING THE INCUBATION OF MACHINE LEARNING, AI, AND IOT IN HEALTHCARE THAT IS BENEFICIAL FOR RESEARCHERS, HEALTHCARE PROFESSIONALS, SCIENTISTS, AND TECHNOLOGISTS.The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. AUDIENCEThis book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector. ROHIT TANWAR, PHD (Kurukshetra University, Kurukshetra, India) is an assistant professor in the School of Computer Science at UPES Dehradun, India.S. BALAMURUGAN, PHD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. R. K. SAINI, PHD (DIT University, Dehradun, India) is an assistant professor in the Department of Computer Science & Applications at DIT University, Dehradun (Uttarakhand). VISHAL BHARTI, PHD is a professor in the Department of Computer Science and Engineering, Chandigarh University, India. He has published more than 75 research papers in both national & international journals. PREMKUMAR CHITHALURU, PHD is an assistant professor in the Department of SCS at the University of Petroleum and Energy Studies (UPES), Dehradun, India. Preface xvii1 INTERNET OF MEDICAL THINGS—STATE-OF-THE-ART 1Kishor Joshi and Ruchi Mehrotra1.1 Introduction 21.2 Historical Evolution of IoT to IoMT 21.2.1 IoT and IoMT—Market Size 41.3 Smart Wearable Technology 41.3.1 Consumer Fitness Smart Wearables 41.3.2 Clinical-Grade Wearables 51.4 Smart Pills 71.5 Reduction of Hospital-Acquired Infections 81.5.1 Navigation Apps for Hospitals 81.6 In-Home Segment 81.7 Community Segment 91.8 Telehealth and Remote Patient Monitoring 91.9 IoMT in Healthcare Logistics and Asset Management 121.10 IoMT Use in Monitoring During COVID-19 131.11 Conclusion 14References 152 ISSUES AND CHALLENGES RELATED TO PRIVACY AND SECURITY IN HEALTHCARE USING IOT, FOG, AND CLOUD COMPUTING 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma2.1 Introduction 222.2 Related Works 232.3 Architecture 252.3.1 Device Layer 252.3.2 Fog Layer 262.3.3 Cloud Layer 262.4 Issues and Challenges 262.5 Conclusion 29References 303 STUDY OF THYROID DISEASE USING MACHINE LEARNING 33Shanu Verma, Rashmi Popli and Harish Kumar3.1 Introduction 343.2 Related Works 343.3 Thyroid Functioning 353.4 Category of Thyroid Cancer 363.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 373.5.1 Decision Tree Algorithm 383.5.2 Support Vector Machines 393.5.3 Random Forest 393.5.4 Logistic Regression 393.5.5 Naïve Bayes 403.6 Conclusion 41References 414 A REVIEW OF VARIOUS SECURITY AND PRIVACY INNOVATIONS FOR IOT APPLICATIONS IN HEALTHCARE 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi4.1 Introduction 444.1.1 Introduction to IoT 444.1.2 Introduction to Vulnerability, Attack, and Threat 454.2 IoT in Healthcare 464.2.1 Confidentiality 464.2.2 Integrity 464.2.3 Authorization 464.2.4 Availability 474.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 484.4 Conclusion 54References 545 METHODS OF LUNG SEGMENTATION BASED ON CT IMAGES 59Amit Verma and Thipendra P. Singh5.1 Introduction 595.2 Semi-Automated Algorithm for Lung Segmentation 605.2.1 Algorithm for Tracking to Lung Edge 605.2.2 Outlining the Region of Interest in CT Images 625.2.2.1 Locating the Region of Interest 625.2.2.2 Seed Pixels and Searching Outline 625.3 Automated Method for Lung Segmentation 635.3.1 Knowledge-Based Automatic Model for Segmentation 635.3.2 Automatic Method for Segmenting the Lung CT Image 645.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 645.5 Conclusion 65References 656 HANDLING UNBALANCED DATA IN CLINICAL IMAGES 69Amit Verma6.1 Introduction 706.2 Handling Imbalance Data 716.2.1 Cluster-Based Under-Sampling Technique 726.2.2 Bootstrap Aggregation (Bagging) 756.3 Conclusion 76References 767 IOT-BASED HEALTH MONITORING SYSTEM FOR SPEECH-IMPAIRED PEOPLE USING ASSISTIVE WEARABLE ACCELEROMETER 81Ishita Banerjee and Madhumathy P.7.1 Introduction 827.2 Literature Survey 847.3 Procedure 867.4 Results 937.5 Conclusion 97References 978 SMART IOT DEVICES FOR THE ELDERLY AND PEOPLE WITH DISABILITIES 101K. N. D. Saile and Kolisetti Navatha8.1 Introduction 1018.2 Need for IoT Devices 1028.3 Where Are the IoT Devices Used? 1038.3.1 Home Automation 1038.3.2 Smart Appliances 1048.3.3 Healthcare 1048.4 Devices in Home Automation 1048.4.1 Automatic Lights Control 1048.4.2 Automated Home Safety and Security 1048.5 Smart Appliances 1058.5.1 Smart Oven 1058.5.2 Smart Assistant 1058.5.3 Smart Washers and Dryers 1068.5.4 Smart Coffee Machines 1068.5.5 Smart Refrigerator 1068.6 Healthcare 1068.6.1 Smart Watches 1078.6.2 Smart Thermometer 1078.6.3 Smart Blood Pressure Monitor 1078.6.4 Smart Glucose Monitors 1078.6.5 Smart Insulin Pump 1088.6.6 Smart Wearable Asthma Monitor 1088.6.7 Assisted Vision Smart Glasses 1098.6.8 Finger Reader 1098.6.9 Braille Smart Watch 1098.6.10 Smart Wand 1098.6.11 Taptilo Braille Device 1108.6.12 Smart Hearing Aid 1108.6.13 E-Alarm 1108.6.14 Spoon Feeding Robot 1108.6.15 Automated Wheel Chair 1108.7 Conclusion 112References 1129 IOT-BASED HEALTH MONITORING AND TRACKING SYSTEM FOR SOLDIERS 115Kavitha N. and Madhumathy P.9.1 Introduction 1169.2 Literature Survey 1179.3 System Requirements 1189.3.1 Software Requirement Specification 1199.3.2 Functional Requirements 1199.4 System Design 1199.4.1 Features 1219.4.1.1 On-Chip Flash Memory 1229.4.1.2 On-Chip Static RAM 1229.4.2 Pin Control Block 1229.4.3 UARTs 1239.4.3.1 Features 1239.4.4 System Control 1239.4.4.1 Crystal Oscillator 1239.4.4.2 Phase-Locked Loop 1249.4.4.3 Reset and Wake-Up Timer 1249.4.4.4 Brown Out Detector 1259.4.4.5 Code Security 1259.4.4.6 External Interrupt Inputs 1259.4.4.7 Memory Mapping Control 1259.4.4.8 Power Control 1269.4.5 Real Monitor 1269.4.5.1 GPS Module 1269.4.6 Temperature Sensor 1279.4.7 Power Supply 1289.4.8 Regulator 1289.4.9 LCD 1289.4.10 Heart Rate Sensor 1299.5 Implementation 1299.5.1 Algorithm 1309.5.2 Hardware Implementation 1309.5.3 Software Implementation 1319.6 Results and Discussions 1339.6.1 Heart Rate 1339.6.2 Temperature Sensor 1359.6.3 Panic Button 1359.6.4 GPS Receiver 1359.7 Conclusion 136References 13610 CLOUD-IOT SECURED PREDICTION SYSTEM FOR PROCESSING AND ANALYSIS OF HEALTHCARE DATA USING MACHINE LEARNING TECHNIQUES 137G. K. Kamalam and S. Anitha10.1 Introduction 13810.2 Literature Survey 13910.3 Medical Data Classification 14110.3.1 Structured Data 14210.3.2 Semi-Structured Data 14210.4 Data Analysis 14210.4.1 Descriptive Analysis 14210.4.2 Diagnostic Analysis 14310.4.3 Predictive Analysis 14310.4.4 Prescriptive Analysis 14310.5 ML Methods Used in Healthcare 14410.5.1 Supervised Learning Technique 14410.5.2 Unsupervised Learning 14510.5.3 Semi-Supervised Learning 14510.5.4 Reinforcement Learning 14510.6 Probability Distributions 14510.6.1 Discrete Probability Distributions 14610.6.1.1 Bernoulli Distribution 14610.6.1.2 Uniform Distribution 14710.6.1.3 Binomial Distribution 14710.6.1.4 Normal Distribution 14810.6.1.5 Poisson Distribution 14810.6.1.6 Exponential Distribution 14910.7 Evaluation Metrics 15010.7.1 Classification Accuracy 15010.7.2 Confusion Matrix 15010.7.3 Logarithmic Loss 15110.7.4 Receiver Operating Characteristic Curve, or ROC Curve 15210.7.5 Area Under Curve (AUC) 15210.7.6 Precision 15310.7.7 Recall 15310.7.8 F1 Score 15310.7.9 Mean Absolute Error 15410.7.10 Mean Squared Error 15410.7.11 Root Mean Squared Error 15510.7.12 Root Mean Squared Logarithmic Error 15510.7.13 R-Squared/Adjusted R-Squared 15610.7.14 Adjusted R-Squared 15610.8 Proposed Methodology 15610.8.1 Neural Network 15810.8.2 Triangular Membership Function 15810.8.3 Data Collection 15910.8.4 Secured Data Storage 15910.8.5 Data Retrieval and Merging 16110.8.6 Data Aggregation 16210.8.7 Data Partition 16210.8.8 Fuzzy Rules for Prediction of Heart Disease 16310.8.9 Fuzzy Rules for Prediction of Diabetes 16410.8.10 Disease Prediction With Severity and Diagnosis 16510.9 Experimental Results 16610.10 Conclusion 169References 16911 CLOUDIOT-DRIVEN HEALTHCARE: REVIEW, ARCHITECTURE, SECURITY IMPLICATIONS, AND OPEN RESEARCH ISSUES 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan11.1 Introduction 17411.2 Background Elements 18011.2.1 Security Comparison Between Traditional and IoT Networks 18511.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 18711.3.1 Security Protocols 18711.3.2 Enabling Technologies 18811.4 CloudIoT Health System Framework 19111.4.1 Data Perception/Acquisition 19211.4.2 Data Transmission/Communication 19311.4.3 Cloud Storage and Warehouse 19411.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 19411.4.5 Design Considerations 19711.5 Security Challenges and Vulnerabilities 19911.5.1 Security Characteristics and Objectives 20011.5.1.1 Confidentiality 20211.5.1.2 Integrity 20211.5.1.3 Availability 20211.5.1.4 Identification and Authentication 20211.5.1.5 Privacy 20311.5.1.6 Light Weight Solutions 20311.5.1.7 Heterogeneity 20311.5.1.8 Policies 20311.5.2 Security Vulnerabilities 20311.5.2.1 IoT Threats and Vulnerabilities 20511.5.2.2 Cloud-Based Threats 20811.6 Security Countermeasures and Considerations 21411.6.1 Security Countermeasures 21411.6.1.1 Security Awareness and Survey 21411.6.1.2 Security Architecture and Framework 21511.6.1.3 Key Management 21611.6.1.4 Authentication 21711.6.1.5 Trust 21811.6.1.6 Cryptography 21911.6.1.7 Device Security 21911.6.1.8 Identity Management 22011.6.1.9 Risk-Based Security/Risk Assessment 22011.6.1.10 Block Chain–Based Security 22011.6.1.11 Automata-Based Security 22011.6.2 Security Considerations 23411.7 Open Research Issues and Security Challenges 23711.7.1 Security Architecture 23711.7.2 Resource Constraints 23811.7.3 Heterogeneous Data and Devices 23811.7.4 Protocol Interoperability 23811.7.5 Trust Management and Governance 23911.7.6 Fault Tolerance 23911.7.7 Next-Generation 5G Protocol 24011.8 Discussion and Analysis 24011.9 Conclusion 241References 24212 A NOVEL USAGE OF ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS IN REMOTE-BASED HEALTHCARE APPLICATIONS 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan12.1 Introduction Machine Learning 25612.2 Importance of Machine Learning 25612.2.1 ML vs. Classical Algorithms 25812.2.2 Learning Supervised 25912.2.3 Unsupervised Learning 26112.2.4 Network for Neuralism 26312.2.4.1 Definition of the Neural Network 26312.2.4.2 Neural Network Elements 26312.3 Procedure 26512.3.1 Dataset and Seizure Identification 26512.3.2 System 26512.4 Feature Extraction 26612.5 Experimental Methods 26612.5.1 Stepwise Feature Optimization 26612.5.2 Post-Classification Validation 26812.5.3 Fusion of Classification Methods 26812.6 Experiments 26912.7 Framework for EEG Signal Classification 26912.8 Detection of the Preictal State 27012.9 Determination of the Seizure Prediction Horizon 27112.10 Dynamic Classification Over Time 27212.11 Conclusion 273References 27313 USE OF MACHINE LEARNING IN HEALTHCARE 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi13.1 Introduction 27613.2 Uses of Machine Learning in Pharma and Medicine 27613.2.1 Distinguish Illnesses and Examination 27713.2.2 Drug Discovery and Manufacturing 27713.2.3 Scientific Imaging Analysis 27813.2.4 Twisted Therapy 27813.2.5 AI to Know-Based Social Change 27813.2.6 Perception Wellness Realisms 27913.2.7 Logical Preliminary and Exploration 27913.2.8 Publicly Supported Perceptions Collection 27913.2.9 Better Radiotherapy 28013.2.10 Incidence Forecast 28013.3 The Ongoing Preferences of ML in Human Services 28113.4 The Morals of the Use of Calculations in Medicinal Services 28413.5 Opportunities in Healthcare Quality Improvement 28813.5.1 Variation in Care 28813.5.2 Inappropriate Care 28913.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 28913.5.4 The Fact That People Are Unable to do What They Know Works 28913.5.5 A Waste 29013.6 A Team-Based Care Approach Reduces Waste 29013.7 Conclusion 291References 29214 METHODS OF MRI BRAIN TUMOR SEGMENTATION 295Amit Verma14.1 Introduction 29514.2 Generative and Descriptive Models 29614.2.1 Region-Based Segmentation 30014.2.2 Generative Model With Weighted Aggregation 30014.3 Conclusion 302References 30315 EARLY DETECTION OF TYPE 2 DIABETES MELLITUS USING DEEP NEURAL NETWORK–BASED MODEL 305Varun Sapra and Luxmi Sapra15.1 Introduction 30615.2 Data Set 30715.2.1 Data Insights 30815.3 Feature Engineering 31015.4 Framework for Early Detection of Disease 31215.4.1 Deep Neural Network 31315.5 Result 31415.6 Conclusion 315References 31516 A COMPREHENSIVE ANALYSIS ON MASKED FACE DETECTION ALGORITHMS 319Pranjali Singh, Amitesh Garg and Amritpal Singh16.1 Introduction 32016.2 Literature Review 32116.3 Implementation Approach 32516.3.1 Feature Extraction 32516.3.2 Image Processing 32516.3.3 Image Acquisition 32516.3.4 Classification 32516.3.5 MobileNetV2 32616.3.6 Deep Learning Architecture 32616.3.7 LeNet-5, AlexNet, and ResNet-50 32616.3.8 Data Collection 32616.3.9 Development of Model 32716.3.10 Training of Model 32816.3.11 Model Testing 32816.4 Observation and Analysis 32816.4.1 CNN Algorithm 32816.4.2 SSDNETV2 Algorithm 33016.4.3 SVM 33116.5 Conclusion 332References 33317 IOT-BASED AUTOMATED HEALTHCARE SYSTEM 335Darpan Anand and Aashish Kumar17.1 Introduction 33517.1.1 Software-Defined Network 33617.1.2 Network Function Virtualization 33717.1.3 Sensor Used in IoT Devices 33817.2 SDN-Based IoT Framework 34117.3 Literature Survey 34317.4 Architecture of SDN-IoT for Healthcare System 34417.5 Challenges 34517.6 Conclusion 347References 347Index 351
Python for Cybersecurity
DISCOVER AN UP-TO-DATE AND AUTHORITATIVE EXPLORATION OF PYTHON CYBERSECURITY STRATEGIESPython For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today.Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including:* Reconnaissance, resource development, initial access, and execution* Persistence, privilege escalation, defense evasion, and credential access* Discovery, lateral movement, collection, and command and control* Exfiltration and impactEach chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.HOWARD E. POSTON III is a freelance consultant and content creator with a professional focus on blockchain and cybersecurity. He has over ten years’ experience in programming with Python and has developed and taught over a dozen courses teaching cybersecurity. He is a sought-after speaker on blockchain and cybersecurity at international security conferences. Introduction xviiCHAPTER 1 FULFILLING PRE- ATT&CK OBJECTIVES 1Active Scanning 2Scanning Networks with scapy 2Implementing a SYN Scan in scapy 4Performing a DNS Scan in scapy 5Running the Code 5Network Scanning for Defenders 6Monitoring Traffic with scapy 7Building Deceptive Responses 8Running the Code 9Search Open Technical Databases 9Offensive DNS Exploration 10Searching DNS Records 11Performing a DNS Lookup 12Reverse DNS Lookup 12Running the Code 13DNS Exploration for Defenders 13Handling DNS Requests 15Building a DNS Response 15Running the Code 16Summary 17Suggested Exercises 17CHAPTER 2 GAINING INITIAL ACCESS 19Valid Accounts 20Discovering Default Accounts 20Accessing a List of Default Credentials 21Starting SSH Connections in Python 22Performing Telnet Queries in Python 23Running the Code 24Account Monitoring for Defenders 24INTRODUCTION TO WINDOWS EVENT LOGS 25Accessing Event Logs in Python 28Detecting Failed Logon Attempts 28Identifying Unauthorized Access to Default Accounts 30Running the Code 30Replication Through Removable Media 31Exploiting Autorun 31Converting Python Scripts to Windows Executables 32Generating an Autorun File 33Setting Up the Removable Media 34Running the Code 34Detecting Autorun Scripts 34Identifying Removable Drives 35Finding Autorun Scripts 36Detecting Autorun Processes 36Running the Code 36Summary 37Suggested Exercises 37CHAPTER 3 ACHIEVING CODE EXECUTION 39Windows Management Instrumentation 40Executing Code with WMI 40Creating Processes with WMI 41Launching Processes with PowerShell 41Running the Code 42WMI Event Monitoring for Defenders 42WMI in Windows Event Logs 43Accessing WMI Event Logs in Python 45Processing Event Log XML Data 45Running the Code 46Scheduled Task/Job 47Scheduling Malicious Tasks 47Checking for Scheduled Tasks 48Scheduling a Malicious Task 48Running the Code 49Task Scheduling for Defenders 50Querying Scheduled Tasks 51Identifying Suspicious Tasks 52Running the Code 52Summary 53Suggested Exercises 53CHAPTER 4 MAINTAINING PERSISTENCE 55Boot or Logon Autostart Execution 56Exploiting Registry Autorun 56The Windows Registry and Autorun Keys 57Modifying Autorun Keys with Python 60Running the Code 61Registry Monitoring for Defenders 62Querying Windows Registry Keys 63Searching the HKU Hive 64Running the Code 64Hijack Execution Flow 65Modifying the Windows Path 65Accessing the Windows Path 66Modifying the Path 67Running the Code 68Path Management for Defenders 69Detecting Path Modification via Timestamps 69Enabling Audit Events 71Monitoring Audit Logs 73Running the Code 75Summary 76Suggested Exercises 76CHAPTER 5 PERFORMING PRIVILEGE ESCALATION 77Boot or Logon Initialization Scripts 78Creating Malicious Logon Scripts 78Achieving Privilege Escalation with Logon Scripts 79Creating a Logon Script 79Running the Code 79Searching for Logon Scripts 80Identifying Autorun Keys 81Running the Code 81Hijack Execution Flow 81Injecting Malicious Python Libraries 82How Python Finds Libraries 82Creating a Python Library 83Running the Code 83Detecting Suspicious Python Libraries 83Identifying Imports 85Detecting Duplicates 85Running the Code 86Summary 86Suggested Exercises 87CHAPTER 6 EVADING DEFENSES 89Impair Defenses 90Disabling Antivirus 90Disabling Antivirus Autorun 90Terminating Processes 93Creating Decoy Antivirus Processes 94Catching Signals 95Running the Code 95Hide Artifacts 95Concealing Files in Alternate Data Streams 96Exploring Alternate Data Streams 96Alternate Data Streams in Python 97Running the Code 98Detecting Alternate Data Streams 98Walking a Directory with Python 99Using PowerShell to Detect ADS 100Parsing PowerShell Output 101Running the Code 102Summary 102Suggested Exercises 103CHAPTER 7 ACCESSING CREDENTIALS 105Credentials from Password Stores 106Dumping Credentials from Web Browsers 106Accessing the Chrome Master Key 108Querying the Chrome Login Data Database 108Parsing Output and Decrypting Passwords 109Running the Code 109Monitoring Chrome Passwords 110Enabling File Auditing 110Detecting Local State Access Attempts 111Running the Code 113Network Sniffing 114Sniffing Passwords with scapy 114Port- Based Protocol Identification 116Sniffing FTP Passwords 116Extracting SMTP Passwords 117Tracking Telnet Authentication State 119Running the Code 121Creating Deceptive Network Connections 121Creating Decoy Connections 122Running the Code 122Summary 123Suggested Exercises 123CHAPTER 8 PERFORMING DISCOVERY 125Account Discovery 126Collecting User Account Data 126Identifying Administrator Accounts 127Collecting User Account Information 128Accessing Windows Password Policies 128Running the Code 129Monitoring User Accounts 130Monitoring Last Login Times 130Monitoring Administrator Login Attempts 131Running the Code 132File and Directory Discovery 133Identifying Valuable Files and Folders 133Regular Expressions for Data Discovery 135Parsing Different File Formats 135Running the Code 136Creating Honeypot Files and Folders 136Monitoring Decoy Content 136Creating the Decoy Content 137Running the Code 138Summary 138Suggested Exercises 139CHAPTER 9 MOVING LATERALLY 141Remote Services 142Exploiting Windows Admin Shares 142Enabling Full Access to Administrative Shares 143Transferring Files via Administrative Shares 144Executing Commands on Administrative Shares 144Running the Code 144Admin Share Management for Defenders 145Monitoring File Operations 146Detecting Authentication Attempts 147Running the Code 148Use Alternative Authentication Material 148Collecting Web Session Cookies 149Accessing Web Session Cookies 150Running the Code 150Creating Deceptive Web Session Cookies 151Creating Decoy Cookies 151Monitoring Decoy Cookie Usage 153Running the Code 153Summary 154Suggested Exercises 155CHAPTER 10 COLLECTING INTELLIGENCE 157Clipboard Data 158Collecting Data from the Clipboard 158Accessing the Windows Clipboard 159Replacing Clipboard Data 159Running the Code 160Clipboard Management for Defenders 160Monitoring the Clipboard 161Processing Clipboard Messages 161Identifying the Clipboard Owner 161Running the Code 162Email Collection 162Collecting Local Email Data 162Accessing Local Email Caches 163Running the Code 163Protecting Against Email Collection 164Identifying Email Caches 165Searching Archive Files 165Running the Code 166Summary 166Suggested Exercises 166CHAPTER 11 IMPLEMENTING COMMAND AND CONTROL 169Encrypted Channel 170Command and Control Over Encrypted Channels 170Encrypted Channel Client 171Encrypted Channel Server 172Running the Code 173Detecting Encrypted C2 Channels 174Performing Entropy Calculations 175Detecting Encrypted Traffic 175Running the Code 176Protocol Tunneling 176Command and Control via Protocol Tunneling 176Protocol Tunneling Client 177Protocol Tunneling Server 177Running the Code 179Detecting Protocol Tunneling 179Extracting Field Data 181Identifying Encoded Data 181Running the Code 181Summary 182Suggested Exercises 182CHAPTER 12 EXFILTRATING DATA 183Alternative Protocols 184Data Exfiltration Over Alternative Protocols 184Alternative Protocol Client 185Alternative Protocol Server 186Running the Code 188Detecting Alternative Protocols 189Detecting Embedded Data 190Running the Code 191Non- Application Layer Protocols 191Data Exfiltration via Non- Application Layer Protocols 192Non- Application Layer Client 193Non- Application Layer Server 193Running the Code 194Detecting Non- Application Layer Exfiltration 195Identifying Anomalous Type and Code Values 196Running the Code 196Summary 197Suggested Exercises 197CHAPTER 13 ACHIEVING IMPACT 199Data Encrypted for Impact 200Encrypting Data for Impact 200Identifying Files to Encrypt 201Encrypting and Decrypting Files 202Running the Code 202Detecting File Encryption 203Finding Files of Interest 204Calculating File Entropies 204Running the Code 205Account Access Removal 205Removing Access to User Accounts 205Changing Windows Passwords 207Changing Linux Passwords 207Running the Code 207Detecting Account Access Removal 208Detecting Password Changes in Windows 209Detecting Password Changes in Linux 210Running the Code 211Summary 211Suggested Exercises 212Index 213
The Political Philosophy of AI
Political issues people care about such as racism, climate change, and democracy take on new urgency and meaning in the light of technological developments such as AI. How can we talk about the politics of AI while moving beyond mere warnings and easy accusations?This is the first accessible introduction to the political challenges related to AI. Using political philosophy as a unique lens through which to explore key debates in the area, the book shows how various political issues are already impacted by emerging AI technologies: from justice and discrimination to democracy and surveillance. Revealing the inherently political nature of technology, it offers a rich conceptual toolbox that can guide efforts to deal with the challenges raised by what turns out to be not only artificial intelligence but also artificial power.This timely and original book will appeal to students and scholars in philosophy of technology and political philosophy, as well as tech developers, innovation leaders, policy makers, and anyone interested in the impact of technology on society.MARK COECKELBERGH is Professor of Philosophy of Media and Technology at the University of Vienna. Acknowledgements1 Introduction2 Freedom: Manipulation by AI and Robot Slavery3 Equality and Justice: Bias and Discrimination by AI4 Democracy: Echo Chambers and Machine Totalitarianism5 Power: Surveillance and (Self-)disciplining by Data6 What about Non-Humans? Environmental Politics and Posthumanism7 Conclusion: Political TechnologiesReferencesIndex
C++ Software Interoperability for Windows Programmers
Get up-to-speed quickly and connect modern code written in C#, R, and Python to an existing codebase written in C++. This book for practitioners is about software interoperability in a Windows environment from C++ to languages such as C#, R, and Python. Using a series of example projects, the book demonstrates how to connect a simple C++ codebase packaged as a static or dynamic library to modern clients written in C#, R, and Python. The book shows you how to develop the in-between components that allow disparate languages to communicate.This book addresses a fundamental question in software design: given an existing C++ codebase, how does one go about connecting that codebase to clients written in C#, R, and Python? How is the C++ functionality exposed to these clients? One answer may be to rewrite the existing codebase in the target language. This is rarely, if ever, feasible and this book’s goal is to save you the pain and the high cost of throwing out valuable existing code by showing you how to make that older code function alongside and with the more modern languages that are commonly in use today. The knowledge you will gain from reading this book will help you broaden your architectural choices and take advantage of the growing amount of talent around newer languages.WHAT YOU WILL LEARN* Build components that connect C++ to other languages* Translate between the C++ type system and the type systems of C#, R, and Python* Write a managed assembly targeting the .NET framework* Create C++ packages for use in R/Studio* Develop Python modules based on high-performance C++ code* Overcome the difficulties and pitfalls involved in cross-language developmentWHO THIS BOOK IS FORSoftware developers who are looking for ways to extend existing systems written in C++ using modern languages. Readers should have some programming experience, particularly in C++. Readers should also be familiar with common development tools such as Visual Studio, R/Studio, Visual Studio Code, and CodeBlocks.ADAM GLADSTONE is a software developer with more than 20 years of experience in investment banking, building software mostly in C++ and C#. For the last few years, he has been developing data science and machine learning skills, particularly in Python and R after completing a degree in Math & Statistics. He currently works at Virtu Financial Inc. in Madrid as an Analyst Programmer. In his free time, he develops tools for natural language processing.IntroductionPART I. C++1. Preliminaries2. C++ Components and C++ ClientsPART II. C++/CLI AND .NET3. Building a C++/CLI Wrapper4. C# Clients: Consuming the Managed WrapperPART III. R AND RCPP5. Building an R Package6. Exposing Functions using RcppPART IV. PYTHON7. Building a Python Extension Module8. Module Development with Boost and PyBind9. ConclusionPART V. APPENDIXESA. Boost LibrariesB. Cmake
Artificial Intelligence for Renewable Energy Systems
ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMSRENEWABLE ENERGY SYSTEMS, INCLUDING SOLAR, WIND, BIODIESEL, HYBRID ENERGY, AND OTHER RELEVANT TYPES, HAVE NUMEROUS ADVANTAGES COMPARED TO THEIR CONVENTIONAL COUNTERPARTS. THIS BOOK PRESENTS THE APPLICATION OF MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR RENEWABLE ENERGY SYSTEM MODELING, FORECASTING, AND OPTIMIZATION FOR EFFICIENT SYSTEM DESIGN.Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. AUDIENCEThe primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology. AJAY KUMAR VYAS, PHD is an assistant professor at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has authored several research papers in peer-reviewed international journals and conferences, three books, and two Indian patents.S. BALAMURUGAN, PHD SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. KAMAL KANT HIRAN, PHD is an assistant professor at the School of Engineering, Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India, as well as a research fellow at the Aalborg University, Copenhagen, Denmark. He has published more than 35 scientific research papers in SCI/Scopus/Web of Science and IEEE Transactions Journal, conferences, two Indian patents, one Australian patent granted, and nine books. HARSH S. DHIMAN, PHD is an assistant professor in the Department of Electrical Engineering at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has published 12 SCI-indexed journal articles and two books, and his research interests include hybrid operation of wind farms, hybrid wind forecasting techniques, and anomaly detection in wind turbines. Preface xi1 ANALYSIS OF SIX-PHASE GRID CONNECTED SYNCHRONOUS GENERATOR IN WIND POWER GENERATION 1Arif Iqbal and Girish Kumar Singh1.1 Introduction 21.2 Analytical Modeling of Six-Phase Synchronous Machine 41.2.1 Voltage Equation 51.2.2 Equations of Flux Linkage Per Second 51.3 Linearization of Machine Equations for Stability Analysis 101.4 Dynamic Performance Results 121.5 Stability Analysis Results 151.5.1 Parametric Variation of Stator 161.5.2 Parametric Variation of Field Circuit 191.5.3 Parametric Variation of Damper Winding, Kd221.5.4 Parametric Variation of Damper Winding, Kq241.5.5 Magnetizing Reactance Variation Along q-axis 261.5.6 Variation in Load 281.6 Conclusions 29References 30Appendix 31Symbols Meaning 322 ARTIFICIAL INTELLIGENCE AS A TOOL FOR CONSERVATION AND EFFICIENT UTILIZATION OF RENEWABLE RESOURCE 37Vinay N., Ajay Sudhir Bale, Subhashish Tiwari and Baby Chithra R.2.1 Introduction 382.2 AI in Water Energy 392.2.1 Prediction of Groundwater Level 392.2.2 Rainfall Modeling 462.3 AI in Solar Energy 472.3.1 Solar Power Forecasting 472.4 AI in Wind Energy 532.4.1 Wind Monitoring 532.4.2 Wind Forecasting 542.5 AI in Geothermal Energy 552.6 Conclusion 60References 613 ARTIFICIAL INTELLIGENCE–BASED ENERGY-EFFICIENT CLUSTERING AND ROUTING IN IOT-ASSISTED WIRELESS SENSOR NETWORK 79Nitesh Chouhan3.1 Introduction 803.2 Related Study 813.3 Clustering in WSN 843.4 Research Methodology 853.4.1 Creating Wireless Sensor–Based IoT Environment 853.4.2 Clustering Approach 863.4.3 AI-Based Energy-Aware Routing Protocol 873.5 Conclusion 89References 894 ARTIFICIAL INTELLIGENCE FOR MODELING AND OPTIMIZATION OF THE BIOGAS PRODUCTION 93Narendra Khatri and Kamal Kishore Khatri4.1 Introduction 934.2 Artificial Neural Network 964.2.1 ANN Architecture 964.2.2 Training Algorithms 984.2.3 Performance Parameters for Analysis of the ANN Model 984.2.4 Application of ANN for Biogas Production Modeling 994.3 Evolutionary Algorithms 1034.3.1 Genetic Algorithm 1034.3.2 Ant Colony Optimization 1044.3.3 Particle Swarm Optimization 1064.3.4 Application of Hybrid Models (ANN and Evolutionary Algorithms) for Biogas Production Modeling 1064.4 Conclusion 107References 1115 BATTERY STATE-OF-CHARGE MODELING FOR SOLAR PV ARRAY USING POLYNOMIAL REGRESSION 115Siddhi Vinayak Pandey, Jeet Patel and Harsh S. Dhiman5.1 Introduction 1155.2 Dynamic Battery Modeling 1195.2.1 Proposed Methodology 1205.3 Results and Discussion 1225.4 Conclusion 126References 1276 DEEP LEARNING ALGORITHMS FOR WIND FORECASTING: AN OVERVIEW 129M. Lydia and G. Edwin Prem KumarNomenclature 1296.1 Introduction 1316.2 Models for Wind Forecasting 1336.2.1 Persistence Model 1336.2.2 Point vs. Probabilistic Forecasting 1336.2.3 Multi-Objective Forecasting 1346.2.4 Wind Power Ramp Forecasting 1346.2.5 Interval Forecasting 1346.2.6 Multi-Step Forecasting 1346.3 The Deep Learning Paradigm 1356.3.1 Batch Learning 1366.3.2 Sequential Learning 1366.3.3 Incremental Learning 1366.3.4 Scene Learning 1366.3.5 Transfer Learning 1366.3.6 Neural Structural Learning 1366.3.7 Multi-Task Learning 1376.4 Deep Learning Approaches for Wind Forecasting 1376.4.1 Deep Neural Network 1376.4.2 Long Short-Term Memory 1386.4.3 Extreme Learning Machine 1386.4.4 Gated Recurrent Units 1396.4.5 Autoencoders 1396.4.6 Ensemble Models 1396.4.7 Other Miscellaneous Models 1396.5 Research Challenges 1396.6 Conclusion 141References 1427 DEEP FEATURE SELECTION FOR WIND FORECASTING-I 147C. Ramakrishnan, S. Sridhar, Kusumika Krori Dutta, R. Karthick and C. Janamejaya7.1 Introduction 1487.2 Wind Forecasting System Overview 1527.2.1 Classification of Wind Forecasting 1537.2.2 Wind Forecasting Methods 1537.2.2.1 Physical Method 1547.2.2.2 Statistical Method 1547.2.2.3 Hybrid Method 1557.2.3 Prediction Frameworks 1557.2.3.1 Pre-Processing of Data 1557.2.3.2 Data Feature Analysis 1567.2.3.3 Model Formulation 1567.2.3.4 Optimization of Model Structure 1567.2.3.5 Performance Evaluation of Model 1577.2.3.6 Techniques Based on Methods of Forecasting 1577.3 Current Forecasting and Prediction Methods 1587.3.1 Time Series Method (TSM) 1597.3.2 Persistence Method (PM) 1597.3.3 Artificial Intelligence Method 1607.3.4 Wavelet Neural Network 1617.3.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) 1627.3.6 ANFIS Architecture 1637.3.7 Support Vector Machine (SVM) 1657.3.8 Ensemble Forecasting 1667.4 Deep Learning–Based Wind Forecasting 1667.4.1 Reducing Dimensionality 1687.4.2 Deep Learning Techniques and Their Architectures 1697.4.3 Unsupervised Pre-Trained Networks 1697.4.4 Convolutional Neural Networks 1707.4.5 Recurrent Neural Networks 1707.4.6 Analysis of Support Vector Machine and Decision Tree Analysis (With Computation Time) 1707.4.7 Tree-Based Techniques 1727.5 Case Study 173References 1768 DEEP FEATURE SELECTION FOR WIND FORECASTING-II 181S. Oswalt Manoj, J.P. Ananth, Balan Dhanka and Maharaja Kamatchi8.1 Introduction 1828.1.1 Contributions of the Work 1848.2 Literature Review 1858.3 Long Short-Term Memory Networks 1868.4 Gated Recurrent Unit 1908.5 Bidirectional Long Short-Term Memory Networks 1948.6 Results and Discussion 1968.7 Conclusion and Future Work 197References 1989 DATA FALSIFICATION DETECTION IN AMI: A SECURE PERSPECTIVE ANALYSIS 201Vineeth V.V. and S. Sophia9.1 Introduction 2019.2 Advanced Metering Infrastructure 2029.3 AMI Attack Scenario 2049.4 Data Falsification Attacks 2059.5 Data Falsification Detection 2069.6 Conclusion 207References 20810 FORECASTING OF ELECTRICITY CONSUMPTION FOR G20 MEMBERS USING VARIOUS MACHINE LEARNING TECHNIQUES 211Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta and Akshay Srivastava10.1 Introduction 21110.1.1 Why Electricity Consumption Forecasting Is Required? 21210.1.2 History and Advancement in Forecasting of Electricity Consumption 21210.1.3 Recurrent Neural Networks 21310.1.3.1 Long Short-Term Memory 21410.1.3.2 Gated Recurrent Unit 21410.1.3.3 Convolutional LSTM 21510.1.3.4 Bidirectional Recurrent Neural Networks 21610.1.4 Other Regression Techniques 21610.2 Dataset Preparation 21710.3 Results and Discussions 21810.4 Conclusion 225Acknowledgement 225References 22511 USE OF ARTIFICIAL INTELLIGENCE (AI) IN THE OPTIMIZATION OF PRODUCTION OF BIODIESEL ENERGY 229Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini and Dinesh Kumar Saini11.1 Introduction 23011.2 Indian Perspective of Renewable Biofuels 23011.3 Opportunities 23211.4 Relevance of Biodiesel in India Context 23311.5 Proposed Model 23411.6 Conclusion 236References 237Index 239
Beginning Deep Learning with TensorFlow
Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners.You’ll start with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs.Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!WHAT YOU'LL LEARN* Develop using deep learning algorithms* Build deep learning models using TensorFlow 2* Create classification systems and other, practical deep learning applicationsWHO THIS BOOK IS FORStudents, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.LIANGQU LONG is a well-known deep learning educator and engineer in China. He is a successfully published author in the topic area with years of experience in teaching machine learning concepts. His two online video tutorial courses “Deep Learning with PyTorch” and “Deep Learning with TensorFlow 2” have received massive positive comments and allowed him to refine his deep learning teaching methods.XIANGMING ZENG is an experienced data scientist and machine learning practitioner. He has over ten years of experience using machine learning and deep learning models to solve real world problems in both academia and professionally. Xiangming is familiar with deep learning fundamentals and mainstream machine learning libraries such as Tensorflow and scikit-learn.Part 1 Introduction to AI1. Introduction1. Artificial Intelligence2. History of Neural Networks3. Characteristics of Deep Learning4. Applications of Deep Learning5. Deep Learning Frameworks6. Installation of Development Environment2. Regression2.1 Neuron Model2.2 Optimization Methods2.3 Hands-on Linear Models2.4 Linear Regression3. Classification3.1 Hand-writing Digital Picture Dataset3.2 Build a Classification Model3.3 Compute the Error3.4 Is the Problem Solved?3.5 Nonlinear Model3.6 Model Representation Ability3.7 Optimization Method3.8 Hands-on Hand-written Recognition3.9 SummaryPart 2 Tensorflow4. Tensorflow 2 Basics4.1 Datatype4.2 Numerical Precision4.3 What is a Tensor?4.4 Create a Tensor4.5 Applications of Tensors4.6 Indexing and Slicing4.7 Dimension Change4.8 Broadcasting4.9 Mathematical Operations4.10 Hands-on Forward Propagation Algorithm5. Tensorflow 2 Pro5.1 Aggregation and Seperation5.2 Data Statistics5.3 Tensor Comparison5.4 Fill and Copy5.5 Data Clipping5.6 High-level Operations5.7 Load Classic Datasets5.8 Hands-on MNIST Dataset PracticePart 3 Neural Networks6. Neural Network Introduction6.1 Perception Model6.2 Fully-Connected Layers6.3 Neural Networks6.4 Activation Functions6.5 Output Layer6.6 Error Calculation6.7 Neural Network Categories6.8 Hands-on Gas Consuming Prediction7. Backpropagation Algorithm7.1 Derivative and Gradient7.2 Common Properties of Derivatives7.3 Derivatives of Activation Functions7.4 Gradient of Loss Function7.5 Gradient of Fully-Connected Layers7.6 Chain Rule7.7 Back Propagation Algorithm7.8 Hands-on Himmelblau Function Optimization7.9 Hands-on Back Propagation Algorithm8. Keras Basics8.1 Basic Functionality8.2 Model Configuration, Training and Testing8.3 Save and Load Models8.4 Customized Class8.5 Model Zoo8.6 Metrics8.7 Visualization9. Overfitting9.1 Model Capability9.2 Overfitting and Underfitting9.3 Split the Dataset9.4 Model Design9.5 Regularization9.6 Dropout9.7 Data Enhancement9.8 Hands-on OverfittingPart 4 Deep Learning Applications10. Convolutional Neural Network10.1 Problem of Fully-Connected Layers10.2 Convolutional Neural Network10.3 Convolutional Layer10.4 Hands-on LeNet-510.5 Representation Learning10.6 Gradient Propagation10.7 Pooling Layer10.8 BatchNorm Layer10.9 Classical Convolutional Neural Network10.10 Hands-on CIFRA10 and VGG1310.11 Variations of Convolutional Neural Network10.12 Deep Residual Network10.13 DenseNet10.14 Hands-on CIFAR10 and ResNet1811. Recurrent Neural Network11.1 Time Series11.2 Recurrent Neural Network (RNN)11.3 Gradient Propagation11.4 RNN Layer11.5 Hands-on RNN Sentiment Classification11.6 Gradient Vanishing and Exploding11.7 RNN Short Memory11.8 LSTM Principle11.9 LSTM Layer11.10 GRU Basics11.11 Hands-on Sentiment Classification with LSTM/GRU11.12 Pre-trained Word Vectors12. Auto-Encoders12.1 Basics of Auto-Encoders12.2 Hands-on Reconstructing MNIST Pictures12.3 Variations of Auto-Encoders12.4 Variational Auto-Encoders (VAE)12.5 Hands-on VAE13. Generative Adversarial Network (GAN)13.1 Examples of Game Theory13.2 GAN Basics13.3 Hands-on DCGAN13.4 Variants of GAN13.5 Nash Equilibrium13.6 Difficulty of Training GAN13.7 WGAN Principle13.8 Hands-on WGAN-GP14. Reinforcement Learning14.1 Introduction14.2 Reinforcement Learning Problem14.3 Policy Gradient Method14.4 Metric Function Method14.5 Actor-Critic Method14.6 Summary15. Custom Dataset Pipeline15.1 Pokémon Go Dataset15.2 Load Customized Dataset15.3 Hands-on Pokémon Go Dataset15.4 Transfer Learning15.5 Save Model15.6 Model DeploymentAudience: Beginner to Intermediate
Data Mining and Machine Learning Applications
DATA MINING AND MACHINE LEARNING APPLICATIONSTHE BOOK ELABORATES IN DETAIL ON THE CURRENT NEEDS OF DATA MINING AND MACHINE LEARNING AND PROMOTES MUTUAL UNDERSTANDING AMONG RESEARCH IN DIFFERENT DISCIPLINES, THUS FACILITATING RESEARCH DEVELOPMENT AND COLLABORATION.Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features:* A review of the state-of-the-art in data mining and machine learning,* A review and description of the learning methods in human-computer interaction,* Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,* The scope and implementation of a majority of data mining and machine learning strategies.* A discussion of real-time problems.AUDIENCE Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly. ROHIT RAJA, PHD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents.KAPIL KUMAR NAGWANSHI, PHD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKM’s Narsee Monjee Institute of Management Studies Mumbai, India. SANDEEP KUMAR, PHD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents. K. RAMYA LAXMI, PHD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing. Preface xvii1 INTRODUCTION TO DATA MINING 1Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar1.1. Introduction 11.1.1 Data Mining 11.2 Knowledge Discovery in Database (KDD) 21.2.1 Importance of Data Mining 31.2.2 Applications of Data Mining 31.2.3 Databases 41.3 Issues in Data Mining 61.4 Data Mining Algorithms 71.5 Data Warehouse 91.6 Data Mining Techniques 101.7 Data Mining Tools 111.7.1 Python for Data Mining 121.7.2 KNIME 131.7.3 Rapid Miner 17References 182 CLASSIFICATION AND MINING BEHAVIOR OF DATA 21Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala2.1 Introduction 222.2 Main Characteristics of Mining Behavioral Data 232.2.1 Mining Dynamic/Streaming Data 232.2.2 Mining Graph & Network Data 242.2.3 Mining Heterogeneous/Multi-Source Information 252.2.3.1 Multi-Source and Multidimensional Information 262.2.3.2 Multi-Relational Data 262.2.3.3 Background and Connected Data 272.2.3.4 Complex Data, Sequences, and Events 272.2.3.5 Data Protection and Morals 272.2.4 Mining High Dimensional Data 282.2.5 Mining Imbalanced Data 292.2.5.1 The Class Imbalance Issue 292.2.6 Mining Multimedia Data 302.2.6.1 Common Applications Multimedia Data Mining 312.2.6.2 Multimedia Data Mining Utilizations 312.2.6.3 Multimedia Database Management 322.2.7 Mining Scientific Data 342.2.8 Mining Sequential Data 352.2.9 Mining Social Networks 362.2.9.1 Social-Media Data Mining Reasons 392.2.10 Mining Spatial and Temporal Data 402.2.10.1 Utilizations of Spatial and Temporal Data Mining 412.3 Research Method 442.4 Results 482.5 Discussion 492.6 Conclusion 50References 513 A COMPARATIVE OVERVIEW OF HYBRID RECOMMENDER SYSTEMS: REVIEW, CHALLENGES, AND PROSPECTS 57Rakhi Seth and Aakanksha Sharaff3.1 Introduction 583.2 Related Work on Different Recommender System 603.2.1 Challenges in RS 653.2.2 Research Questions and Architecture of This Paper 663.2.3 Background 683.2.3.1 The Architecture of Hybrid Approach 693.2.4 Analysis 783.2.4.1 Evaluation Measures 783.2.5 Materials and Methods 813.2.6 Comparative Analysis With Traditional Recommender System 853.2.7 Practical Implications 853.2.8 Conclusion & Future Work 94References 944 STREAM MINING: INTRODUCTION, TOOLS & TECHNIQUES AND APPLICATIONS 99Naresh Kumar Nagwani4.1 Introduction 1004.2 Data Reduction: Sampling and Sketching 1014.2.1 Sampling 1014.2.2 Sketching 1024.3 Concept Drift 1034.4 Stream Mining Operations 1054.4.1 Clustering 1054.4.2 Classification 1064.4.3 Outlier Detection 1074.4.4 Frequent Itemsets Mining 1084.5 Tools & Techniques 1094.5.1 Implementation in Java 1104.5.2 Implementation in Python 1164.5.3 Implementation in R 1184.6 Applications 1204.6.1 Stock Prediction in Share Market 1204.6.2 Weather Forecasting System 1214.6.3 Finding Trending News and Events 1214.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 1214.6.5 Pollution Control Systems 1224.7 Conclusion 122References 1225 DATA MINING TOOLS AND TECHNIQUES: CLUSTERING ANALYSIS 125Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja5.1 Introduction 1265.2 Data Mining Task 1295.2.1 Data Summarization 1295.2.2 Data Clustering 1295.2.3 Classification of Data 1295.2.4 Data Regression 1305.2.5 Data Association 1305.3 Data Mining Algorithms and Methodologies 1315.3.1 Data Classification Algorithm 1315.3.2 Predication 1325.3.3 Association Rule 1325.3.4 Neural Network 1325.3.4.1 Data Clustering Algorithm 1335.3.5 In-Depth Study of Gathering Techniques 1345.3.6 Data Partitioning Method 1345.3.7 Hierarchical Method 1345.3.8 Framework-Based Method 1365.3.9 Model-Based Method 1365.3.10 Thickness-Based Method 1365.4 Clustering the Nearest Neighbor 1365.4.1 Fuzzy Clustering 1375.4.2 K-Algorithm Means 1375.5 Data Mining Applications 1385.6 Materials and Strategies for Document Clustering 1405.6.1 Features Generation 1425.7 Discussion and Results 1435.7.1 Discussion 1465.7.2 Conclusion 149References 1496 DATA MINING IMPLEMENTATION PROCESS 151Kamal K. Mehta, Rajesh Tiwari and Nishant Behar6.1 Introduction 1516.2 Data Mining Historical Trends 1526.3 Processes of Data Analysis 1536.3.1 Data Attack 1536.3.2 Data Mixing 1536.3.3 Data Collection 1536.3.4 Data Conversion 1546.3.4.1 Data Mining 1546.3.4.2 Design Evaluation 1546.3.4.3 Data Illustration 1546.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 1546.3.5 Business Understanding 1556.3.6 Data Understanding 1566.3.7 Data Preparation 1586.3.8 Modeling 1596.3.9 Evaluation 1606.3.10 Deployment 1616.3.11 Contemporary Developments 1626.3.12 An Assortment of Data Mining 1626.3.12.1 Using Computational & Connectivity Tools 1636.3.12.2 Web Mining 1636.3.12.3 Comparative Statement 1636.3.13 Advantages of Data Mining 1636.3.14 Drawbacks of Data Mining 1656.3.15 Data Mining Applications 1656.3.16 Methodology 1676.3.17 Results 1696.3.18 Conclusion and Future Scope 171References 1727 PREDICTIVE ANALYTICS IN IT SERVICE MANAGEMENT (ITSM) 175Sharon Christa I.L. and Suma V.7.1 Introduction 1767.2 Analytics: An Overview 1787.2.1 Predictive Analytics 1807.3 Significance of Predictive Analytics in ITSM 1817.4 Ticket Analytics: A Case Study 1867.4.1 Input Parameters 1887.4.2 Predictive Modeling 1887.4.3 Random Forest Model 1897.4.4 Performance of the Predictive Model 1917.5 Conclusion 191References 1928 MODIFIED CROSS-SELL MODEL FOR TELECOM SERVICE PROVIDERS USING DATA MINING TECHNIQUES 195K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan8.1 Introduction 1968.2 Literature Review 1988.3 Methodology and Implementation 2008.3.1 Selection of the Independent Variables 2008.4 Data Partitioning 2038.4.1 Interpreting the Results of Logistic Regression Model 2038.5 Conclusions 204References 2059 INDUCTIVE LEARNING INCLUDING DECISION TREE AND RULE INDUCTION LEARNING 209Raj Kumar Patra, A. Mahendar and G. Madhukar9.1 Introduction 2109.2 The Inductive Learning Algorithm (ILA) 2129.3 Proposed Algorithms 2139.4 Divide & Conquer Algorithm 2149.4.1 Decision Tree 2149.5 Decision Tree Algorithms 2159.5.1 ID3 Algorithm 2159.5.2 Separate and Conquer Algorithm 2179.5.3 RULE EXTRACTOR-1 2269.5.4 Inductive Learning Applications 2269.5.4.1 Education 2269.5.4.2 Making Credit Decisions 2279.5.5 Multidimensional Databases and OLAP 2289.5.6 Fuzzy Choice Trees 2289.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 2299.5.8 Execution and Results 2309.6 Conclusion and Future Work 231References 23210 DATA MINING FOR CYBER-PHYSICAL SYSTEMS 235M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar10.1 Introduction 23610.1.1 Models of Cyber-Physical System 23810.1.2 Statistical Model-Based Methodologies 23910.1.3 Spatial-and-Transient Closeness-Based Methodologies 24010.2 Feature Recovering Methodologies 24010.3 CPS vs. IT Systems 24110.4 Collections, Sources, and Generations of Big Data for CPS 24210.4.1 Establishing Conscious Computation and Information Systems 24310.5 Spatial Prediction 24310.5.1 Global Optimization 24410.5.2 Big Data Analysis CPS 24510.5.3 Analysis of Cloud Data 24510.5.4 Analysis of Multi-Cloud Data 24710.6 Clustering of Big Data 24810.7 NoSQL 25110.8 Cyber Security and Privacy Big Data 25110.8.1 Protection of Big Computing and Storage 25210.8.2 Big Data Analytics Protection 25210.8.3 Big Data CPS Applications 25610.9 Smart Grids 25610.10 Military Applications 25810.11 City Management 25910.12 Clinical Applications 26110.13 Calamity Events 26210.14 Data Streams Clustering by Sensors 26310.15 The Flocking Model 26310.16 Calculation Depiction 26410.17 Initialization 26510.18 Representative Maintenance and Clustering 26610.19 Results 26710.20 Conclusion 268References 26911 DEVELOPING DECISION MAKING AND RISK MITIGATION: USING CRISP-DATA MINING 281Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma11.1 Introduction 28211.2 Background 28311.3 Methodology of CRISP-DM 28411.4 Stage One—Determine Business Objectives 28611.4.1 What Are the Ideal Yields of the Venture? 28711.4.2 Evaluate the Current Circumstance 28811.4.3 Realizes Data Mining Goals 28911.5 Stage Two—Data Sympathetic 29011.5.1 Portray Data 29111.5.2 Investigate Facts 29111.5.3 Confirm Data Quality 29211.5.4 Data Excellence Description 29211.6 Stage Three—Data Preparation 29211.6.1 Select Your Data 29411.6.2 The Data Is Processed 29411.6.3 Data Needed to Build 29411.6.4 Combine Information 29511.7 Stage Four—Modeling 29511.7.1 Select Displaying Strategy 29611.7.2 Produce an Investigation Plan 29711.7.3 Fabricate Ideal 29711.7.4 Evaluation Model 29711.8 Stage Five—Evaluation 29811.8.1 Assess Your Outcomes 29911.8.2 Survey Measure 29911.8.3 Decide on the Subsequent Stages 30011.9 Stage Six—Deployment 30011.9.1 Plan Arrangement 30111.9.2 Plan Observing and Support 30111.9.3 Produce the Last Report 30211.9.4 Audit Venture 30211.10 Data on ERP Systems 30211.11 Usage of CRISP-DM Methodology 30411.12 Modeling 30611.12.1 Association Rule Mining (ARM) or Association Analysis 30711.12.2 Classification Algorithms 30711.12.3 Regression Algorithms 30811.12.4 Clustering Algorithms 30811.13 Assessment 31011.14 Distribution 31011.15 Results and Discussion 31011.16 Conclusion 311References 31412 HUMAN–MACHINE INTERACTION AND VISUAL DATA MINING 317Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain12.1 Introduction 31812.2 Related Researches 32012.2.1 Data Mining 32312.2.2 Data Visualization 32312.2.3 Visual Learning 32412.3 Visual Genes 32512.4 Visual Hypotheses 32612.5 Visual Strength and Conditioning 32612.6 Visual Optimization 32712.7 The Vis 09 Model 32712.8 Graphic Monitoring and Contact With Human–Computer 32812.9 Mining HCI Information Using Inductive Deduction Viewpoint 33212.10 Visual Data Mining Methodology 33412.11 Machine Learning Algorithms for Hand Gesture Recognition 33812.12 Learning 33812.13 Detection 33912.14 Recognition 34012.15 Proposed Methodology for Hand Gesture Recognition 34012.16 Result 34312.17 Conclusion 343References 34413 MSDTRA: A BOOSTING BASED-TRANSFER LEARNING APPROACH FOR CLASS IMBALANCED SKIN LESION DATASET FOR MELANOMA DETECTION 349Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu13.1 Introduction 34913.2 Literature Survey 35213.3 Methods and Material 35313.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 35513.4 Experimental Results 35713.5 Libraries Used 35713.6 Comparing Algorithms Based on Decision Boundaries 35713.7 Evaluating Results 35813.8 Conclusion 361References 36114 NEW ALGORITHMS AND TECHNOLOGIES FOR DATA MINING 365Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel14.1 Introduction 36614.2 Machine Learning Algorithms 36814.3 Supervised Learning 36814.4 Unsupervised Learning 36914.5 Semi-Supervised Learning 36914.6 Regression Algorithms 37114.7 Case-Based Algorithms 37114.8 Regularization Algorithms 37214.9 Decision Tree Algorithms 37214.10 Bayesian Algorithms 37314.11 Clustering Algorithms 37414.12 Association Rule Learning Algorithms 37514.13 Artificial Neural Network Algorithms 37514.14 Deep Learning Algorithms 37614.15 Dimensionality Reduction Algorithms 37714.16 Ensemble Algorithms 37714.17 Other Machine Learning Algorithms 37814.18 Data Mining Assignments 37814.19 Data Mining Models 38114.20 Non-Parametric & Parametric Models 38114.21 Flexible vs. Restrictive Methods 38214.22 Unsupervised vs. Supervised Learning 38214.23 Data Mining Methods 38414.24 Proposed Algorithm 38714.24.1 Organization Formation Procedure 38714.25 The Regret of Learning Phase 38814.26 Conclusion 392References 39215 CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURE USING RESTRICTED BOLTZMANN MACHINE CLASSIFIER 397Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu15.1 Introduction 39815.2 Related Work 40015.3 Material and Methods 40115.3.1 Dataset Description 40115.3.2 Proposed Methodology 40315.3.3 Normalization 40415.3.4 Preprocessing Using PCA 40415.3.5 Restricted Boltzmann Machine (RBM) 40615.3.6 Stochastic Binary Units (Bernoulli Variables) 40715.3.7 Training 40815.3.7.1 Gibbs Sampling 40915.3.7.2 Contrastive Divergence (CD) 40915.4 Experimental Framework 41015.5 Experimental Results and Discussion 41215.5.1 Performance Measurement Criteria 41215.5.2 Experimental Results 41215.6 Discussion 41415.7 Conclusion 418References 41916 AN ENHANCED SECURITY OF WOMEN AND CHILDREN USING MACHINE LEARNING AND DATA MINING TECHNIQUES 423Nanda R. Wagh and Sanjay R. Sutar16.1 Introduction 42416.2 Related Work 42416.2.1 WoSApp 42416.2.2 Abhaya 42516.2.3 Women Empowerment 42516.2.4 Nirbhaya 42516.2.5 Glympse 42616.2.6 Fightback 42616.2.7 Versatile-Based 42616.2.8 RFID 42616.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 42616.2.10 Safe: A Women Security Framework 42716.2.11 Intelligent Safety System For Women Security 42716.2.12 A Mobile-Based Women Safety Application 42716.2.13 Self-Salvation—The Women’s Security Module 42716.3 Issue and Solution 42716.3.1 Inspiration 42716.3.2 Issue Statement and Choice of Solution 42816.4 Selection of Data 42816.5 Pre-Preparation Data 43016.5.1 Simulation 43116.5.2 Assessment 43116.5.3 Forecast 43416.6 Application Development 43616.6.1 Methodology 43616.6.2 AI Model 43716.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 43716.7 Use Case For The Application 43716.7.1 Application Icon 43716.7.2 Enlistment Form 43816.7.3 Login Form 43916.7.4 Misconduct Place Detector 43916.7.5 Help Button 44016.8 Conclusion 443References 44317 CONCLUSION AND FUTURE DIRECTION IN DATA MINING AND MACHINE LEARNING 447Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar17.1 Introduction 44817.2 Machine Learning 45117.2.1 Neural Network 45217.2.2 Deep Learning 45217.2.3 Three Activities for Object Recognition 45317.3 Conclusion 457References 457Index 461
Beginning Scala 3
Learn the latest version of Scala through simple, practical examples. This book introduces you to the Scala programming language, its object-oriented and functional programming characteristics, and then guides you through Scala constructs and libraries that allow you to assemble small components into high-performance, scalable systems.Beginning Scala 3 explores new Scala 3 language features such as Top-level declarations, Creator applications, Extension methods to add extra functionality to existing types, and Enums. You will also learn new ways to manipulate types via Union types, intersection, literal, and opaque type aliases. Additionally, you’ll see how Implicits are replaced by given and using clauses.After reading this book, you will understand why Scala is judiciously used for critical business applications by leading companies such as Twitter, LinkedIn, Foursquare, the Guardian, Morgan Stanley, Credit Suisse, UBS, and HSBC – and you will be able to use it in your own projects.WHAT YOU WILL LEARN* Get started with Scala 3 or Scala language programming in general* Understand how to utilitze OOP in Scala* Perform functional programming in Scala* Master the use of Scala collections, traits and implicits* Leverage Java and Scala interopability* Employ Scala for DSL programming* Use patterns and best practices in ScalaWHO THIS BOOK IS FORThose with a background in Java and/or Kotlin who are new to Scala. This book is also for those with some prior Scala experience who want to learn Scala version 3.DAVID POLLAK has been writing commercial software since 1977. He wrote the award-winning Mesa spreadsheet, which in 1992 was the first real-time spreadsheet. Wall Street companies traded billions of dollars a day through Mesa. In 1996, David sold his company to CMP Media and became CTO of CMP Media's NetGuide Live and was one of the first large-scale users of Java and WebLogic to power an Internet site. In 1998, David released Integer, the world's first browser-accessible, multiuser spreadsheet. Since 2000, David has been consulting for companies including Hewlett-Packard, Pretzel Logic/WebGain, BankServ, Twitter, and SAP. David has been using Scala since 2006 and is the lead developer of the Lift Web framework.VISHAL LAYKA is the chief technology officer of Star Protocol. He is involved in the architecture, design, and implementation of distributed business systems, and his focus is on consulting and training with the JVM languages. His language proficiencies include Java, Groovy, Scala, and Haskell. Vishal is also the lead author of Beginning Groovy, Grails, and Griffon (Apress, 2012). When he needs a break from technology, Vishal reads eclectically from calculus to star formation.ANDRES SACCO has been a professional developer since 2007, working with a variety of languages, including Java, Scala, PHP, NodeJs, and Kotlin. Most of his background is in Java and the libraries or frameworks associated with it, but since 2016, he has utilized Scala as well, depending on the situation. He is focused on researching new technologies to improve the performance, stability, and quality of the applications he develops.BEGINNING SCALA 3 (3E)1. Getting started with Scala2. Basics of Scala3. OOP in Scala4. Functional programming in Scala5. Pattern matching6. Scala Collections7. Traits8. Types and Implicits9. Scala and Java Interoperability10. SBT11. Building web applications with Scala12. DSL13. Scala Best practices
Learn JavaFX 17
This unique in-depth tutorial shows you how to start developing rich-client desktop applications using your Java skills and provides comprehensive coverage of JavaFX 17's features. Each chapter starts with an introduction to the topic at hand, followed by a step-by-step discussion of the topic with small snippets of code. The book contains numerous figures aiding readers in visualizing the GUI that is built at every step in the discussion. This book has been revised to include JavaFX 17 and earlier releases since previous edition.It starts with an introduction to JavaFX and its history. It lists the system requirements and the steps to start developing JavaFX applications. It shows you how to create a Hello World application in JavaFX, explaining every line of code in the process. Later in the book, authors Kishori Sharan and Peter Späth discuss advanced topics such as 2D and 3D graphics, charts, FXML, advanced controls, and printing. Some of the advanced controls such as TableView, and WebView are covered at length in separate chapters.This book provides complete and comprehensive coverage of JavaFX 17 features; uses an incremental approach to teach JavaFX, assuming no prior GUI knowledge; includes code snippets, complete programs, and pictures; covers MVC patterns using JavaFX; and covers advanced topics such as FXML, effects, transformations, charts, images, canvas, audio and video, DnD, and more. So, after reading and using this book, you'll come away with a comprehensive introduction to the JavaFX APIs.WHAT YOU WILL LEARN* How to build JavaFX User Interfaces and Java clients* What are properties, bindings, observable collections, stages, scenes; how to use these* How to play with colors, styling nodes and event handling* How to add user interactivity (mouse, keyboard, DnD)* How to do tables, trees and tree tables* How to do 2D shapes, text nodes, 3D shapes* How to apply effects, transformations, animations, images* How to draw; play audio and videoWHO IS THIS BOOK FOR:Developers new to the JavaFX platform. Some prior Java experience is recommended.KISHORI SHARAN has earned a Master of Science in Computer Information Systems degree from Troy State University, Alabama. He is a Sun Certified Java 2 programmer. He has vast experience in providing training to professional developers in Java, JSP, EJB, and Web technology. He possesses over ten years of experience in implementing enterprise level Java application.PETER SPÄTH graduated in 2002 as a physicist and soon afterward became an IT consultant, mainly for Java-related projects. In 2016, he decided to concentrate on writing books on various aspects, but with a main focus on software development. With two books about graphics and sound processing, three books on Android app development, and a beginner’s book on Jakarta EE development, the author continues his effort in writing software development-related literature.Chapter 1. Getting Started with JavaFXChapter 2. Properties and BindingsChapter 3. Observable CollectionsChapter 4. Managing StagesChapter 5. Making ScenesChapter 6. Understanding NodesChapter 7. Playing with ColorsChapter 8. Styling NodesChapter 9. Event HandlingChapter 10. Understanding Layout PanesChapter 11. Model-View-Controller PatternChapter 12. Understanding ControlsChapter 13. Understanding TableViewChapter 14. Understanding TreeViewChapter 15. Understanding TreeTableViewChapter 16. Browsing Web PagesChapter 17. Understanding 2D ShapesChapter 18. Understanding Text NodesChapter 19. Understanding 3D ShapesChapter 20. Applying EffectsChapter 21. Understanding TransformationsChapter 22. Understanding AnimationChapter 24. Understanding ImagesChapter 25. Drawing on a CanvasChapter 26. Understanding Drag-and-DropChapter 27. Understanding Concurrency in JavaFXChapter 28. Playing Audios and VideosChapter 29. Understanding FXMLChapter 30. Printing
Building Offline Applications with Angular
Get a complete overview of offline installable applications. Businesses need reliable applications that enable users to access data and their applications in spite of a bad network connection.Traditional websites work only when connected to the network. With a large number of users depending on mobile phones and tablets for work, social interactions, and media consumption, it’s important that the web applications can work on a weak network connection and even offline.This step-by-step guide shows you how to build an Angular application that considers offline access and uses its ready-made features and configurations. Build Offline Applications with Angular helps bridge the gap between native apps and web applications.WHAT YOU WILL LEARN* Get started with an installable Angular application* Understand the importance of performant, reliable, and offline access of a web application* Discover solutions for building Angular applications for speedy response in low bandwidth scenarios* Use IndexedDB as an offline data store within a browserWHO IS THIS BOOK FORIdeal for beginner-to-intermediate-level readers with basic understanding of JavaScript and Angular.V Keerti Kotaru has been in software development for almost two decades. He helped design and develop scalable, performant, modern software solutions for multiple clients. He holds a master's degree in software systems from the University of St. Thomas, Minneapolis and St. Paul, USA.He is an author of books on Angular, contributes to the developer community by blogging, writing articles and speaking at technology, events. He wrote for Dotnet Curry (DNC Magazine). He presented technology sessions at AngularJS Hyderabad, AngularJS Chicago and Google Developer Groups at Hyderabad including the annual event Dev Fest. He is a three time Microsoft MVP.Chapter 1: Introduction Build Modern Web Application.- Chapter 2: Getting Started.- Chapter 3: Install Angular Application.- Chapter 4: Service Workers.- Chapter 5: Cache Data with Service Workers .- Chapter 6: Upgrade Applications .- Chapter 7: Introduction to IndexedDB.- Chapter 8: Create Entity - Use case.- Chapter 9: Create Data Offline.- Chapter 10: Dexie.JS for IndexedDB.- Addendum.- Reference.
The CISO Evolution
LEARN TO EFFECTIVELY DELIVER BUSINESS ALIGNED CYBERSECURITY OUTCOMESIn The CISO Evolution: Business Knowledge for Cybersecurity Executives, information security experts Matthew K. Sharp and Kyriakos “Rock” Lambros deliver an insightful and practical resource to help cybersecurity professionals develop the skills they need to effectively communicate with senior management and boards. They assert business aligned cybersecurity is crucial and demonstrate how business acumen is being put into action to deliver meaningful business outcomes.The authors use illustrative stories to show professionals how to establish an executive presence and avoid the most common pitfalls experienced by technology experts when speaking and presenting to executives. The book will show you how to:* Inspire trust in senior business leaders by properly aligning and setting expectations around risk appetite and capital allocation * Properly characterize the indispensable role of cybersecurity in your company’s overall strategic plan * Acquire the necessary funding and resources for your company’s cybersecurity program and avoid the stress and anxiety that comes with underfunding Perfect for security and risk professionals, IT auditors, and risk managers looking for effective strategies to communicate cybersecurity concepts and ideas to business professionals without a background in technology. The CISO Evolution is also a must-read resource for business executives, managers, and leaders hoping to improve the quality of dialogue with their cybersecurity leaders.MATTHEW K. SHARP is Chief Information Security Officer of Logicworks. He is responsible for security governance, risk management, strategy, and architecture in a business that provides comprehensive cloud services to help customers successfully onboard and operate complex and compliant workloads on the AWS and Azure public clouds.KYRIAKOS “ROCK” LAMBROS is CEO and Founder of RockCyber, a cybersecurity strategy consulting firm focused on helping firms align cybersecurity to their enterprise business goals. He has extensive experience building security programs and overseeing security architecture, operations, threat intelligence, governance, and risk management.Foreword ixPreface xiAcknowledgments xvIntroduction 1PART I FOUNDATIONAL BUSINESS KNOWLEDGE 7Chapter 1 Financial Principles 9Chapter 2 Business Strategy Tools 29Chapter 3 Business Decisions 55Chapter 4 Value Creation 91Chapter 5 Articulating the Business Case 129PART II COMMUNICATION AND EDUCATION 167Chapter 6 Cybersecurity: A Concern of the Business, Not Just IT 169Chapter 7 Translating Cyber Risk into Business Risk 197Chapter 8 Communication – You Do It Every Day (or Do You?) 239PART III CYBERSECURITY LEADERSHIP 273Chapter 9 Relationship Management 275Chapter 10 Recruiting and Leading High Performing Teams 307Chapter 11 Managing Human Capital 339Chapter 12 Negotiation 367Conclusion 383Index 385
Pivot im Büroalltag
Pivottabellen werden in der Praxis immer wichtiger. Sie sind inzwischen DAS Wunschthema in jedem Excel-Kurs. Viele Nutzer halten die Erstellung für wahres Hexenwerk - völlig zu Unrecht! In diesem Heft lernen Sie alles zur Erstellung aussagekräftiger und übersichtlicher Pivottabellen aus Excel-Daten: Ausgangsbedingungen, Aggregationen, Datenauswahl, Formatierung, Erstellung eigener Gruppen und Felder, sowie die unerreicht flexiblen Pivotdiagramme.Ina Koys ist langjährige Trainerin für MS-Office-Produkte. Viele Fragen werden in den Kursen immer wieder gestellt, aber selten in Fachbüchern behandelt. Einige davon beantwortet sie jetzt in der Reihe "kurz & knackig".
Autonomy and Independence
THIS BOOK LOOKS AT HOW AGETECH CAN SUPPORT THE AUTONOMY AND INDEPENDENCE OF PEOPLE AS THEY GROW OLDER. The authors challenge readers to reflect on the concepts of autonomy and independence not as absolutes but as experiences situated within older adults’ social connections and environments. Eleven personas of people around the world provide the context for readers to consider the influence of culture and values on how we understand autonomy and independence and the potential role of technology-based supports.The global pandemic provides a backdrop for the unprecedentedly rapid adoption of AgeTech, such as information and communication technologies or mobile applications that benefit older adults. Each persona in the book demonstrates the opportunity for AgeTech to facilitate autonomy and independence in supporting one’s identity, decision making, advance care planning, self care, health management, economic and social participation, enjoyment and self fulfillment and mobility in the community. The book features AgeTech from around the world to provide examples of commercially available products as well as research and development within the field. Despite the promise of AgeTech, the book highlights the “digital divide,” where some older people experience inadequate access to technology due to their geographic location, socio-economic status, and age.This book is accessible and relevant to everyday readers. Older adults will recognize themselves or peers in the personas and may glean insight from the solutions. Care partners and service providers will identify with the challenges of the personas. AgeTech entrepreneurs, especially “seniorpreneurs,” will appreciate that their endeavours represent a growing trend. Researchers will be reminded that the most important research questions are those that will enhance the quality of life of older adults and their sense of autonomy and independence, or relational autonomy and interdependence.* Acknowledgments* Abbreviations* Introduction* Part I: Technology for Autonomy and Independence: An Overview* What is Autonomy and Independence in the Context of Aging in an Era of Technology* International Frameworks on Health and Technology* Part II: How Can Technology Support One's Autonomy?* Sense of Self and Identity* Capacity* Advance Care Planning* Risk* Privacy* Part III: How Can Technology Support One's Independence?* Technology to Facilitate Independence in Self Care-ADL and IADL* Technology to Facilitate Independence in Self Care-Health Management* Technology to Facilitate Independence in Activities for Economic and Social Participation* Technology to Facilitate Enjoyment and Self-Fulfillment* Technnology for Independence in Mobility in the Community* Usability of Technologies to Support Independence* Part IV: Challenges and Future Directions* AgeTech for Autonomy and Independence: Challenges and Future Directions* Glossary* References* Authors' Biographies
Impact of Artificial Intelligence on Organizational Transformation
IMPACT OF ARTIFICIAL INTELLIGENCE ON ORGANIZATIONAL TRANSFORMATIONDISCUSSES THE IMPACT OF AI ON ORGANIZATIONAL TRANSFORMATION WHICH IS A MIX OF COMPUTATIONAL TECHNIQUES AND MANAGEMENT PRACTICES, WITH IN-DEPTH ANALYSIS ABOUT THE ROLE OF AUTOMATION & DATA MANAGEMENT, AND STRATEGIC MANAGEMENT IN RELATION TO HUMAN CAPITAL, PROCUREMENT & PRODUCTION, FINANCE, AND MARKETING. The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms. AUDIENCE Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business. S. BALAMURUGAN, PHD, SMIEEE and ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.SONAL PATHAK, PHD is an associate professor at the Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India. ANUPRIYA JAIN, PHD is an associate professor at the Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India. SACHIN GUPTA, PHD is at the Department of Business Administration, Mohanlal Sukhadia University, Udaipur, Rajasthan, India. SACHIN SHARMA is an assistant professor at the Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India. SONIA DUGGAL, PHD is an assistant professor at the Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India. Foreword xxiiiPreface xxv1 ARTIFICIAL INTELLIGENCE DISRUPTION ON THE BRINK OF REVOLUTIONIZING HR AND MARKETING FUNCTIONS 1Akansha Mer and Amarpreet Singh Virdi1.1 Introduction 21.2 Research Methodology 41.2.1 Research Objectives 41.2.2 Data Collection 41.3 Artificial Intelligence in HRM 41.3.1 Recruitment 51.3.2 Engaging the Applicants and Employees 51.3.3 Orientation and Onboarding 61.3.4 Performance Appraisal 61.3.5 Training 71.3.6 Compensation 71.3.7 Employee Retention 81.4 Artificial Intelligence in Marketing 81.4.1 Creation of Customer Profiles/Market Segmentation 91.4.2 Cognizance of Consumers Purchase Behavior/Intention 101.4.3 Pricing 111.4.4 Content/Product/Service Recommendations/Search Optimization 111.4.5 Sales Prediction Based on Consumer’s Demographics 121.4.6 Virtual Assistants/Real-Time Conversations 121.4.7 Visual Searching 121.4.8 CRM 131.5 Discussion and Findings 131.6 Implication for Managers 141.7 Conclusion 15References 162 RING TRADING TO ALGO TRADING—A PARADIGM SHIFT MADE POSSIBLE BY ARTIFICIAL INTELLIGENCE 21Aditi R. Khandelwal2.1 Introduction 222.2 Ring Trading 222.3 Features of Generation 1: Ring Trading 222.4 Generation 2: Shifting to Online Platform 242.5 Generation 3: Algo Trading 282.6 Artificial Intelligence 292.7 AI Stock Trading 302.8 Algorithmic (Algo Trading) Trading 312.9 Conclusion 31References 323 AI IN HR A FAIRY TALE OF COMBINING PEOPLE, PROCESS, AND TECHNOLOGY IN MANAGING THE HUMAN RESOURCE 33Jyoti Jain and Sachin Gupta3.1 Introduction 343.2 Problem Recognition 353.3 Journey of AI in HR “From Where Till What” 363.4 Work Methodology of AI in HR 393.5 Branches of AI in HR 393.5.1 Machine Learning 393.5.1.1 Variance Detection 393.5.1.2 Background Verification 403.5.1.3 Employees Abrasion/Attrition 403.5.1.4 Personalized Content 403.5.2 Deep Learning 403.5.2.1 Important Use of Deep Learning in HR Context 403.5.3 Natural Language Processing 413.5.4 Recommendation Engines 413.6 Implication Stages of AI in HR 413.6.1 Automate 423.6.2 Augment 423.6.3 Amplify 423.7 Process Model of AI in HR 433.8 Key Roles of AI in HRM 443.9 Broad Area of Uses of AI in HR 453.9.1 Recruitment 463.9.2 Interviews 463.9.3 Reduction in the Human Biases 463.9.4 Retention 473.9.5 AI in Learning and Advancement 473.9.6 Diminish Gender Bias Equality 473.9.7 Candidate Engagement 483.9.8 Prediction 483.9.9 Smart People Analytics 483.9.10 Employee Experience 483.10 Dark Side of AI 503.10.1 Technical Requirements and Acceptance 513.10.2 Cost Involvement 523.10.3 Machine Biases 523.10.4 Job Losses 523.10.5 Emotional Turmoil 533.10.6 Fake Identity 533.10.7 Having an Audit Trail 533.10.8 Question on Decisions 543.11 Conclusion 54References 554 EFFECT OF ARTIFICIAL INTELLIGENCE ON HUMAN RESOURCE PROFESSION: A PARADIGM SHIFT 57Jyoti Dashora and Karunesh Saxena4.1 Introduction 584.2 Evolution of Artificial Intelligence 594.2.1 Phases of Artificial Intelligence 614.3 Changing Role of Human Resource Professionals 614.4 Effect of Artificial Intelligence on Human Resource Profession 634.4.1 Symbiotic Relationship Between Artificial Intelligence and Human Resource Profession 674.5 Limitations of Artificial Intelligence in HRM 684.6 Conclusion 69References 705 ARTIFICIAL INTELLIGENCE IN ANIMAL SURVEILLANCE AND CONSERVATION 73Devendra Kumar and Saha Dev Jakhar5.1 History 745.2 Introduction 745.3 Need of Artificial Intelligence 755.4 Applications of AI in Animal Surveillance and Conservation 765.4.1 In Livestock Monitoring 775.4.1.1 Chip and Sensor (RFID) 785.4.1.2 Microchip (GPS Tracker) 795.4.1.3 Mobile Application 795.4.1.4 Drone With Thermal Camera 795.4.2 In Wildlife Animal Monitoring 805.4.2.1 Motion Sensor Camera 805.4.2.2 GPS Base Animal Tracker 815.4.2.3 Smart Camera (Thermal Camera) 825.4.2.4 Satellite Base Tag (Ringing, Callers) 825.4.2.5 Acoustics/Sound Monitoring 825.4.2.6 Radio Transmitter (Transponder) 835.5 Some Other Tools of Artificial Intelligence 845.5.1 Computer Software and Application 845.5.1.1 Wildbook Comb (Bot) 845.5.1.2 Betty 845.5.1.3 Sensing Clues 845.5.2 Resolve’s Trail Guard 84References 856 IMPACT OF ARTIFICIAL INTELLIGENCE ON DIGITAL MARKETING 87Giuseppe Granata and Vincenzo Palumbo6.1 Introduction 886.2 The Impact That AI Has on Marketing 896.2.1 The Data of Artificial Intelligence in Marketing 906.2.1.1 The Audience: Highly Targeted Marketing Segmentation 926.2.1.2 Journey to: The Customer’s Road 926.2.1.3 Offer to: Advice-Based Behavioral Marketing 926.2.2 Number of Efficiency Powered by the Al Global Consumer Statistics 946.2.3 Cloud Computing: How it Interfaces to Marketing Thanks to Big Data 956.2.4 AI World is Made Also With BOT. Exactly What Are BOT? 986.2.5 The Chatbot: Service Robot as Support of Customer Care 996.3 The Community Regulation “GDPE” and Artificial Intelligence: Here’s How Technology is Governed 1016.4 The Case Study Estée Lauder 1036.5 Conclusion 104References 1057 ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING THE FACE OF BANKING ORGANIZATIONS 109Shweta Solanki, MeeraMathur and BhumikaRathore7.1 Objectives 1107.2 Introduction 1107.2.1 Three Stages of Artificial Intelligence 1117.2.2 Different Types of Artificial Intelligence 1117.2.3 Trends and Need of Artificial Intelligence in Context of Indian Banking 1117.2.4 Uses and Role of Artificial Intelligence in Banks in the Opinion of 1137.2.5 Importance of Artificial Intelligence in Banking Practices and Operation 1147.2.5.1 Chat Bots 1157.2.5.2 Analytics 1157.2.5.3 Robotics Process Automation 1157.2.5.4 Generating Reports 1157.2.6 Impact of AI in Banking Operations 1167.2.6.1 Front Office Operations/Customer Centric 1167.2.6.2 Middle Office/Operation Centric 1167.2.6.3 Back Office/Decision Centric 1167.2.7 Future of Artificial Intelligence in Banks 1167.3 Existing Technology 1177.4 Methodology 1177.4.1 Search Process 1187.4.2 Selection Criteria and Review Process 1187.5 Findings 1187.6 Conclusion 1197.7 Suggestions 119References 1208 ARTIFICIAL INTELLIGENCE AND ENERGY SECTOR 123Oum Kumari R8.1 Introduction 1238.1.1 Increase in the Emission of Greenhouse Gases 1248.1.2 Increase in the Financial Burden 1248.1.3 Huge Power Deficit 1248.1.4 Water Scarcity 1248.2 Challenges of Indian Power Sector 1258.2.1 Global Warming 1258.2.2 Depletion of Coal 1258.2.3 Huge Financial Stress 1268.2.4 Power Crisis 1268.2.5 Health Issues 1278.2.6 Plant Load Factor 1278.2.7 Transmission and Distribution (T&D) Losses 1288.3 Artificial Intelligence for Energy Solutions 128References 1299 IMPACT OF ARTIFICIAL INTELLIGENCE ON DEVELOPMENT AND GROWTH OF ENTREPRENEURSHIP 131Pooja Meena, Ankita Chaturvedi and Sachin Gupta9.1 Introduction 1329.2 Entrepreneurship 1339.3 Artificial Intelligence 1339.4 Artificial Intelligence and Entrepreneurship 1349.5 Process of Entrepreneurship 1359.5.1 Entrepreneurial Recognition 1359.5.2 Human Capital 1369.5.3 Technology Requirements and Idea Generation 1369.5.4 Opportunity Recognition Phase 1369.5.5 Opportunity Development 1369.5.6 Resource Requirements 1369.5.7 Entrepreneurship 1379.5.8 Financial Resources 1379.5.9 Opportunity Exploitation 1379.5.10 Knowledge Networks 1379.5.11 Validation of the Product 1379.6 The Need of Artificial Intelligence for Business Development 1389.6.1 Consumer Satisfaction 1389.6.2 Cybercrime Protection 1389.6.3 CRMs 1399.6.4 AI-Based Analytics 1399.6.5 Demand and Supply Management 1399.6.6 Improved Maintenance and Better Equipment Safety 1399.6.7 Searching Capable Employees 1409.6.8 Virtual Assistance for Sales 1409.6.9 Improvements With Self-Driven Technologies 1409.7 Some Important Facts About AI 1419.8 Opportunities for Artificial Intelligence in Business 1419.8.1 AI in the Field of Marketing 1419.8.2 For Track Competitors 1429.8.3 Make Less Work of Huge Data 1429.8.4 AI as Customer Support System 1429.8.5 Artificial Intelligence in CRMs 1439.9 Further Research Possibilities 1449.10 Conclusion 144References 14510 AN EXPLORATORY STUDY ON ROLE OF ARTIFICIAL INTELLIGENCE IN OVERCOMING BIASES TO PROMOTE DIVERSITY AND INCLUSION PRACTICES 147Bhumika Rathore, Meeera Mathur and Shweta Solanki10.1 Introduction 14810.1.1 Objectives of the Study 14910.1.2 Background of the Study 14910.1.3 Relevance and Scope of the Study 14910.2 Research Gaps Identified 15010.3 Experiential Framework 15010.3.1 Hypothetical Research Model 15110.3.2 Methodology 15110.3.3 Search Process 15210.3.4 Selection Criteria and Review Process 15210.3.5 Systematic Representation of Literature Review 15310.3.6 Understanding Workforce Diversity 15410.3.7 Benefits and Challenges of Workforce Diversity 15510.3.8 Biases as Obstacles in Diversity and Inclusion Practices 15710.3.9 AI as a Tool to Prevent Bias and Promote D&I Practices 15910.4 Synthesis of the Study 16110.5 Managerial Implications and Conclusion 161References 16311 ARTIFICIAL INTELLIGENCE: REVOLUTIONIZING INDIA BYTE BY BYTE 165Priyanka Jingar, Anju Singh and Sachin Gupta11.1 Introduction 16511.2 Objectives of the Chapter 16611.3 AI for India’s Transformation 16711.4 Economic Impact of Artificial Intelligence 16911.5 Artificial Intelligence and its Impact on Various Sectors 17011.5.1 AI in Healthcare 17111.5.2 AI in Banking and Finance 17211.5.3 Artificial Intelligence in Education 17311.5.4 Artificial Intelligence in Agriculture Sector 17511.5.5 Artificial Intelligence in Smart Cities and Infrastructure 17611.5.6 AI in Smart Mobility and Transportation 17711.6 SWOT Analysis of Artificial Intelligence 17811.6.1 Strength 17811.6.2 Weakness 17911.6.3 Opportunity 17911.6.4 Threat 18011.7 Conclusion 181References 18112 AI: A NEW STRATEGIC METHOD FOR MARKETING AND SALES PLATFORMS 183Ravindar Meena, Ashmi Chhabra, Sachin Gupta and Manoj Gupta12.1 Introduction 18412.2 Objectives of the Chapter 18412.3 Importance of Artificial Intelligence 18512.4 Research Methodology 18612.5 AI: The Ultimate B2B Growth Accelerator 18712.5.1 AI Can Help Get Better Leads 18712.5.2 Predictive Analysis Improves Pitches 18812.5.3 Better Upsell Opportunities 18812.5.4 AI is an Excessive Digital Assistant 18812.5.5 AI and Improved Customer Conversations 18812.6 The Existing Methods of Marketing and Sales 18912.6.1 Being Lazy About Self-Promotion 18912.6.2 Avoiding Networking 18912.6.3 Bridging the New Product Launch Gap 19012.7 AI Will Shape Marketing Strategies of Startup in the Future 19012.7.1 Winning the Loots of Artificial Intelligence 19212.7.2 The Control of Artificial Intelligence and Recorded Data 19212.7.3 Artificial Intelligence the Game Changer for Small Businesses 19212.7.4 AI Selling and Marketing for E-Commerce 19212.7.5 Marketing Computerization to Modified Knowledge 19312.8 Artificial Intelligence is Shaking up the Job Market 19312.9 The Role of Artificial Intelligence and Machine Learning on Marketing 19512.9.1 Traditional and Modern Marketing 19612.10 Conclusion 197References 19813 BRAIN AND BEHAVIOR: BLENDING OF HUMAN AND ARTIFICIAL MINDS TOWARD STRESS RECOGNITION AND INTERVENTION IN ORGANIZATIONAL WELL-BEING 201Manisha D. Solanky and Sachin Gupta13.1 Introduction 20213.2 Research Methodology 20313.3 Fundamentals of Stress 20313.3.1 Stress at Workplace 20513.3.2 Symptoms and Outcome of Stress 20613.4 Embracing AI Opportunity in Stress Management Interventions 20713.5 Existing Technology for Stress Recognition 20813.5.1 Smart Detection Devices 20913.5.2 Stress Detection Through Physiological Signals 20913.5.3 Sensor-Based Detection 21013.5.4 Deep Learning Approaches for Stress Detection 21013.5.5 Stress Detection Through Biofeedback Systems 21013.5.6 Stress Detection Through Virtual Reality 21213.5.7 Stress Detection Through Keyboard Strokes 21313.5.7.1 Chatbots for Depression, Stress, and Anxiety 21313.5.7.2 WYSA Chatbot 21413.5.8 Stress Intervention Based on Human-Technology Interaction 21413.5.8.1 Individual Level of Intervention 21513.5.8.2 Organization Level Intervention 21513.5.8.3 Devices Supporting Stress Interventions 21613.6 Discussion and Findings 21813.7 An AI—Eye to the Future 22013.7.1 Implications to Managers 22013.7.2 Implication to the Entrepreneurs 22113.8 Conclusion 22213.9 Limitations of AI in Human Resource Management 22313.10 Conclusion 223References 22414 ALTERNATIVE FINANCING 229Suhasini Verma14.1 Introduction 22914.1.1 Sources of Funds for Individuals 23014.1.2 Sources of Funds for Organizations 23114.2 Alternative Financing 23114.2.1 Features of Alternative Financing 23114.3 Models of Alternative Financing 23514.3.1 Peer-to-Peer Lending 23514.3.1.1 Peer-to-Peer Lending Types 23514.3.2 Crowdfunding 23614.3.2.1 Equity-Based Crowdfunding 23614.3.2.2 Profit Sharing Crowdfunding 23614.3.2.3 Reward-Based Crowdfunding 23714.3.2.4 Donation-Based Crowdfunding 23714.4 Scope of Alternative Financing in India 23714.5 Alternative Finance as a Tool of Financial Inclusion 24114.6 Regulation of Alternative Finance 241References 242Further Web Links 243Dissertation 24315 APPLICATION OF MACHINE LEARNING IN OPEN GOVERNMENT DATABASE 245Shantanu P. Chakraborty, Parul Dashora and Sachin Gupta15.1 Introduction 24615.2 Literature Review 24615.3 Overview of Open Government Data 24715.4 Open Government Data in India 24815.5 How to Create Value from Data 25115.6 Artificial Intelligence 25115.7 Why AI is Important? 25215.8 Machine Learning 25215.9 Concerns About Machine Learning on Government Database 25415.10 Conclusion 255References 25516 ARTIFICIAL INTELLIGENCE: AN ASSET FOR THE FINANCIAL SECTOR 259Swati Bandi and Anil Kothari16.1 Introduction 25916.1.1 Phase I 1950–1983 Origin of AI and the First Hype Cycle 26016.1.2 II Phase 1983–2010 Reawakening of Artificial Intelligence 26116.1.3 III Phase 2011–2017 AI Domains Competing Humans 26216.1.4 The Present and the Future Phase (2018–2035) 26416.2 Types, Technology, and Application of AI 26516.2.1 Types of Artificial Intelligence 26516.2.2 Artificial Intelligence Technologies 26516.2.3 Applications of Artificial Intelligence 26616.3 Artificial Intelligence and Financial Services 26816.3.1 Artificial Intelligence and Insurance 26916.3.2 Artificial Intelligence and Stock Market 27516.3.2.1 From the History of Stock Exchange to the Development of Algo Trading in India 27616.3.2.2 What is Algorithmic Trading? 27616.3.2.3 Benefits of Algo Trading 27716.3.2.4 Algorithmic Trading Platforms 27716.3.2.5 Algo Trading Strategies 27816.3.2.6 Impact of Artificial Intelligence on Stock Market 28016.3.3 Artificial Intelligence and Mutual Funds 28116.3.3.1 Mutual Funds Use AI in the Following Ways 28216.3.3.2 Quantitative Fund’s Investment Process 28216.3.3.3 Quantitative Fund—Choosing Stocks Strategy 28316.3.3.4 The Other Way Around 28416.4 Conclusion 28416.5 Glossary 285References 286Bibliography 28717 ARTIFICIAL INTELLIGENCE WITH SPECIAL REFERENCE TO BLOCKCHAIN TECHNOLOGY: A FUTURE OF ACCOUNTING 289Ashish Porwal, Ankita Chaturvedi and Sachin Gupta17.1 Introduction 29017.1.1 Artificial Intelligence and Accounting 29017.1.2 Blockchain in Finance and Accounting 29017.2 Objectives 29217.3 Literature Review 29217.3.1 Janling Shi 29217.3.2 Nordgren et al. 29317.3.3 Kiwilinski 29317.3.4 Ahmed Farah 29317.3.5 Odoh Longinus Chukwudi 29417.3.6 Potekhina and Rumkin 29417.4 Research Methodology 29517.5 Usage of Artificial Intelligence in Accounting 29517.6 Usage of Blockchain in Accounting 29717.6.1 Bitcoin 29717.6.2 Interbank Transactions 29817.6.3 Property Registry 29917.7 Impact of AI on the Field of HRM 30017.8 Challenges in Execution 30117.9 Conclusion 301References 30218 AI-IMPLANTED E-LEARNING 4.0: A NEW PARADIGM IN HIGHER EDUCATION 305Garima Kothari and B.L. Verma18.1 Introduction 30618.2 Research Methodology 30718.2.1 Objective 30718.2.2 Research Approach 30718.2.3 Types and Sources of Data 30718.3 Progression of Web and E-Learning 30718.3.1 Some Relevant Definitions Distance Education 30718.3.2 E-Learning 30818.3.3 E-Learning 1.0–4.0 30818.3.3.1 Web 1.0 E-Learning 1.0 (Link to Anything): 1997 to 2003 30818.3.3.2 Web 2.0 E-Learning 2.0 (User Involvement): 2004 to 2006 30818.3.3.3 Web 3.0 E-Learning 3.0 (Existing Data Reconnected): 2007 to 2011 30818.3.3.4 Web 4.0 (Read/Write/Execute/Concurrency From 2012) 30918.4 Artificial Intelligence in Learning 31318.4.1 What is Artificial Intelligence? 31318.4.2 AI En Routed the Learning 31418.4.2.1 Smart Learning Content 31418.4.2.2 Intelligent Tutoring Systems 31518.4.2.3 Virtual Facilitators and Learning Environments 31518.4.2.4 Content Analytics 31518.4.2.5 Paving New Learning Pathways in the Coming Decade 31618.5 Impact of Artificial Intelligence in Education (AIEd) 31618.5.1 Will AI Take Over From Humans? 31618.5.2 AI-Implanted E-Learning 31718.5.2.1 Avatars 31718.5.2.2 Hyper-Reality 31818.5.2.3 The Hyper Class in Virtual Universities 31818.5.2.4 JITAITs 31918.5.3 Recommendations to Help Unleash Intelligence 31918.5.3.1 Pedagogy 32018.5.3.2 Technology 32018.5.3.3 System Change 32118.6 Conclusion 321Concise Summary 322References 32219 ARTIFICIAL INTELLIGENCE IN BANKING INDUSTRY 327GarimaKaneria19.1 Introduction 32719.2 Banking on Artificial Intelligence 32919.3 Role of Artificial Intelligence in Shaping Indian Banking Industry 33019.3.1 Detection of Anti-Money Laundering Pattern 33019.3.2 Chatbots 33019.3.3 Algorithmic Trading 33119.3.4 Fraud Detection 33219.3.5 Customer Suggestions 33219.3.6 Personalized Banking 33219.3.7 Digital Payments 33319.3.8 Robo Advisors 33419.4 Influence of Artificial Intelligence on Indian Banking Industry 33419.5 Reasons Behind Elongated Adoption of Artificial Intelligence in Banking Industry 33619.5.1 Cut-Throat Competition in Banking Sector 33619.5.2 Push for Process-Driven Services 33619.5.3 Introduction of Self-Service at Banks 33619.5.4 Customer Demand for More Customized Solutions 33619.5.5 Creating Operational Efficiencies 33619.5.6 Increasing Employee Productivity 33719.5.7 To Help Focus on Profitability and Compliance 33719.5.8 Use of Robotics Software 33719.5.9 To Reduce Fraud and Risk Associated With Security 33719.5.10 To Manage Large Information and Derive Value Insight 33719.5.11 To Bring in Effective Decision-Making 33819.6 Indian Banks Using Artificial Intelligence 33819.6.1 State Bank of India 33819.6.2 Bank of Baroda 33919.6.3 Allahabad Bank 33919.6.4 Andhra Bank 33919.6.5 YES Bank 33919.6.6 Housing Development Finance Corporation (HDFC) 33919.6.7 Industrial Credit and Investment Corporation of India (ICICI) 34019.6.8 Axis Bank 34019.6.9 Canara Bank 34019.6.10 Punjab National Bank 34019.6.11 IndusInd Bank 34019.6.12 City Union Bank 34119.7 Pros and Cons of Artificial Intelligence in Banking Sector 34119.7.1 Pros 34119.7.1.1 Tracking of Transactional and Other Data Sources 34119.7.1.2 Identification of Pattern Which May Be Eluded by Human Observers 34119.7.1.3 Risk Assessment 34119.7.1.4 Secure and Swift Transaction 34219.7.1.5 Protection of Personal Data 34219.7.1.6 Hedge Fund Trading and Management 34219.7.1.7 Quick Transaction 34219.7.1.8 Reduce Cost and Time 34219.7.1.9 Upgraded Personnel Effectiveness and Customer Observation 34219.7.1.10 Enhanced Banking Services 34319.7.2 Cons 34319.7.2.1 High Cost 34319.7.2.2 Bad Calls 34319.7.2.3 Distribution of Power 34319.7.2.4 Unemployment 34319.8 Intelligent Mobile Applications Drive Growth in Banking 34419.8.1 Investment 34419.8.2 Accounting 34419.8.3 Banking Apps 34519.8.4 Digital Wallet Apps 34519.9 Conclusion 345References 34620 THE POTENTIAL OF ARTIFICIAL INTELLIGENCE IN PUBLIC HEALTHCARE INDUSTRY 349Megha Shrivastava and Devendra Kumar20.1 Introduction 35020.1.1 Drug Discovery 35020.1.1.1 The Main Stages of Drug Discovery Might Take Several Years in Completion 35120.1.1.2 Companies or Startups Used AI Techniques for Drug Discovery 35120.1.2 Medical Imaging 35220.1.2.1 Areas of Medical Imaging 35220.1.2.2 Some Applications for AI in Medical Imaging Are at Present Applied in General Healthcare 35320.1.3 Disease Prevention 35420.1.3.1 Areas of Disease Prevention, Supported by AI System 35420.1.3.2 Some Recent Software Used for Disease Prevention 35420.1.4 Medical Diagnosis 35520.1.4.1 Categories of AI Tools for Disease Diagnosis 35520.1.4.2 Software Developed for Disease Diagnosis 35620.1.4.3 Making Smartphone as Powerful Diagnostic Tools 35720.1.5 Robotic AI 35720.2 The Future of Artificial Intelligence in Healthcare 358References 35921 BANKS TO LEAD DIGITAL TRANSFORMATION WITH ARTIFICIAL INTELLIGENCE 361Lavika Jaroli, Sachin Gupta and Parul Dashora21.1 Artificial Intelligence 36221.1.1 Human Versus Artificial Intelligence 36321.1.2 Difference Between AI, NLP, NN, ML, or DL 36321.1.3 Types of Artificial Intelligence 36521.1.4 Innovations in Indian Banking Through IT 36621.1.5 A Short History of Artificial Intelligence 36621.2 Artificial Intelligence History Timeline 36721.2.1 Objectives 36721.2.2 Scope 36721.2.3 Methodology 36921.3 Why Artificial Intelligence in Banks 36921.4 Goal of Artificial Intelligence 37021.4.1 Innovations in Indian Banking Through IT 37021.4.2 Innovation in Indian Banking Sectors 37021.5 Artificial Intelligences Using by Different Banks 37221.6 Implementation of Artificial Intelligence in Banking 37521.7 Path Ahead Chatbots in Banking 37721.8 Advantage of Artificial Intelligence in Banking Sector 37921.9 Types of Risks and Threats Associated With Banking 38021.10 Nature of Risks in Wireless Banking 38021.11 Advent of Information Technology in Indian Banking Sector 38321.12 Future Scope of AI 38421.13 Conclusion 384References 38422 EFFECTIVENESS OF E-HRM TOOLS USING THE FUNCTIONALITIES OF ARTIFICIAL INTELLIGENCE DURING REMOTE WORKING IN LOCKDOWN PERIOD 387Nidhi Saxena and Aditi R. Khandelwal22.1 Introduction 38822.1.1 Artificial Intelligence in Electronic HR Management 38922.1.1.1 Prospective Employee Engagement and Development 38922.1.1.2 Employee Training 39022.1.1.3 Candidate Selection for Recruitment 39022.1.1.4 Development Needs of Employees 39022.2 Literature Review 39022.3 Objective of the Study 39122.4 Research Methodology 39222.5 Impact and Efficiency of AI-Enabled EHRM Tools in Work From Home Scenario Under Lockdown 39222.6 Conclusion 395Reading List 396Index 399
Can. Trust. Will.
BUILDING A SUCCESSFUL CYBERSECURITY TEAM IS NO LONGER OPTIONAL.Cyberthreats evolve at a staggering pace, and effective cybersecurity operations depend on successful teams. Unfortunately, statistics continue to illustrate that employers are not finding the people they need.The Can. Trust. Will. system guides the C-Suite, HR professionals and talent acquisition to build unbeatable cybersecurity teams through advanced hiring processes and focused on-boarding programs. Additionally, this book details how successful cybersecurity ecosystems are best built and sustained, with expert analysis from high-level government officials, Fortune 500 CSOs and CISOs, risk managers, and even a few techies.Those already in the field (and newbies) will glean invaluable knowledge about how to find their most effective position within a cybersecurity ecosystem. In a tech-driven environment, cybersecurity is fundamentally a human problem: and the first step is to hire for the human element.
Synchronization of Multi-Agent Systems in the Presence of Disturbances and Delays
This monograph explores the synchronization of large-scale, multi-agent dynamical systems in the presence of disturbances, delays, and time-varying networks. Drawing upon their extensive work in this area, the authors provide a thorough treatment of agents with higher-order dynamics, different classes of models for agents, and the underlying networks representing the agents’ actions. The high technical level of their presentation and their rigorous mathematical approach make this a timely and valuable resource that will fill a gap in the existing literature. Divided into two sections, the first part of the book focuses on state synchronization of homogeneous multi-agent systems. The authors consider state synchronization by determining control strategies for both continuous- and discrete-time systems that achieve state synchronization under both full- and partial-state coupling. The chapters that follow examine multi-agent systems with both linear and nonlinear time-varying agents, input-delays for continuous- and discrete-time systems, and communication delays for continuous-time systems. The second part of the book is dedicated to regulated output synchronization of heterogeneous multi-agent systems with linear and nonlinear agents. Both sections of the book include performance considerations in H2- and H-infinity norms in the presence of external disturbances. Research on synchronization of multi-agent systems has been growing in popularity and is highly interdisciplinary, with applications to automobile systems, aerospace systems, multiple-satellite GPS and high-resolution satellite imagery, aircraft formations, highway traffic platooning, industrial process control with multiple processes, and more. Synchronization of Multi-Agent Systems in the Presence of Disturbances and Delays will therefore be of interest to upper-level graduate students, researchers, and engineers in industry working on interconnected dynamical systems. Notation and preliminaries.- Part I Synchronization of homogeneous systems.- Synchronization of continuous-time linear MAS.- Synchronization of discrete-time linear MAS.- Synchronization of linear MAS subject to actuator saturation.- Synchronization of continuous-time MAS with nonlinear time-varying agents.- Synchronization of continuous-time linear MAS with unknown input delay.- Synchronization of discrete-time linear MAS with unknown input delay.- Synchronization of continuous-time linear MAS with unknown communication delay.- Synchronization of discrete-time linear MAS with unknown communication delay.- Synchronization of linear MAS subject to actuator saturation and unknown input delay.- Synchronization of continuous-time linear time-varying MAS.- Synchronization of continuous-time nonlinear time-varying MAS.- H1 and H2 almost synchronization of continuous-time linear MAS.- Part II Synchronization of heterogeneous systems.- Necessary conditions for synchronization of heterogeneous MAS.- Regulated output synchronization of heterogeneous continuous-time linear MAS.- Regulated output synchronization of heterogeneous continuous-time nonlinear MAS.- Regulated output synchronization of heterogeneous continuous-time linear time-varying MAS.- Exact regulated output synchronization for heterogeneous continuous-time MAS in the presence of disturbances and measurement noise with known frequencies.- H1 almost output synchronization for heterogeneous continuous-time MAS.- H2 almost regulated output synchronization for heterogeneous continuous-time MAS.- Almost output synchronization of heterogeneous continuous-time linear MAS with passive agents.- Synchronization of heterogeneous continuous-/and discrete-time linear MAS with introspective agents.- A special coordinate basis (SCB) of linear multivariable systems.- Squaring down of general MIMO systems to invertible uniform rank systems via pre- and/or post-compensators.- Index.- References.
Digital Decarbonization
Decarbonization through optimized energy flows. In this book you will learn how a significant reduction in climate changing greenhouse gas emissions can be achieved through systemic optimization of our energy systems. The authors clearly demonstrate how energy-intensive processes can be optimized flexibly by using technology-neutral simulation methods to ensure that significantly fewer greenhouse gases are emitted. Such field-tested, data-based energy models described in this publication prove that "digital decarbonization" enables an economy that releases significantly fewer climate changing emissions while maintaining its production output. This is a promising message in view of ongoing climate change.
Maschinelles Lernen mit R
Wie bringt man Computern das Lernen aus Daten bei?Diese praxisorientierte Einführung vermittelt anhand zahlreicher Beispiele die Grundlagen des maschinellen Lernens mit R, H2O und Keras. Sie werden in die Lage versetzt, den jeweils zielführenden Ansatz auszuwählen und auf eigene Fragestellungen wie Bild-Klassifizierung oder Vorhersagen anzuwenden. Da fehlerhafte Daten den Lernerfolg gefährden können, wird der Datenvorbereitung und -analyse besondere Aufmerksamkeit gewidmet. R stellt hierfür hochentwickelte und wissenschaftlich fundierte Analyse-Bibliotheken zur Verfügung, deren Funktionsweise und Anwendung gezeigt werden. Sie erfahren, für welche Anwendungsfälle statistische Verfahren wie Regression, Klassifikation, Faktoren-, Cluster- und Zeitreihenanalyse ausreichen und wann Sie besser mit neuronalen Netzen wie z. B. CNNs oder RNNs arbeiten sollten. Hier kommen das Framework H20 sowie Keras zum Einsatz. Anhand von Beispielen wird gezeigt, wie Sie Stolpersteine beim Lernvorgang analysieren oder von vornherein vermeiden können. Darüber hinaus erfahren Sie, unter welchen Umständen Sie die Ergebnisse des maschinellen Lernens weiterverwenden können und wie Sie dabei vorgehen.Leseprobe (PDF-Link)Autor:Prof. Dr. Uli Schell lehrt seit 1997 an der Hochschule Kaiserslautern. Er ist stellvertretender Direktor des „Chinesisch-Deutschen Kollegs für Intelligente Produktion“ an der Shanghai DianJi University sowie Leiter der Technischen Akademie Südwest Kaiserslautern. Zuvor war er Software-Entwickler und Methoden-Berater bei BBC und der SAP AG.
Service als Prinzip
7 Management-Prinzipien für glückliche KundenService ist aus unserem Leben nicht wegzudenken. Jeder von uns nimmt täglich verschiedene Services in Anspruch: Vom Friseur über öffentliche Verkehrsmittel bis hin zu Telefon, Internet und komplexen B2B Services. Das Management solcher Service ist dabei reifer geworden, aber auch komplexer. Und so existiert inzwischen eine unübersichtliche Vielzahl von Methoden, Werkzeugen und Techniken, die sich auch noch nach Branchen unterscheiden. Sie alle spiegeln den Versuch wider, die unterschiedlichsten Erfahrungen in konkrete Handlungsanweisungen zu überführen. Manager wie Mitarbeiter in Serviceorganisationen erhoffen sich davon Unterstützung in der täglichen Arbeit. In der Praxis führt das aber zu unübersichtlich vielen Regeln und Ausnahmen.In dieser Situation helfen wenige einfache, aber starke Prinzipien, die – mit gesundem Menschenverstand eingesetzt – Sinn und Nutzen stiften. Das Buch stellt diese Prinzipien mit Hilfe von Beispielen aus der Praxis vor und gibt Ihnen Anstöße und Tipps zur praktischen Anwendung.Aus dem Inhalt: Der Service der ZukunftDie Welt des Kunden verstehenDen Menschen in den Mittelpunkt stellenSysteme zur Zusammenarbeit schaffenVom Ende her denkenRelevante Ergebnisse erzeugenMit Vertrauen und Verantwortung führenEinfach machenLeseprobe (PDF-Link)Autoren:Martin Beims ist ein geschätzter Impulsgeber für Servicemanagement und Gründer der aretas GmbH. Neben seiner Arbeit als Servicementor gibt er bereits seit vielen Jahren seine Erfahrungen in seinen Büchern weiter.Dr. Roland Fleischer ist geschäftsführender Gesellschafter bei der aretas GmbH. Er verfügt über 20 Jahre Erfahrungen im Service Management.Nico Kroker, MBA Gründer und Geschäftsführer der aretas. Er verfügt über langjährige Erfahrung als Produktmanager, verantwortlicher Prozessmanager und als Managementberater.
Pro Go
Best-selling author Adam Freeman explains how to get the most from Go, starting from the basics and building up to the most advanced and sophisticated features. You will learn how Go builds on a simple and consistent type system to create a comprehensive and productive development experience that produces fast and robust applications that run across platforms.Go, also known as Golang, is the concise and efficient programming language designed by Google for creating high-performance, cross-platform applications. Go combines strong static types with simple syntax and a comprehensive standard library to increase programmer productivity, while still supporting features such as concurrent/parallel programming.Each topic is covered in a clear, concise, no-nonsense approach that is packed with the details you need to learn to be truly effective. Chapters include common problems and how to avoid them.WHAT YOU WILL LEARN* Gain a solid understanding of the Go language and tools* Gain in-depth knowledge of the Go standard library* Use Go for concurrent/parallel tasks* Use Go for client- and server-side development WHO THIS BOOK IS FORExperienced developers who want to use Go to create applicationsADAM 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 1 - Understanding the Go Language1. Your First Go App2. Putting Go in Context3. Using the Go Tools4. Basic Types, Values, and Pointers5. Operations and Conversions6. Flow Control7. Using Arrays, Slice, and Maps8. Defining and Using Functions9. Using Function Types10. Defining Structs11. Using Methods and Interfaces12. Creating and Using Packages13. Type and Interface Composition14. Using Goroutines and Channels15. Error HandlingPart 2 - Using the Go Standard Library16. String Processing and Regular Expressions 17. Formatting and Scanning Strings 18. Math Functions and Data Sorting 19. Dates, Times, and Durations 20. Reading and Writing Data 21. Working with JSON Data 22. Working with Files 23. Using HTML and Text Templates 24. Creating HTTP Servers 25. Creating HTTP Clients 26. Working with Databases 27. Using Reflection 28. Using Reflection, Part 2 29. Using Reflection, Part 3 30. Coordinating Goroutines 31. Unit Testing, Benchmarking, and LoggingPart 3 - Applying Go32. Creating a Web Platform33. Middleware, Templates, and Handlers34. Actions, Sessions, and Authorization 35. SportsStore: A Real Application 36. SportsStore: Cart and Database 37. SportsStore: Checkout and Administration 38. SportsStore: Finishing and Deployment
Natürliche Kognition technologisch begreifen
Im Kern dieser Arbeit geht es um das Begreifen von Kognition. Der Kognitionsbegriff wird zur Schlüsselkategorie in den basalen Gedanken- bzw. Modellgebäuden und den daraus entwickelten Algorithmen. Es ist eine Arbeit, die unter anderem die philosophischen Positionen des Reduktionismus, Funktionalismus und Konstruktivismus mit einer kognitiven Theorie so in Verbindung bringt, um diese erkenntnistheoretischen Ismen mit den Erkenntnissen einer technologisierten Kognitionswissenschaft zu synchronisieren und als algorithmisierte Theorie im Rahmen eines Entwicklungsprojekt als artifizielle Kognition zu realisieren. Die Arbeit ist somit theoretisch fundiert und praktisch orientiert. PROFESSOR DR. MATTHIAS HAUN ist für die Entwicklung und weltweite Einführung „Kognitiver Lösungen“ verantwortlich. Ziel ist es, durch die Entwicklung solcher intelligenten, lernenden und vorausschauenden Systeme die Digitalisierung und Kognitivierung verantwortungsbewusst voranzutreiben, um damit auch deren Einsatz im Sinne der Nachhaltigkeit auszugestalten. Herr Haun leitet zudem zwei interdisziplinäre Forschungsprogramme, in deren Rahmen unter anderem zukünftige Paradigmen, Methoden und Techniken des Cognitive Computing entwickelt werden. Er hat weltweit mehrere Professuren inne, unter anderem seit Januar 2018 eine Shared Professorship für Kognitive Kybernetik und Philosophie der Kognitionswissenschaften. Hier widmet er sich der Technologisierung der Wissenschaften und der Lebenswelt sowie den Implikationen für die Gesellschaft. Er bringt 30 Jahre Erfahrung in der Entwicklung intelligenter Lösungssysteme im Finanzdienstleistungssektor, in der Forschung und Industrie mit. Herleitung als Motivation.- Methodik als Entwicklungs- und Erkenntnispfad.- Natürliche Kognition als Modell.- Artifizielle Kognition als Simulation.- Wissenschaftsphilosophie als Reflexionsinstrument.- Ausblick als Motivation.
Healthcare CIO
Die digitale Transformation der Gesundheitswirtschaft ist in vollem Gange. Trotzdem weist der digitale Reifegrad in allen Versorgungsbereichen noch deutliche Potentiale auf. Dies betrifft den stationären, ambulanten und post-akutstationären Bereich ebenso wie die Rehabilitation und Pflege. Führungskräfte stehen vor der Herausforderung, sich mit Digitalisierungs-/Health-IT-Strategien auseinanderzusetzen, um die Anforderungen erfüllen zu können.Die Weiterbildung zum Certified Healthcare CIO (CHCIO) qualifiziert Führungskräfte, Digitalisierungsstrategien zu entwickeln und umzusetzen, zugeschnitten auf den Bedarf der eigenen Gesundheitseinrichtung. Das Buch liefert einen Einblick in die wesentlichen Kompetenzfelder, d. h. Krankenhaus-/Digitalstrategie, Technologiemanagement, Change Management, Management des IT-Wertbeitrages, Service Management, Talent Management und Relationship Management.Dr. Pierre-Michael Meier, Geschäftsführer ENTSCHEIDERFABRIK und AHIME Academy und Generalbevollmächtigter der Hospitalgemeinschaft Hosp.Do.IT; Prof. Dr. Gregor Hülsken, Professor für Wirtschafts- und Medizininformatik an der FOM Hochschule für Oekonomie und Management und Geschäftsführer AHIME Academy; Prof. Dr. Björn Maier, Duale Hochschule Baden-Württemberg Mannheim - Gesundheitswirtschaft und Soziale Einrichtungen.
Cyber Security and Digital Forensics
CYBER SECURITY AND DIGITAL FORENSICSCYBER SECURITY IS AN INCREDIBLY IMPORTANT ISSUE THAT IS CONSTANTLY CHANGING, WITH NEW METHODS, PROCESSES, AND TECHNOLOGIES COMING ONLINE ALL THE TIME. BOOKS LIKE THIS ARE INVALUABLE TO PROFESSIONALS WORKING IN THIS AREA, TO STAY ABREAST OF ALL OF THESE CHANGES.Current cyber threats are getting more complicated and advanced with the rapid evolution of adversarial techniques. Networked computing and portable electronic devices have broadened the role of digital forensics beyond traditional investigations into computer crime. The overall increase in the use of computers as a way of storing and retrieving high-security information requires appropriate security measures to protect the entire computing and communication scenario worldwide. Further, with the introduction of the internet and its underlying technology, facets of information security are becoming a primary concern to protect networks and cyber infrastructures from various threats. This groundbreaking new volume, written and edited by a wide range of professionals in this area, covers broad technical and socio-economic perspectives for the utilization of information and communication technologies and the development of practical solutions in cyber security and digital forensics. Not just for the professional working in the field, but also for the student or academic on the university level, this is a must-have for any library. AUDIENCE: Practitioners, consultants, engineers, academics, and other professionals working in the areas of cyber analysis, cyber security, homeland security, national defense, the protection of national critical infrastructures, cyber-crime, cyber vulnerabilities, cyber-attacks related to network systems, cyber threat reduction planning, and those who provide leadership in cyber security management both in public and private sectors MANGESH M. GHONGE, PhD, is currently working at Sandip Institute of Technology and Research Center, Nashik, Maharashtra, India. He authored or co-authored more than 60 published articles in prestigious journals, book chapters, and conference papers. He is also the author or editor of ten books and has organized and chaired many national and international conferences.SABYASACHI PRAMANIK, PhD, is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He earned his doctorate in computer science and engineering from the Sri Satya Sai University of Technology and Medical Sciences, Bhopal, India. He has many publications in various reputed international conferences, journals, and online book chapter contributions and is also serving as the editorial board member of many international journals. He is a reviewer of journal articles in numerous technical journals and has been a keynote speaker, session chair and technical program committee member in many international conferences. He has authored a book on wireless sensor networks and is currently editing six books for multiple publishers, including Scrivener Publishing.RAMCHANDRA MANGRULKAR, PhD, is an associate professor in the Department of Computer Engineering at SVKM’s Dwarkadas J. Sanghvi College of Engineering, Mumbai, Maharashtra, India. He has published 48 papers and 12 book chapters and presented significant papers at technical conferences. He has also chaired many conferences as a session chair and conducted various workshops and is also a ICSI-CNSS Certified Network Security Specialist. He is an active member on boards of studies in various universities and institutes in India.DAC-NHUONG LE, PhD, is an associate professor and associate dean at Haiphong University, Vietnam. He earned his MSc and PhD in computer science from Vietnam National University, and he has over 20 years of teaching experience. He has over 50 publications in reputed international conferences, journals and online book chapter contributions and has chaired numerous international conferences. He has served on numerous editorial boards for scientific and technical journals and has authored or edited over 15 books by various publishers, including Scrivener Publishing.Preface xviiAcknowledgment xxvii1 A COMPREHENSIVE STUDY OF SECURITY ISSUES AND RESEARCH CHALLENGES IN DIFFERENT LAYERS OF SERVICE-ORIENTED IOT ARCHITECTURE 1Ankur O. Bang, Udai Pratap Rao and Amit A. Bhusari1.1 Introduction and Related Work 21.2 IoT: Evolution, Applications and Security Requirements 41.2.1 IoT and Its Evolution 51.2.2 Different Applications of IoT 51.2.3 Different Things in IoT 71.2.4 Security Requirements in IoT 81.3 Service-Oriented IoT Architecture and IoT Protocol Stack 101.3.1 Service-Oriented IoT Architecture 101.3.2 IoT Protocol Stack 111.3.2.1 Application Layer Protocols 121.3.2.2 Transport Layer Protocols 131.3.2.3 Network Layer Protocols 151.3.2.4 Link Layer and Physical Layer Protocols 161.4 Anatomy of Attacks on Service-Oriented IoT Architecture 241.4.1 Attacks on Software Service 241.4.1.1 Operating System–Level Attacks 241.4.1.2 Application-Level Attacks 251.4.1.3 Firmware-Level Attacks 251.4.2 Attacks on Devices 261.4.3 Attacks on Communication Protocols 261.4.3.1 Attacks on Application Layer Protocols 261.4.3.2 Attacks on Transport Layer Protocols 281.4.3.3 Attacks on Network Layer Protocols 281.4.3.4 Attacks on Link and Physical Layer Protocols 301.5 Major Security Issues in Service-Oriented IoT Architecture 311.5.1 Application – Interface Layer 321.5.2 Service Layer 331.5.3 Network Layer 331.5.4 Sensing Layer 341.6 Conclusion 35References 362 QUANTUM AND POST-QUANTUM CRYPTOGRAPHY 45Om Pal, Manoj Jain, B.K. Murthy and Vinay Thakur2.1 Introduction 462.2 Security of Modern Cryptographic Systems 462.2.1 Classical and Quantum Factoring of A Large Number 472.2.2 Classical and Quantum Search of An Item 492.3 Quantum Key Distribution 492.3.1 BB84 Protocol 502.3.1.1 Proposed Key Verification Phase for BB84 512.3.2 E91 Protocol 512.3.3 Practical Challenges of Quantum Key Distribution 522.3.4 Multi-Party Quantum Key Agreement Protocol 532.4 Post-Quantum Digital Signature 532.4.1 Signatures Based on Lattice Techniques 542.4.2 Signatures Based on Multivariate Quadratic Techniques 552.4.3 Hash-Based Signature Techniques 552.5 Conclusion and Future Directions 55References 563 ARTIFICIAL NEURAL NETWORK APPLICATIONS IN ANALYSIS OF FORENSIC SCIENCE 59K.R. Padma and K.R. Don3.1 Introduction 603.2 Digital Forensic Analysis Knowledge 613.3 Answer Set Programming in Digital Investigations 613.4 Data Science Processing with Artificial Intelligence Models 633.5 Pattern Recognition Techniques 633.6 ANN Applications 653.7 Knowledge on Stages of Digital Forensic Analysis 653.8 Deep Learning and Modelling 673.9 Conclusion 68References 694 A COMPREHENSIVE SURVEY OF FULLY HOMOMORPHIC ENCRYPTION FROM ITS THEORY TO APPLICATIONS 73Rashmi Salavi, Dr. M. M. Math and Dr. U. P. Kulkarni4.1 Introduction 734.2 Homomorphic Encryption Techniques 764.2.1 Partial Homomorphic Encryption Schemes 774.2.2 Fully Homomorphic Encryption Schemes 784.3 Homomorphic Encryption Libraries 794.4 Computations on Encrypted Data 834.5 Applications of Homomorphic Encryption 854.6 Conclusion 86References 875 UNDERSTANDING ROBOTICS THROUGH SYNTHETIC PSYCHOLOGY 91Garima Saini and Dr. Shabnam5.1 Introduction 915.2 Physical Capabilities of Robots 925.2.1 Artificial Intelligence and Neuro Linguistic Programming (NLP) 935.2.2 Social Skill Development and Activity Engagement 935.2.3 Autism Spectrum Disorders 935.2.4 Age-Related Cognitive Decline and Dementia 945.2.5 Improving Psychosocial Outcomes through Robotics 945.2.6 Clients with Disabilities and Robotics 945.2.7 Ethical Concerns and Robotics 955.3 Traditional Psychology, Neuroscience and Future Robotics 955.4 Synthetic Psychology and Robotics: A Vision of the Future 975.5 Synthetic Psychology: The Foresight 985.6 Synthetic Psychology and Mathematical Optimization 995.7 Synthetic Psychology and Medical Diagnosis 995.7.1 Virtual Assistance and Robotics 1005.7.2 Drug Discovery and Robotics 1005.8 Conclusion 101References 1016 AN INSIGHT INTO DIGITAL FORENSICS: HISTORY, FRAMEWORKS, TYPES AND TOOLS 105G Maria Jones and S Godfrey Winster6.1 Overview 1056.2 Digital Forensics 1076.2.1 Why Do We Need Forensics Process? 1076.2.2 Forensics Process Principles 1086.3 Digital Forensics History 1086.3.1 1985 to 1995 1086.3.2 1995 to 2005 1096.3.3 2005 to 2015 1106.4 Evolutionary Cycle of Digital Forensics 1116.4.1 Ad Hoc 1116.4.2 Structured Phase 1116.4.3 Enterprise Phase 1126.5 Stages of Digital Forensics Process 1126.5.1 Stage 1 - 1995 to 2003 1126.5.2 Stage II - 2004 to 2007 1136.5.3 Stage III - 2007 to 2014 1146.6 Types of Digital Forensics 1156.6.1 Cloud Forensics 1166.6.2 Mobile Forensics 1166.6.3 IoT Forensics 1166.6.4 Computer Forensics 1176.6.5 Network Forensics 1176.6.6 Database Forensics 1186.7 Evidence Collection and Analysis 1186.8 Digital Forensics Tools 1196.8.1 X-Ways Forensics 1196.8.2 SANS Investigative Forensics Toolkit – SIFT 1196.8.3 EnCase 1196.8.4 The Sleuth Kit/Autopsy 1226.8.5 Oxygen Forensic Suite 1226.8.6 Xplico 1226.8.7 Computer Online Forensic Evidence Extractor (COFEE) 1226.8.8 Cellebrite UFED 1226.8.9 OSForeniscs 1236.8.10 Computer-Aided Investigative Environment (CAINE) 1236.9 Summary 123References 1237 DIGITAL FORENSICS AS A SERVICE: ANALYSIS FOR FORENSIC KNOWLEDGE 127Soumi Banerjee, Anita Patil, Dipti Jadhav and Gautam Borkar7.1 Introduction 1277.2 Objective 1287.3 Types of Digital Forensics 1297.3.1 Network Forensics 1297.3.2 Computer Forensics 1427.3.3 Data Forensics 1477.3.4 Mobile Forensics 1497.3.5 Big Data Forensics 1547.3.6 IoT Forensics 1557.3.7 Cloud Forensics 1577.4 Conclusion 161References 1618 4S FRAMEWORK: A PRACTICAL CPS DESIGN SECURITY ASSESSMENT & BENCHMARKING FRAMEWORK 163Neel A. Patel, Dhairya A. Parekh, Yash A. Shah and Ramchandra Mangrulkar8.1 Introduction 1648.2 Literature Review 1668.3 Medical Cyber Physical System (MCPS) 1708.3.1 Difference between CPS and MCPS 1718.3.2 MCPS Concerns, Potential Threats, Security 1718.4 CPSSEC vs. Cyber Security 1728.5 Proposed Framework 1738.5.1 4S Definitions 1748.5.2 4S Framework-Based CPSSEC Assessment Process 1758.5.3 4S Framework-Based CPSSEC Assessment Score Breakdown & Formula 1818.6 Assessment of Hypothetical MCPS Using 4S Framework 1878.6.1 System Description 1878.6.2 Use Case Diagram for the Above CPS 1888.6.3 Iteration 1 of 4S Assessment 1898.6.4 Iteration 2 of 4S Assessment 1958.7 Conclusion 2008.8 Future Scope 201References 2019 ENSURING SECURE DATA SHARING IN IOT DOMAINS USING BLOCKCHAIN 205Tawseef Ahmed Teli, Rameez Yousuf and Dawood Ashraf Khan9.1 IoT and Blockchain 2059.1.1 Public 2089.1.1.1 Proof of Work (PoW) 2099.1.1.2 Proof of Stake (PoS) 2099.1.1.3 Delegated Proof of Stake (DPoS) 2109.1.2 Private 2109.1.3 Consortium or Federated 2109.2 IoT Application Domains and Challenges in Data Sharing 2119.3 Why Blockchain? 2149.4 IoT Data Sharing Security Mechanism On Blockchain 2169.4.1 Double-Chain Mode Based On Blockchain Technology 2169.4.2 Blockchain Structure Based On Time Stamp 2179.5 Conclusion 219References 21910 A REVIEW OF FACE ANALYSIS TECHNIQUES FOR CONVENTIONAL AND FORENSIC APPLICATIONS 223Chethana H.T. and Trisiladevi C. Nagavi10.1 Introduction 22410.2 Face Recognition 22510.2.1 Literature Review on Face Recognition 22610.2.2 Challenges in Face Recognition 22810.2.3 Applications of Face Recognition 22910.3 Forensic Face Recognition 22910.3.1 Literature Review on Face Recognition for Forensics 23110.3.2 Challenges of Face Recognition in Forensics 23310.3.3 Possible Datasets Used for Forensic Face Recognition 23510.3.4 Fundamental Factors for Improving Forensics Science 23510.3.5 Future Perspectives 23710.4 Conclusion 238References 23811 ROADMAP OF DIGITAL FORENSICS INVESTIGATION PROCESS WITH DISCOVERY OF TOOLS 241Anita Patil, Soumi Banerjee, Dipti Jadhav and Gautam Borkar11.1 Introduction 24211.2 Phases of Digital Forensics Process 24411.2.1 Phase I - Identification 24411.2.2 Phase II - Acquisition and Collection 24511.2.3 Phase III - Analysis and Examination 24511.2.4 Phase IV - Reporting 24511.3 Analysis of Challenges and Need of Digital Forensics 24611.3.1 Digital Forensics Process has following Challenges 24611.3.2 Needs of Digital Forensics Investigation 24711.3.3 Other Common Attacks Used to Commit the Crime 24811.4 Appropriateness of Forensics Tool 24811.4.1 Level of Skill 24811.4.2 Outputs 25211.4.3 Region of Emphasis 25211.4.4 Support for Additional Hardware 25211.5 Phase-Wise Digital Forensics Techniques 25311.5.1 Identification 25311.5.2 Acquisition 25411.5.3 Analysis 25611.5.3.1 Data Carving 25711.5.3.2 Different Curving Techniques 25911.5.3.3 Volatile Data Forensic Toolkit Used to Collect and Analyze the Data from Device 26011.5.4 Report Writing 26511.6 Pros and Cons of Digital Forensics Investigation Process 26611.6.1 Advantages of Digital Forensics 26611.6.2 Disadvantages of Digital Forensics 26611.7 Conclusion 267References 26712 UTILIZING MACHINE LEARNING AND DEEP LEARNING IN CYBESECURITY: AN INNOVATIVE APPROACH 271Dushyant Kaushik, Muskan Garg, Annu, Ankur Gupta and Sabyasachi Pramanik12.1 Introduction 27112.1.1 Protections of Cybersecurity 27212.1.2 Machine Learning 27412.1.3 Deep Learning 27612.1.4 Machine Learning and Deep Learning: Similarities and Differences 27812.2 Proposed Method 28112.2.1 The Dataset Overview 28212.2.2 Data Analysis and Model for Classification 28312.3 Experimental Studies and Outcomes Analysis 28312.3.1 Metrics on Performance Assessment 28412.3.2 Result and Outcomes 28512.3.2.1 Issue 1: Classify the Various Categories of Feedback Related to the Malevolent Code Provided 28512.3.2.2 Issue 2: Recognition of the Various Categories of Feedback Related to the Malware Presented 28612.3.2.3 Issue 3: According to the Malicious Code, Distinguishing Various Forms of Malware 28712.3.2.4 Issue 4: Detection of Various Malware Styles Based on Different Responses 28712.3.3 Discussion 28812.4 Conclusions and Future Scope 289References 29213 APPLICATIONS OF MACHINE LEARNING TECHNIQUES IN THE REALM OF CYBERSECURITY 295Koushal Kumar and Bhagwati Prasad Pande13.1 Introduction 29613.2 A Brief Literature Review 29813.3 Machine Learning and Cybersecurity: Various Issues 30013.3.1 Effectiveness of ML Technology in Cybersecurity Systems 30013.3.2 Machine Learning Problems and Challenges in Cybersecurity 30213.3.2.1 Lack of Appropriate Datasets 30213.3.2.2 Reduction in False Positives and False Negatives 30213.3.2.3 Adversarial Machine Learning 30213.3.2.4 Lack of Feature Engineering Techniques 30313.3.2.5 Context-Awareness in Cybersecurity 30313.3.3 Is Machine Learning Enough to Stop Cybercrime? 30413.4 ML Datasets and Algorithms Used in Cybersecurity 30413.4.1 Study of Available ML-Driven Datasets Available for Cybersecurity 30413.4.1.1 KDD Cup 1999 Dataset (DARPA1998) 30513.4.1.2 NSL-KDD Dataset 30513.4.1.3 ECML-PKDD 2007 Discovery Challenge Dataset 30513.4.1.4 Malicious URL’s Detection Dataset 30613.4.1.5 ISOT (Information Security and Object Technology) Botnet Dataset 30613.4.1.6 CTU-13 Dataset 30613.4.1.7 MAWILab Anomaly Detection Dataset 30713.4.1.8 ADFA-LD and ADFA-WD Datasets 30713.4.2 Applications ML Algorithms in Cybersecurity Affairs 30713.4.2.1 Clustering 30913.4.2.2 Support Vector Machine (SVM) 30913.4.2.3 Nearest Neighbor (NN) 30913.4.2.4 Decision Tree 30913.4.2.5 Dimensionality Reduction 31013.5 Applications of Machine Learning in the Realm of Cybersecurity 31013.5.1 Facebook Monitors and Identifies Cybersecurity Threats with ML 31013.5.2 Microsoft Employs ML for Security 31113.5.3 Applications of ML by Google 31213.6 Conclusions 313References 31314 SECURITY IMPROVEMENT TECHNIQUE FOR DISTRIBUTED CONTROL SYSTEM (DCS) AND SUPERVISORY CONTROL-DATA ACQUISITION (SCADA) USING BLOCKCHAIN AT DARK WEB PLATFORM 317Anand Singh Rajawat, Romil Rawat and Kanishk Barhanpurkar14.1 Introduction 31814.2 Significance of Security Improvement in DCS and SCADA 32214.3 Related Work 32314.4 Proposed Methodology 32414.4.1 Algorithms Used for Implementation 32714.4.2 Components of a Blockchain 32714.4.3 MERKLE Tree 32814.4.4 The Technique of Stack and Work Proof 32814.4.5 Smart Contracts 32914.5 Result Analysis 32914.6 Conclusion 330References 33115 RECENT TECHNIQUES FOR EXPLOITATION AND PROTECTION OF COMMON MALICIOUS INPUTS TO ONLINE APPLICATIONS 335Dr. Tun Myat Aung and Ni Ni Hla15.1 Introduction 33515.2 SQL Injection 33615.2.1 Introduction 33615.2.2 Exploitation Techniques 33715.2.2.1 In-Band SQL Injection 33715.2.2.2 Inferential SQL Injection 33815.2.2.3 Out-of-Band SQL Injection 34015.2.3 Causes of Vulnerability 34015.2.4 Protection Techniques 34115.2.4.1 Input Validation 34115.2.4.2 Data Sanitization 34115.2.4.3 Use of Prepared Statements 34215.2.4.4 Limitation of Database Permission 34315.2.4.5 Using Encryption 34315.3 Cross Site Scripting 34415.3.1 Introduction 34415.3.2 Exploitation Techniques 34415.3.2.1 Reflected Cross Site Scripting 34515.3.2.2 Stored Cross Site Scripting 34515.3.2.3 DOM-Based Cross Site Scripting 34615.3.3 Causes of Vulnerability 34615.3.4 Protection Techniques 34715.3.4.1 Data Validation 34715.3.4.2 Data Sanitization 34715.3.4.3 Escaping on Output 34715.3.4.4 Use of Content Security Policy 34815.4 Cross Site Request Forgery 34915.4.1 Introduction 34915.4.2 Exploitation Techniques 34915.4.2.1 HTTP Request with GET Method 34915.4.2.2 HTTP Request with POST Method 35015.4.3 Causes of Vulnerability 35015.4.3.1 Session Cookie Handling Mechanism 35015.4.3.2 HTML Tag 35115.4.3.3 Browser’s View Source Option 35115.4.3.4 GET and POST Method 35115.4.4 Protection Techniques 35115.4.4.1 Checking HTTP Referer 35115.4.4.2 Using Custom Header 35215.4.4.3 Using Anti-CSRF Tokens 35215.4.4.4 Using a Random Value for each Form Field 35215.4.4.5 Limiting the Lifetime of Authentication Cookies 35315.5 Command Injection 35315.5.1 Introduction 35315.5.2 Exploitation Techniques 35415.5.3 Causes of Vulnerability 35415.5.4 Protection Techniques 35515.6 File Inclusion 35515.6.1 Introduction 35515.6.2 Exploitation Techniques 35515.6.2.1 Remote File Inclusion 35515.6.2.2 Local File Inclusion 35615.6.3 Causes of Vulnerability 35715.6.4 Protection Techniques 35715.7 Conclusion 358References 35816 RANSOMWARE: THREATS, IDENTIFICATION AND PREVENTION 361Sweta Thakur, Sangita Chaudhari and Bharti Joshi16.1 Introduction 36116.2 Types of Ransomwares 36416.2.1 Locker Ransomware 36416.2.1.1 Reveton Ransomware 36516.2.1.2 Locky Ransomware 36616.2.1.3 CTB Locker Ransomware 36616.2.1.4 TorrentLocker Ransomware 36616.2.2 Crypto Ransomware 36716.2.2.1 PC Cyborg Ransomware 36716.2.2.2 OneHalf Ransomware 36716.2.2.3 GPCode Ransomware 36716.2.2.4 CryptoLocker Ransomware 36816.2.2.5 CryptoDefense Ransomware 36816.2.2.6 CryptoWall Ransomware 36816.2.2.7 TeslaCrypt Ransomware 36816.2.2.8 Cerber Ransomware 36816.2.2.9 Jigsaw Ransomware 36916.2.2.10 Bad Rabbit Ransomware 36916.2.2.11 WannaCry Ransomware 36916.2.2.12 Petya Ransomware 36916.2.2.13 Gandcrab Ransomware 36916.2.2.14 Rapid Ransomware 37016.2.2.15 Ryuk Ransomware 37016.2.2.16 Lockergoga Ransomware 37016.2.2.17 PewCrypt Ransomware 37016.2.2.18 Dhrama/Crysis Ransomware 37016.2.2.19 Phobos Ransomware 37116.2.2.20 Malito Ransomware 37116.2.2.21 LockBit Ransomware 37116.2.2.22 GoldenEye Ransomware 37116.2.2.23 REvil or Sodinokibi Ransomware 37116.2.2.24 Nemty Ransomware 37116.2.2.25 Nephilim Ransomware 37216.2.2.26 Maze Ransomware 37216.2.2.27 Sekhmet Ransomware 37216.2.3 MAC Ransomware 37216.2.3.1 KeRanger Ransomware 37316.2.3.2 Go Pher Ransomware 37316.2.3.3 FBI Ransom Ransomware 37316.2.3.4 File Coder 37316.2.3.5 Patcher 37316.2.3.6 ThiefQuest Ransomware 37416.2.3.7 Keydnap Ransomware 37416.2.3.8 Bird Miner Ransomware 37416.3 Ransomware Life Cycle 37416.4 Detection Strategies 37616.4.1 Unevil 37616.4.2 Detecting File Lockers 37616.4.3 Detecting Screen Lockers 37716.4.4 Connection-Monitor and Connection-Breaker Approach 37716.4.5 Ransomware Detection by Mining API Call Usage 37716.4.6 A New Static-Based Framework for Ransomware Detection 37716.4.7 White List-Based Ransomware Real-Time Detection Prevention (WRDP) 37816.5 Analysis of Ransomware 37816.5.1 Static Analysis 37916.5.2 Dynamic Analysis 37916.6 Prevention Strategies 38016.6.1 Access Control 38016.6.2 Recovery After Infection 38016.6.3 Trapping Attacker 38016.7 Ransomware Traits Analysis 38016.8 Research Directions 38416.9 Conclusion 384References 384Index 389