Allgemein
Medical Imaging and Health Informatics
MEDICAL IMAGING AND HEALTH INFORMATICSPROVIDES A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL IMAGING AS WELL AS PRACTICAL RECOMMENDATIONS FOR THE USAGE OF MACHINE LEARNING (ML) AND DEEP LEARNING (DL) TECHNIQUES FOR CLINICAL APPLICATIONS.Medical imaging and health informatics is a subfield of science and engineering which applies informatics to medicine and includes the study of design, development, and application of computational innovations to improve healthcare. The health domain has a wide range of challenges that can be addressed using computational approaches; therefore, the use of AI and associated technologies is becoming more common in society and healthcare. Currently, deep learning algorithms are a promising option for automated disease detection with high accuracy. Clinical data analysis employing these deep learning algorithms allows physicians to detect diseases earlier and treat patients more efficiently. Since these technologies have the potential to transform many aspects of patient care, disease detection, disease progression and pharmaceutical organization, approaches such as deep learning algorithms, convolutional neural networks, and image processing techniques are explored in this book.This book also delves into a wide range of image segmentation, classification, registration, computer-aided analysis applications, methodologies, algorithms, platforms, and tools; and gives a holistic approach to the application of AI in healthcare through case studies and innovative applications. It also shows how image processing, machine learning and deep learning techniques can be applied for medical diagnostics in several specific health scenarios such as COVID-19, lung cancer, cardiovascular diseases, breast cancer, liver tumor, bone fractures, etc. Also highlighted are the significant issues and concerns regarding the use of AI in healthcare together with other allied areas, such as the Internet of Things (IoT) and medical informatics, to construct a global multidisciplinary forum.AUDIENCEThe core audience comprises researchers and industry engineers, scientists, radiologists, healthcare professionals, data scientists who work in health informatics, computer vision and medical image analysis.TUSHAR H. JAWARE, PHD, received his degree in Medical Image Processing and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published more than 50 research articles in refereed journals and IEEE conferences, and has three international patents granted and two Indian patents published.K. SARAT KUMAR, PHD, received his degree in Electronics Engineering and is now a professor in the Department of Electronics & Communication Engineering, K L University, Andhra Pradesh, India. RAVINDRA D. BADGUJAR, PHD, received his degree in Electronics Engineering and is now an assistant professor in the Department of Electronics and Telecommunication Engineering, R C Patel Institute of Technology, Shirpur, India. He has published many research articles in refereed journals and IEEE conferences as well as one international patent granted and two Indian patents published. SVETLIN ANTONOV, PHD, received his degree in Telecommunications and is now a lecturer in the Faculty of Telecommunications, TU-Sofia, Bulgaria. He is the author of several books and more than 60 peer-reviewed articles. PREFACE XVII1 MACHINE LEARNING APPROACH FOR MEDICAL DIAGNOSIS BASED ON PREDICTION MODEL 1Hemant Kasturiwale, Rajesh Karhe and Sujata N. Kale1.1 Introduction 21.1.1 Heart System and Major Cardiac Diseases 21.1.2 ECG for Heart Rate Variability Analysis 21.1.3 HRV for Cardiac Analysis 31.2 Machine Learning Approach and Prediction 31.3 Material and Experimentation 41.3.1 Data and HRV 41.3.1.1 HRV Data Analysis via ECG Data Acquisition System 51.3.2 Methodology and Techniques 61.3.2.1 Classifiers and Performance Evaluation 71.3.3 Proposed Model With Layer Representation 81.3.4 The Model Using Fixed Set of Features and Standard Dataset 111.3.4.1 Performance of Classifiers With Feature Selection 111.4 Performance Metrics and Evaluation of Classifiers 131.4.1 Cardiac Disease Prediction Through Flexi Intra Group Selection Model 131.4.2 HRV Model With Flexi Set of Features 141.4.3 Performance of the Proposed Modified With ISM-24 151.5 Discussion and Conclusion 181.5.1 Conclusion and Future Scope 19References 202 APPLICATIONS OF MACHINE LEARNING TECHNIQUES IN DISEASE DETECTION 23M.S. Roobini, Sowmiya M., S. Jancy and L. Suji Helen2.1 Introduction 242.1.1 Overview of Machine Learning Types 242.1.2 Motivation 252.1.3 Organization the Chapter 252.2 Types of Machine Learning Techniques 252.2.1 Supervised Learning 252.2.2 Classification Algorithm 252.2.3 Regression Analysis 262.2.4 Linear Regression 272.2.4.1 Applications of Linear Regression 272.2.5 KNN Algorithm 282.2.5.1 Working of KNN 282.2.5.2 Drawbacks of KNN Algorithm 292.2.6 Decision Tree Classification Algorithm 292.2.6.1 Attribute Selection Measures 292.2.6.2 Information Gain 292.2.6.3 Gain Ratio 292.2.7 Random Forest Algorithm 292.2.7.1 How the Random Forest Algorithm Works 292.2.7.2 Advantage of Using Random Forest 302.2.7.3 Disadvantage of Using the Random Forest 312.2.8 Naive Bayes Classifier Algorithm 312.2.8.1 For What Reason is it Called Naive Bayes? 312.2.8.2 Disservices of Naive Bayes Classifier 312.2.9 Logistic Regression 312.2.9.1 Logistic Regression for Machine Learning 312.2.10 Support Vector Machine 322.2.11 Unsupervised Learning 322.2.11.1 Clustering 332.2.11.2 PCA in Machine Learning 352.2.12 Semi-Supervised Learning 382.2.12.1 What is Semi-Supervised Clustering? 382.2.12.2 How Semi-Supervised Learning Functions? 382.2.13 Reinforcement Learning 392.2.13.1 Artificial Intelligence 392.2.13.2 Deep Learning 402.2.13.3 Points of Interest of Machine Learning 412.2.13.4 Why Machine Learning is Popular 412.2.13.5 Test Utilizations of ML 422.3 Future Research Directions 432.3.1 Privacy 432.3.2 Accuracy 43References 433 DENGUE INCIDENCE RATE PREDICTION USING NONLINEAR AUTOREGRESSIVE NEURAL NETWORK TIME SERIES MODEL 47S. Dhamodharavadhani and R. Rathipriya3.1 Introduction 473.2 Related Literature Study 483.2.1 Limitations of Existing Works 503.2.2 Contributions of Proposed Methodology 503.3 Methods and Materials 503.3.1 NAR-NNTS 503.3.2 Fit/Train the Model 513.3.3 Training Algorithms 543.3.3.1 Levenberg-Marquardt (LM) Algorithm 543.3.3.2 Bayesian Regularization (BR) Algorithm 553.3.3.3 Scaled Conjugate Gradient (SCG) Algorithm 553.3.4 DIR Prediction 553.4 Result Discussions 563.4.1 Dataset Description 563.4.2 Evaluation Measure for NAR-NNTS Models 573.4.3 Analysis of Results 573.5 Conclusion and Future Work 65Acknowledgment 66References 664 EARLY DETECTION OF BREAST CANCER USING MACHINE LEARNING 69G. Lavanya and G. Thilagavathi4.1 Introduction 704.1.1 Objective 704.1.2 Anatomy of Breast 704.1.3 Breast Imaging Modalities 714.2 Methodology 714.2.1 Database 714.2.2 Image Pre-Processing 714.3 Segmentation 724.4 Feature Extraction 724.5 Classification 724.5.1 Naive Bayes Neural Network Classifier 724.5.2 Radial Basis Function Neural Network 734.5.2.1 Input 734.5.2.2 Hidden Layer 734.5.2.3 Output Nodes 744.6 Performance Evaluation Methods 744.7 Output 754.7.1 Dataset 754.7.2 Pre-Processing 754.7.3 Segmentation 754.7.4 Geometric Feature Extraction 774.8 Results and Discussion 784.8.1 Database 784.9 Conclusion and Future Scope 81References 815 MACHINE LEARNING APPROACH FOR PREDICTION OF LUNG CANCER 83Hemant Kasturiwale, Swati Bhisikar and Sandhya Save5.1 Introduction 845.1.1 Disorders in Lungs 845.1.2 Background 845.1.3 Material, Datasets, and Techniques 855.2 Feature Extraction and Lung Cancer Analysis 865.3 Methodology 875.3.1 Proposed Algorithm Steps 875.3.2 Classifiers in Concurrence With Datasets 885.4 Proposed System and Implementation 895.4.1 Interpretation via Artificial Intelligence 895.4.2 Training of Model 905.4.3 Implementation and Results 905.5 Conclusion 995.5.1 Future Scope 99References 1006 SEGMENTATION OF LIVER TUMOR USING ANN 103Hema L. K. and R. Indumathi6.1 Introduction 1036.2 Liver Tumor 1046.2.1 Overview of Liver Tumor 1046.2.2 Classification 1056.2.2.1 Benign 1056.2.2.2 Malignant 1076.3 Benefits of CT to Diagnose Liver Cancer 1086.4 Literature Review 1086.5 Interactive Liver Tumor Segmentation by Deep Learning 1096.6 Existing System 1096.7 Proposed System 1106.7.1 Pre-Processing 1106.7.2 Segmentation 1116.7.3 Feature Extraction 1126.7.4 GLCM 1126.7.5 Backpropagation Network 1136.8 Result and Discussion 1136.8.1 Processed Images 1146.8.2 Segmentation 1166.9 Future Enhancements 1176.10 Conclusion 118References 1187 DMSAN: DEEP MULTI-SCALE ATTENTION NETWORK FOR AUTOMATIC LIVER SEGMENTATION FROM ABDOMEN CT IMAGES 121Devidas T. Kushnure and Sanjay N. Talbar7.1 Introduction 1217.2 Related Work 1227.3 Methodology 1237.3.1 Proposed Architecture 1237.3.2 Multi-Scale Feature Characterization Using Res2Net Module 1257.4 Experimental Analysis 1267.4.1 Dataset Description 1267.4.2 Pre-Processing Dataset 1277.4.3 Training Strategy 1287.4.4 Loss Function 1287.4.5 Implementation Platform 1297.4.6 Data Augmentation 1297.4.7 Performance Metrics 1297.5 Results 1317.6 Result Comparison With Other Methods 1357.7 Discussion 1367.8 Conclusion 137Acknowledgement 138References 1388 AI-BASED IDENTIFICATION AND PREDICTION OF CARDIAC DISORDERS 141Rajesh Karhe, Hemant Kasturiwale and Sujata N. Kale8.1 Introduction 1428.1.1 Cardiac Electrophysiology and Electrocardiogram 1438.1.2 Heart Arrhythmia 1448.1.2.1 Types of Arrhythmias 1458.1.3 ECG Database 1478.1.3.1 Association for the Advancement of Medical Instrumentation (AAMI) Standard 1478.1.4 An Overview of ECG Signal Analysis 1488.2 Related Work 1498.3 Classifiers and Methodology 1518.3.1 Databases for Cardiac Arrhythmia Detection 1528.3.2 MIT-BIH Normal Sinus Rhythm and Arrhythmia Database 1528.3.3 Arrhythmia Detection and Classification 1538.3.4 Methodology 1538.3.4.1 Database Gathering and Pre-Processing 1538.3.4.2 QRST Wave Detection 1538.3.4.3 Features Extraction 1548.3.4.4 Neural Network 1558.3.4.5 Performance Evaluation 1568.4 Result Analysis 1568.4.1 Arrhythmia Detection and Classification 1568.4.2 Dataset 1568.4.3 Evaluations and Results 1568.4.4 Evaluating the Performance of Various Neural Network Classifiers (Arrhythmia Detection) 1578.5 Conclusions and Future Scope 1598.5.1 Arrhythmia Detection and Classification 1598.5.2 Future Scope 161References 1619 AN IMPLEMENTATION OF IMAGE PROCESSING TECHNIQUE FOR BONE FRACTURE DETECTION INCLUDING CLASSIFICATION 165Rocky Upadhyay, Prakash Singh Tanwar and Sheshang Degadwala9.1 Introduction 1659.2 Existing Technology 1669.2.1 Pre-Processing 1669.2.2 Denoise Image 1679.2.3 Histogram 1689.3 Image Processing 1699.3.1 Canny Edge 1699.4 Overview of System and Steps 1709.4.1 Workflow 1709.4.2 Classifiers 1719.4.2.1 Extra Tree Ensemble Method 1719.4.2.2 SVM 1729.4.2.3 Trained Algorithm 1739.4.3 Feature Extraction 1739.5 Results 1749.5.1 Result Analysis 1759.6 Conclusion 176References 17610 IMPROVED OTSU ALGORITHM FOR SEGMENTATION OF MALARIA PARASITE IMAGES 179Mosam K. Sangole, Sanjay T. Gandhe and Dipak P. Patil10.1 Introduction 17910.2 Literature Review 18010.3 Related Works 18210.4 Proposed Algorithm 18310.5 Experimental Results 18410.6 Conclusion 193References 19311 A RELIABLE AND FULLY AUTOMATED DIAGNOSIS OF COVID-19 BASED ON COMPUTED TOMOGRAPHY 195Bramah Hazela, Saad Bin Khalid and Pallavi Asthana11.1 Introduction 19611.2 Background 19611.3 Methodology 19911.3.1 Models Used 19911.3.2 Architecture of the Image Source Classification Model 19911.3.3 Architecture of the CT Scan Classification Model 20011.3.4 Architecture of the Ultrasound Image Classification Model 20111.3.5 Architecture of the X-Ray Classification Model 20111.3.6 Dataset 20211.3.6.1 Training 20211.4 Results 20411.5 Conclusion 206References 20712 MULTIMODALITY MEDICAL IMAGES FOR HEALTHCARE DISEASE ANALYSIS 209B. Rajalingam, R. Santhoshkumar, P. Santosh Kumar Patra, M. Narayanan, G. Govinda Rajulu and T. Poongothai12.1 Introduction 21012.1.1 Background 21012.2 Brief Survey of Earlier Works 21212.3 Medical Imaging Modalities 21312.3.1 Computed Tomography (CT) 21412.3.2 Magnetic Resonance Imaging (MRI) 21412.3.3 Positron Emission Tomography (PET) 21412.3.4 Single-Photon Emission Computed Tomography (SPECT) 21512.4 Image Fusion 21612.4.1 Different Levels of Image Fusion 21612.4.1.1 Pixel Level Fusion 21612.4.1.2 Feature Level Fusion 21712.4.1.3 Decision Level Fusion 21712.5 Clinical Relevance for Medical Image Fusion 21812.5.1 Clinical Relevance for Neurocyticercosis (NCC) 21812.5.2 Clinical Relevance for Neoplastic Disease 21812.5.2.1 Clinical Relevance for Astrocytoma 21812.5.2.2 Clinical Relevance for Anaplastic Astrocytoma 21912.5.2.3 Clinical Relevance for Metastatic Bronchogenic Carcinoma 22012.5.3 Clinical Relevance for Alzheimer’s Disease 22112.6 Data Sets and Softwares Used 22112.7 Generalized Image Fusion Scheme 22112.7.1 Input Image Modalities 22212.7.2 Image Registration 22212.7.3 Fusion Process 22312.7.4 Fusion Rule 22312.7.5 Evaluation 22412.7.5.1 Subjective Evaluation 22412.7.5.2 Objective Evaluation 22412.8 Medical Image Fusion Methods 22412.8.1 Traditional Image Fusion Techniques 22412.8.1.1 Spatial Domain Image Fusion Approach 22512.8.1.2 Transform Domain Image Fusion Approach 22512.8.1.3 Fuzzy Logic–Based Image Fusion Approach 22712.8.1.4 Filtering Technique–Based Image Fusion Approach 22712.8.1.5 Neural Network–Based Image Fusion Approach 22712.8.2 Hybrid Image Fusion Techniques 22812.8.2.1 Transforms with Fuzzy Logic–Based Medical Image Fusion 22812.8.2.2 Transforms With Guided Image Filtering–Based Medical Image Fusion 22912.8.2.3 Transforms With Neural Network–Based Image Fusion 22912.9 Conclusions 23312.9.1 Future Work 234References 23413 HEALTH DETECTION SYSTEM FOR COVID-19 PATIENTS USING IOT 237Dipak P. Patil, Kishor Badane, Amit Kumar Mishra and Vishal A. Wankhede13.1 Introduction 23713.1.1 Overview 23713.1.2 Preventions 23813.1.3 Symptoms 23813.1.4 Present Situation 23813.2 Related Works 23913.3 System Design 23913.3.1 Hardware Implementation 23913.3.1.1 NodeMCU 24013.3.1.2 DHT 11 Sensor 24013.3.1.3 MAX30100 Oxygen Sensor 24113.3.1.4 ThingSpeak Server 24213.3.1.5 Arduino IDE 24313.4 Proposed System for Detection of Corona Patients 24513.4.1 Introduction 24513.4.2 Arduino IDE 24613.4.3 Hardware Implementation 24613.5 Results and Performance Analysis 24713.5.1 Hardware Implementation 24713.5.1.1 Implementation of NodeMCU With Temperature Sensor 24713.5.2 Software Implementation 24813.5.2.1 Simulation of Temperature Sensor With Arduino on Proteus Software 24813.5.2.2 Interfacing of LCD With Arduino 25013.6 Conclusion 250References 25014 INTELLIGENT SYSTEMS IN HEALTHCARE 253Rajiv Dey and Pankaj Sahu14.1 Introduction 25314.2 Brain Computer Interface 25514.2.1 Types of Signals Used in BCI 25614.2.2 Components of BCI 25714.2.3 Applications of BCI in Health Monitoring 25814.3 Robotic Systems 25814.3.1 Advantages of Surgical Robots 25814.3.2 Centralization of the Important Information to the Surgeon 25914.3.3 Remote-Surgery, Software Development, and High SpeedConnectivity Such as 5G 26014.4 Voice Recognition Systems 26014.5 Remote Health Monitoring Systems 26014.5.1 Tele-Medicine Health Concerns 26214.6 Internet of Things–Based Intelligent Systems 26214.6.1 Ubiquitous Computing Technologies in Healthcare 26414.6.2 Patient Bio-Signals and Acquisition Methods 26514.6.3 Communication Technologies Used in Healthcare Application 26714.6.4 Communication Technologies Based on Location/Position 26914.7 Intelligent Electronic Healthcare Systems 27014.7.1 The Background of Electronic Healthcare Systems 27014.7.2 Intelligent Agents in Electronic Healthcare System 27014.7.3 Patient Data Classification Techniques 27114.8 Conclusion 271References 27215 DESIGN OF ANTENNAS FOR MICROWAVE IMAGING TECHNIQUES 275Dnyaneshwar D. Ahire, Gajanan K. Kharate and Ammar Muthana15.1 Introduction 27515.1.1 Overview 27615.2 Literature 27715.2.1 Microstrip Patch Antenna 27815.2.2 Early Detection of Breast Cancer and Microstrip Patch Antenna for Biomedical Application 27915.2.3 UWB for Microwave Imaging 27915.3 Design and Development of Wideband Antenna 28015.3.1 Overview 28015.3.2 Design of Rectangular Microstrip Patch Antenna 28115.3.3 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna 28315.3.4 Design of Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 28515.3.5 Key Shape Monopole Rectangular Microstrip Patch Antenna With Rounded Corner in Partial Ground 28615.4 Results and Inferences 29015.4.1 Overview 29015.4.2 Rectangular Microstrip Patch Antenna 29015.4.2.1 Reflection and VSWR Bandwidth 29015.4.2.2 Surface Current Distribution 29115.4.3 Microstrip Line Feed Rectangular Microstrip Patch Antenna With Partial Ground 29215.4.3.1 Reflection and VSWR Bandwidth 29215.4.3.2 Surface Current Distribution 29215.4.3.3 Inference 29315.4.4 Key Shape Monopole Rectangular Microstrip Patch Antenna with Rounded Corner in Partial Ground 29415.4.4.1 Reflection and VSWR Bandwidth 29415.4.4.2 Surface Current Distribution 29415.4.4.3 Results of the Fabricated Antenna 29515.4.4.4 Inference 29615.5 Conclusion 297References 29816 COVID-19: A GLOBAL CRISIS 303Savita Mandan and Durgeshwari Kalal16.1 Introduction 30316.1.1 Structure 30416.1.2 Classification of Corona Virus 30416.1.3 Types of Human Coronavirus 30416.1.4 Genome Organization of Corona Virus 30516.1.5 Coronavirus Replication 30516.1.6 Host Defenses 30616.2 Clinical Manifestation and Pathogenesis 30616.2.1 Symptoms 30716.2.2 Epidemiology 30716.3 Diagnosis and Control 30816.3.1 Molecular Test 30816.3.2 Serology 30816.3.3 Concerning Lab Assessments 30916.3.4 Significantly Improved D-Dimer 30916.3.5 Imaging 30916.3.6 HRCT 30916.3.7 Lung Ultrasound 31016.4 Control Measures 31016.4.1 Prevention and Patient Education 31116.5 Immunization 31216.5.1 Medications 31216.6 Conclusion 313References 31317 SMART HEALTHCARE FOR PREGNANT WOMEN IN RURAL AREAS 317D. Shanthi17.1 Introduction 31717.2 National/International Surveys Reviews 31917.2.1 National Family Health Survey Review-11 31917.2.2 National Family Health Survey Review-2.2 31917.2.3 National Family Health Survey Reviews-3 32017.3 Architecture 32017.4 Anganwadi’s Collaborative Work 32117.5 Schemes Offered by Central/State Governments 32117.5.1 AAH (Anna Amrutha Hastham) 32117.5.2 Programme Arogya Laxmi 32317.5.3 Balamrutham-Kids’ Weaning Food from 7 Months to 3 Years 32317.5.4 Nutri TASC (Tracking of Group Responsibility for Services) 32317.5.5 Akshyapatra Foundation (ISKCON) 32417.5.6 Mahila Sishu Chaitanyam 32417.5.7 Community Management of Acute Malnutrition 32517.5.8 Child Health Nutrition Committee 32517.5.9 Bharat Ratna APJ Abdul Kalam Amrut Yojna 32517.6 Smart Healthcare System 32617.7 Data Collection 32817.8 Hardware and Software Features of HCS 32817.9 Implementation 32917.9.1 Modules 32917.9.2 Modules Description 32917.9.2.1 Data Preprocessing 32917.9.2.2 Component Features Extraction 32917.9.2.3 User Sentimental Measurement 33017.9.2.4 Sentiment Evaluation 33017.10 Results and Analysis 33117.11 Conclusion 333References 33318 COMPUTER-AIDED INTERPRETATION OF ECG SIGNAL—A CHALLENGE 335Shalini Sahay and A.K. Wadhwani18.1 Introduction 33618.1.1 Electrical Activity of the Heart 33618.2 The Cardiovascular System 33818.3 Electrocardiogram Leads 34018.4 Artifacts/Noises Affecting the ECG 34218.4.1 Baseline Wander 34318.4.2 Power Line Interference 34318.4.3 Motion Artifacts 34418.4.4 Muscle Noise 34418.4.5 Instrumentation Noise 34418.4.6 Other Interferences 34518.5 The ECG Waveform 34618.5.1 Normal Sinus Rhythm 34718.6 Cardiac Arrhythmias 34718.6.1 Sinus Bradycardia 34718.6.2 Sinus Tachycardia 34818.6.3 Atrial Flutter 34818.6.4 Atrial Fibrillation 34918.6.5 Ventric ular Tachycardia 34918.6.6 AV Block 2 First Degree 35018.6.7 Asystole 35018.7 Electrocardiogram Databases 35118.8 Computer-Aided Interpretation (CAD) 35118.9 Computational Techniques 35418.10 Conclusion 356References 357Index 359
Up and Running with DAX for Power BI
Take a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX.While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it’s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Knowledge of everything else in DAX is built on top of these mandatory aspects.Author Alison Box has been teaching and working with DAX for over eight years, starting with DAX for PowerPivot, the Excel add-in, before moving into the Power BI platform. The guide you hold in your hands is an outcome of these years of experience explaining difficult concepts in a way that people can understand. Over the years she has refined her approach, distilling down the truth of DAX which is “you can take people through as many functions as you like, but it’s to no avail if they don’t truly understand how it all works.”You will learn to use DAX to gain powerful insights into your data by generating complex and challenging business intelligence calculations including, but not limited to:* Calculations to control the filtering of information to gain better insight into the data that matters to you* Calculations across dates such as comparing data for the same period last year or the previous period* Finding rolling averages and rolling totals* Comparing data against targets and KPIs or against average and maximum values* Using basket analysis, such as “of customers who bought product X who also bought product Y”* Using “what if” analysis and scenarios* Finding “like for like” sales* Dynamically showing TopN/BottomN percent of customers or products by sales* Finding new and returning customers or sales regions in each month or each yearWHO THIS BOOK IS FORExcel users and non-technical users of varying levels of ability or anyone who wants to learn DAX for Power BI but lacks the confidence to do soALISON BOX is Director of Burningsuit Ltd, and an IT trainer and consultant with over 30 years of experience delivering computer applications training to all skill levels, from basic users to advanced technical experts. Currently, she specializes in delivering training in Microsoft Power BI Service and Desktop, Data Modeling, DAX (Data Analysis Expressions), and Excel. Alison also works with organizations as a DAX and Data Analysis consultant. She was one of the first Excel trainers to move into delivering courses in Power Pivot and DAX, from where Power BI was born. Part of her job entails promoting Burningsuit as a knowledge base for Power BI and includes writing regular blog posts on all aspects of Power BI that are published on her website.Chapter 1: Show Me the DataChapter 2: DAX Objects, Syntax & FormattingChapter 3: Calculated Columns & MeasuresChapter 4: Evaluation ContextChapter 5: IteratorsChapter 6: The CALCULATE FunctionChapter 7: DAX Table FunctionsChapter 8: The ALL Function and All its VariationsChapter 9: Calculations on Dates: Using DAX Time IntelligenceChapter 10: Empty Values Versus ZeroChapter 11: Using Variables: Making Our Code More ReadableChapter 12: Returning Values in the Current FilterChapter 13: Controlling the Direction of Filter PropagationChapter 14: Working with Multiple Relationships Between TablesChapter 15: Understanding Context TransitionChapter 16: Leveraging Context TransitionChapter 17: Virtual Relationships: the LOOKUPVALUE and TREATAS FunctionsChapter 18: Table ExpansionChapter 19: The CALCULATETABLE Function
Electronic Governance
Noch nie sind die technologischen Entwicklungen und die Veränderungen der Märkte so rasant verlaufen wie heutzutage. Die Digitalisierung von Wirtschaft und Gesellschaft stehen dabei erst am Anfang. Viele Menschen beobachten die Entwicklungen misstrauisch. Sie können mit den in Verbindung stehenden Methoden und Begriffen kaum etwas anfangen. Dieses Buch schafft Abhilfe, in dem es umfassend und verständlich aufklärt und erklärt. Beispiele aus Theorie und Praxis veranschaulichen die Inhalte. Die Beherrschung der zugehörigen Komplexitäten ist noch nicht gelungen, wie z.B. die lange Liste gescheiterter Digitalisierungsvorhaben anschaulich belegt. Es geht darum, die Unternehmen zukunftsfest zu machen und die Beschäftigten zu befähigen. Hierfür braucht es einer Art digitaler bzw. technisierter „Leitplanken“, die mit einer „Electronic Governance“ entwickelt und spezifiziert werden. Es handelt sich um ein Steuerungs- und Regelungssystem, welches Organisationen und ihre Beschäftigten in Zeiten der Digitalisierung erfolgreich in die Zukunft führt. PROF. DR. ANDREAS SCHMID lehrt und forscht an der Hochschule Hannover. Er hat zahlreiche (IT-) Projekte und Organisationen evaluiert. Electronic Governance.- Digitalisierung.-Scheitern von Digitalisierungsprojekten.- Disruption.-(Digitale) Strategie.- (Digitales) Geschäftsmodell.-Industrie 4.0.- Robotic Process Automation.- Agilität.-Elektronische Akte.- Design Thinking.-Customer Journey.-Blockchain.-Kryptowährungen.- Künstliche Intelligenz.- Big Data inklusive Praxisbeispiel.
Echtzeit 2021
Mit seiner Tagung 2021 zum Thema „Echtzeitkommunikation“ greift der GI/GMA/ITG-Fachausschuss Echtzeitsysteme ein immer wichtiger werdendes Thema in einer mehr und mehr vernetzten Welt auf. Die präsentierten Lösungen reichen dabei von Hardware über Systementwurf bis hin zu einzelnen Applikationen.Das Buch stellt die auf der Tagung Echtzeit präsentierten Ergebnisse der Forscher auf dem diesjährigen Themengebiet der Echtzeitkommunikation dar. Diese Tagung des Fachausschusses Echtzeitsysteme der Gesellschaft für Informatik ist einzigartig im deutschem Sprachraum und fand 2021 zum 42. Mal statt.HERWIG UNGER und MARCEL SCHAIBLE sind Sprecher bzw. stellv. Sprecher des GI/GMA/ITG-Fachausschuss Echtzeitsysteme, der sich dem immer wichtiger werdenden Thema zeitkritischer Systeme von Hardware bis hin zu einzelnen Applikationen widmet.Real-Time Systems Through the Ages - Dynamische Migrationsentscheidungen in Multicore-Systemen - Ausführungszeit und Stromverbrauch von Inferenzen künstlicher neuronaler Netze auf einem Tensorprozessor - Dynamic Vision-Sensoren zur Texturklassifikation in der automatischen Sichtprüfung - Sind Bitcoin-Transaktionen sicher, echtzeitfähig und ressourcenadäquat? - Analysemethodiken zur Berechnung der WCET mit asynchroner Ein-/Ausgabeverarbeitung - Ein auf Bluetooth 5.1 und Ultrabreitband basierendes Innenraum- Positionssystem - Hardware-Beschleuniger für automobile Multicore-Mikrocontroller mit einer harten Echtzeitanforderung - Fault Tolerance in Heterogeneous Automotive Real-time Systems - Echtzeitfähige Ethernet-Kommunikation in automobilen Multicore-Systemen mit hierarchischem Speicherlayout - Zeitgesteuerte Kommunikationsschnittstellen in unterschiedlichen Anwendungskontexten - Ein Konferenzsystem mit biometrisch basierter Gesichtsvisualisierung für sehr große Teilnehmerzahlen - Machine Learning für die Temperaturermittlung eines Permanentmagnet-Synchronmotors.- Zeitoptimierungsuntersuchungen für Algorithmen des maschinellen Lernens
Introducing Microsoft Orleans
Welcome to Orleans, a virtual actor framework from Microsoft that allows a single developer to create immensely scalable, available applications while maintaining a high throughput. This guide is designed to give you a foundational understanding of Orleans, an overview of its implementations, and plenty of hands-on coding experience. Side-by-side monolithic and microservice patterns alongside Orleans' framework features are also discussed, to help readers without an actor model background understand how they can enhance applications.Author Nelson’s approach is to introduce patterns as needed for business requirements, including monolithic microservices and to convert monolithic to microservices, in order to keep a microservice from growing into a monolithic application. Orleans is a good choice for either of these scenarios as the next step to build your backend services and reduce unnecessary orchestration, overhead, and tooling.The Orleans framework was designed to handle tedious overhead, allowing the developer to focus on the solution. You will learn how Orleans can support billions of virtually parallel transactions while sustaining low latency and high availability. In addition, you will glimpse under the hood at Orleans to discover its useful attributes. All key learning points include hands-on coding examples to reinforce understanding.This book goes beyond what Orleans is to explain where it fits within the realm of development. You will gain an in-depth understanding to build a foundation for future growth.WHAT YOU WILL LEARN* Understand how Orleans can benefit your monolithic and/or microservice applications* Gain a brief overview of actor models and how they relate to Orleans* Observe the design patterns and how Orleans can simplify or reduce tooling requirements* Know the pros and cons of microservices and Orleans to determine the best course of action based on the needs of an application* Discover Orleans' design patterns and practices, including life cycle, messaging guarantees, cluster management, streams, load balancing, and more* Build your first Orleans' application; build base knowledge of application structure, unit testing, dashboard, scheduled eventsWHO THIS BOOK IS FORThis book is for developers. A basic understanding of .NET development and an understanding of service concepts is helpful. Readers will need a connection to download Nuget packages and a code editor (Community Edition Visual Studio or VS Code).THOMAS NELSON, a Lead Cloud Architect and a Microsoft Certified Azure Solutions Architect Expert, has worked in several technical fields spanning from the graphic design of websites to development and architecture. During his 10 years of backend development, his interest has gravitated towards DevSecOps and automation. He enjoys teaching others and is often found at local meetups presenting various technologies, patterns, and software examples. He is thrilled to be using Orleans and considers it one of those wonderful and valuable frameworks that should be in the tool kit of every architect and backend developer. Also, he is pleased to have graduated from monolithic and microservice systems to build cloud-native solutions, including actor model backends. He has an associate's degree in Graphic Design, bachelor's degree in Computer Information Systems, and is currently attending Harvard Extension pursuing his master's degree in Information Management Systems.Chapter 1: A Primer on Microsoft Orleans and the Actor ModChapter 2: Introducing Microsoft OrleansChapter 3: LifecyclesChapter 4: Enhancing Current DesignsChapter 5: Starting DevelopmentChapter 6: Timers and RemindersChapter 7: Unit TestsChapter 8: Orleans' DashboardChapter 9: DeploymentChapter 10: Conclusion
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICSPROVIDES COVERAGE OF DEVELOPMENTS AND STATE-OF-THE-ART METHODS IN THE BROAD AND DIVERSIFIED DATA ANALYTICS FIELD AND APPLICABLE AREAS SUCH AS BIG DATA ANALYTICS, DATA MINING, AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS.The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. AUDIENCEResearchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning. SUNIL KUMAR DHAL, PHD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.SUBHENDU KUMAR PANI, PHD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents. SRINIVAS PRASAD, PHD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters. SUDHIR KUMAR MOHAPATRA, PHD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains. Preface xiii1 AN INTRODUCTION TO BIG DATA ANALYTICS TECHNIQUES IN HEALTHCARE 1Anil Audumbar Pise1.1 Introduction 11.2 Big Data in Healthcare 31.3 Areas of Big Data Analytics in Medicine 51.4 Healthcare as a Big Data Repository 91.5 Applications of Healthcare Big Data 101.6 Challenges in Big Data Analytics 161.7 Big Data Privacy and Security 171.8 Conclusion 181.9 Future Work 182 IDENTIFY DETERMINANTS OF INFANT AND CHILD MORTALITY BASED USING MACHINE LEARNING: CASE STUDY ON ETHIOPIA 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique2.1 Introduction 222.2 Literature Review 232.3 Methodology and Data Source 252.4 Implementation and Results 282.5 Conclusion 443 PRE-TRAINED CNN MODELS IN EARLY ALZHEIMER'S PREDICTION USING POST-PROCESSED MRI 47Kalyani Gunda and Pradeepini Gera3.1 Introduction 483.2 Experimental Study 513.3 Data Exploration 553.4 OASIS Dataset Pre-Processing 613.5 Alzheimer's 4-Class-MRI Features Extraction 693.6 Alzheimer 4-Class MRI Image Dataset 693.7 RMSProp (Root Mean Square Propagation) 803.8 Activation Function 813.9 Batch Normalization 813.10 Dropout 813.11 Result--I 823.12 Conclusion and Future Work 894 ROBUST SEGMENTATION ALGORITHMS FOR RETINAL BLOOD VESSELS, OPTIC DISC, AND OPTIC CUP OF RETINAL IMAGES IN MEDICAL IMAGING 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal4.1 Introduction 984.2 Basics of Proposed Methods 1004.3 Experimental Results and Discussion 1074.4 Conclusion 1155 ANALYSIS OF HEALTHCARE SYSTEMS USING COMPUTATIONAL APPROACHES 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani5.1 Introduction 1205.2 AI & ML Analysis in Health Systems 1245.3 Healthcare Intellectual Approaches 1275.4 Precision Approaches to Medicine 1335.5 Methodology of AI, ML With Healthcare Examples 1345.6 Big Analytic Data Tools 1365.7 Discussion 1415.8 Conclusion 1426 EXPERT SYSTEMS IN BEHAVIORAL AND MENTAL HEALTHCARE: APPLICATIONS OF AI IN DECISION-MAKING AND CONSULTANCY 147Shrikaant Kulkarni6.1 Introduction 1486.2 AI Methods 1496.3 Turing Test 1566.4 Barriers to Technologies 1576.5 Advantages of AI for Behavioral & Mental Healthcare 1576.6 Enhanced Self-Care & Access to Care 1586.7 Other Considerations 1606.8 Expert Systems in Mental & Behavioral Healthcare 1616.9 Dynamical Approaches to Clinical AI and Expert Systems 1656.10 Conclusion 1736.11 Future Prospects 1757 A MATHEMATICAL-BASED EPIDEMIC MODEL TO PREVENT AND CONTROL OUTBREAK OF CORONA VIRUS 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra7.1 Introduction 1887.2 Related Work 1897.3 Proposed Frameworks 1907.4 Results and Discussion 1947.5 Conclusion 2018 AN ACCESS AUTHORIZATION MECHANISM FOR ELECTRONIC HEALTH RECORDS OF BLOCKCHAIN TO SHEATHE FRAGILE INFORMATION 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy8.1 Introduction 2068.2 Related Work 2128.3 Need for Blockchain in Healthcare 2168.4 Proposed Frameworks 2198.5 Use Cases 2238.6 Discussions 2298.7 Challenges and Limitations 2318.8 Future Work 2318.9 Conclusion 2329 AN EPIDEMIC GRAPH'S MODELING APPLICATION TO THE COVID-19 OUTBREAK 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani9.1 Introduction 2379.2 Related Work 2399.3 Theoretical Approaches 2409.4 Frameworks 2439.5 Evaluation of COVID-19 Outbreak 2469.6 Conclusions and Future Works 25010 BIG DATA AND DATA MINING IN E-HEALTH: LEGAL ISSUES AND CHALLENGES 257Amita Verma and Arpit BansalObject of Study 25710.1 Introduction 25810.2 Big Data and Data Mining in e-Health 26010.3 Big Data and e-Health in India 26210.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 26310.5 Big Data and Issues of Privacy in e-Health 27110.6 Conclusion and Suggestions 27211 BASIC SCIENTIFIC AND CLINICAL APPLICATIONS 275Manna Sheela Rani Chetty and Kiran Babu C. V.11.1 Introduction 27511.2 Case Study-1: Continual Learning Using ML for Clinical pplications 28311.3 Case Study-2 28611.4 Case Study-3: ML Will Improve the Radiology Patient Experience 28911.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 29211.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 29511.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 29811.8 Conclusion 30012 HEALTHCARE BRANDING THROUGH SERVICE QUALITY 305Saraju Prasad and Sunil Dhal12.1 Introduction to Healthcare 30512.2 Quality in Healthcare 30712.3 Service Quality 31112.4 Conclusion and Road Ahead 315References 316Index 321
Human-Computer Interaction in Game Development with Python
Deepen your understanding of human-computer interaction (HCI) in game development and learn how to develop video games that grab players and don't let them go. This book explores HCI design in computer games to maximize collaborative and interactive functions.You'll first gain a basic introduction to fundamental concepts and practices of HCI before diving into the fundamental concepts of game interface design and technology. You'll learn how to design a gaming interface through practical examples using Python. This is followed by a brief look at how HCI can offer immersive gaming experiences for players and a review of key elements such as interface, usability, user-centered design, and user interface in terms of efficacy. You will also learn how to implement usability aspects in gaming interfaces with examples using Python.Additionally, the book discusses major challenges that game publishers and developers face, and how they can be resolved using HCI techniques. The question of playability is reviewed throughout the game production process. After working through this book's practical examples, you'll have the knowledge required to begin developing compelling, can't-put-the-controller down games of your own.WHAT YOU'LL LEARN* Master HCI tools and methodologies * Understand the concept of HCI strategies in the game development cycle* Develop a game in Python using the HCI approach* Utilize gamification techniques in Human-Computer Interaction* Grasp concepts of usability, user experience and user-centered design processes and their applicationWHO THIS BOOK IS FORProgrammers, engineers, and students interested in creating and implementing computer games using HCI technologies. Prior experience with game development is recommended.JOSEPH THACHIL GEORGE is an IT Security Engineer based in Germany. He also worked as a technical consultant for International Game Technology (IGT) in Italy. Joseph is currently pursuing his doctorate (PhD) in computer science and engineering at the University of Lisbon, Portugal. He has an M.S. in cybersecurity from the Università degli Studi di Firenze, Italy. He is also part of the DISIA research group at the University of Florence, Italy, and the research group (INESC-ID Lisbon) at the University of Lisbon, Portugal. His research interests cover automatic exploit generation; exploitation of vulnerabilities; chaining of vulnerabilities; security of web applications; and JavaScript code exploits. At IGT, he has been a part of various projects related to game configuration and integration in various platforms, specializing in Java and Spring Boot-based projects. He has also worked for various companies in India, Angola, Portugal, and UK and has seven years of experience with various IT companies.MEGHNA JOSEPH GEORGE is a Cloud Engineer based in Germany. She is an AWS-certified solutions architect. She received a B.S. in System Management and M.S. in economics.Chapter 1: Human–Computer Interaction Tools and MethodologiesSub-Topics• Fundamentals of HCI• Tools and techniques• Eye tracking technique and usability• Use of effective interface• Advantage of HCI toolsChapter 2: Human–Computer Interaction and Game Design and DevelopmentSub-Topics• Games and game world• Concept of game design and development• Connection between HCI and game design and development• Interactive design of the game interface• Window and Icon design• Impact of eye tracking and usability• Effect of Thumbnail• Communication, dynamic Interface, and better user experience• Gamification in HCI• Project overviewChapter 3: Game Interface DevelopmentSub-Topics• What is game interface?• What need to be addressed for effective game interface?• Project - Explaining game interface using Python• Best practice for developing game interfaces• Different standard that companies use for game interface developmentChapter 4: Applying Usability in Computer Game InterfaceSub-Topics• Connection between usability and game interface• Sample project for explaining usability• Project description• Design phase• Applying usability tools• Evaluation of interface based on tools outcome• Conclusion of usability projectChapter 5: Project - Gamification in Human-Computer InteractionSub-Topics:• Relationship between the various processes of game development• Game programming• FAN translation• Game design document and production• Development of game using Python• Discussing HCI technique• Expected problems and solutions• Effectiveness of HCI techniqueChapter 6: Human-Computer Interaction New Trends: Research, DevelopmentSub-Topics:• Research, development• HCI new trends• Research project description of HCI in game development• Examples of HCI in game development - From various game developing companies• European policy and standard for game developmentChapter 7: Tips for Developers and StudentsSub-Topics:• Overview• Tips for developers and students for creating HCI-based games• Advantage in terms of cost and effectiveness of game development• Various industry standards in game development• Impact of games in the global economyChapter 8: ConclusionSub-Topics:• Overview• Recommendation and concluding comments
Artificial Intelligence in Medical Sciences and Psychology
Get started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques.The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification.This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers.WHAT YOU WILL LEARN* Apply artificial neural networks when modelling medical data* Know the standard method for Markov decision making and medical data simulation* Understand survival analysis methods for investigating data from a clinical trial* Understand medical record categorization* Measure personality differences using psychological modelsWHO THIS BOOK IS FORMachine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work settingTSHEPO CHRIS NOKERI harnesses advanced analytics and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, medical sciences, and manufacturing industries. He initially completed a bachelor’s degree in information management. Afterward, he graduated with an Honours degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. Chapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:● Context of the book.● The book’s central point.● Artificial Intelligence subsets covered in this book.● Structure of the book.● Tools that this book implements.○ Python distribution package.○ Anaconda distribution package.○ Jupyter Notebook.○ Python libraries.● Encapsulating Artificial Intelligence.● Debunking algorithms.● Debunking supervised algorithms.● Debunking unsupervised algorithms.● Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:● Classifying patients’ Cardiovascular disease diagnosis outcome data by executing a deepbelief network.● Preprocessing the Cardiovascular disease diagnosis outcome data.● Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.● Training the deep belief network.● Outlining the deep belief networks predictions.● Considering the deep belief network’s performance.● Classifying patients’ diabetes diagnosis outcome data by executing a deep belief network.● Outlining the deep belief networks predictions .● Considering the deep belief network’s performance.● Conclusion.Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation ResultsChapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the patternChapter content:● Debunking the Hidden Markov Model● Descriptive analysis● Carrying Out the Gaussian Hidden Markov Modelo Considering the Hidden States in US COVID-19 Confirmed Cases with the GaussianHidden Markov Model● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Methodo US COVID-19 confirmed cases simulation results● ConclusionChapter 4: Cancer Segmentation with Neural NetworksChapter goal: This chapter typically exhibits the practical application of computer vision andconvolutional neural networks for breast and skin Cancer realization and segmentation. Equally, it shows an approach to filter medical scans by applying canny, luplican, and sobel filters. It concludes by ascertaining the extent to which the networks accurately differentiate scans of patients with and without Cancer.Chapter content:● Debunking Cancer.● Debunking Skin Cancer● Depicting scans of a patient with Skin Cancer.● Classifying Patients’ Skin Cancer Diagnosis Image Data by Executing a Convolutional Neural Network.o Preprocessing the training Skin Cancer Image Data.o Preprocessing the Validation Skin Cancer Image Data.o Generating the Training Skin Cancer Diagnosis Image Data.o Tuning the Training Skin Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Skin CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.o Debunking Breast Cancer.● Classifying Ultrasound Scans of Breast Cancer Patients by Executing a Convolutional Neural Network.o Preprocessing the Validation Breast Cancer Image Data .o Preprocessing the Validation Breast Cancer Image Data .o Generating the Training Breast Cancer Diagnosis Image Data.o Tuning the Training Breast Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Breast CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 5: Modelling Magnetic Resonance Imaging and X-Rays by Carrying out Artificial Neural NetworksChapter goal: This chapter intimately acquaints you with the practical application of computer vision and artificial neural networks in neurology and radiology. It promptly carries out convolutional neural networks for image classification. The initial network models MRI scans to set apart patients with and without a brain tumor, and the second network models X-ray scans to set apart patients with and without pneumonia. Besides that, it unveils an effective technique for appraising networks in medical image classification.Sub-topics:● Debunking Brain Tumors.● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Executing aConvolutional Neural Network.o Depicting MRI Scan of Patients with a Brain Tumor.o Depicting Brain Scans without a Brain Tumor.o Preprocessing the Training MRI Image Data.o Preprocessing the Validation MRI Image Data.o Generating the Training MRI Image Data.o Tuning the Training MRI Image Data.o Executing the Convolutional Neural Network to Classify Patients’ MRI Image Data.o Considering the Convolutional Neural Network’s Performance.● Debunking Pneumonia.o Classifying Patients’ CT scan Data by Executing a Convolutional Neural Network.o Depicting an X-Ray scan of a Patient with Pneumonia.o Depicting an X-Ray scan of a Patient without Pneumonia.o Processing the X-Ray Image Data.o Generating the Training Chest X-Ray Image Data.o Preprocessing the Validation Chest X-Ray Image Data.o Generating the Validation Chest X-Ray Image Data.o Tuning the Training Chest X-Ray Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Chest X-Ray ImageData.▪ Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 6: A Case for COVID-19 CT Scan SegmentationChapter goal: This chapter presents an approach for carrying out convolutional neural networks to model chest CT scan images and differentiate between patients with and without COVID-19.Sub-topics:● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Carrying out aConvolutional Neural Network.o Depicting a Chest CT scan of a COVID-19 Negative Patient.o Depicting a CT scan of COVID-19 Negative Patient.o Preprocessing the Training COVID-19 Data.o Preprocessing the Validation COVID-19 CT Scan Data.o Generating the Training COVID-19 CT Scan Data.o Tuning the Training COVID-19 CT Scan Data.● Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 7 Modelling Clinical Trial DataChapter goal: This chapter familiarizes you with the prime essentials of the most widespread method for adequately investigating data from a clinical trial, recognized as a survival method. It debunks the Nelson-Aalen additive model. To begin with, it encapsulates the method. Subsequently, it promptly presents exploratory analysis, then correlation analysis by carrying out the Pearson correlation method. Following that, it outlines the survival table, then fits the model. It concludes by carefully outlining the profile table, confidence interval, and reproducing the cumulative and baseline hazard.sub-topics:● Debunking Clinical Trials.● An Overview of Survival Analysis.● Context of the Chapter.● Exploring the Nelson-Aalen Additive Model.● Descriptive Analysis.● Realizing a Correlation Relationship.● Outlining the Survival Table.● Carrying out the Nelson-Aalen Additive Model.o Outlining the Nelson-Aalen additive Model’s Confidence Intervalo Discerning the Survival Hazard.o Discerning the Cumulative Survival Hazard.o Baseline Survival Hazard.● Conclusion.● References.Chapter 8: Medical Record CategorizationChapter goal: This chapter sufficiently apprises a wholesome approach for realizing patterns in medical records by carrying out a linear discriminant analysis model. To begin with, it summarizes medical recording. Subsequently, it exhibits a technique of cleansing textual data by carrying out fundamental methods like regularization and TfidfVectorizer. Afterward, it executes the method to classify the medical specialty, then it assesses the extent to which it segregates classes.Sub-topics:● Medical Records.● Context of Chapter.● Debunking Categorization with Linear Discriminant Analysis.o Descriptive Statistics.o Preprocessing the Medical Records Data.o Carrying out Regular Expression.o Carrying Out Word Vectorization.o Carrying out the Linear Discriminant Analysis Model to Classify Patients’ MedicalRecords.o Considering the Linear Discriminant Analysis Model’s Performance.● Conclusion.Chapter 9: A Case for Psychology: Factoring and Clustering Personality DimensionsChapter goal: This chapter introduces you to analyzing the underlying patterns in human behavior by promptly carrying out exploratory factor analysis and cluster analysis. To begin with, it covers the big five personality dimensions. Following that, it presents an approach for typically collecting data by retaining a Likert scale and measuring the reliability of the scale with Cronbach’s reliability testing strategy. Subsequently, it performs factor analysis; beginning with estimating Bartlett Sphericity statistics, then the Kaiser-Meyer-Olkin statistic. Following that, it rotates the eigenvalues by carrying out the varimax rotation method and estimates the proportional variances and cumulative variances. In addition, it executes the K-Means method to observe clusters in the data; beginning with standardizing the data and carrying out principal component analysis.Sub-topics:● Debunking Personality Dimensions.● Questionnaires.● Likert Scale.● Reliability.o Spearman-Brown Reliability Testing Strategy.o Carrying out Cronbach's Reliability Testing Strategy.● Carrying out Factor Model.o Carrying out the Bartlett Sphericity Test.o Carrying out the Kaiser-Meyer-Olkin Test.o Discerning K with a Scree Plot.o Carrying out Eigenvalue Rotation.▪ Varimax Rotation.● Carrying out Cluster Analysis.o Carrying out Principal Component Analysis.O Returning K-Means label.
Festschrift zum 90. Geburtstag von Prof. Dr. Dr. h.c. mult. Günter Hotz
Die vorliegende Festschrift zum 90. Geburtstag von Prof. Dr. Dr. h.c. mult. Günter Hotz zeigt insbesondere über Kurzberichte der Doktorkinder die Nachwirkung von Günter Hotz’ Schaffen auf. Sie gibt damit auch einen schönen Überblick über die Informatik in Deutschland.Dr. Jan Messerschmidt, Jg. 1954, war nach seiner Zeit als wissenschaftlicher Mitarbeiter am Lehrstuhl von Prof. Hotz viele Jahre als geschäftsführender Gesellschafter der DIaLOGIKa GmbH tätig. Seit der 2018 erfolgten Übergabe der Geschäftsleitung der DIaLOGIKa in jüngere Hände engagiert er sich im Rahmen der eigens zu diesem Zweck neu gegründeten LibroDuct GmbH & Co. KG für Projekte aus dem Bereich der Elektromobilität im öffentlichen Verkehr.Prof. Dr. Paul Molitor, Jg. 1959, war von 1982 bis 1993 als wissenschaftlicher Mitarbeiter am Lehrstuhl von Prof. Hotz im Sonderforschungsbereich 124 „VLSI-Entwurfsmethoden und Parallelität“ tätig. In 1993 wurde er auf eine Professur für Schaltungstechnik an die HU Berlin (1993) berufen. Seit 1994 ist er Professor für Technische Informatik an der Martin-Luther-Universität Halle-Wittenberg. Unter anderem war er von 1995 bis 2015 Vorsitzender der Hochschul-DV-Kommission des Landes Sachsen-Anhalt, von 2003 bis 2019 Hauptherausgeber der Zeitschrift it – Information Technology des Oldenbourg Verlages, jetzt de Gruyter, und von 2012 bis 2019 Vorsitzender des Wissenschaftlich-Technischen Beirates der GISA GmbH Halle.Prof. Dr. Jürgen Steimle ist seit 2016 Professor für Mensch-Computer-Interaktion an der Fachrichtung Informatik der Universität des Saarlandes. Derzeit ist er auch Dekan der Fakultät für Mathematik und Informatik. Er hat an den Universitäten Freiburg und Lyon studiert und unter der Betreuung von Prof. Dr. Max Mühlhäuser an der TU Darmstadt promoviert. Seine Dissertation wurde mit dem Dissertationspreis der Gesellschaft für Informatik ausgezeichnet. Vor seiner Berufung an die Universität des Saarlandes war Jürgen Steimle als Visiting Assistant Professor am Massachusetts Institute of Technology und als Senior Researcher am Max-Planck-Institut für Informatik tätig.
Functional Aesthetics for Data Visualization
What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https://www.functionalaestheticsbook.com VIDYA SETLUR, PHD, is the head of Tableau Research. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Her expertise is in natural language processing and computer graphics, and she seeks to develop new algorithms and user interfaces that enhance communication and understanding.BRIDGET COGLEY is the Chief Visualization Officer at Versalytix and is a Tableau Hall of Fame Visionary. As an American Sign Language interpreter turned analyst, her practice incorporates semantics to draw meaning in her designs. She focuses on innovative use cases in data visualization. Acknowledgments ixAbout the Authors xiAbout the Technical Editor xiiForeword by Pat Hanrahan xiiiIntroduction xvPART A: PERCEPTION 1CHAPTER 1: THE SCIENCE BEHIND PERCEPTION 3Seeing and Understanding Imagery 3Color Cognition 6Saccade and Directed Attention 10The Notion of Space and Spatial Cognition 11Diagramming the World 13Summary 20CHAPTER 2: PERCEPTION IN CHARTS 21Visualization and Task 23Chart as an Informational Unit 24Unboxing Functional Aesthetics in the Physical World 27Recursive Proportions 28The Digitized Space: Creating Experiences on the Screen 31Summary 34CHAPTER 3: CHARTS IN USE 35The First Charts 36Standardizing Visualization 40The Shifting Role of Data Visualization 43Maturity within the Profession 49Summary 56PART B: SEMANTICS 57CHAPTER 4: COMING TO TERMS 59Statistical Graphics Are Inherently Abstract 60Flattening the Curve 63Toward Meaningful Depictions 65Situating with Semiotics 68Summary 69CHAPTER 5: VAGUENESS AND AMBIGUITY 71How Tall Is Tall? 71Spicy or Hot—What’sthe Difference? 76Clarification, Repair, and Refinement 78Iconicity of Representation 80The Art of Chart 82Summary 85CHAPTER 6: DATA LITERACY 87Navigating Data Literacy 89The Impact of Writing 90Data Orality 92Changing Exposition Styles 96Data Literacy Democratization 97Summary 99CHAPTER 7: DATA PREPARATION 101Hairy Dates 102Common Transformations 103Clarity in Conversation 107Shaping for Intent 109Prepping for the Future 110Data Enrichment 113Summary 115CHAPTER 8: SCALING IT DOWN 117Generalization 118Natural Sizes 119Fat Fingers and Small Screens 120Color as a Function of Size 123Thumbnails and Visual Summaries 124Summary 128CHAPTER 9: COHESIVE DATA MESSAGES 129Cohesion in Designing Visualizations 131Analytical Conversation 144Summary 152CHAPTER 10: TEXT AND CHARTS 153Medium Being the Message 154Types of Text 155Balancing Text with Charts 161Chart and Text Agreement 163Text in Analytical Conversation 166Making Data More Accessible 168Text for Supporting Reading Fluency 170Summary 171PART C: INTENT 173CHAPTER 11: DEFINING AND FRAMING 175Analytical Intent 176Register 178Repair and Refinement 179Pragmatics 181Practicing Intent 182Summary 185CHAPTER 12: VISUAL COMMUNICATION 187Do What I Mean, Not What I Say 189Register in Charts 192Registers in Composition 194Mood and Metaphor 197Beyond Language Communication 197Expansion and Contraction 200Summary 201CHAPTER 13: SCAFFOLDS 203Visualization Scaffolding 206Scaffolding Data Discovery 210Scaffolding Natural Language Recommendations 213Analytical Conversation to Repair and Refine 217Summary 221CHAPTER 14: BALANCING EMPHASIS 223Individual Choices 224Collective Choices 225Correcting Common Problems 228View Snapping 232Summary 238CHAPTER 15: MODE 239Navigate Like a Local 241Revisiting Analytical Chatbots 247Video Killed the Radio Star 249Beyond the Desktop 251Future Forward 255Summary 257PART D: PUTTING IT ALL TOGETHER 259CHAPTER 16: BRINGING EVERYTHING TOGETHER 261Addressing the Paper Towel Problem 263Crafting Recipes for Functional Aesthetics 267Summary 286CHAPTER 17: CLOSE 287Data in Everything and Everywhere 288New Tools and New Experiences 295Sign-off 297Technical Glossary 299Index 305
Planung und Reporting im BI-gestützten Controlling
Planungs- und Reportinglösungen leiden in vielen Unternehmen immer noch unter mangelnder Datenqualität, sind unzureichend integriert und häufig zeit- und kostenintensiv. Dieses praxisorientierte Buch zeigt Schritt für Schritt, wie es anders geht. Es wird systematisch gezeigt, wie moderne Planungs- und Reportingsysteme im BI-gestützten Controlling mit dem Einsatz von Data-Warehouse- und Big-Data-Technologie aufgebaut und sinnvoll um KI-gestützte Features ergänzt werden können. Für die 4. Auflage wurde das Buch umfassend aktualisiert. Hierbei wurde das umfangreiche Controlling-Cockpit-Beispiel erweitert. Es enthält nun Vorschläge für die Bereiche Unternehmensleitung (operatives und strategisches Controlling), Vertrieb, Produktion, Einkauf und Projektsteuerung. Zudem werden die neusten Entwicklungen im BI-gestützten Controlling mit Unterstützung der traditionellen und explorativen BI aufgezeigt, u.a. Data Mining, Predictive Analytics, Künstliche Intelligenz, RPA, Chatbots, Data Discovery, Data Visualization, App-Technologie, Self Service BI sowie Cloud Computing. Weitere Neuerungen betreffen die Themen Datenqualität und Datenmodellierung. Den Abschluss bildet weiterhin das Kapitel „Mobile BI“, bei dem es um den Ausbau von leistungsfähigen mobilen Analyse- und Planungslösungen mit Hilfe von Tablets, Handys und anderen mobilen Endgeräten geht. Einführung.- Grundlagen.- Fachliche inhaltliche Ausgestaltung.- Organisation und Prozesse.- IT-Unterstützung.- Zusammenfassung und Ausblick.
Cloud Computing Solutions
CLOUD COMPUTING SOLUTIONSTHE MAIN PURPOSE OF THIS BOOK IS TO INCLUDE ALL THE CLOUD-RELATED TECHNOLOGIES IN A SINGLE PLATFORM, SO THAT RESEARCHERS, ACADEMICIANS, POSTGRADUATE STUDENTS, AND THOSE IN THE INDUSTRY CAN EASILY UNDERSTAND THE CLOUD-BASED ECOSYSTEMS.This book discusses the evolution of cloud computing through grid computing and cluster computing. It will help researchers and practitioners to understand grid and distributed computing cloud infrastructure, virtual machines, virtualization, live migration, scheduling techniques, auditing concept, security and privacy, business models, and case studies through the state-of-the-art cloud computing countermeasures. This book covers the spectrum of cloud computing-related technologies and the wide-ranging contents will differentiate this book from others. The topics treated in the book include:* The evolution of cloud computing from grid computing, cluster computing, and distributed systems;* Covers cloud computing and virtualization environments;* Discusses live migration, database, auditing, and applications as part of the materials related to cloud computing;* Provides concepts of cloud storage, cloud strategy planning, and management, cloud security, and privacy issues;* Explains complex concepts clearly and covers information for advanced users and beginners.AUDIENCEThe primary audience for the book includes IT, computer science specialists, researchers, graduate students, designers, experts, and engineers who are occupied with research. SOUVIK PAL is an associate professor in the Department of Computer Science and Engineering at Sister Nivedita University (Techno India Group), Kolkata, India. He has edited about 15 books and published numerous articles in research journals. His research area includes cloud computing, big data, internet of things, wireless sensor network, and data analytics.DAC-NHUONG LE OBTAINED HIS PHD in computer science from Vietnam National University, Vietnam in 2015. He is Deputy-Head of the Faculty of Information Technology, Haiphong University, Vietnam. His area of research includes evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT, and image processing in biomedicine. He has more than 50 publications and edited/authored 15 computer science books. PRASANT KUMAR PATTNAIK, PHD is a professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He has published many research papers in peer-reviewed international journals and conferences and has authored many computer science books in the fields of data mining, robotics, graph theory, Turing machine, cryptography, security solutions in cloud computing, mobile computing, and privacy preservation. List of Figures xviiList of Tables xixForeword xxiPreface xxiiiAcknowledgments xxvAcronyms xxviiPART I: CLOUD COMPUTING ARCHITECTURE 11 BASICS OF CLOUD COMPUTING 3Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik1.1 Evolution of Cloud Computing 41.2 Cluster Computing 71.2.1 The Architecture of Cluster Computing Environment 71.2.2 Components of Computer Cluster 81.3 Grid Computing 91.3.1 Grid-Related Technologies 101.3.2 Levels of Deployment 111.3.3 Architecture of Grid Computing Environment 131.4 Mobile Computing 161.4.1 Characteristics of Mobile Computing 171.4.2 Characteristics of Mobile Networks 171.5 Summary 18Exercises 18References 192 INTRODUCTION TO CLOUD COMPUTING 21Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik2.1 Definition of Cloud Computing 222.2 Characteristics of Cloud 222.2.1 Elasticity and Scalability 232.2.2 Metered and Billing of Service 232.2.3 Self-Service Allocation of Resources 232.2.4 Application Programming Interface (APIs) 242.2.5 Efficiency Measurement Service 242.2.6 Device and Location Interdependency 242.2.7 Customization 242.2.8 Security 252.3 Cloud Computing Environment 252.3.1 Access to Supporting Business Agility 252.3.2 Minimizing Investment Expenditures 252.3.3 Public Cloud Computing Environment 252.3.4 Private Cloud Computing Environment 262.3.5 Hybrid Cloud Computing Environment 272.3.6 Community Cloud Computing Environment 272.4 Cloud Services 282.4.1 Resources as a Service (RaaS) 282.4.2 Infrastructure as a Service (IaaS) 282.4.3 Platform as a Service (PaaS) 292.4.4 Software as a Service (SaaS) 302.4.5 Network as a Service (NaaS) 312.4.6 Desktop as a Service (DaaS/VDI) 322.4.7 Recovery as a Service (DRaaS) 322.5 Security Paradigms and Issues of Cloud Computing 322.6 Major Cloud Service Providers 332.6.1 IaaS CSPs 332.6.2 PaaS CSPs 352.6.3 SaaS CSPs 352.7 Summary 35Exercises 36References 373 ARCHITECTURAL FRAMEWORK FOR CLOUD COMPUTING 39Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik3.1 Challenges of Cloud Computing Environment 403.2 Architectural Framework for Cloud Computing 413.2.1 Service-Oriented Architecture (SOA) 413.2.2 SOA Characterization 423.2.3 Life Cycle of Services in SOA 433.2.4 Integrating SOA and the Cloud 453.2.5 Cloud Architecture 463.3 Architectural Workflow and Co-ordination of Multiple Activities 493.3.1 Characteristics of Workflow 503.3.2 Need for Workflow 503.4 Examples of Workflow Tools 523.5 Summary 53Exercises 53References 544 VIRTUALIZATION ENVIRONMENT IN CLOUD COMPUTING 57Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik4.1 Introduction 584.1.1 Need of Virtualization in Cloud Computing Environment 584.1.2 Virtualization versus Traditional Approach 584.2 Virtualization and Virtual Machine 594.2.1 Advantages of Virtualization Technique in Cloud Computing Environment 604.2.2 Category of Virtual Machine 614.3 Virtualization Model for Cloud Computing 644.3.1 Distributed Resources of Physical Hosts 654.3.2 Hypervisor Monitoring Environment (HME) 654.3.3 Platform Service 664.3.4 Software Service 664.3.5 Broker Service 674.3.6 Business Service 674.4 Categorization of Guest OS Virtualization Techniques 684.4.1 Full Virtualization 684.4.2 Paravirtualization 694.4.3 Hardware-Assisted Virtualization 704.5 Mapping Technique of Virtual Machine to Physical Machine in a Private Cloud 714.6 Drawbacks of Virtualization 724.7 Summary 73Exercises 74References 755 CLASSIFICATION OF VIRTUALIZATION ENVIRONMENT 77Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik5.1 Introduction 785.2 Classification 785.2.1 Scheduling-Based Environment 795.2.2 Load Distribution-Based Environment 805.2.3 Energy-Aware-Based Environment 815.2.4 Operational-Based Environment 825.2.5 Distribution Pattern-Based Environment 855.2.6 Transaction-Based Environment 865.3 Summary 87Exercises 87References 88PART II: CLOUD COMPUTING DATA STORAGE 916 AN APPROACH TO LIVE MIGRATION OF VIRTUAL MACHINES IN CLOUD COMPUTING ENVIRONMENT 93Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik6.1 Introduction 946.2 Need of Live Migration of Virtual Machine 946.3 Advantages of Live Migration 946.4 A Design Approach to Live Migration 956.4.1 Live Migration Process 976.5 Security Issues 996.5.1 Possible Attacks 996.5.2 Solutions 1006.6 Summary 100Exercises 100References 1017 RELIABILITY ISSUES IN CLOUD COMPUTING ENVIRONMENT 103Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik7.1 Introduction 1047.1.1 Research Problem Statement 1057.1.2 Research Aim 1057.1.3 Research Question 1057.2 Literature Review 1067.2.1 Cloud Service Models 1077.2.2 Elements of Reliable Cloud Computing 1087.2.3 Cloud Computing Gaps and Concerns 1097.2.4 Trends in Cloud Computing 1107.3 Reliability Issues in Cloud Computing Research 1117.3.1 Research Methodology 1117.3.2 Research Strategy 1117.3.3 Data Collection 1127.3.4 Sampling 1127.3.5 Data Analysis and Findings 1127.4 Findings 1147.4.1 Lack of Effort to Address Reliability and Availability Issues 1147.4.2 Performance Issues 1157.4.3 Privacy Issues 1157.5 Summary 115Exercises 116References 1168 CLOUD DATABASE 123Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik8.1 Introduction 1248.2 Non-Relational Data Models 1248.2.1 Transactions in Cloud Databases 1258.2.2 Advantages of Cloud Database 1258.3 Heterogeneous Databases in DaaS 1268.3.1 Relational and Non-Relational Database 1268.3.2 Centralized and Distributed Database 1268.3.3 Structured and Unstructured Database 1278.3.4 Infrastructure-Based and Infrastructureless Databases 1278.3.5 SQL-Based and NoSQL-Based Databases 1288.4 Study of a Document-Oriented Cloud Database - MongoDB 1298.4.1 Data Model 1298.4.2 Replication 1298.4.3 Sharding 1308.4.4 Architecture 1318.4.5 Consistency 1328.4.6 Failure Handling 1328.5 CAP Theorem for Cloud Database Transaction 1328.6 Issues in Live Migration of Databases in Cloud 1338.7 Cloud Database Classification Based on Transaction Processing 1348.7.1 ACID-Based Cloud Database 1348.7.2 NoACID-Based Cloud Database 1358.8 Commercially Available Cloud Database Platform 1368.8.1 Amazon Web Services 1378.8.2 Microsoft Windows Azure 1388.8.3 Google App Engine 1388.9 Summary 138Exercises 138References 1409 CLOUD-BASED DATA STORAGE 143Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik9.1 Relevant Hadoop Tools 1449.2 Hadoop Distributed File System (HDFS) 1459.2.1 HDFS Architecture 1459.2.2 Data Read Process in HDFS 1469.2.3 Data Write Process in HDFS 1479.2.4 Authority Management of HDFS 1489.2.5 Limitations of HDFS 1489.3 Data Mining Challenges with Big Data 1499.4 MapReduce 1519.4.1 MapReduce Architecture 1519.4.2 MapReduce Procedure 1529.4.3 Limitations of MapReduce 1539.5 Next Generation of MapReduce: YARN 1549.5.1 YARN Compared to MapReduce 1.0 (MRv1) 1559.5.2 YARN and MapReduce 2.0 (MRv2) 1569.5.3 YARN Architecture 1569.5.4 Advantages of YARN 1599.6 Classification of Data Mining Systems 1609.6.1 Classification According to Kind of Databases Mined 1609.6.2 Classification According to Kind of Knowledge Mined 1609.6.3 Classification According to Kind of Techniques Utilized 1619.6.4 Classification According to the Applications Adapted 1619.7 Summary 162Exercises 162References 16310 AUDITING CONCEPT IN CLOUD COMPUTING 165Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik10.1 Introduction 16610.2 Data Security in Cloud Computing Environment 16610.2.1 Characteristics of a Secure Cloud Computing Environment 16610.2.2 Need for Auditing in Cloud Computing Environment 16710.2.3 Auditing Background Within Third-Party Service Provider 16710.3 Cloud Auditing Outsourcing Life Cycle Phases 16710.4 Auditing Classification 16810.5 Auditing Service 16910.5.1 How Third-Party Service Provider is Enabling Auditing Service 17110.5.2 Auditing Process Analysis 17110.5.3 Privacy and Integrity 17410.5.4 Cloud-Auditing Architecture Analysis 17610.6 Summary 177Exercises 178References 178PART III: CLOUD COMPUTING IMPLEMENTATION, SECURITY AND APPLICATIONS 18111 SECURITY PARADIGMS IN CLOUD COMPUTING 183Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal11.1 Security Paradigms and Issues 18411.2 Cloud Security Challenges 18511.3 Cloud Economics 18711.4 Security of Big Data in Cloud 18711.4.1 The Biggest Risk: Data Breach 18811.4.2 Data Loss 18811.4.3 Account or Service Traffic Hijacking 18911.4.4 Insecure Interfaces and APIs 18911.4.5 Denial of Service 19011.4.6 Malicious Insiders 19011.4.7 Abuse of Cloud Users 19011.4.8 Inadequate Due Diligence 19111.4.9 Vulnerabilities in Shared Technology 19111.5 Security as a Service in Cloud 19111.6 Summary 194Exercises 194References 19512 PRIVACY PRESERVATION ISSUES IN CLOUD COMPUTING 197Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal12.1 Privacy Issues in Cloud Storage 19812.1.1 Encryption Methods 19812.1.2 Access Control Mechanisms 19912.1.3 Query Integrity/Keyword Searches 20012.1.4 Auditability Schemes 20012.2 Privacy and Security 20112.2.1 Performance Unpredictability, Latency and Reliability 20212.2.2 Portability and Interoperability 20312.2.3 Data Breach Through Fiber-Optic Networks 20412.2.4 Data Storage over IP Networks 20412.2.5 Data Storage and Security in Cloud 20512.3 Threats to Security in Cloud Computing 20812.3.1 Basic Security 20812.3.2 Network-Level Security 20912.3.3 Application-Level Security 21112.4 Security Issues in Cloud Deployment Models 21512.4.1 Security Issues in a Public Cloud 21512.4.2 Security Issues in a Private Cloud 21612.5 Ensuring Security Against Various Types of Attacks 21712.6 Survey of Privacy Preservation Using Fuzzy Set and Genetic Algorithm 21912.6.1 Fuzzy-Based Approach for Privacy-Preserving Publication of Data 21912.6.2 Privacy-Preserving Fuzzy Association Rules Hiding in Quantitative Data 22012.6.3 A Rough Computing-Based Performance Evaluation Approach for Educational Institutions 22212.6.4 A New Method for Preserving Privacy in Quantitative Association Rules Using Genetic Algorithm 22312.6.5 Privacy Preserving in Association Rules Using a Genetic Algorithm 22412.7 Summary 225Exercises 225References 22613 APPLICATIONS OF WIRELESS SENSOR NETWORK IN CLOUD 233Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal13.1 Introduction 23413.2 Architectural Issues of Combining Cloud Computing and Wireless Sensor Networks 23413.3 Sensor Network Overview 23513.3.1 Terminology 23513.3.2 Routing Protocols in WSNs 23613.4 Application Scenarios 23713.4.1 Military Use 23713.4.2 Weather Forecasting 23713.4.3 Healthcare 23813.4.4 Transport Monitoring 23813.5 Summary 238Exercises 239References 23914 APPLICATIONS OF MOBILE CLOUD COMPUTING 243Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal14.1 What is Mobile Cloud Computing? 24414.2 The Architecture of Mobile Cloud Computing 24514.3 Characteristics of Mobile Cloud Computing 24514.4 Advantages of Mobile Cloud Computing 24614.5 Mobile Cloud Applications 24814.5.1 Mobile Commerce 24814.5.2 Mobile Learning 24914.5.3 Mobile Healthcare 24914.5.4 Mobile Gaming 25014.5.5 Mobile Social Network 25114.5.6 Multimedia Sharing 25214.6 Summary 252Exercises 252References 25315 BIG DATA IN CLOUD COMPUTING 257Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal15.1 Introduction to Big Data 25815.2 Big Data vs. Cloud Computing 25915.3 Big Data and the Cloud 26115.4 Cloud Computing to Support Big Data 26215.4.1 Cloud Storage for Big Data Storage 26215.4.2 Cloud Computing for Big Data Processing 26215.4.3 Cloud Computing for Big Data Analytics 26315.4.4 Cloud Computing for Big Data Sharing and Remote Collaboration 26315.5 Opportunities and Challenges 26315.5.1 Pros of Putting Big Data in the Cloud 26315.5.2 Potential Challenges of Big Data in the Cloud 26415.6 Summary 265Exercises 265References 266PART IV: CLOUD COMPUTING SIMULATOR TOOLS 26916 CLOUDSIM: A SIMULATOR FOR CLOUD COMPUTING ENVIRONMENT 271Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik16.1 Introduction 27216.2 Main Features 27216.3 CloudSim Architecture 27316.3.1 Modeling the Cloud 27416.3.2 Modeling the VM Allocation 27516.3.3 Modeling the Cloud Market 27616.3.4 Modeling the Network Behavior 27616.3.5 Modeling a Federation of Clouds 27616.3.6 Modeling Dynamic Workloads 27716.3.7 Modeling Data Center Power Consumption 27816.3.8 Modeling Dynamic Entities Creation 27816.4 Design and Implementation of CloudSim 27916.5 Setting up Development Environments 28216.6 How to Use CloudSim with Eclipse 282References 28517 OPENFAAS 287Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal17.1 Introduction 28817.2 OpenFaaS Architecture 28817.3 OpenFaaS Installation 28917.3.1 Development Environment with Docker Swarm 29017.3.2 Multi-Node Cluster with Docker Swarm 29117.3.3 Production Environment with Kubernetes 29317.3.4 Installing OpenFaaS Using Helm 29717.3.5 Install OpenShift 29817.4 Considerations 30017.5 Operation of OpenFaaS 30017.5.1 Setup and Configuration of the Open FaaS Command Line Tool 30017.5.2 OpenFaaS Store 30117.5.3 Management and Usage of Functions 30117.5.4 Development of Functions 30217.5.5 Working with Docker Registries 30217.5.6 Web UI 303References 30318 OPENNEBULA 305Prasant Kumar Pattnaik, Dac-Nhuong Le, Souvik Pal18.1 Project Goal and Environment 30618.2 Set Up Masternode with Frontend 30618.2.1 Install Components 30618.2.2 Starting the Frontend 30618.3 Set Up Worker Node with KVM 30718.3.1 Install Components 30718.3.2 Establish an SSH Communication Pipeline between Master and Worker 30818.3.3 Network Configuration 30818.4 Register Worker Node 30818.5 Deploy VM 309References 31119 OPENSTACK 313Dac-Nhuong Le, Souvik Pal, Prasant Kumar Pattnaik19.1 OpenStack 31419.2 Terminologies in OpenStack 31419.3 OpenStack Architecture 31519.3.1 Compute (Nova) 31619.3.2 Networking (Neuron) 31619.3.3 Image 31619.3.4 Object Storage (Swift) 31619.3.5 Block Storage (Cinder) 31619.4 Logical Architecture 31719.5 OpenStack Installation Guide 31819.5.1 Hardware Requirements 31819.5.2 Networking Requirements 31919.6 OpenStack Work 321References 32220 EUCALYPTUS 325Souvik Pal, Dac-Nhuong Le, Prasant Kumar Pattnaik20.1 Introduction to Eucalyptus 32620.1.1 Eucalyptus Overview 32620.1.2 Eucalyptus Architecture 32620.1.3 Eucalyptus Components 32720.2 Eucalyptus Installation 32820.2.1 System Requirements 32920.2.2 Services Placement 33020.2.3 Eucalyptus Features 33120.2.4 Networking Modes 33220.2.5 Install Repositories 33220.3 Configure Eucalyptus 33520.4 Amazon Web Services Compatibility 337References 337Glossary 339Authors 365
Practical Industrial Cybersecurity
A PRACTICAL ROADMAP TO PROTECTING AGAINST CYBERATTACKS IN INDUSTRIAL ENVIRONMENTSIn Practical Industrial Cybersecurity: ICS, Industry 4.0, and IIoT, veteran electronics and computer security author Charles J. Brooks and electrical grid cybersecurity expert Philip Craig deliver an authoritative and robust discussion of how to meet modern industrial cybersecurity challenges. The book outlines the tools and techniques used by practitioners in the industry today, as well as the foundations of the professional cybersecurity skillset required to succeed on the SANS Global Industrial Cyber Security Professional (GICSP) exam. Full of hands-on explanations and practical guidance, this book also includes:* Comprehensive coverage consistent with the National Institute of Standards and Technology guidelines for establishing secure industrial control systems (ICS)* Rigorous explorations of ICS architecture, module and element hardening, security assessment, security governance, risk management, and morePractical Industrial Cybersecurity is an indispensable read for anyone preparing for the Global Industrial Cyber Security Professional (GICSP) exam offered by the Global Information Assurance Certification (GIAC). It also belongs on the bookshelves of cybersecurity personnel at industrial process control and utility companies. Practical Industrial Cybersecurity provides key insights to the Purdue ANSI/ISA 95 Industrial Network Security reference model and how it is implemented from the production floor level to the Internet connection of the corporate network. It is a valuable tool for professionals already working in the ICS/Utility network environment, IT cybersecurity personnel transitioning to the OT network environment, and those looking for a rewarding entry point into the cybersecurity field. CHARLES J. BROOKS is the co-Owner and Vice President of Educational Technologies Group Inc and the co-Owner of eITPrep LLP. He oversees research and product development at those organizations and has authored several books, including the A+ Certification Training Guide and The Complete Introductory Computer Course. For the past eight years Charles has been lecturing and providing Instructor training for cybersecurity teachers throughout the U.S. and abroad. His latest projects have been associated with IT and OT cybersecurity courses and hands-on lab activities that include Cybersecurity Essentials — Concepts & Practices; Cybersecurity Essentials – Environments & Testing; and Industrial Network Cybersecurity.PHILIP A. CRAIG JR is the founder of BlackByte Cyber Security, LLC, a consultancy formed to develop new cybersecurity tools and tactics for use in U.S Critical Infrastructure. He oversees research and product development for the U.S. Department of Energy (DOE), the Defense Advanced Research Projects Agency (DARPA), and the National Rural Electric Cooperative Association (NRECA), as well as providing expert knowledge in next generation signal isolation techniques to protect automated controls in energy generation, transmission, and distribution systems. Mr. Craig has authored regulation for both the Nuclear Regulatory Commission (NRC) and National Energy Reliability Corporation (NERC) and is an active cyber responder in federal partnerships for incident response. Introduction xxiiiCHAPTER 1 INDUSTRIAL CONTROL SYSTEMS 1Introduction 2Basic Process Control Systems 3Closed- Loop Control Systems 5Industrial Process Controllers 6Supervisory Control and Data Acquisition Systems 20System Telemetry 21Utility Networks 23OT/IT Network Integration 25Industrial Safety and Protection Systems 28Safety Instrument Systems 29Review Questions 39Exam Questions 41CHAPTER 2 ICS ARCHITECTURE 43Introduction 44Network Transmission Media 45Copper Cabling 45Fiber- Optic Cabling 46Industrial Network Media Standards 49Ethernet Connectivity 52External Network Communications 53Transmission Media Vulnerabilities 55Field Device Architecture 56PLC I/O Sections 58PLC Implementations 62Industrial Sensors 63Final Control Elements/Actuators 71Relays 73Process Units 76Industrial Network Protocols 79Common Industrial Protocols 79EtherNet/IP Protocol 79Modbus 80ProfiNet/ProfiBus 81Dnp3 82Iccp 83Opc 83BACnet 83Enterprise Network Protocols 84Tcp/ip 84Dynamic Host Configuration Protocol 89Review Questions 90Exam Questions 91CHAPTER 3 SECURE ICS ARCHITECTURE 95Introduction 96Boundary Protection 97Firewalls 98Proxies 104Security Topologies 105Network Switches 106Routers 108Security Zoning Models 109Flat Network Topologies 113Network Segmentation 122Controlling Intersegment Data Movement 128Tunneling 128Wireless Networking 129Wireless Sensors 131Wireless Gateways 134Modems 135Review Questions 137Exam Questions 139CHAPTER 4 ICS MODULE AND ELEMENT HARDENING 143Introduction 145Endpoint Security and Hardening 145User Workstation Hardening 145BIOS Security Subsystems 147Additional Outer Perimeter Access Hardening 148Mobile Device Protection 154OS Security/Hardening 155File System Security 156Operating System Security Choices 160Linux SystemV vs Systemd 160Hardening Operating Systems 162Common Operating System Security Tools 162Virtualization 169Application Software Security 172Software Exploitation 172Information Leakage 173Applying Software Updates and Patches 174Database Hardening 174SQL Injection 175Anti-Malware 177Antivirus 178Anti-spyware 178Anti- Malware: Sanitization 181Embedded Device Security 182Meters 184Network Hardening 189OT/IT Network Security 189Server Security 191Hardening the Server OS 193Logical Server Access Control 194Hardening Network Connectivity Devices 196Review Questions 201Exam Questions 202CHAPTER 5 CYBERSECURITY ESSENTIALS FOR ICS 205Introduction 207Basic Security Tenets 208Confidentiality, Integrity, and Availability 208Availability in ICS Networks 209Nonrepudiation 210Principle of Least Privilege 211Separation of Duties 211Vulnerability and Threat Identification 212Nation- States 213Cyberterrorists 213Cybercriminals 214Insider Threats 216Events, Incidents, and Attacks 217Threat Vectors 217Weaponization 230Delivery 230Exploitation 231Installation 232Command and Control 233Actions on Objectives 233Attack Methods 234Unauthorized Access 251Cryptographics 260Encryption 262Digital Certificates 264Public Key Infrastructure 264Hashing 266Resource Constraints 267Review Questions 268Exam Questions 268CHAPTER 6 PHYSICAL SECURITY 271Introduction 272Infrastructure Security 273Access Control 274Physical Security Controls 276Authentication Systems 278Remote Access Monitoring and Automated Access Control Systems 286Intrusion Detection and Reporting Systems 289Security Controllers 290Video Surveillance Systems 295Cameras 297IP Cameras 297Pan- Tilt- Zoom Cameras 298Physical Security for ICS 306Industrial Processes/Generating Facilities 307Control Center/Company Offices 307Nerc Cip-006-1 309Review Questions 311Exam Questions 312CHAPTER 7 ACCESS MANAGEMENT 315Introduction 316Access Control Models 317Mandatory Access Control 317Discretionary Access Control 318Role- Based Access Control 318Rule- Based Access Control 319Attribute- Based Access Control 319Context- Based Access Control 320Key Security Components within Access Controls 320Directory Services 321Active Directory 321Linux Directory Services 324Application Runtime and Execution Control 326User Access Management 326Establishing User and Group Accounts 328Group Account Security 330Network Authentication Options 331Establishing Resource Controls 332ICS Access Control 334Remote ICS Access Control 336Access Control for Cloud Systems 340Review Questions 343Exam Questions 344CHAPTER 8 ICS SECURITY GOVERNANCE AND RISK MANAGEMENT 347Introduction 348Security Policies and Procedure Development 348Requirements 349Exceptions and Exemptions 350Standards 351ICS Security Policies 356Risk Management 357Asset Identification 358Risk Assessment 359Risk Identification Vulnerability Assessment 362Impact Assessment 363ICS Risk Assessments 364Risk Mitigation 366Nerc Cip-008 367Review Questions 369Exam Questions 370CHAPTER 9 ICS SECURITY ASSESSMENTS 373Introduction 374Security Assessments 374ICS Device Testing 376Vulnerability 376Supply Chain 377Communication Robustness Testing 382Fuzzing 382ICS Penetration Testing 384The Pentest Process 385Security Testing Tools 392Packet Sniffers 392Network Enumeration/Port Scanning 393Port Scanning 395Vulnerability Scanning 395Review Questions 401Exam Questions 402CHAPTER 10 ICS SECURITY MONITORING AND INCIDENT RESPONSE 405Introduction 407ICS Lifecycle Challenges 408Change Management 408Establishing a Security Baseline 409Change Management Documentation 411Configuration Change Management 412Controlling Patch Distribution and Installation for Systems 414Monitoring 419Event Monitoring 420Network Monitoring 421Security Monitoring 423Logging and Auditing 424Event Logging 425Incident Management 433The Incident Response Lifecycle 434Preparation 435Incident Response 442Recovery 445Post- Incident Activities 446Review Questions 449Exam Questions 450CHAPTER 11 DISASTER RECOVERY AND BUSINESS CONTINUITY 453Introduction 454Business Continuity Plans 455System Redundancy 455Local Virtualized Storage 459System Backup and Restoration 462Backup Options 463Backup Media Rotation 466Securing Backup Media 467Other BCP Considerations 467Disaster Recovery 469Planning 470Documenting the Disaster Recovery Plan 472The Disaster Response/Recovery Team 473Nerc Cip-009-6 475Review Questions 477Exam Questions 478APPENDIX A GICSP OBJECTIVE MAP 481ICS410.1 ICS: Global Industrial Cybersecurity Professional (GICSP) Objectives 482Overview 482ICS410.2: Architecture and Field Devices 483ICS410.3: Communications and Protocols 484ICS410.4: Supervisory Systems 485ICS410.5: Security Governance 485APPENDIX B GLOSSARY 487APPENDIX C STANDARDS AND REFERENCES 533Reference Links 536APPENDIX D REVIEW AND EXAM QUESTION ANSWERS 539Chapter 1: Industrial Control Systems 540Review Question Answers 540Exam Question Answers 541Chapter 2: ICS Architecture 542Review Question Answers 542Exam Question Answers 544Chapter 3: Secure ICS Architecture 545Review Question Answers 545Exam Question Answers 547Chapter 4: ICS Modules and Element Hardening 548Review Question Answers 548Exam Question Answers 550Chapter 5: Cybersecurity Essentials for ICS 551Review Question Answers 551Exam Question Answers 553Chapter 6: Physical Security 554Review Question Answers 554Exam Question Answers 556Chapter 7: Access Management 556Review Question Answers 556Exam Question Answers 558Chapter 8: ICS Security Governance and Risk Management 559Review Question Answers 559Exam Question Answers 560Chapter 9: ICS Security Assessments 561Review Question Answers 561Exam Question Answers 563Chapter 10: ICS Security Monitoring and Incident Response 564Review Question Answers 564Exam Question Answers 565Chapter 11: Disaster Recovery and Business Continuity 567Review Question Answers 567Exam Question Answers 568Index 571
Selbstorganisation braucht Führung
SELBSTORGANISATION BRAUCHT FÜHRUNG // - Erfahren Sie, warum agile Unternehmenskulturen mehr und vor allem echte Führung brauchen - Schaffen Sie mit einfachen Werkzeugen die Rahmenbedingungen für die Selbstorganisation Ihres Teams - Lernen Sie aus den Erfahrungen, Erfolgen und Misserfolgen der Autoren als Manager - Nutzen Sie die Tipps und Übungen, um Ihr persönliches Führungsverständnis zu formen - Neue Themen in der 3. Auflage: Legitimation, Mut, Remote-Führung und selbstbestimmte Gehälter - Ihr exklusiver Vorteil: E-Book inside beim Kauf des gedruckten Buches Durch die Herausforderungen der Digitalisierung ist das Thema »Agilität« an die Spitze der Management-Agenda gerückt. Waren selbstorganisierte Arbeitsweisen bis vor wenigen Jahren noch eine Grassroots-Bewegung, so kommen die Initiativen zur agilen Transformation ganzer Organisationen heute von oben, aus den Führungs- und Vorstandsetagen. Vielen Managern ist klargeworden, dass sie Agilität selbst leben müssen und durch ihr eigenes Verhalten maßgeblich fördern. Nur, wie geht das? Führungskräfte müssen immer wieder auf das System einwirken, damit es die nächste Entwicklungsstufe der Selbstorganisation erreichen kann. Das funktioniert nicht durch Mikromanagement oder Delegation von Veränderung. Es bedeutet: konsequente Arbeit an der eigenen Haltung, Vorbild sein im Verhalten und die Wahrnehmung von Mitarbeitern als Menschen – nicht als Ressourcen. Boris Gloger und Dieter Rösner entwerfen keine agile Führungslehre, sondern leiten zur Selbstreflexion an. Sie erzählen von eigenen und beobachteten Krisen, vom eigenen Scheitern und dem Erkennen, wie Selbstorganisation entsteht. Daraus leiten sie ein modernes Führungsverständnis für eine Kultur des Gelingens ab. AUS DEM INHALT // Warum Führen heute so schwierig ist/Wie Selbstorganisation funktioniert/Mensch, Modell, Manager: Agilität als Kultur des Gelingens/Welche Strukturen die Selbstorganisation anregen/Vom Anreizsystem zum Anerkennungssystem
Gefühlte Zukunft
Welche Rolle spielen Emotionen bei der Konstruktion, Kommunikation und Nutzung von Zukunftsbildern? Sind Emotionen primär eine Komponente, die Vorurteile transportiert? In welchem Maße sind Forschende selbst Emotionen ausgesetzt, die die Methodenwahl und die Ergebnisse beeinflussen? In dem Sammelband kommt die gesamte Breite der Thematik „Zukunftsforschung und Emotionalität“ zur Sprache, von den philosophischen Grundlagen bis zu methodischen Fragestellungen und Erkenntnissen aus der Praxis, die die enge Verschränkung von Kognition und Emotion in allen Phasen von Vorausschauprozessen belegen. Psychodynamik und Foresight. Zum subjektiven Faktor in der prospektiven Forschung.- Gefühl und Zukunftsbild.- Sein zur Lust. Zukünfte als Modalstrukturen.- Narrative Scharniere – Zur Vermittlung von Emotionalität und Zukunftsperspektiven.- Zur Uneindeutigkeit von Zukunft – Widerspruchstoleranz im Umgang mit mehrdeutigen Zukünften.- Der „subjektive Faktor“: Angst, Hoffnung und Zuversicht in drei Berichten an den Club of Rome.- Risikoanalysen als Austauschformat im Rahmen verantwortungsvoller Forschung und Innovation.- Im emotionalisierten Raum. Human Factors in Hardware- und Software-Design von Robotern und Künstlicher Intelligenz.- Personas, ein Instrument zur erleichterten Handhabung emotionaler Aspekte in Foresightprozessen?.- Einflussreiche Metaphern: Funktionen und Wirkungspotentiale von metaphorisch-emotionalen Ausdrücken beim Formulieren und Kommunizieren von Zukunftsbildern.- Ist Angst tatsächlich ein schlechter Ratgeber? Über den Zusammenhang von Technik, Emotionen und Vorsorge.- Wilde Zukünfte. Zur Emotionalität beim Umgang mit Wild Cards.- Geschichten aus der Zukunft – das Unfassbare erlebbar machen.- Tabuisierte Zukünfte – Wie Tabus die Analyse des zukünftigen Möglichkeitenraums beeinflussen.- Von der Furcht, konkret zu werden.
Advanced Analytics and Deep Learning Models
ADVANCED ANALYTICS AND DEEP LEARNING MODELSTHE BOOK PROVIDES READERS WITH AN IN-DEPTH UNDERSTANDING OF CONCEPTS AND TECHNOLOGIES RELATED TO THE IMPORTANCE OF ANALYTICS AND DEEP LEARNING IN MANY USEFUL REAL-WORLD APPLICATIONS SUCH AS E-HEALTHCARE, TRANSPORTATION, AGRICULTURE, STOCK MARKET, ETC.Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. AUDIENCEResearchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning. ARCHANA MIRE, PHD, is an assistant professor in the Computer Engineering Department, Terna Engineering College, Navi Mumbai, India. She has published many research articles in peer-reviewed journals. SHAVETA MALIK, PHD, is an associate professor in the Computer Engineering Department (NBA accredited), Terna Engineering College, Nerul, India. She has published many research articles in peer-reviewed journals. AMIT KUMAR TYAGI, PHD, is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber-physical systems, and computer vision. Preface xixPART 1: INTRODUCTION TO COMPUTER VISION 11 ARTIFICIAL INTELLIGENCE IN LANGUAGE LEARNING: PRACTICES AND PROSPECTS 3Khushboo Kuddus1.1 Introduction 41.2 Evolution of CALL 51.3 Defining Artificial Intelligence 71.4 Historical Overview of AI in Education and Language Learning 71.5 Implication of Artificial Intelligence in Education 81.5.1 Machine Translation 91.5.2 Chatbots 91.5.3 Automatic Speech Recognition Tools 91.5.4 Autocorrect/Automatic Text Evaluator 111.5.5 Vocabulary Training Applications 121.5.6 Google Docs Speech Recognition 121.5.7 Language MuseTM Activity Palette 131.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 131.6.1 Autonomous Learning 131.6.2 Produce Smart Content 131.6.3 Task Automation 131.6.4 Access to Education for Students with Physical Disabilities 141.7 Conclusion 14References 152 REAL ESTATE PRICE PREDICTION USING MACHINE LEARNING ALGORITHMS 19Palak Furia and Anand Khandare2.1 Introduction 202.2 Literature Review 202.3 Proposed Work 212.3.1 Methodology 212.3.2 Work Flow 222.3.3 The Dataset 222.3.4 Data Handling 232.3.4.1 Missing Values and Data Cleaning 232.3.4.2 Feature Engineering 242.3.4.3 Removing Outliers 252.4 Algorithms 272.4.1 Linear Regression 272.4.2 LASSO Regression 272.4.3 Decision Tree 282.4.4 Support Vector Machine 282.4.5 Random Forest Regressor 282.4.6 XGBoost 292.5 Evaluation Metrics 292.6 Result of Prediction 30References 313 MULTI-CRITERIA–BASED ENTERTAINMENT RECOMMENDER SYSTEM USING CLUSTERING APPROACH 33Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan3.1 Introduction 343.2 Work Related Multi-Criteria Recommender System 353.3 Working Principle 383.3.1 Modeling Phase 393.3.2 Prediction Phase 393.3.3 Recommendation Phase 403.3.4 Content-Based Approach 403.3.5 Collaborative Filtering Approach 413.3.6 Knowledge-Based Filtering Approach 413.4 Comparison Among Different Methods 423.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 423.4.1.1 Discussion and Result 433.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 463.4.2.1 Dataset and Evaluation Matrix 463.4.2.2 Training Setting 493.4.2.3 Result 493.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 493.4.3.1 Evaluation Setting 503.4.3.2 Experimental Result 503.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 513.4.4.1 Experimental Dataset 513.4.4.2 Experimental Result 523.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 533.4.5.1 Experimental Evaluation 533.4.5.2 Result and Analysis 533.5 Advantages of Multi-Criteria Recommender System 543.5.1 Revenue 573.5.2 Customer Satisfaction 573.5.3 Personalization 573.5.4 Discovery 583.5.5 Provide Reports 583.6 Challenges of Multi-Criteria Recommender System 583.6.1 Cold Start Problem 583.6.2 Sparsity Problem 593.6.3 Scalability 593.6.4 Over Specialization Problem 593.6.5 Diversity 593.6.6 Serendipity 593.6.7 Privacy 603.6.8 Shilling Attacks 603.6.9 Gray Sheep 603.7 Conclusion 60References 614 ADOPTION OF MACHINE/DEEP LEARNING IN CLOUD WITH A CASE STUDY ON DISCERNMENT OF CERVICAL CANCER65Jyothi A. P., S. Usha and Archana H. R.4.1 Introduction 664.2 Background Study 694.3 Overview of Machine Learning/Deep Learning 724.4 Connection Between Machine Learning/Deep Learning and Cloud Computing 744.5 Machine Learning/Deep Learning Algorithm 744.5.1 Supervised Learning 744.5.2 Unsupervised Learning 774.5.3 Reinforcement or Semi-Supervised Learning 774.5.3.1 Outline of ML Algorithms 774.6 A Project Implementation on Discernment of Cervical Cancer by Using Machine/Deep Learning in Cloud 934.6.1 Proposed Work 944.6.1.1 MRI Dataset 944.6.1.2 Pre Processing 954.6.1.3 Feature Extraction 964.6.2 Design Methodology and Implementation 974.6.3 Results 1004.7 Applications 1014.7.1 Cognitive Cloud 1024.7.2 Chatbots and Smart Personal Assistants 1034.7.3 IoT Cloud 1034.7.4 Business Intelligence 1034.7.5 AI-as-a-Service 1044.8 Advantages of Adoption of Cloud in Machine Learning/ Deep Learning 1044.9 Conclusion 105References 1065 MACHINE LEARNING AND INTERNET OF THINGS–BASED MODELS FOR HEALTHCARE MONITORING 111Shruti Kute, Amit Kumar Tyagi, Aswathy S.U. and Shaveta Malik5.1 Introduction 1125.2 Literature Survey 1135.3 Interpretable Machine Learning in Healthcare 1145.4 Opportunities in Machine Learning for Healthcare 1165.5 Why Combining IoT and ML? 1195.5.1 ML-IoT Models for Healthcare Monitoring 1195.6 Applications of Machine Learning in Medical and Pharma 1215.7 Challenges and Future Research Direction 1225.8 Conclusion 123References 1236 MACHINE LEARNING–BASED DISEASE DIAGNOSIS AND PREDICTION FOR E-HEALTHCARE SYSTEM 127Shruti Suhas Kute, Shreyas Madhav A. V., Shabnam Kumari and Aswathy S. U.6.1 Introduction 1286.2 Literature Survey 1296.3 Machine Learning Applications in Biomedical Imaging 1326.4 Brain Tumor Classification Using Machine Learning and IoT 1346.5 Early Detection of Dementia Disease Using Machine Learning and IoT-Based Applications 1356.6 IoT and Machine Learning-Based Diseases Prediction and Diagnosis System for EHRs 1376.7 Machine Learning Applications for a Real-Time Monitoring of Arrhythmia Patients Using IoT 1406.8 IoT and Machine Learning–Based System for Medical Data Mining 1416.9 Conclusion and Future Works 143References 144PART 2: INTRODUCTION TO DEEP LEARNING AND ITS MODELS 1497 DEEP LEARNING METHODS FOR DATA SCIENCE 151K. Indira, Kusumika Krori Dutta, S. Poornima and Sunny Arokia Swamy Bellary7.1 Introduction 1527.2 Convolutional Neural Network 1527.2.1 Architecture 1547.2.2 Implementation of CNN 1547.2.3 Simulation Results 1577.2.4 Merits and Demerits 1587.2.5 Applications 1597.3 Recurrent Neural Network 1597.3.1 Architecture 1607.3.2 Types of Recurrent Neural Networks 1617.3.2.1 Simple Recurrent Neural Networks 1617.3.2.2 Long Short-Term Memory Networks 1627.3.2.3 Gated Recurrent Units (GRUs) 1647.3.3 Merits and Demerits 1677.3.3.1 Merits 1677.3.3.2 Demerits 1677.3.4 Applications 1677.4 Denoising Autoencoder 1687.4.1 Architecture 1697.4.2 Merits and Demerits 1697.4.3 Applications 1707.5 Recursive Neural Network (RCNN) 1707.5.1 Architecture 1707.5.2 Merits and Demerits 1727.5.3 Applications 1727.6 Deep Reinforcement Learning 1737.6.1 Architecture 1747.6.2 Merits and Demerits 1747.6.3 Applications 1747.7 Deep Belief Networks (DBNS) 1757.7.1 Architecture 1767.7.2 Merits and Demerits 1767.7.3 Applications 1767.8 Conclusion 177References 1778 A PROPOSED LSTM-BASED NEUROMARKETING MODEL FOR CONSUMER EMOTIONAL STATE EVALUATION USING EEG 181Rupali Gill and Jaiteg Singh8.1 Introduction 1828.2 Background and Motivation 1838.2.1 Emotion Model 1838.2.2 Neuromarketing and BCI 1848.2.3 EEG Signal 1858.3 Related Work 1858.3.1 Machine Learning 1868.3.2 Deep Learning 1918.3.2.1 Fast Feed Neural Networks 1938.3.2.2 Recurrent Neural Networks 1938.3.2.3 Convolutional Neural Networks 1948.4 Methodology of Proposed System 1958.4.1 DEAP Dataset 1968.4.2 Analyzing the Dataset 1968.4.3 Long Short-Term Memory 1978.4.4 Experimental Setup 1978.4.5 Data Set Collection 1978.5 Results and Discussions 1988.5.1 LSTM Model Training and Accuracy 1988.6 Conclusion 199References 1999 AN EXTENSIVE SURVEY OF APPLICATIONS OF ADVANCED DEEP LEARNING ALGORITHMS ON DETECTION OF NEURODEGENERATIVE DISEASES AND THE TACKLING PROCEDURE IN THEIR TREATMENT PROTOCOL 207Vignesh Baalaji S., Vergin Raja Sarobin M., L. Jani Anbarasi, Graceline Jasmine S. and Rukmani P.9.1 Introduction 2089.2 Story of Alzheimer’s Disease 2089.3 Datasets 2109.3.1 ADNI 2109.3.2 OASIS 2109.4 Story of Parkinson’s Disease 2119.5 A Review on Learning Algorithms 2129.5.1 Convolutional Neural Network (CNN) 2129.5.2 Restricted Boltzmann Machine 2139.5.3 Siamese Neural Networks 2139.5.4 Residual Network (ResNet) 2149.5.5 U-Net 2149.5.6 LSTM 2149.5.7 Support Vector Machine 2159.6 A Review on Methodologies 2159.6.1 Prediction of Alzheimer’s Disease 2159.6.2 Prediction of Parkinson’s Disease 2219.6.3 Detection of Attacks on Deep Brain Stimulation 2239.7 Results and Discussion 2249.8 Conclusion 224References 22710 EMERGING INNOVATIONS IN THE NEAR FUTURE USING DEEP LEARNING TECHNIQUES 231Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi10.1 Introduction 23210.2 Related Work 23410.3 Motivation 23510.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 23610.4.1 Deep Learning for Image Classification and Processing 23710.4.2 Deep Learning for Medical Image Recognition 23710.4.3 Computational Intelligence for Facial Recognition 23810.4.4 Deep Learning for Clinical and Health Informatics 23810.4.5 Fuzzy Logic for Medical Applications 23910.4.6 Other Intelligent-Based Methods for Biomedical and Healthcare 23910.4.7 Other Applications 23910.5 Open Issues and Future Research Directions 24410.5.1 Joint Representation Learning From User and Item Content Information 24410.5.2 Explainable Recommendation With Deep Learning 24510.5.3 Going Deeper for Recommendation 24510.5.4 Machine Reasoning for Recommendation 24610.5.5 Cross Domain Recommendation With Deep Neural Networks 24610.5.6 Deep Multi-Task Learning for Recommendation 24710.5.7 Scalability of Deep Neural Networks for Recommendation 24710.5.8 Urge for a Better and Unified Evaluation 24810.6 Deep Learning: Opportunities and Challenges 24910.7 Argument with Machine Learning and Other Available Techniques 25010.8 Conclusion With Future Work 251Acknowledgement 252References 25211 OPTIMIZATION TECHNIQUES IN DEEP LEARNING SCENARIOS: AN EMPIRICAL COMPARISON 255Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma11.1 Introduction 25611.1.1 Background and Related Work 25611.2 Optimization and Role of Optimizer in DL 25811.2.1 Deep Network Architecture 25911.2.2 Proper Initialization 26011.2.3 Representation, Optimization, and Generalization 26111.2.4 Optimization Issues 26111.2.5 Stochastic GD Optimization 26211.2.6 Stochastic Gradient Descent with Momentum 26311.2.7 SGD With Nesterov Momentum 26411.3 Various Optimizers in DL Practitioner Scenario 26511.3.1 AdaGrad Optimizer 26511.3.2 RMSProp 26711.3.3 Adam 26711.3.4 AdaMax 26911.3.5 AMSGrad 26911.4 Recent Optimizers in the Pipeline 27011.4.1 EVE 27011.4.2 RAdam 27111.4.3 MAS (Mixing ADAM and SGD) 27111.4.4 Lottery Ticket Hypothesis 27211.5 Experiment and Results 27311.5.1 Web Resource 27311.5.2 Resource 27711.6 Discussion and Conclusion 278References 279PART 3: INTRODUCTION TO ADVANCED ANALYTICS 28312 BIG DATA PLATFORMS 285Sharmila Gaikwad and Jignesh Patil12.1 Visualization in Big Data 28612.1.1 Introduction to Big Data 28612.1.2 Techniques of Visualization 28712.1.3 Case Study on Data Visualization 30212.2 Security in Big Data 30512.2.1 Introduction of Data Breach 30512.2.2 Data Security Challenges 30612.2.3 Data Breaches 30712.2.4 Data Security Achieved 30712.2.5 Findings: Case Study of Data Breach 30912.3 Conclusion 309References 30913 SMART CITY GOVERNANCE USING BIG DATA TECHNOLOGIES 311K. Raghava Rao and D. Sateesh Kumar13.1 Objective 31213.2 Introduction 31213.3 Literature Survey 31413.4 Smart Governance Status 31413.4.1 International 31413.4.2 National 31613.5 Methodology and Implementation Approach 31813.5.1 Data Generation 31913.5.2 Data Acquisition 31913.5.3 Data Analytics 31913.6 Outcome of the Smart Governance 32213.7 Conclusion 323References 32314 BIG DATA ANALYTICS WITH CLOUD, FOG, AND EDGE COMPUTING 325Deepti Goyal, Amit Kumar Tyagi and Aswathy S. U.14.1 Introduction to Cloud, Fog, and Edge Computing 32614.2 Evolution of Computing Terms and Its Related Works 33014.3 Motivation 33214.4 Importance of Cloud, Fog, and Edge Computing in Various Applications 33314.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing 33414.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing) 33514.6.1 CloudSim 33514.6.2 SPECI 33614.6.3 Green Cloud 33614.6.4 OCT (Open Cloud Testbed) 33714.6.5 Open Cirrus 33714.6.6 GroudSim 33814.6.7 Network CloudSim 33814.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment) 33814.7.1 Microsoft HDInsight 33814.7.2 Skytree 33914.7.3 Splice Machine 33914.7.4 Spark 33914.7.5 Apache SAMOA 33914.7.6 Elastic Search 33914.7.7 R-Programming 33914.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems 34014.8.1 Risk Management 34014.8.2 Predictive Models 34014.8.3 Secure With Penetration Testing 34014.8.4 Bottom Line 34114.8.5 Others: Internet of Things-Based Intelligent Applications 34114.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing 34114.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments) 34214.10.1 Cloud Issues 34314.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps) 34414.12 Conclusion 345References 34615 BIG DATA IN HEALTHCARE: APPLICATIONS AND CHALLENGES 351V. Shyamala Susan, K. Juliana Gnana Selvi and Ir. Bambang Sugiyono Agus Purwono15.1 Introduction 35215.1.1 Big Data in Healthcare 35215.1.2 The 5V’s Healthcare Big Data Characteristics 35315.1.2.1 Volume 35315.1.2.2 Velocity 35315.1.2.3 Variety 35315.1.2.4 Veracity 35315.1.2.5 Value 35315.1.3 Various Varieties of Big Data Analytical (BDA) in Healthcare 35315.1.4 Application of Big Data Analytics in Healthcare 35415.1.5 Benefits of Big Data in the Health Industry 35515.2 Analytical Techniques for Big Data in Healthcare 35615.2.1 Platforms and Tools for Healthcare Data 35715.3 Challenges 35715.3.1 Storage Challenges 35715.3.2 Cleaning 35815.3.3 Data Quality 35815.3.4 Data Security 35815.3.5 Missing or Incomplete Data 35815.3.6 Information Sharing 35815.3.7 Overcoming the Big Data Talent and Cost Limitations 35915.3.8 Financial Obstructions 35915.3.9 Volume 35915.3.10 Technology Adoption 36015.4 What is the Eventual Fate of Big Data in Healthcare Services? 36015.5 Conclusion 361References 36116 THE FOG/EDGE COMPUTING: CHALLENGES, SERIOUS CONCERNS, AND THE ROAD AHEAD 365Varsha. R., Siddharth M. Nair and Amit Kumar Tyagi16.1 Introduction 36616.1.1 Organization of the Work 36816.2 Motivation 36816.3 Background 36916.4 Fog and Edge Computing–Based Applications 37116.5 Machine Learning and Internet of Things–Based Cloud, Fog, and Edge Computing Applications 37416.6 Threats Mitigated in Fog and Edge Computing–Based Applications 37616.7 Critical Challenges and Serious Concerns Toward Fog/Edge Computing and Its Applications 37816.8 Possible Countermeasures 38116.9 Opportunities for 21st Century Toward Fog and Edge Computing 38316.9.1 5G and Edge Computing as Vehicles for Transformation of Mobility in Smart Cities 38316.9.2 Artificial Intelligence for Cloud Computing and Edge Computing 38416.10 Conclusion 387References 387Index 391
Hacking Artificial Intelligence
Sheds light on the ability to hack AI and the technology industry’s lack of effort to secure vulnerabilities.We are accelerating towards the automated future. But this new future brings new risks. It is no surprise that after years of development and recent breakthroughs, artificial intelligence is rapidly transforming businesses, consumer electronics, and the national security landscape. But like all digital technologies, AI can fail and be left vulnerable to hacking. The ability to hack AI and the technology industry’s lack of effort to secure it is thought by experts to be the biggest unaddressed technology issue of our time. Hacking Artificial Intelligence sheds light on these hacking risks, explaining them to those who can make a difference.Today, very few people—including those in influential business and government positions—are aware of the new risks that accompany automated systems. While society hurdles ahead with AI, we are also rushing towards a security and safety nightmare. This book is the first-ever layman’s guide to the new world of hacking AI and introduces the field to thousands of readers who should be aware of these risks. From a security perspective, AI is today where the internet was 30 years ago. It is wide open and can be exploited. Readers from leaders to AI enthusiasts and practitioners alike are shown how AI hacking is a real risk to organizations and are provided with a framework to assess such risks, before problems arise.Davey Gibian is a technologist and artificial intelligence practitioner. His career has spanned Wall Street, the White House, and active war zones as he has brought cutting-edge data science tools to solve hard problems. He has built two start-ups, Calypso AI and OMG, was a White House Presidential Innovation Fellow for AI and Cybersecurity, and helped scale Palantir Technologies. He holds patents in machine learning and served in the US Air Force. He currently resides in New York City.Introduction: Hacking facial recognitionChapter 1: A brief overview of artificial intelligenceChapter 2: How AI is different from traditional softwareChapter 3: Data biasChapter 4: Hacking AI systemsChapter 5: Evasion AttacksChapter 6: Data PoisoningChapter 7: Model Inversion (“Privacy”) AttacksChapter 8: Obfuscation attacksChapter 9: Talking to AI: Model interpretabilityChapter 10: Machine vs. machineChapter 11: Will someone hack my AI?About the Author
C++ mit Visual Studio 2022 und Windows Forms-Anwendungen
Dieses Buch stellt C++ umfassend dar. Zahlreiche Beispiele veranschaulichen die Theorie. Dabei werden die Neuerungen von C++11, C++14 und C++17 von Anfang an integriert und ihre Vorteile gezeigt. Im Unterschied zu den allermeisten anderen C++-Büchern werden Windows-Programme mit einer grafischen Benutzeroberfläche entwickelt.Dieses Buch ist ein Lehrbuch, das sich an Studenten von Fachhochschulen und Universitäten richtet. Da es keine Vorkenntnisse voraussetzt, ist es auch zum Selbststudium geeignet. Es entstand aus zahlreichen Vorlesungen und Firmenseminaren. Der Aufbau, die Beispiele und Übungsaufgaben sind erprobt und bewährt.Und es ist gleichzeitig auch ein Fachbuch, das erfahrene C++-Programmierer auf den Stand von C++17 bringt. Es zeigt, wie die zahlreichen Neuerungen selbst elementare Programmiertechniken einfacher und sicherer machen. Dazu kommen neue Konzepte, die bessere und effizientere Lösungen als noch vor einigen Jahren ermöglichen. Viele dieser neuen Möglichkeiten sind in der industriellen Praxis noch nicht verbreitet.Übungsaufgaben ermöglichen dem Leser, das Gelernte zu vertiefen. Lösungen stehen auf www.rkaiser.de zum Download bereit.Dieses Buch erscheint in zwei weitgehend identischen Ausgaben:• In der vorliegenden Ausgabe werden Programme mit einer grafischen Benutzeroberfläche geschrieben, in denen alle Ein- und Aus-gaben über eine Windows-Benutzeroberfläche erfolgen.• In der anderen Ausgabe „C++ mit Visual Studio 2019“ (ISBN 978-3-662-594759) werden C++-Programme ohne eine grafische Benutzeroberfläche geschrieben. Alle Ein- und Ausgaben erfolgen mit cin und cout über die Konsole.Nach seinem Mathematikstudium an der Universität Tübingen war RICHARD KAISER einige Jahre in der Lehrerausbildung tätig, Trainer in der Industrie, Software-Entwickler (vor allem für technische Anwendungen) und Leiter der Software-Abteilung. Seit 1991 ist er Professor an der Dualen Hochschule Baden-Württemberg (Lörrach), wo er vor allem Vorlesungen über Programmiersprachen (C/C++/C#) und Mathematik hält. In den letzten Jahren hat er viele Seminare über C++ und C# für Firmen durchgeführt.Die Entwicklungsumgebung.- Steuerelemente für die Benutzeroberfläche.- Elementare Datentypen und Anweisungen in C und C++.- Sie Stringklassen string und wstring.- Arrays und Container.- Einfache selbstdefinierte Datentypen.- Zeiger, Strings und dynamisch erzeugte Variablen.- Überladene Funktionen und Operatoren.- Objektorientierte Programmierung.- Namensbereiche.- Exception-Handling.- Containerklassen der C++-Standardbibliothek.- Dateibearbeitung mit den Stream-Klassen.- Funktionsobjekte und Lambda-Ausdrücke.- Templates und STL.- C++11 Smart Pointer: shared_ptr, unique_ptr und weak_ptr.- Literatur.- Index.
Cyber-Sicherheit
Dieses Lehrbuch gibt Ihnen einen Überblick über die Themen der IT-Sicherheit Die digitale Transformation eröffnet viele neue Möglichkeiten, den dadurch lassen sich Geschäftsmodelle und Verwaltungsprozesse radikal verändern. Aber mit fortschreitender Digitalisierung nimmt jedoch die Komplexität der IT-Systeme- und Infrastrukturen zu. Zudem werden die Methoden der professionellen Angreifer ausgefeilter und die Angriffsziele kontinuierlich lukrativer, insgesamt führt dies bei Unternehmen und der Gesellschaft zu hohen Schäden. Für eine erfolgreiche Zukunft unserer Gesellschaft ist es daher entscheidend, diesen gestiegenen Risiken entgegenzuwirken und eine sichere sowie vertrauenswürdige IT zu gestalten. Von daher ist es notwendig, dass mit den wachsenden Herausforderungen auch neue Entwicklungen und Prozessen in der Cyber-Sicherheit einhergehen. Was sich hier getan hat können Sie in der 2. Auflage des Lehrbuchs ‚Cyber-Sicherheit‘, von Prof. Norbert Pohlmann, nachlesen. Denn inder Überarbeitung der sehr erfolgreichen Erst-Auflage wurden die bestehenden Kapitel ergänzt und aktualisiert sowie zusätzlich für neue Themen weitere Kapitel hinzugefügt. Aber auch Lehrmaterialien, wie 19 komplette Vorlesungen und Überbungen auf den Webseiten wurden angepasst und erweitert. Auf insgesamt 746 Seiten bietet Informatikprofessor Norbert Pohlmann grundlegendes Wissen über die Cyber-Sicherheit und geht bei innovativen Themen, wie Self Sovereign Identity oder dem Vertrauenswürdigkeits-Modell, detailliert in die Tiefe. Dabei ist dem Autor wichtig, nicht nur theoretisches Fachwissen zu vermitteln, sondern auch den Leser in die Lage zu versetzen, die Cyber-Sicherheit aus der anwendungsorientierten Perspektive zu betrachten. Lernen Sie mithilfe dieses Lehrbuchs mehr über Mechanismen, Prinzipien, Konzepte und Eigenschaften von Cyber-Sicherheitssystemen. So sind Sie in der Lage, die Sicherheit und Vertrauenswürdigkeit von IT-Lösungen zu beurteilen. Grundlegende Aspekte der Cyber-Sicherheit Im einführenden Abschnitt werden den Lesenden die Grundlagen der IT-Sicherheit vermittelt: · Cyber-Sicherheitsstrategien · Motivationen von Angreifern · Sicherheitsbedürfnisse der Bürger und Mitarbeiter von Unternehmen · Aktuelle Cyber-Sicherheitsprobleme · Herausforderungen für eine sicher und vertrauenswürdige digitale Zukunft · Wirksamkeitskonzepte von Cyber-Sicherheitsmechanismen Detaillierte Darstellung relevanter Systeme, Prozesse und Prinzipien In den weiteren Kapiteln wird auf besonders relevante Teilbereiche der Cyber-Sicherheit fokussiert: · Kryptographie · Hardware-Sicherheitsmodule zum Schutz von sicherheitsrelevanten Informationen · Digitale Signatur, elektronische Zertifikate sowie PKIs und PKAs · Identifikation und Authentifikation · Enterprise Identity und Access Management · Trusted Computing · Cyber-Sicherheit Frühwarn- und Lagebildsysteme · Firewall-Systeme · E-Mail-Sicherheit · Blockchain-Technologie · Künstliche Intelligenz und Cyber-Security · Social Web Cyber-Sicherheit · Self-Sovereign Identity (SSI) - neu · Vertrauen und Vertrauenswürdigkeit - neu · Weitere Aspekte der Cyber-Sicherheit - neu Zudem erfahren Sie mehr über neue Standards und Methoden bei IPSec-Verschlüsselung, Transport Layer Security (TLS) sowie Sicherheitsmaßnahmen gegen DDoS-Angriffe. Anschauliche Grafiken und Tabellen bilden Prozesse und Zusammenhänge verständlich ab. Didaktisch gut aufbereitet, können Sie die Inhalte mit zahlreichen neuen Übungsaufgaben vertiefen. Das Lehrbuch richtet sich insbesondere an Lesende, für die ein umfassendes Know-how zu Cyber-Security im Arbeits-, Lehr- oder Privatumfeld relevant und interessant ist: · Studierende der Informatik, IT- oder Cyber-Sicherheit, aber auch angrenzende Disziplinen · Auszubildende im Bereich Fachinformatik, digitale Medien · Mitarbeitende/Führungspersonen aller Branchen, die sich mit Digitalisierung beschäftigen Die zweite Auflage des Lehrbuchs Cyber-Sicherheit von Prof. Norbert Pohlmann wurde umfassend überarbeitet, aktualisiert und um drei neue Kapitel sowie ein Glossar erweitert.
Werde ein Data Head
Data Science, Machine Learning und Statistik verstehen und datenintensive Jobs meistern. Fundierte Datenkompetenz für den Arbeitsplatz entwickeln – auch ohne Programmierkenntnisse.Dieses Buch ist ein umfassender Leitfaden für das Verständnis von Datenanalyse am Arbeitsplatz. Alex Gutman und Jordan Goldmeier lüften den Vorhang der Data Science und geben Ihnen die Sprache und die Werkzeuge an die Hand, die Sie benötigen, um informiert mitreden zu können, kritisch über die Auswertung von Daten zu sprechen und die richtigen Fragen zu stellen. Dank dieses Buchs kann jede:r ein Data Head werden und aktiv an Data Science, Statistik und Machine Learning teilnehmen – auch ohne einen technischen Background.In diesem unterhaltsamen und gut verständlichen Buch werden die aktuellen, zum Teil komplexen Data-Science- und Statistik-Konzepte anhand einfacher Beispiele und Analogien veranschaulicht. Sie lernen statistisches Denken, das Vermeiden häufiger Fallstricke bei der Interpretation von Daten, und Sie erfahren, was es mit Machine Learning, Textanalyse, Deep Learning und künstlicher Intelligenz wirklich auf sich hat. Wenn Sie in Ihrem Unternehmen konkret mit Daten arbeiten, Führungskraft oder angehender Data Scientist sind, zeigt Ihnen dieses Buch, wie Sie ein echter Data Head werden.Die Autoren:Alex J. Gutman ist Data Scientist, Unternehmenstrainer und Accredited Professional Statistician®. Sein beruflicher Schwerpunkt liegt auf statistischem und maschinellem Lernen, und er verfügt über umfangreiche Erfahrungen als Data Scientist für das US-Verteidigungsministerium und zwei Fortune-50-Unternehmen. Seinen Doktortitel in angewandter Mathematik erhielt er vom Air Force Institute of Technology.Jordan Goldmeier ist ein international anerkannter Analytik- und Datenvisualisierungs-Experte, Autor und Redner. Er wurde sieben Mal mit dem Microsoft Most Valuable Professional Award ausgezeichnet und hat Mitglieder von Pentagon und Fortune-500-Unternehmen in Analytik unterrichtet. Er ist Autor der Bücher Advanced Excel Essentials und Dashboards for Excel.
Software Architecture by Example
Design system solutions using modern architectural patterns and practices. This book discusses methods to keep a system responsive, even when it is being constantly updated, extending a system's functionality without changing the core code, methods of maintaining data history, and designing a distributed transactional system.This book will guide you in understanding how a software solution is designed using different architectural processes and scenarios. Each scenario explains if and why a software solution is required to resolve a given issue, and discusses possible architectural approaches to solve the problem. You will learn specific implementations of software architecture for each case along with different approaches to achieve the solutions. Each chapter is structured as a real-world requirement from a client and describes a process to meet that requirement.After reading this book, you should have a high-level understanding of the architectural patterns used in the book, and you should have a methodology for approaching system design.WHAT YOU WILL LEARN* Understand design principles and considerations for various stages of software development* Translate patterns into code samples* Create a blueprint for approaching system design* Understand architectural patterns: CQRS, event sourcing, distributed systems, distributed transactions, and plug-in architectureWHO THIS BOOK IS FORDevelopers who wish to move into architecture, and junior software architects also will find the book usefulPAUL MICHAELS is the Head of Development at musicMagpie. He started his career as a professional software engineer in 1997. Paul is a regular speaker, published author, and Microsoft MVP. He enjoys programming, playing with new technology, and finding neat solutions to problems. When he's not working, you can find him cycling or walking around The Peak District, playing table tennis, or trying to cook for his wife and two children. You can follow him on twitter @paul_michaels or find him on LinkedIn. He also writes a blog at pmichaels.net.CHAPTER 1: THE TICKET SALES PROBLEMCHAPTER GOAL: DESIGN A SOFTWARE SOLUTION THAT ADDRESSES THE PROBLEM OF SELLING TICKETS FOR EVENTS. TICKET SALES WILL BE INITIALLY LOW, BUT FOR CERTAIN EVENTS THERE WILL BE HUGE SPIKES.NO OF PAGES: 50SUB -TOPICS1. Message queues / pub / sub2. CQRS – which it doesn’t fitCHAPTER 2: THE CASH DESK PROBLEMCHAPTER GOAL: DESIGN A SOLUTION THAT CATERS FOR A SYSTEM TRACKING MONEY IN AND OUT OF A CASH DESK.NO OF PAGES: 50SUB - TOPICS1. Event Sourcing2.CHAPTER 3: THE TRAVEL AGENT PROBLEMCHAPTER GOAL: DESIGN A SOLUTION THAT CATERS FOR A SYSTEM WHEREBY YOU NEED TO INTERFACE WITH MANY DIFFERENT THIRD-PARTY SYSTEMS AND COLLATE THE RESULTS.NO OF PAGES: 50SUB - TOPICS:1. Distributed systems2. Microservices3. Service bus4. Scheduler Agent Supervisor PatternCHAPTER 4: THE SOCIAL MEDIA PROBLEMCHAPTER GOAL: DESIGN A SOLUTION THAT CATERS FOR A SYSTEM WHEREBY A HIGH FREQUENCY OF UPDATES ARE MADE, AND YET THE EXACT ORDER OF THE TRANSACTIONS IS UNIMPORTANT.NO OF PAGES: 50SUB - TOPICS:1. CQRSCHAPTER 5: THE ADMIN APPLICATION PROBLEMCHAPTER GOAL: DESIGN A SOLUTION WHERE A USER IS ABLE TO CONFIGURE THE RULES WITHIN A GIVEN APPLICATION.NO OF PAGES: 501. N-Tier2. Plug-in architectureCHAPTER 6: THE TRAVEL REP PROBLEMCHAPTER GOAL: DESIGN A SOLUTION TO ALLOW AN APPLICATION TO ACCEPT TRAVEL AGENT QUERIES, BUT TO POLL THE SERVER OFFLINENO OF PAGES: 501. Ambassador pattern2. Message Queues
Creating Apps with React Native
Produce high-quality, cross-platform apps with user experiences almost identical to pure native apps. When evaluating cross-platform frameworks, developers make an assumption that quality will be compromised. But that doesn't have to be true. The principles in this book will show you how to meet quality expectations both from engineering and consumer standpoints.You’ll also realize the ideal of a greater front end. That means your whole front-end team, including app side and web side, will be optimized. The shared knowledge base as well as mobilization potential give more flexibility and strength in all front-end facets without the need of increasing team sizes.The market has seen a large amount of high quality React Native apps and successful stories about them. Nevertheless, under optimized apps and unsuccessful stories shadow. The fundamental difference between the two opposing groups is understanding. Discover the critical points in the React and React Native architecture, and develop general best practices that can lead to consistently developing 0 crash, 5 star apps based on an understanding of fundamentals.WHAT YOU'LL LEARN* Measure and define successful app design* Create animation based on user need* Reduce performance bottleneck throughout your appsWHO THIS BOOK IS FORMobile developers who want to expand their front end skill set, and web developers who want to enter mobile development.Muyang (Holmes) He is a software engineer. He spent four years working with Tencent on hyperscale social network products. At the time when this book is written, he is a mobile software engineer with Microsoft. He is an active advocate and a practice leader of using React Native to create 0 crash, 5 star apps (05 apps).Chapter 1 Start thinking in ReactThe hello world app in piecesProps and StatesFunction componentsChapter 2 React ToolsFlexbox – A Practical GuideScrollView and FlatListInheritance v.s. Composition, HOCError handlingState Management, ReduxReact NavigationApp architecture in action – a boilerplateChapter 3 React Native ArchitectureApp initializationUnder the hood of componentsCommunication between JavaScript and NativeChapter 4 Custom NativeCustom native moduleCustom native componentChapter 5 NetworksPromise chainAwait for async eventConnectivityCommon error handlingPutting it all togetherChapter 6 PerformanceHow to measure and what defines successCritical points in RN architectureLong listPerformance bottlenecks in FlatListCommon optimization techniquesCase studiesItem 1Item 2Item 3Chapter 7 AnimationLayout animationValue driven animationGesture driven animationChapter 8 3rd-Party Components
A Complete Guide to Docker for Operations and Development
Harness the power of Docker by containerizing your code with all its libraries and file systems to consistently run anywhere. This book is your source for learning all about Docker operations and development. It’s divided into two units and focuses on the topics that the Docker Certified Associate exam covers.Unit 1 covers the Docker fundamentals, such as Docker Enterprise for Operations, Docker Enterprise for Developers, Swarm, and an introduction to Kubernetes. You will learn how to install Docker Community Edition, Docker Enterprise Edition with Kubernetes and CLI. Also, you will learn the Docker cycle, container lifecycle, develop applications in any language, wrap, build, ship, and deploy them for production. Finally, you will learn how to create a Swarm cluster, deploy an app to it, and manage it with the best practices according to the current technologies.Unit 2 provides quizzes to help you prepare for the certification test. The DCA exam format and the question style has changed since Mirantis acquired Docker. To accommodate this, the quizzes mirror these changes.WHAT YOU’LL LEARN* Understand the difference between containerization and virtualization* Install Docker CE on various platforms and manage the resources* Write Dockerfile, Docker Compose YAML, and Kubernetes manifest YAML files* Compare microservices and monolithic applications * Containerize monolithic applicationsWHO THIS BOOK IS FORSoftware developers, Cloud Architects, and DevOps operation managers.Engy Fouda is an adjunct lecturer at Suny New Paltz University. She also teaches SAS, Docker Fundamentals, Docker for Enterprise Developers, Docker for Enterprise Operations, and Kubernetes at a Microsoft Training Partner, ONLC Training Centers, and at several other venues as a freelance instructor. She is an Apress and Packt Publishing author. Moreover, she has more than 20 years of experience passing technical certificates. All her students always pass the certifications from the first try. She holds two master’s degrees, one in journalism with a Data Science Professional Graduate Certificate from Harvard University, Extension School, and another in computer engineering from Cairo University, Egypt.UNIT 1: DOCKER FUNDAMENTALSChapter 1: IntroductionChapter 2: Installation and ConfigurationChapter 3: Image and Containers Creation, Management, and RegistryChapter 4: NetworkingChapter 5: Storage and VolumesChapter 6: Docker Enterprise Edition/Mirantis Kubernetes Edition installationChapter 7: Universal Control Plane (UCP)Chapter 8: Docker Trusted Registry (DTR)Chapter 9: MicroservicesChapter 10: OrchestrationChapter 11: Security, RBAC, and DCTChapter 12: DCA exam guideUNIT 2: EXAM PREP QUIZZESChapter 13: Orchestration QuizChapter 14: Image Creation, Management, and Registry QuizChapter 15: Installation and Configuration QuizChapter 16: Networking QuizChapter 17: Security QuizChapter 18: Storage and Volumes Quiz
Beginning Ansible Concepts and Application
Learn the concepts and develop the skills to be a true Ansible artist and use it inside and outside the box. This book applies key concepts immediately while building up your Ansible skills layer by layer through easy to grasp examples and engaging projects. You’ll also think about security, why testing is important, and how to use version control safely.As a beginner to Ansible, you'll be guided step-by-step through creating your first Ansible playbook to deploying your first server and writing more complex cross-dependency playbooks. From the first line of code to the last, you'll constantly iterate and simplify your playbooks, iwhile taking on more complex topics as you construct a full Wordpress website stack consisting of a database, web servers, and load balancer. This book will prompt you to think about how these fit together and will explain what to do to ensure maintainability long into the future.Don’t just use Ansible. Completely change how you go about provisioning, configuring, and managing servers, applications, and their dependencies with this powerful, open source automation tool. In exchange for this power and efficiency, Ansible demands a very different way of thinking about resources and how they all fit together. This can be hard to get your head around if you’ve never done it before. Every step of the way, Beginning Ansible Concepts and Application show you best practices so that you can confidently start using Ansible right away.WHAT YOU'LL LEARN* Set up an Ansible environment* Create and run playbooks* Organize groups of variables, vaults, roles, and tasks to ensure your playbooks are scalable* Protect secrets using Ansible Vaults* Automate the build of a Wordpress websiteWHO THIS BOOK IS FORDevelopers looking for a better way to manage their servers other than by logging in and typing commands. Also enthusiasts who want to learn not just how to use Ansible but how to use it correctly and confidently.SHAUN SMITH is a Fellow of the British Computer Society (BCS) and holds degrees in Computer Science and Psychology. He has a wealth of experience across a broad range of technology, which he combines in novel ways to build out industry-leading, scalable and highly-secure solutions to complex problems. He evangelises simple, elegant and secure designs and these days is focusing on making the Internet a safer place to be and up-skilling the next generation.PETER MEMBREY is a Chartered Fellow of the British Computer Society, a Chartered IT Professional and a Chartered Engineer with nearly a quarter of a century in the field. He has a doctorate in engineering and a masters degree in IT specialising in Information Security. He's co-authored over a dozen books, a number of research papers on a variety of topics and has recently been awarded the Distinguished Contributor award by the IEEE Computer Society. Chapter 1 – Setting the SceneFoundations of AnsiblCreate an environmentChallenges to comeChapter 2 – Say Hello to AnsibleIntroduce AnsibleHistoryToolsChapter 3 – Getting Ansible and Setting Up the EnvironmentDownload and set up AnsibleUsing virtual python environmentsUsing VirtualBoxChapter 4 – Your First Steps with AnsiblePlay with AnsibleRevision control and security aspectsPython 2 vs 3Chapter 5 – Run Your First playbookCreate and run your first playbookStructure of a playbookPut servers under source controlChapter 6 – Designing an InventoryUsing localhostInventoriesChapter 7 – Setting Your Sights – Target the Servers You WantSetting up real serversPlaybook skills and inventory skillsWriting the playbookChapter 8 – Batteries Included – The Core ModulesCore modulesInstalling packages, copying config files, and making changes to system configWeb based documentationChapter 9 – Gathering Data and the Power of FactsUsing fact gatheringAutomatic (implicit) fact-gathering for every playbookExplicit fact gatheringStat to gain information on files, directories, and symbolic linksChapter 10 – The Building Blocks of Ansible – RolesChapter 11 – Making Decisions and Controlling FlowConditionalsOptionsincludes and when clausesChapter 12 – Repeating YourselfLoopsSyntaxChapter 13 – Jinja 2 and the Power of TemplatesChapter 14 – Structuring Your Repo for SuccessBasic directory structureOrganize groups of variables, vaults, roles, and tasks to ensure your playbooks are scalableChapter 15 – Locking Away Your SecretsAnsible-vaultsEnvironment specific encrypted storesChapter 16 – Extending the Power of AnsibleCreation of custom modulesModule types (actions, filters, callback to name a few)Hints and tips on when a plugin is the right course of actionChapter 17 – Dynamically Generating Your InventoryInventory, or CMDBInventory sourceSimple web service to pull in the ansible inventory at runtimeMeta groupsChapter 18 – CommunityShare playbooks with like-minded sysadminsAnsible GalaxyChapter 19 - Troubleshooting AnsibleChapter 20 – Other Projects Around AnsiblePOSSIBLE: document interesting projects that make use of Ansible's power, such as ansible-cmdb