LEADER 03386nam 22006372 450 001 9910783233403321 005 20151005020621.0 010 $a1-107-13410-2 010 $a1-283-32934-4 010 $a0-511-64329-2 010 $a9786613329349 010 $a1-139-14827-3 010 $a0-511-06505-1 010 $a0-511-05872-1 010 $a0-511-56173-3 010 $a0-511-61527-2 010 $a0-511-07351-8 035 $a(CKB)1000000000030850 035 $a(EBL)218005 035 $a(OCoLC)437069041 035 $a(SSID)ssj0000178174 035 $a(PQKBManifestationID)11169790 035 $a(PQKBTitleCode)TC0000178174 035 $a(PQKBWorkID)10221579 035 $a(PQKB)10758341 035 $a(UkCbUP)CR9780511615276 035 $a(MiAaPQ)EBC218005 035 $a(Au-PeEL)EBL218005 035 $a(CaPaEBR)ebr10074094 035 $a(EXLCZ)991000000000030850 100 $a20090914d2003|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIndelible shadows $efilm and the Holocaust /$fAnnette Insdorf$b[electronic resource] 205 $aThird edition. 210 1$aCambridge :$cCambridge University Press,$d2003. 215 $a1 online resource (xix, 410 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-01630-4 311 $a0-521-81563-0 320 $aIncludes bibliographical references (p. 383-387) and index. 327 $aCover; Half-title; Title; Copyright; Dedication; Contents; Foreword by Elie Wiesel; Preface; Introduction; Part I Finding an Appropriate Language; Part II Narrative Strategies; Part III Responses to Nazi Atrocity; Part IV Shaping Reality; Part V Third Edition Update; Annotated Filmography (Third Edition); Filmography (Second Edition); Notes; Bibliography (Second Edition); Bibliography (Third Edition); Relevant Websites; Index 330 $aIndelible Shadows investigates questions raised by films about the Holocaust. How does one make a movie that is both morally just and marketable? Annette Insdorf provides sensitive readings of individual films and analyzes theoretical issues such as the 'truth claims' of the cinematic medium. The third edition of Indelible Shadows includes five additional chapters that cover recent trends, as well as rediscoveries of motion pictures made during and just after World War II. It addresses the treatment of rescuers, as in 'Schindler's List'; the controversial use of humor, as in 'Life is Beautiful'; the distorted image of survivors, and the growing genre of documentaries that return to the scene of the crime or rescue. The annotated filmography offers capsule summaries and information about another hundred Holocaust films from around the world, making this edition an extremely comprehensive discussion of films about the Holocaust, and an invaluable resource for film programmers and educators. 606 $aHolocaust, Jewish (1939-1945), in motion pictures 615 0$aHolocaust, Jewish (1939-1945), in motion pictures. 676 $a791.43/658 700 $aInsdorf$b Annette$0554067 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910783233403321 996 $aIndelible shadows$93837036 997 $aUNINA LEADER 11773oam 22005292 450 001 9910954327303321 005 20251116172048.0 010 $a0-429-55882-1 010 $a0-429-26379-1 010 $a0-429-55435-4 035 $a(CKB)4100000009374777 035 $a(MiAaPQ)EBC5904936 035 $a(OCoLC)1110660822 035 $a(OCoLC-P)1110660822 035 $a(FlBoTFG)9780429263798 035 $a(EXLCZ)994100000009374777 100 $a20190710d2020 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData science $etheory, analysis, and applications /$fedited by Qurban A Memon, Shakeel Ahmed Khoja 205 $a1st ed. 210 1$aBoca Raton :$cCRC Press,$d[2020] 215 $a1 online resource (345 pages) 311 08$a1-03-224024-5 311 08$a0-367-20861-X 320 $aIncludes bibliographical references and index. 327 $aCover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Editors -- Contributors -- PART I: Data Science: Theory, Concepts, and Algorithms -- Chapter 1 Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark -- 1.1 Introduction -- 1.1.1 Integration of Spark and MongoDB -- 1.2 Literature Survey -- 1.3 Proposed System -- 1.3.1 Methodology for Processing Spatial Queries -- 1.3.2 Spark Master-Slave Framework -- 1.3.3 Algorithms for Sharding -- 1.3.3.1 Algorithm for Range Sharding -- 1.3.3.2 Algorithms for Zone Sharding -- 1.3.4 Dataset and Statistics -- 1.4 Results and Performance Evaluation -- 1.5 Conclusion -- References -- Chapter 2 A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes -- 2.1 Introduction -- 2.2 Supervised Image Segmentation -- 2.3 Literature Review -- 2.4 Artificial Neural Networks -- 2.5 Optimization -- 2.6 Metaheuristic Algorithms -- 2.6.1 Genetic Algorithm -- 2.6.2 Particle Swarm Optimization Algorithm -- 2.6.3 Imperialist Competitive Algorithm -- 2.7 Optimization of the Neural Networks Weights Using Optimization Algorithms -- 2.8 Experimental Setup and Method Analysis -- 2.9 Conclusions -- References -- Chapter 3 A Study and Analysis of a Feature Subset Selection Technique Using Penguin Search Optimization Algorithm -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Proposed Work -- 3.3.1 Pseudocode of the Proposed FS-PeSOA Algorithm -- 3.3.2 Discussion -- 3.3.2.1 Hunting Strategy of Penguins -- 3.3.2.2 Fitness Function Evaluation -- 3.3.2.3 Position Update Logic -- 3.3.2.4 Oxygen Update Logic -- 3.4 Result Analysis -- 3.5 Conclusions -- References -- Chapter 4 A Physical Design Strategy on a NoSQL DBMS -- 4.1 Introduction -- 4.2 Motivation Example -- 4.3 Neo4j -- 4.4 Design Guidelines -- 4.5 Physical Design. 327 $a4.5.1 Query Rewriting Using Path Redundancy Pattern -- 4.5.2 Query Rewriting Using Minimal Query Pattern -- 4.5.3 Path Materialization -- 4.5.4 Index Creation -- 4.6 Experimental Study -- 4.6.1 Experimental Design -- 4.6.2 Impact of the Proposed Physical Design on Query Performance for a 1 GB Database -- 4.6.3 Impact of the Proposed Physical Design on Query Performance for a 10 GB Database -- 4.6.4 Impact of the Proposed Physical Design on Query Performance for a 100 GB Database -- 4.7 Related Work -- 4.8 Discussion -- 4.9 Future Research Directions -- 4.10 Conclusion -- References -- Chapter 5 Large-Scale Distributed Stream Data Collection Schemes -- 5.1 Introduction -- 5.2 Data Collection Scheme for Distributed TBPS -- 5.2.1 Assumed Environment -- 5.2.1.1 Assumed TBPS Architecture -- 5.2.1.2 Assumed Overlay for Distributed TBPS -- 5.2.2 Proposed Method -- 5.2.2.1 Methodology Principle -- 5.2.2.2 Collective Store and Forwarding -- 5.2.2.3 Adaptive Data Collection Tree -- 5.2.3 Evaluation -- 5.2.3.1 Simulation Parameters -- 5.2.3.2 Simulation Results -- 5.3 Data Collection Scheme Considering Phase Differences -- 5.3.1 Problems Addressed -- 5.3.1.1 Assumed Environment -- 5.3.1.2 Input Setting -- 5.3.1.3 Definition of a Load -- 5.3.2 Proposed Method -- 5.3.2.1 Skip Graphs -- 5.3.2.2 Phase Differences -- 5.3.3 Evaluation -- 5.3.3.1 Collection Target Nodes -- 5.3.3.2 Communication Loads and Hops -- 5.4 Discussion -- 5.5 Related Work -- 5.6 Conclusion -- Acknowledgements -- References -- PART II: Data Design and Analysis -- Chapter 6 Big Data Analysis and Management in Healthcare -- 6.1 Introduction -- 6.2 Preliminary Studies -- 6.3 Healthcare Data -- 6.4 Need of Big Data Analytics in Healthcare -- 6.5 Challenges in Big Data Analysis in Healthcare -- 6.5.1 Capture -- 6.5.2 Cleaning -- 6.5.3 Storage -- 6.5.4 Security -- 6.5.5 Stewardship -- 6.5.6 Querying. 327 $a6.5.7 Reporting -- 6.5.8 Visualization -- 6.5.9 Updating -- 6.5.10 Sharing -- 6.6 Collection of Healthcare Data -- 6.6.1 Importance in Healthcare Data Collection -- 6.6.2 Complications and Clarifications of Healthcare Data Collection -- 6.6.3 Current Data Collection Methods -- 6.6.4 Advanced Data Collection Tools -- 6.6.5 Healthcare Data Standards -- 6.6.6 Inferences of Patient Data Collection in Healthcare -- 6.7 Analysis of Healthcare Data -- 6.8 Healthcare Data Management -- 6.8.1 Big Data and Care Management -- 6.8.2 Advantages of Healthcare Data Management -- 6.9 Big Data in Healthcare -- 6.9.1 Big Data and IoT -- 6.9.2 Patient Prophecies for Upgraded Staffing -- 6.9.3 Electronic Health Records -- 6.9.4 Real-Time Warning -- 6.9.5 Augmenting Patient Engagement -- 6.9.6 Using Health Data for Informed Strategic Planning -- 6.9.7 Extrapolative Analytics in Healthcare -- 6.9.8 Diminish Fraud and Enrich Security -- 6.9.9 Telemedicine -- 6.9.10 Assimilating Big Data per Medical Imaging -- 6.9.11 A Method to Avert Pointless ER (Emergency Room) Visits -- 6.10 Future for Big Data in Healthcare -- 6.11 Conclusion -- References -- Chapter 7 Healthcare Analytics: A Case Study Approach Using the Framingham Heart Study -- 7.1 Introduction and Background to the Case Study: Framingham Heart Study -- 7.2 Literature Review -- 7.3 Introduction to the Data Analytics Framework -- 7.3.1 Step 1. Defining the Healthcare Problem -- 7.3.2 Step 2. Explore the Healthcare Data -- 7.3.3 Step 3. Predict What Is Likely to Happen -- or Perform Classification Analysis -- 7.3.4 Step 4. Check the Modeling Results -- 7.3.5 Step 5. Optimize (Find the Best Solution) -- 7.3.6 Step 6. Derive a Clinical Strategy for Patient Care and Measure the Outcome -- 7.3.7 Step 7. Update the CDS System -- 7.4 Data Exploration and Understanding of the Healthcare Problem. 327 $a7.5 Machine-Learning Model Application -- 7.6 Evaluation of the Machine-Learning Model Results -- 7.7 Conclusion -- 7.8 Future Direction -- Acknowledgements -- References -- Chapter 8 Bioinformatics Analysis of Dysfunctional (Mutated) Proteins of Cardiac Ion Channels Underlying the Brugada Syndrome -- 8.1 Introduction -- 8.2 Results -- 8.2.1 Brief Description of Unique BrS-Related Proteins -- 8.2.2 PIM-Based Analysis of the Unique BrS-Related Proteins -- 8.2.3 Intrinsic Disorder Analysis of the BrS-Related Proteins -- 8.2.4 Kolmogorov-Smirnov Test -- 8.3 Discussion -- 8.4 Materials and Methods -- 8.4.1 Evaluation of Polar Profile -- 8.4.1.1 Weighting of Polar Profiles -- 8.4.1.2 Comparison of Polar Profiles -- 8.4.1.3 Graphics of Polar Profiles -- 8.4.2 Evaluation of Intrinsic Disorder Predisposition -- 8.4.3 Data Files -- 8.4.4 Kolmogorov-Smirnov Test -- 8.4.5 Test Plan -- 8.4.5.1 Polar Profile -- 8.5 Conclusions -- References -- Chapter 9 Discrimination of Healthy Skin, Superficial Epidermal Burns, and Full-Thickness Burns from 2D-Colored Images Using Machine Learning -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Skin Burns -- 9.2.2 Causes of Burn Injuries -- 9.2.3 Burns Category -- 9.2.4 Burn Assessment Techniques -- 9.2.4.1 Clinical Assessment -- 9.2.4.2 Blood Perfusion Measurement -- 9.3 Machine Learning -- 9.3.1 Convolutional Neural Networks -- 9.3.1.1 Convolution Layer -- 9.3.1.2 Pooling Layer -- 9.3.1.3 Output/Classification Layer -- 9.3.2 Training a ConvNet -- 9.3.3 Common ConvNet Models -- 9.3.3.1 AlexNet -- 9.3.3.2 GoogleNet -- 9.3.3.3 VGGNet -- 9.3.3.4 Residual Network -- 9.4 Goals and Methodology -- 9.4.1 Image Acquisition and Preprocessing -- 9.4.2 Feature Extraction and Classification -- 9.5 Results and Discussion -- 9.5.1 Terms Related to Contingency Table -- 9.5.2 Classifier Performance -- 9.6 Conclusions -- References. 327 $aChapter 10 A Study and Analysis of an Emotion Classification and State-Transition System in Brain Computer Interfacing -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed Work -- 10.3.1 Classification Processes -- 10.3.1.1 SVM Classifier -- 10.3.1.2 KNN Classifier -- 10.3.1.3 Random Forest Classifier -- 10.3.2 State-Transition Machine -- 10.3.2.1 Proposed Algorithm of Emotional State Transition Based on Channel Value for a Fixed Time Interval -- 10.4 Result Analysis -- 10.4.1 Requirement -- 10.4.2 Result Comparisons of SVM, KNN, and Random Forest Classifiers -- 10.4.3 SVM Polynomial Kernel Performance Analysis -- 10.4.4 Analysis of the State-Transition Machine -- 10.4.5 Comparison with Previous Works -- 10.4.6 Computational Complexity -- 10.5 Conclusion -- Acknowledgment -- References -- PART III: Applications and New Trends in Data Science -- Chapter 11 Comparison of Gradient and Textural Features for Writer Retrieval in Handwritten Documents -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Adopted Features -- 11.3.1 Local Binary Pattern -- 11.3.2 Histogram of Oriented Gradients -- 11.3.3 Gradient Local Binary Pattern -- 11.3.4 Pixel Density -- 11.3.5 Run Length Feature -- 11.4 Matching Step -- 11.5 Experimental Evaluation -- 11.5.1 Evaluation Criteria -- 11.5.2 Experimental Setup -- 11.5.3 Retrieval Results -- 11.6 Discussion and Comparison -- 11.7 Conclusion -- References -- Chapter 12 A Supervised Guest Satisfaction Classification with Review Text and Ratings -- 12.1 Introduction -- 12.2 Related Literature -- 12.2.1 Guest Satisfaction and Online Reviews -- 12.3 Methodology -- 12.3.1 Data Description and Analysis -- 12.3.2 Data Cleaning -- 12.3.3 Latent Semantic Analysis -- 12.3.4 Classifiers and Performance Measures -- 12.4 Experimental Results -- 12.4.1 Features Related to Guest Satisfaction. 327 $a12.4.2 Hotel Guest Satisfaction Prediction. 330 $a"The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: Part 1: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science"--$cProvided by publisher. 606 $aData mining$xStatistical methods 615 0$aData mining$xStatistical methods. 676 $a006.312 702 $aMemon$b Qurban A$g(Qurban Ali), 702 $aKhoja$b Shakeel Ahmed 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910954327303321 996 $aData Science$91562261 997 $aUNINA