Search Results - (((((((kent OR mantis) OR wantin) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Computational models of argument : Proceedings of COMMA 2012

Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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Banking and finance issues in emerging markets.

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Symbolic computation and education

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Informatics for Health by Randell, R.

Table of Contents: “…Connected and Digital Health -- Design and Validation of a Platform to Evaluate mHealth Apps -- Reasoning and Data Representation in a Health and Lifestyle Support System -- Feasibility of Representing a Danish Microbiology Model Using FHIR -- Establishment of Requirements and Methodology for the Development and Implementation of GreyMatters, a Memory Clinic Information System -- Nurses' Perspectives on In-Home Monitoring of Elderlies's Motion Pattern -- Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments -- Design, Implementation and Operation of a Reading Center Platform for Clinical Studies -- Web Validation Service for Ensuring Adherence to the DICOM Standard -- A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods -- Severity Summarization and Just in Time Alert Computation in mHealth Monitoring -- Towards Safe and Efficient Child Primary Care -- Gaps in the Use of Unique Identifiers in Europe -- Why Are Children's Interests Invisible in European National E-Health Strategies? -- Shared Decision Making via Personal Health Record Technology for Routine Use of Diabetic Youth: A Study Protocol -- A Medication Reminder Mobile App: Does It Work for Different Age Ranges -- Internet of Things in Health Trends Through Bibliometrics and Text Mining -- Developing the Safety Case for MediPi: An Open-Source Platform for Self Management -- UK Health and Social Care Case Studies: Iterative Technology Development -- 2. …”
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Progress reports on impedance spectroscopy : measurements, modeling, and application

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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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Digital information ecosystems : smart press by Augey, Dominique

Table of Contents: “…The problem of revenue sharing between media and social networks; 6.2. The social network eco-system; 6.2.1. …”
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Binary decision diagrams and extensions for system reliability analysis by Xing, Liudong

Table of Contents: “…7.5 Applications to Phased-Mission Systems -- 7.5.1 Mini-Component Concept -- 7.5.2 Extended SEA Method for PMS -- 7.5.3 An Illustrative Example -- 7.6 Summary -- 8 Shared Decision Diagrams -- 8.1 Multi-Rooted Decision Diagrams -- 8.2 Multi-Terminal Decision Diagrams -- 8.3 Performance Study on Multi-State Systems -- 8.3.1 Example Analyses -- 8.3.2 Benchmark Studies -- 8.4 Application to Phased-Mission Systems -- 8.4.1 PMS Analysis Using MDDs -- 8.4.1.1 Step 1-Variable Encoding -- 8.4.1.2 Step 2-Input Variable Ordering -- 8.4.1.3 Step 3-PMS MDD Generation -- 8.4.1.4 Step 4-PMS MDD Evaluation -- 8.4.2 An Illustrative Example -- 8.5 Application to Multi-State k-out-of-n Systems -- 8.5.1 Multi-State k-out-of-n System Analysis Using MDDs -- 8.5.1.1 Step 1- BDDkl Generation -- 8.5.1.2 Step 2- MDDkl Generation -- 8.5.1.3 Step 3- MDDSj Generation -- 8.5.1.4 Step 4-System MDDSj Evaluation -- 8.5.2 An Illustrative Example -- 8.6 Importance Measures -- 8.6.1 Capacity Networks and Reliability Modeling -- 8.6.2 Composite Importance Measures (Type 1) -- 8.6.2.1 General CIMs -- 8.6.2.2 Alternative CIMs -- 8.6.3 Computing CIMs Using MDD -- 8.6.4 An Illustrative Example -- 8.7 Failure Frequency Based Measures -- 8.8 Summary -- Conclusions -- References -- Index -- EULA.…”
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Advanced security solutions for multimedia

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Security, Privacy and Reliability in Computer Communications and Networks. by Sha, Kewei

Table of Contents: “…Front Cover -- Half Title Page -- RIVER PUBLISHERS SERIES IN INNOVATION AND CHANGE IN EDUCATION -- CROSS-CULTURAL PERSPECTIVE -- Title Page -- Security, Privacy and Reliability in Computer Communications and Networks -- Copyright Page -- Contents -- Preface -- Acknowledgments -- List of Contributors -- List of Figures -- List of Tables -- List of Algorithms -- List of Abbreviations -- PART I -- Privacy -- Chapter 1 -- Distributed Beamforming Relay Selection to Increase Base Station Anonymity in Wireless Ad Hoc Networks -- Abstract -- 1.1 Introduction -- 1.2 Anonymity Definition, Metrics, and Contemporary Measures -- 1.2.1 Anonymity Definition and Assessment -- 1.2.2 Antitraffic Analysis Measures -- 1.3 System Assumptions and Attack Model -- 1.3.1 Network Model -- 1.3.2 Adversary Model -- 1.3.3 Evidence Theory and Belief Metric -- 1.4 Distributed Beamforming to Increase the BS Anonymity -- 1.4.1 Overview of the DiBAN Protocol -- 1.4.2 DiBAN Illustrative Example -- 1.4.3 DiBAN Energy Analysis -- 1.5 Distributed Beamforming Relay Selection Approach -- 1.6 Validation Experiments -- 1.6.1 Simulation Environment -- 1.6.2 Simulation Results -- 1.7 Conclusions and FutureWork -- Appendix I: Numerical Evidence Theory Belief Calculation Example -- References -- Chapter 2 -- A Privacy-Preserving and Efficient Information Sharing Scheme for VANET Secure Communication -- Abstract -- 2.1 Introduction -- 2.2 Related Works -- 2.3 System Model and Preliminaries -- 2.3.1 Network Model -- 2.3.2 Attack Model -- 2.3.3 Security Requirements -- 2.4 The Proposed PETS Scheme -- 2.4.1 Scheme Overview -- 2.4.2 System Initiation -- 2.4.3 Vehicle-RSU Key Agreement -- 2.4.4 Traffic Information Collection and Aggregation -- 2.4.5 Traffic Jam Message Propagation -- 2.5 Security Analysis -- 2.6 Performance Evaluation -- 2.6.1 Traffic Information Sending/Collection Overhead.…”
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SCADA Security : Machine Learning Concepts for Intrusion Detection and Prevention. by Almalawi, Abdulmohsen

Table of Contents: “…CHAPTER 4 Efficient k-Nearest Neighbour Approach Based on Various-Widths Clustering -- 4.1 INTRODUCTION -- 4.2 RELATED WORK -- 4.3 THE kNNVWC APPROACH -- 4.3.1 FWC Algorithm and Its Limitations -- 4.3.2 Various-Widths Clustering -- 4.3.3 The k-NN Search -- 4.4 EXPERIMENTAL EVALUATION -- 4.4.1 Data Sets -- 4.4.2 Performance Metrics -- 4.4.3 Impact of Cluster Size -- 4.4.4 Baseline Methods -- 4.4.5 Distance Metric -- 4.4.6 Complexity Metrics -- 4.5 CONCLUSION -- CHAPTER 5 SCADA Data-Driven Anomaly Detection -- 5.1 INTRODUCTION -- 5.2 SDAD APPROACH -- 5.2.1 Observation State of SCADA Points…”
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The Johns Hopkins guide to digital media

Table of Contents: “…Berry -- Cognitive implications of new media / Anne Mangen and Jean-Luc Velay -- Collaborative narrative / Scott Rettberg -- Collective intelligence / John Duda -- Combinatory and automatic text generation / Philippe Bootz and Christopher Funkhouser -- Computational linguistics / Inderjeet Mani -- Conceptual writing / Darren Wershler -- Copyright / Benjamin J. …”

Machine Learning with Apache Spark Quick Start Guide : Uncover Patterns, Derive Actionable Insights, and Learn from Big Data Using MLlib. by Quddus, Jillur

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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…<i>3</i>Big Data Analytics. 31</p> <p>1.7.4 Visualizing Big Data. 32</p> <p>1.8 Big Data Technology. 32</p> <p>1.8.1 Challenges faced by Big Data technology. 34</p> <p>1.8.1 Heterogeneity and incompleteness. 34</p> <p>1.8.2 Volume and velocity of the Data. 35</p> <p>1.8.3 Data Storage. 35</p> <p>1.8.4 Data Privacy. 36</p> <p>1.9 Big Data Applications. 36</p> <p>1.10 Big Data Use Cases. 37</p> <p>1.9. 1 Healthcare. 37</p> <p>1.9.2 Telecom.. 38</p> <p>1.9.3 Financial Services. 39</p> <p>Chapter 1 refresher: 40</p> <p>Conceptual short Questions with answers. 43</p> <p>Frequently asked Interview questions. 45</p> <p>Chapter Objective. 46</p> <p>Big Data Storage Concepts. 46</p> <p>2.1 Cluster computing. 47</p> <p>2.1.1 Types of cluster. 49</p> <p>2.1.1.1 High availability cluster. 50</p> <p>2.1.1.2 Load balancing cluster. 50</p> <p>2.1.2 Cluster structure. 51</p> <p>2.3 Distribution Models. 53</p> <p>2.3.1 Sharding. 54</p> <p>2.3.2 Data Replication. 56</p> <p>2.3.2.1 Master-Slave model 57</p> <p>2.3.2.2 Peer-to-Peer model 58</p> <p>2.3.3 Sharding and Replication. 59</p> <p>2.4 Distributed file system.. 60</p> <p>2.5 Relational and Non Relational Databases. 61</p> <p>CoursesOffered. 62</p> <p>Figure 2.12 Data divided across multiple related tables. 62</p> <p>2.4.2 RDBMS Databases. 63</p> <p>2.4.3 NoSQL Databases. 63</p> <p>2.4.4 NewSQL Databases. 64</p> <p>2.5 Scaling Up and Scaling Out Storage. 65</p> <p>Chapter 2 refresher. 67</p> <p>Conceptual short questions with answers. 69</p> <p>Chapter Objective. 72</p> <p>3.1 Introduction to NoSQL. 72</p> <p>3.2 Why NoSQL. 72</p> <p>3.3 CAP theorem.. 73</p> <p>3.4 ACID.. 75</p> <p>3.5 BASE. 76</p> <p>3.6 Schemaless Database. 77</p> <p>3.7 NoSQL (Not Only SQL) 77</p> <p>3.7.1 NoSQL Vs RDBMS. 78</p> <p>3.7.2Features of NoSQL database. 79</p> <p>3.7.3Types of NoSQL Technologies. 80</p> <p>3.7.3.1 Key-Value store database. 81</p> <p>3.7.3.2 Column-store database. 82</p> <p>3.7.3.3 Document Oriented Database. 84</p> <p>3.7.3.4 Graph-oriented Database. 86</p> <p>3.7.4 NoSQL Operations. 93</p> <p>3.9 Migrating from RDBMS to NoSQL. 98</p> <p>Chapter 3 refresher. 99</p> <p>Conceptual short questions with answers. 102</p> <p>Chapter Objective. 104</p> <p>4.1 Data Processing. 104</p> <p>4.2 Shared Everything Architecture. 106</p> <p>4.2.1 Symmetric multiprocessing architecture. 107</p> <p>4.2.2 Distributed Shared memory. 108</p> <p>4.3 Shared nothing architecture. 109</p> <p>4.4 Batch Processing. 110</p> <p>4.5 Real-Time Data Processing. 111</p> <p>4.6 Parallel Computing. 112</p> <p>4.7 Distributed Computing. 113</p> <p>4.8 Big Data Virtualization. 113</p> <p>4.8.1 Attributes of Virtualization. 114</p> <p>4.8.1.1 Encapsulation. 115</p> <p>4.8.1.2 Partitioning. 115</p> <p>4.8.1.3 Isolation. 115</p> <p>4.8.2Big Data Server Virtualization. 116</p> <p>4.9 Introduction. 116</p> <p>4.10 Cloud computing types. 118</p> <p>4.11Cloud Services. 120</p> <p>4.12 Cloud Storage. 121</p> <p>4.12.1 Architecture of GFS. 121</p> <p>4.12.1.1 Master. 123</p> <p>4.12.1.2 Client. 123</p> <p>4.13 Cloud Architecture. 127</p> <p>Cloud Challenges. 129</p> <p>Chapter 4 Refresher. 130</p> <p>Conceptual short questions with answers. 133</p> <p>Chapter Objective. 139</p> <p>5.1 Apache Hadoop. 139</p> <p>5.1.1 Architecture of Apache Hadoop. 140</p> <p>5.1.2Hadoop Ecosystem Components Overview.. 140</p> <p>5.2 Hadoop Storage. 142</p> <p>5.2.1HDFS (Hadoop Distributed File System). 142</p> <p>5.2.2Why HDFS?. …”
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Language, vision, and music : selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing, Galway, Ireland, 1999

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Politics of disinformation : the influence of fake news on the public sphere

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Big data analytics : turning big data into big money by Ohlhorst, Frank, 1964-

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AI and the future of banking by Boobier, Tony, 1956-

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Applied mechanics and manufacturing technology : selected, peer reviewed papers of the 2011 International Conference on Applied Mechanics and Manufacturing Technology (AMMT 2011),...

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