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361
SCADA Security : Machine Learning Concepts for Intrusion Detection and Prevention.
Published 2020Table 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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Electronic eBook -
362
The Johns Hopkins guide to digital media
Published 2014Table 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. …”
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364
Big data : concepts, technology and architecture
Published 2021Table 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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365
Language, vision, and music : selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing, Galway, Ireland, 1999
Published 2002Full text (MFA users only)
Electronic Conference Proceeding eBook -
366
Politics of disinformation : the influence of fake news on the public sphere
Published 2021Full text (MFA users only)
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367
Big data analytics : turning big data into big money
Published 2013Full text (MFA users only)
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368
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369
Nonlinear science and complexity
Published 2007Table of Contents: “…Bruzón -- Applying a new algorithm to derive nonclassical symmetries / M.S. …”
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371
Handbook of safety principles
Published 2018Table of Contents: “…Success or Failure / Ann Enander -- 30.8. Relations to Other Safety Principles / Ann Enander -- References / Ann Enander -- Further Reading / Ann Enander -- 31. …”
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Decision Intelligence for Dummies
Published 2022Table of Contents: “…Preventing Wrong Influences from Affecting Decisions -- Bad influences in AI and analytics -- The blame game -- Ugly politics and happy influencers -- Risk Factors in Decision Intelligence -- DI and Hyperautomation -- Part 5 The Part of Tens -- Chapter 17 Ten Steps to Setting Up a Smart Decision -- Check Your Data Source -- Track Your Data Lineage -- Know Your Tools -- Use Automated Visualizations -- Impact = Decision -- Do Reality Checks -- Limit Your Assumptions -- Think Like a Science Teacher -- Solve for Missing Data -- Partial versus incomplete data -- Clues and missing answers -- Take Two Perspectives and Call Me in the Morning -- Chapter 18 Bias In, Bias Out (and Other Pitfalls) -- A Pitfalls Overview -- Relying on Racist Algorithms -- Following a Flawed Model for Repeat Offenders -- Using A Sexist Hiring Algorithm -- Redlining Loans -- Leaning on Irrelevant Information -- Falling Victim to Framing Foibles -- Being Overconfident -- Lulled by Percentages -- Dismissing with Prejudice -- Index -- EULA.…”
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Business Hack : the Wealth Dragon Way to Build a Successful Business in the Digital Age.
Published 2018Full text (MFA users only)
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376
Machine learning and cognitive computing for mobile communications and wireless networks
Published 2020Table of Contents: “…KNN and SVM Models for Wireless 60 3.4.2 Bayesian Learning for Cognitive Radio 60 3.4.3 Deep Learning in Wireless Network 61 3.4.4 Deep Reinforcement Learning in Wireless Network 62 3.4.5 Traffic Engineering and Routing 63 3.4.6 Resource Sharing and Scheduling 64 3.4.7 Power Control and Data Collection 64 3.5 Conclusion 65 References 66 4 Cognitive Computing for Smart Communication 73; Poonam Sharma,…”
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378
Experiencing architecture in the nineteenth century : buildings and society in the modern age
Published 2019Full text (MFA users only)
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379
Advances in intelligent transportation : system and technology : selected, peer reviewed papers from the 1st International Doctoral Annual Symposium on Intelligent Transportation T...
Published 2012Full text (MFA users only)
Electronic Conference Proceeding eBook -
380
Information engineering for mechanics and materials research
Published 2013Full text (MFA users only)
Electronic Conference Proceeding eBook