Search Results - (((((((kant OR wants) OR canton) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Technologies for sustainable development : proceedings of the 7th Nirma University International Conference on Engineering (NUiCONE 2019), November 21-22, 2019, Ahmedabad, India

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Handbook of safety principles

Table 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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DEEP LEARNING FOR HEALTHCARE DECISION MAKING.

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Network Performance Analysis. by Bonald, Thomas

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XML for DB2 information integration

Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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Danforth's obstetrics and gynecology.

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Advanced manufacturing technology and systems : selected, peer reviewed papers from the 2012 International Conference on Advanced Manufacturing Technology and Systems (AMTS 2012),...

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IBM TotalStorage : SAN product, design, and optimization guide

Table of Contents: “…-- 1.1.2 The Internet brings increased risks -- 1.1.3 Planning for business continuity -- 1.2 Using a SAN for business continuance -- 1.2.1 SANs and business continuance -- 1.3 SAN business benefits -- 1.3.1 Storage consolidation and sharing of resources -- 1.3.2 Data sharing -- 1.3.3 Nondisruptive scalability for growth -- 1.3.4 Improved backup and recovery -- 1.3.5 High performance -- 1.3.6 High availability server clustering -- 1.3.7 Improved disaster tolerance -- 1.3.8 Allow selection of best of breed storage -- 1.3.9 Ease of data migration -- 1.3.10 Reduced total costs of ownership -- 1.3.11 Storage resources match e-business enterprise needs -- Chapter 2. …”
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Advances in intelligent transportation : system and technology : selected, peer reviewed papers from the 1st International Doctoral Annual Symposium on Intelligent Transportation T...

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Information engineering for mechanics and materials research

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Mobile phones : technology, networks, and user issues

Table of Contents: “…Seamless Sensor Fusion -- 4.1. Particle Filter Algorithm -- 4.2. Motion Model -- 4.3. Measurement Model -- 4.4. …”
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Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language by Fischetti, Tony

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Atlas of minimally invasive surgery for lung and esophageal cancer

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Artificial intelligence and data mining approaches in security frameworks

Table of Contents: “…87 -- 5.1.2 Purpose of Spamming 88 -- 5.1.3 Spam Filters Inputs and Outputs 88 -- 5.2 Content-Based Spam Filtering Techniques 89 -- 5.2.1 Previous Likeness–Based Filters 89 -- 5.2.2 Case-Based Reasoning Filters 89 -- 5.2.3 Ontology-Based E-Mail Filters 90 -- 5.2.4 Machine-Learning Models 90 -- 5.2.4.1 Supervised Learning 90 -- 5.2.4.2 Unsupervised Learning 90 -- 5.2.4.3 Reinforcement Learning 91 -- 5.3 Machine Learning–Based Filtering 91 -- 5.3.1 Linear Classifiers 91 -- 5.3.2 Naïve Bayes Filtering 92 -- 5.3.3 Support Vector Machines 94 -- 5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94 -- 5.4 Performance Analysis 97 -- 5.5 Conclusion 97 -- References 98 -- 6 Artificial Intelligence in the Cyber Security Environment 101 Jaya Jain -- 6.1 Introduction 102 -- 6.2 Digital Protection and Security Correspondences Arrangements 104 -- 6.2.1 Operation Safety and Event Response 105 -- 6.2.2 AI2 105 -- 6.2.2.1 CylanceProtect 105 -- 6.3 Black Tracking 106 -- 6.3.1 Web Security 107 -- 6.3.1.1 Amazon Macie 108 -- 6.4 Spark Cognition Deep Military 110 -- 6.5 The Process of Detecting Threats 111 -- 6.6 Vectra Cognito Networks 112 -- 6.7 Conclusion 115 -- References 115 -- 7 Privacy in Multi-Tenancy Frameworks Using AI 119 Shweta Solanki -- 7.1 Introduction 119 -- 7.2 Framework of Multi-Tenancy 120 -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI 122 -- 7.4 Related Work 125 -- 7.5 Conclusion 125 -- References 126 -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129 Shubhi Srivastava, Ankit Kumar and Shiv Prakash -- 8.1 Introduction 129 -- 8.1.1 Biometric 131 -- 8.1.2 Categories of Biometric 131 -- 8.1.2.1 Advantages of Biometric 132 -- 8.1.3 Significance and Scope 132 -- 8.1.4 Biometric Face Recognition 132 -- 8.1.5 Related Work 136 -- 8.1.6 Main Contribution 136 -- 8.1.7 Novelty Discussion 137 -- 8.2 The Proposed Methodolgy 139 -- 8.2.1 Face Detection Using Haar Algorithm 139 -- 8.2.2 Feature Extraction Using ILBP 141 -- 8.2.3 Dataset 143 -- 8.2.4 Classification Using SVM 143 -- 8.3 Experimental Results 145 -- 8.3.1 Face Detection 146 -- 8.3.2 Feature Extraction 146 -- 8.3.3 Recognize Face Image 147 -- 8.4 Conclusion 151 -- References 152 -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155 S K Rajesh Kanna, O. …”
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Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics

Table of Contents: “…3.3.1.2 Computation Task Models 68 -- 3.3.1.3 Quality of Experience 71 -- 3.3.2 Computation Offloading Game 71 -- 3.3.2.1 Game Formulation 71 -- 3.3.2.2 Algorithm Development 74 -- 3.3.2.3 Price of Anarchy 74 -- 3.3.2.4 Performance Evaluation 75 -- 3.4 Conclusion 80 -- References 80 -- 4 Pricing Tradeoffs for Data Analytics in Fog-Cloud Scenarios 83 /Yichen Ruan, Liang Zheng, Maria Gorlatova, Mung Chiang, and Carlee Joe-Wong -- 4.1 Introduction: Economics and Fog Computing 83 -- 4.1.1 Fog Application Pricing 85 -- 4.1.2 Incentivizing Fog Resources 86 -- 4.1.3 A Fogonomics Research Agenda 86 -- 4.2 Fog Pricing Today 87 -- 4.2.1 Pricing Network Resources 87 -- 4.2.2 Pricing Computing Resources 89 -- 4.2.3 Pricing and Architecture Trade-offs 89 -- 4.3 Typical Fog Architectures 90 -- 4.3.1 Fog Applications 90 -- 4.3.2 The Cloud-to-Things Continuum 90 -- 4.4 A Case Study: Distributed Data Processing 92 -- 4.4.1 A Temperature Sensor Testbed 92 -- 4.4.2 Latency, Cost, and Risk 95 -- 4.4.3 System Trade-off: Fog or Cloud 98 -- 4.5 Future Research Directions 101 -- 4.6 Conclusion 102 -- Acknowledgments 102 -- References 103 -- 5 Quantitative and Qualitative Economic Benefits of Fog 107 /Joe Weinman -- 5.1 Characteristics of Fog Computing Solutions 108 -- 5.2 Strategic Value 109 -- 5.2.1 Information Excellence 110 -- 5.2.2 Solution Leadership 110 -- 5.2.3 Collective Intimacy 110 -- 5.2.4 Accelerated Innovation 111 -- 5.3 Bandwidth, Latency, and Response Time 111 -- 5.3.1 Network Latency 113 -- 5.3.2 Server Latency 114 -- 5.3.3 Balancing Consolidation and Dispersion to Minimize Total Latency 114 -- 5.3.4 Data Traffic Volume 115 -- 5.3.5 Nodes and Interconnections 116 -- 5.4 Capacity, Utilization, Cost, and Resource Allocation 117 -- 5.4.1 Capacity Requirements 117 -- 5.4.2 Capacity Utilization 118 -- 5.4.3 Unit Cost of Delivered Resources 119 -- 5.4.4 Resource Allocation, Sharing, and Scheduling 120 -- 5.5 Information Value and Service Quality 120 -- 5.5.1 Precision and Accuracy 120.…”
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The language of mathematics education : an expanded glossary of key terms and concepts in mathematics teaching and learning by Dingman, Shannon W., 1977-

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Android Sensor Programming By Example. by Nagpal, Varun

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Mastering D3.js. by Castillo, Pablo Navarro

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Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

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Experimental Economics and Culture by Gunnthorsdottir, Anna

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