Search Results - (((((((ant OR hwkantis) OR wanting) OR makart) OR cantor) OR anne) OR shared) OR hints) algorithms.

Thermal Management of Electronics : an Experimental Approach Using Phase-Change-Based Materials in Composite Heat Sinks. by Baby, Rajesh

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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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Medical and Care Compunetics 2 : Medical and Care Compunetics 2. by Bos, L.

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Network Performance Analysis. by Bonald, Thomas

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Machine learning and cognitive computing for mobile communications and wireless networks

Table 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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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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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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Experimental Economics and Culture by Gunnthorsdottir, Anna

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