Search Results - (((((((kent OR kwanta) OR mantis) OR markant) OR cantor) OR anne) OR share) OR hints) algorithms.

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. …”

Networks-on-chip : from implementations to programming paradigms by Ma, Sheng, Huang, Libo, 1976-, Lai, Mingche, Shi, Wei

Table of Contents: “…3.2.1 DVC scheme3.2.2 Congestion avoidance scheme; 3.3 Multiple-port shared buffer with congestion awareness; 3.3.1 DVC scheme among multiple ports; 3.3.2 Congestion avoidance scheme; 3.4 DVC router microarchitecture; 3.4.1 VC control module; 3.4.2 Metric aggregation and congestion avoidance; 3.4.3 VC allocation module; 3.5 HiBB router microarchitecture; 3.5.1 VC control module; 3.5.2 VC allocation and output port allocation; 3.5.3 VC regulation; 3.6 Evaluation; 3.6.1 DVC router evaluation; 3.6.2 HiBB router evaluation; 3.7 Chapter summary; References.…”
Full text (MFA users only)

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

Full text (MFA users only)

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.…”
Full text (MFA users only)

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

Full text (MFA users only)

Reconfigurable computing : the theory and practice of FPGA-based computation

Full text (MFA users only)

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

Full text (MFA users only)

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. …”
Full text (MFA users only)

DEEP LEARNING FOR HEALTHCARE DECISION MAKING.

Full text (MFA users only)

Network Performance Analysis. by Bonald, Thomas

Full text (MFA users only)

Business Hack : the Wealth Dragon Way to Build a Successful Business in the Digital Age. by Lee, John

Full text (MFA users only)

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,…”
Full text (MFA users only)

Danforth's obstetrics and gynecology.

Full text (MFA users only)

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. …”
Full text (MFA users only)

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. …”
Full text (MFA users only)

Collaborative Annotation for Reliable Natural Language Processing: Technical and Sociological Aspects by Fort, Karen

Full text (MFA users only)

Hands-On Deep Learning with TensorFlow. by Boxel, Dan Van

Full text (MFA users only)

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. …”
Full text (MFA users only)

Artificial intelligence in society.

Full text (MFA users only)

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.…”
Full text (MFA users only)