Search Results - (((((((kent OR wiant) OR ken) OR wants) OR cantor) OR anne) OR carter) OR wanting) algorithms.

OpenCV Android Programming By Example. by Muhammad, Amgad

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Apache Spark for Data Science Cookbook. by Chitturi, Padma Priya

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IP multicast with applications to IPTV and mobile DVB-H by Minoli, Daniel, 1952-

Table of Contents: “…Cover -- TOC36;CONTENTS -- Preface -- About the Author -- CH36;1 INTRODUCTION TO IP MULTICAST -- 146;1 Introduction -- 146;2 Why Multicast Protocols are Wanted47;Needed -- 146;3 Basic Multicast Protocols and Concepts -- 146;4 IPTV and DVB45;H Applications -- 146;5 Course of Investigation -- Appendix 146;A58; Multicast IETF Request for Comments -- Appendix 146;B58; Multicast Bibliography -- References -- CH36;2 MULTICAST ADDRESSING FOR PAYLOAD -- 246;1 IP Multicast Addresses -- 246;146;1 Limited Scope Addresses -- 246;146;2 GLOP Addressing -- 246;146;3 Generic IPv4 Addressing -- 246;2 Layer 2 Multicast Addresses -- 246;246;1 Ethernet MAC Address Mapping -- 246;3 MPEG45;Layer Addresses -- References -- CH36;3 MULTICAST PAYLOAD FORWARDING -- 346;1 Multicasting on a LAN Segment -- 346;2 Multicasting between Network Segments -- 346;3 Multicast Distribution Trees -- 346;4 Multicast Forwarding58; Reverse Path Forwarding -- 346;5 Multicast Forwarding58; Center45;Based Tree Algorithm -- 346;6 Implementing IP Multicast in a Network -- References -- CH36;4 DYNAMIC HOST REGISTRATION8212;INTERNET GROUP MANAGEMENT PROTOCOL -- 446;1 IGMP Messages -- 446;2 IGMPv3 Messages -- 446;3 IGMP Operation -- Appendix 446;A58; Protocol Details for IGMPv2 -- 446;A46;1 Overview -- 446;A46;2 Protocol Description -- 446;A46;3 Receiver 40;Host41; State Diagram -- 446;A46;4 Router State Diagram -- Appendix 446;B58; IGMP Snooping Switches -- Appendix 446;C58; Example of Router Configurations -- References -- CH36;5 MULTICAST ROUTING8212;SPARSE45;MODE PROTOCOLS58; PROTOCOL INDEPENDENT MULTICAST -- 546;1 Introduction to PIM -- 546;2 PIM SM Details -- 546;246;1 Approach -- 546;246;2 PIM SM Protocol Overview -- 546;246;3 Detailed Protocol Description -- 546;246;4 Packet Formats -- References -- CH36;6 MULTICAST ROUTING8212;SPARSE45;MODE PROTOCOLS58; CORE45;BASED TREES -- 646;1 Motivation -- 646;2 Basic Operation -- 646;3 CBT Components and Functions -- 646;346;1 CBT Control Message Retransmission Strategy -- 646;346;2 Nonmember Sending -- 646;4 Core Router Discovery -- 646;5 Protocol Specification Details -- 646;546;1 CBT HELLO Protocol -- 646;546;2 JOIN_REQUEST Processing -- 646;546;3 JOIN_ACK Processing -- 646;546;4 QUIT_NOTIFICATION Processing -- 646;546;5 ECHO_REQUEST Processing -- 646;546;6 ECHO_REPLY Processing -- 646;546;7 FLUSH_TREE Processing -- 646;546;8 Nonmember Sending -- 646;546;9 Timers and Default Values -- 646;546;10 CBT Packet Formats and Message Types -- 646;546;11 Core Router Discovery -- 646;6 CBT Version 3 -- 646;646;1 The First Step58; Joining the Tree -- 646;646;2 Transient State -- 646;646;3 Getting 34;On Tree34; -- 646;646;4 Pruning and Prune State -- 646;646;5 The Forwarding Cache -- 646;646;6 Packet Forwarding -- 646;646;7 The 34;Keepalive34; Protocol -- 646;646;8 Control Message Precedence and Forwarding Criteria -- 646;646;9 Broadcast LANs -- 646;646;10 The 34;all45;cbt45;routers34; Group -- 646;646;11 Nonmember Sending -- References -- CH36;7 MULTICAST ROUTING8212;DENSE45;MODE PROTOCOLS58; PIM DM.…”
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Principles of artificial neural networks by Graupe, Daniel

Table of Contents: “…Fundamentals of biological neural networks -- ch. 3. Basic principles of ANNs and their early structures. 3.1. Basic principles of ANN design. 3.2. …”
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Machine Learning with Swift : Artificial Intelligence for iOS. by Sosnovshchenko, Oleksandr

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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI). by Tan, Khee Giap

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MATLAB for Machine Learning. by Ciaburro, Giuseppe

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Hack proofing your network

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Frontiers of Artificial Intelligence in Medical Imaging. by Razmjooy, Navid

Table of Contents: “…5.5 Electromagnetic field optimization algorithm -- 5.6 Developed electromagnetic field optimization algorithm -- 5.7 Simulation results -- 5.7.1 Image acquisition -- 5.7.2 Pre-processing stage -- 5.7.3 Processing stage -- 5.7.4 Classification -- 5.8 Final evaluation -- 5.9 Conclusions -- References -- Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation -- 6.1 Introduction -- 6.2 Context -- 6.3 Methodology -- 6.3.1 COVID-19 database -- 6.3.2 Image conversion and pre-processing -- 6.3.3 Image thresholding…”
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Machine Learning for Asset Management New Developments and Financial Applications

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Advanced Analytics with R and Tableau. by Stirrup, Jen

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Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R by Rai, Bharatendra K.

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Power and energy systems III : selected, peer reviewed papers from the 2013 3rd International Conference on Power and Energy Systems (ICPES 2013), November 23-24, Bangkok, Thailand

Table of Contents: “…Appropriate Electric Energy Conservation Measures for Big Mosques in Riyadh CityBayesian Algorithm Based on Airborne Power Supply System; Study on the Cooling System of Super-Capacitors for Hybrid Electric Vehicle; A Charging Management of Electric Vehicles Based on Campus Survey Data; Back-EMF Position Detection Technology for Brushless DC Motor; Stress State of Turbine Blade Root and Rim Considering Manufacturing Variations; Analysis on a Gas Turbine Sealing Disk Structure and Material Strength; A Kind of Adjustable Electric Heating Pipe Power Electrode Preparation Equipment.…”
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Java Data Mining - Strategy, Standard, and Practice : a Practical Guide for architecture, design, and implementation. by Hornick, Mark F.

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Mathematical Methods in Interdisciplinary Sciences. by Chakraverty, Snehashish

Table of Contents: “…1.2.2.1 Architecture of Single-Layer LgNN Model -- 1.2.2.2 Training Algorithm of Laguerre Neural Network (LgNN) -- 1.2.2.3 Gradient Computation of LgNN -- 1.3 Methodology for Solving a System of Fredholm Integral Equations of Second Kind -- 1.3.1 Algorithm -- 1.4 Numerical Examples and Discussion -- 1.4.1 Differential Equations and Applications -- 1.4.2 Integral Equations -- 1.5 Conclusion -- References -- Chapter 2 Deep Learning in Population Genetics: Prediction and Explanation of Selection of a Population -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset Description…”
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A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Digital wealth : an automatic way to invest successfully by Moore, Simon, 1978-

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Building Machine Learning Systems with Python. by Richert, Willi

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