Search Results - (((((((ant OR wanting) OR wanted) OR alter) OR cantor) OR anne) OR carter) OR wanting) algorithms.

Metaheuristics for vehicle routing problems. by Labadie, Nacima

Table of Contents: “…Memetic algorithm; 4.1.3. Memetic algorithm with population management; 4.1.4. …”
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Industrial control systems

Table of Contents: “…EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION ; 3. PLANT MODEL BASED ON ANN ; 3.1. ANN Configurations.…”
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Parallel computing : technology trends

Table of Contents: “…Feedback-Driven Performance and Precision Tuning for Automatic Fixed Point Exploitation -- Parallel Programming -- A GPU-CUDA Framework for Solving a Two-Dimensional Inverse Anomalous Diffusion Problem -- Parallelization Strategies for GPU-Based Ant Colony Optimization Applied to TSP -- DBCSR: A Blocked Sparse Tensor Algebra Library -- Acceleration of Hydro Poro-Elastic Damage Simulation in a Shared-Memory Environment -- BERTHA and PyBERTHA: State of the Art for Full Four-Component Dirac-Kohn-Sham Calculations -- Prediction-Based Partitions Evaluation Algorithm for Resource Allocation…”
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The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; What Is Cryptography?; Still Want to Be a Miner?; Do We Need Another Bitcoin?; 7 Nautiluscoin-0 to 1 Million in 60 Days; Creating the Coin; Did It Work?…”
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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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Java programming interviews exposed by Markham, Noel

Table of Contents: “…Chapter 18: Useful LibrariesRemoving Boilerplate Code with Apache Commons; Developing with Guava Collections; Using Joda Time; Summary; Chapter 19: Developing with Build Tools; Building Applications with Maven; Ant; Summary; Chapter 20: Android; Basics; User Interface; Persistence; Android Hardware; Summary; Appendix: Introducing Scala; Scala Basics; Functions as Values; Immutability; Summary; Index.…”
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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Data clustering in C++ : an object-oriented approach by Gan, Guojun, 1979-

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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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Handbook of biometrics for forensic science

Table of Contents: “…5.2 Background of Fingerprint Alterations5.2.1 Obliteration; 5.2.2 Distortion; 5.2.3 Imitation; 5.3 Related Work; 5.3.1 Orientation Field Analysis; 5.3.2 Minutiae Distribution Analysis; 5.4 Recent Algorithms for Fingerprint Alteration Detection; 5.4.1 Preprocessing; 5.4.2 Singular Point Density Analysis; 5.4.3 Minutia Orientation Analysis; 5.4.4 Orientation Difference Map; 5.4.5 Orientation Density Map; 5.5 Evaluation and Results; 5.6 Conclusion; References; Face and Video Analysis; 6 Face Sketch Recognition via Data-Driven Synthesis; 6.1 Introduction; 6.2 Related Work.…”
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Applied mechatronics and android robotics : selected, peer reviewed papers from the International Conference on Applied Mechatronics and Android Robotics (ICAMAR 2013), July 13-14,...

Table of Contents: “…Applied Mechatronics and Android Robotics; Preface, Committees and Sponsors; Table of Contents; Chapter 1: Robot Design, Motion Planning and Robot Control; Design of a Master Manipulator with Dynamical Simplification for Master-Slave Robot; Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots; Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms; A Study of Robot Platform Based on WiFi Remote Control; Fire Detection System Based-On Mobile Robots; Phase Plane Analysis of Battery Series Balancing on Robot…”
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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Comorbidity in migraine

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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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Anaphora Resolution. by Mitkov, Ruslan

Table of Contents: “…4.1 Early work in anaphora resolution4.2 Student; 4.3 Shrdlu; 4.4 Lunar; 4.5 Hobbs's naïve approach; 4.5.1 The algorithm; 4.5.2 Evaluation of Hobbs' s algorithm; 4.6 The BFP algorithm; 4.7 Carter's shallow processing approach; 4.8 Rich and LuperFoy's distributed architecture; 4.9 Carbonell and Brown's multi-strategy approach; 4.10 Other work; 4.11 Summary; Chapter Five: The present: knowledge-poor and corpus-based approaches in the 1990s and beyond; 5.1 Main trends in recent anaphora resolution research; 5.2 Collocation patterns-based approach; 5.3 Lappin and Leass's algorithm; 5.3.1 Overview.…”
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Advances in digital technologies : proceedings of the 6th International Conference on Applications of Digital Information and Web Technologies 2015

Table of Contents: “…Application of Genetic Algorithms to Context-Sensitive Text MiningA Decision Tree Classification Model for Determining the Location for Solar Power Plant; A Framework for Multi-Label Learning Using Label Ranking and Correlation; A Comparative Analysis of Pruning Methods for C4.5 and Fuzzy C4.5; Subject Index; Author Index.…”
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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. by Karim, Rezaul

Table of Contents: “…; Artificial Neural Networks; Biological neurons; A brief history of ANNs; How does an ANN learn?; ANNs and the backpropagation algorithm; Forward and backward passes; Weights and biases; Weight optimization; Activation functions.…”
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Optimization advances in electric power systems

Table of Contents: “…Unreliability Costs -- 3.5. Proposed Algorithms -- 3.5.1. ES and TS Algorithms -- 3.5.2. …”
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Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. by Deshpande, Anand

Table of Contents: “…Snowball stemming -- Lancaster stemming -- Lovins stemming -- Dawson stemming -- Lemmatization -- N-grams -- Feature extraction -- One hot encoding -- TF-IDF -- CountVectorizer -- Word2Vec -- CBOW -- Skip-Gram model -- Applying NLP techniques -- Text classification -- Introduction to Naive Bayes' algorithm -- Random Forest -- Naive Bayes' text classification code example -- Implementing sentiment analysis -- Frequently asked questions -- Summary -- Chapter 7: Fuzzy Systems -- Fuzzy logic fundamentals -- Fuzzy sets and membership functions -- Attributes and notations of crisp sets -- Operations on crisp sets -- Properties of crisp sets -- Fuzzification -- Defuzzification -- Defuzzification methods -- Fuzzy inference -- ANFIS network -- Adaptive network -- ANFIS architecture and hybrid learning algorithm -- Fuzzy C-means clustering -- NEFCLASS -- Frequently asked questions -- Summary -- Chapter 8: Genetic Programming -- Genetic algorithms structure -- KEEL framework -- Encog machine learning framework -- Encog development environment setup -- Encog API structure -- Introduction to the Weka framework -- Weka Explorer features -- Preprocess -- Classify -- Attribute search with genetic algorithms in Weka -- Frequently asked questions -- Summary -- Chapter 9: Swarm Intelligence -- Swarm intelligence -- Self-organization -- Stigmergy -- Division of labor -- Advantages of collective intelligent systems -- Design principles for developing SI systems -- The particle swarm optimization model -- PSO implementation considerations -- Ant colony optimization model -- MASON Library -- MASON Layered Architecture -- Opt4J library -- Applications in big data analytics -- Handling dynamical data -- Multi-objective optimization -- Frequently asked questions -- Summary -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept.…”
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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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