Search Results - (((((((anti OR wpanting) OR span) OR wantis) OR cantor) OR anne) OR carter) OR wanting) algorithms.

  1. 61

    Artificial intelligence : approaches, tools, and applications

    Published 2011
    Table of Contents: “…EVOLUTIONARY COMPUTING ; 2.1. Genetic Algorithms ; 2.2. Mechanism of a Genetic Algorithm ; 3. …”
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  2. 62

    Industrial Engineering Foundations : Bridging the Gap Between Engineering and Management. by Sassani, Farrokh

    Published 2016
    Table of Contents: “…5.2 Definition of Scheduling Terms5.3 Scheduling Algorithms; 5.3.1 Objectives in Scheduling Problems; 5.3.2 Industrial Problems; 5.3.3 The Practice; 5.4 n-Job 1-Machine Problem; 5.4.1 Classification of Scheduling Problems; 5.5 Johnson's Algorithm; 5.6 Johnson's Extended Algorithm; 5.7 Jackson's Algorithm; 5.8 Akers' Algorithm; 5.9 The Branch and Bound Method; 5.9.1 Lower Bounds for Completion Time; 5.9.2 Branch and Bound Algorithm; 5.10 Mathematical Solutions; 5.11 Closing Remarks; Exercises; Chapter 6 Forecasting; 6.1 Introduction; 6.1.1 Pre-Forecasting Analysis; 6.1.2 Forecasting Horizon.…”
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  3. 63

    Data Analytics in Bioinformatics : A Machine Learning Perspective. by Satpathy, Rabinarayan

    Published 2021
    Table of Contents: “…3.4.1 Comparative Analysis of ANN With Broadly Used Traditional ML Algorithms -- 3.5 Critical Analysis -- 3.6 Conclusion -- References -- Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION -- 4 Dimensionality Reduction Techniques: Principles, Benefits, and Limitations -- 4.1 Introduction -- 4.2 The Benefits and Limitations of Dimension Reduction Methods -- 4.3 Components of Dimension Reduction -- 4.3.1 Feature Selection -- 4.3.2 Feature Reduction -- 4.4 Methods of Dimensionality Reduction -- 4.4.1 Principal Component Analysis (PCA)…”
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  4. 64

    Bridge health monitoring, maintenance and safety : special topic volume with invited peer reviewed papers only

    Published 2011
    Table of Contents: “…Modal test and analysis of a bridge under the varying temperature condition -- Bridge significant failure mode identification strategy under traffic-load -- Fatigue reliability analysis of the stay cables of cable-stayed bridge under combined loads of stochastic traffic and wind -- Updating the finite element model of a bridge model using a hybrid optimization method -- An improved Taguchi method and its application in finite element model updating of bridges -- Nonlinear seismic response analysis of half through CFST arch bridge under 3-D earthquake waves -- Simplified seismic response assessment method and parametric study of multi-girder skew bridges -- Seismic testing of a long-span concrete filled steel tubular arch bridge -- Study on finite element model of bridge multi-pile foundation -- Sensor optimal placement for bridge structure based on single parents genetic algorithm with different fitness functions.…”
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  5. 65

    Media technologies : essays on communication, materiality, and society

    Published 2014
    Table of Contents: “…Bowker -- "What Do We Want?" "Materiality!" "When Do We Want It?" "Now!" …”
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  6. 66

    Agent-based computing

    Published 2010
    Table of Contents: “…Analysis of Algorithm -- 2.1.1. Chain-Like Agent Structure -- 2.1.2. …”
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  7. 67

    Integer and Combinatorial Optimization. by Wolsey, Laurence A.

    Published 2014
    Table of Contents: “…The Ellipsoid Algorithm.…”
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    Discriminant Analysis and Clustering.

    Published 1988
    Table of Contents: “…COVER -- COPYRIGHT -- PREFACE -- TABLE OF CONTENTS -- CHAPTER 1 INTRODUCTION -- CHAPTER 2 METHODS -- 2.1 INTRODUCTION -- 2.2 METHODS OF DISCRIMINANT ANALYSIS -- 2.2.1 General Remarks -- 2.2.2 Classical Two-group Linear Discriminant Analysis -- 2.2.3 Classification Into One of Several Populations -- 2.2.4 Heterogeneous Covariance Matrices Case -- 2.2.5 Two-group Classification by Logistic Regression -- 2.2.6 Kernel and Nearest Neighbor Methods -- 2.2.7 Classification Trees -- 2.3 METHODS OF CLUSTER ANALYSIS -- 2.3.1 General Remarks -- 2.3.2 Algorithms -- 2.3.3 Perspective -- CHAPTER 3 THEORY -- 3.1 INTRODUCTION -- 3.2 THEORETICAL ISSUES IN DISCRIMINANT ANALYSIS -- 3.2.1 Introduction -- 3.2.2 The Fisher Linear Discriminant and Some of Its Children -- 3.2.3 Estimating Misclassification Costs -- 3.2.4 Nonparametric Techniques -- 3.3 STATISTICAL THEORY IN CLUSTERING -- 3.3.1 Introduction -- 3.3.2 High Density Clusters -- 3.3.3 Agglomerative Methods for High Density Clusters -- 3.3.4 Single Linkage, the Minimum Spanning Tree and Percolation -- 3.3.5 Mixtures -- 3.3.6 The Number of Clusters: Modes -- 3.3.7 The Number of Clusters: Components -- 3.3.8 Ultrametric and Evolutionary Distances -- CHAPTER 4 SOFTWARE AND ALGORITHM IMPLEMENTATION -- 4.1 INTRODUCTION -- 4.2 DISCRIMINANT ANALYSIS -- 4.2.1 Linear and Quadratic Discriminant Functions -- 4.2.2 Review of Packages -- 4.2.3 Logistic Regression -- 4.2.4 Classification Trees -- 4.3 CLUSTER ANALYSIS -- 4.3.1 Collections of Subroutines and Algorithms -- 4.3.2 Cluster Analysis Packages -- 4.3.3 Simple Cluster Analysis Programs -- 4.4 NEEDS -- CHAPTER 5 CLOSING PERSPECTIVE.…”
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  10. 70

    Introduction to network security : theory and practice by Wang, Jie, 1961-

    Published 2015
    Table of Contents: “…Front Matter -- Network Security Overview -- Data Encryption Algorithms -- Public-Key Cryptography and Key Management -- Data Authentication -- Network Security Protocols in Practice -- Wireless Network Security -- Cloud Security -- Network Perimeter Security -- Intrusion Detections -- The Art of Anti-Malicious Software -- 7-bit ASCII code -- SHA-512 Constants (in Hexadecimal) -- Data Compression Using ZIP -- Base64 Encoding -- Cracking WEP Keys Using WEPCrack -- Acronyms -- Further Readings -- Index.…”
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  11. 71

    Fast facts for nurses

    Published 2004
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  12. 72

    Future Farming. by Shukla, Praveen Kumar

    Published 2023
    Table of Contents: “…PROPOSED METHODOLOGY -- Pre-processing -- Leaf Image from Plants -- Segmentation Model Using Improved Canny Algorithm -- Steps of Improved Canny Algorithm -- Leaf Image Feature Selection Using Hybrid Black Widow Optimization Algorithm with Mayfly Optimization Algorithm (BWO-MA) -- Pseudo-Code of the Hybrid (BWO-MA) Algorithm -- Output: Objective Function's -RMSE -- Leaf Image Classification Using (BWO-MA) with ANN -- Hyper-Parameter Tuning With (BWO-MA) -- RESULT AND DISCUSSION -- Dataset Description -- Evaluation & Results -- CONCLUSION -- REFERENCES…”
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  13. 73

    Rough fuzzy image analysis : foundations and methodologies

    Published 2010
    Table of Contents: “…1. Cantor, fuzzy, near, and rough sets in image analysis / James F. …”
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  15. 75

    Advances in nonlinear waves and symbolic computation by Yan, Zhenya

    Published 2009
    Table of Contents: “…Senthilnathan [and others] -- Direct methods and symbolic software for conservation laws of nonlinear equations / Willy Hereman [and others] -- Nonclassical potential symmetries for a nonlinear thermal wave equation / M.L. Gandarias -- Anti-dark solitons of the resonant nonlinear Schrödinger equation / D.W.C. …”
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  16. 76

    Discrete Mathematics for Computer Science : An Example-Based Introduction. by Fortney, Jon Pierre

    Published 2021
    Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- CHAPTER 1: Introduction to Algorithms -- 1.1. WHAT ARE ALGORITHMS? -- 1.2. CONTROL STRUCTURES -- 1.3. …”
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  19. 79

    Communication System Security.

    Published 2012
    Table of Contents: “…Public-Key Cryptographic Algorithms; II. Security Mechanisms and Protocols; 6. …”
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  20. 80

    Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

    Published 2012
    Table of Contents: “…Regression Analysis -- The Regression Model -- Correlation and Causation -- Algorithms for Regression Analysis -- Assessing Regression Model Performance -- Model Validity -- Looking Beyond R2 -- Polynomial Regression -- Artificial Neural Networks for Regression Analysis -- Dataset Preparation -- Tutorial -- A Regression Model for Home Appraisal -- Modeling with the Right Set of Observations -- Exercise 6.1 -- ANN Modeling -- The Advantage of ANN Regression -- Top-Down Attribute Selection -- Issues in Model Interpretation -- Model Validation -- Model Application -- Summary…”
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