Search Results - empirical (algorithmics OR (algorithmes OR algorithms))

Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI). by Tan, Khee Giap

Table of Contents: “…; 1.1 A More Liveable Life; 1.2 Thinking Broadly about the Concept of Liveability; 1.3 Thinking Sensibly about the Concept of Sustainability; 1.4 Preview; CHAPTER 2: THE EMPIRICAL FRAMEWORK: METHODOLOGY, DATA AND COMPUTATION ALGORITHM; 2.1 Comparison of Empirical Frameworks Used in Ranking; 2.2 Fleshing Out the Global Liveable Cities Index Framework; 2.2.1 Economic Vibrancy & Competitiveness; 2.2.2 Environmental Friendliness & Sustainability.…”
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Wireless Sensor and Ad Hoc Networks Under Diversified Network Scenarios. by Sarkar, Subir Kumar

Table of Contents: “…3.4.10 Optimized Link State Routing Protocol3.4.11 Temporally Ordered Routing Algorithm (TORA); 3.5 Network Simulator, NS-2; 3.6 Simulation Environment; 3.7 Result Analysis; 3.7.1 Evaluation of Performance Metrics; References; 4 An Empirical Study of Various Mobility Models in MANETs; 4.1 Introduction; 4.2 Literature Background; 4.3 Description of Various Mobility Models; 4.3.1 Random Waypoint Mobility Model; 4.3.2 Random Waypoint Mobility Model with Steady State; 4.3.3 Random Walk Mobility Model with Reflection; 4.3.4 Random Walk Mobility Model with Wrapping.…”
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Modelling forest systems

Table of Contents: “…Colour Plates; Principal Contributors; Preface; Part 1 Forest Reality and Modelling Strategies; 1 Suggestions for Choosing an Appropriate Level for Modelling Forest Stands; 2 Mapping Lodgepole Pine Site Index in Alberta; 3 Growth Modelling of Eucalyptus regnans for Carbon Accounting at the Landscape Scale; 4 Spatial Distribution Modelling of Forest Attributes Coupling Remotely Sensed Imagery and GIS Techniques; 5 Algorithmic and Interactive Approaches to Stand Growth Modelling; 6 Linking Process-based and Empirical Forest Models in Eucalyptus Plantations in Brazil.…”
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Cognitive foundations of musical pitch by Krumhansl, Carol L.

Table of Contents: “…Statistical analyses of tonal compositions Tonal distributions and tonal hierarchies; Tonal hierarchies, tonal consonance, and tonal distributions; 4. A KEY-FINDING ALGORITHM BASED ON TONAL HIERARCHIES; The key-finding algorithm; Application I: initial segments of preludes of J.S. …”
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Wireless technologies in intelligent transportation systems

Table of Contents: “…Adaptive Beamforming Algorithms; 4.e.1. Sample Matrix Inversion (SMI) Algorithm; 4.e.2. …”
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Intelligent materials, applied mechanics and design science : selected, peer reviewed papers from the 2011 international conference on intelligent materials, applied mechanics and...

Table of Contents: “…Intelligent Materials, Applied Mechanics and Design Science; Preface and Committee; Table of Contents; Chapter 1: Intelligent Materials, Energy Science and Dynamic System; Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum; Numerical Analysis of Nonlinear Behaviors of a Flexible Rotor Dynamic System with Turbulent Journal Bearings Support; Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation; The Hybrid Genetic Algorithm of Single-Machine Materials Manufacturing Process with Periodic Maintenance.…”
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The importance of health informatics in public health during a pandemic

Table of Contents: “…Confirmatory Factors to Nursing Practice for Patient Safety of Nurses in Kalasin Hospital, Kalasin Province, Thailand -- Patient Awareness of the Severity of Their Information and Medication Management-Related Adverse Events -- The Quality Improvement Information System for Surveillance and Monitoring for Patient Safety and Personal Safety in Kalasin Hospital, Kalasin Province, Thailand -- Iconic Visualization of Sickle Cell Patients Current and Past Health Status -- Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment…”
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Wireless communications systems design by Kim, Haesik

Table of Contents: “…6.1.2 Example of Turbo Encoding and Decoding6.1.3 Hardware Implementation of Turbo Encoding and Decoding; 6.2 Turbo Product Codes; 6.2.1 Turbo Product Encoding and Decoding Algorithm; 6.2.2 Example of Turbo Product Encoding and Decoding; 6.2.3 Hardware Implementation of Turbo Product Encoding and Decoding; 6.3 Low-Density Parity Check Codes; 6.3.1 LDPC Encoding and Decoding Algorithms; 6.3.2 Example of LDPC Encoding and Decoding; 6.3.3 Hardware Implementation of LDPC Encoding and Decoding; 6.4 Problems; References; Chapter 7 Orthogonal Frequency-Division Multiplexing; 7.1 OFDM System Design.…”
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Numerical models for submerged breakwaters : coastal hydrodynamics and morphodynamics by Ahmadian, Amir Sharif

Table of Contents: “…3 Literature Review and BackgroundReferences; 4 Theories and Methodologies; 4.1 Introduction; 4.2 Traditional Models for Water Waves; 4.3 New Approaches; 4.3.1 Meshless Methods; 4.3.2 Artificial Intelligence Methods; MLP Networks; Back-Propagation Algorithm; Levenberg-Marquardt Algorithm; RBF Networks; References; 5 Mathematical Modeling and Algorithm Development; 5.1 Navier-Stokes Equations; 5.2 The Turbulent Model; 5.3 Initial and Boundary Conditions; 5.4 Shallow Waters; 5.5 The Extended Mild-Slope Equation; 5.6 Boussinesq Equations; 5.7 Smoothed Particles Hydrodynamics.…”
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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning by Narsky, Ilya, Porter, Frank Clifford

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Combinatorial Development Of Solid Catalytic Materials : Design Of High-Throughput Experiments, Data Analysis, Data Mining.

Table of Contents: “…Tuning Evolutionary Algorithms with Artificial Neural Networks (M. Holena) -- 7.1. …”
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Machine Learning for Financial Engineering. by Gyorfi, Laszlo

Table of Contents: “…Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. …”
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Data mining with decision trees : theory and applications by Rokach, Lior

Table of Contents: “…2.1 Algorithmic Framework for Decision Trees2.2 Stopping Criteria; 3. …”
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Marvels of Artificial and Computational Intelligence in Life Sciences. by Sivaraman, Thirunavukkarasu

Table of Contents: “…Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword I -- Foreword II -- Preface -- List of Contributors -- Artificial Intelligence for Infectious Disease Surveillance -- Sathish Sankar1,*, Pitchaipillai Sankar Ganesh1 and Rajalakshmanan Eswaramoorthy2 -- INTRODUCTION -- CONCLUSION -- ACKNOWLEDGEMENT -- REFERENCES -- Recent Innovations in Artificial Intelligence (AI) Algorithms in Electrical and Electronic Engineering for Future Transformations…”
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Machine Learning for Asset Management New Developments and Financial Applications

Table of Contents: “…A review of machine learning algorithms for prediction of market direction -- 2.3.1. …”
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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Inverse problems of vibrational spectroscopy by Yagola, A. G.

Table of Contents: “…Ill-posed problems and the regularization method. Regularizing algorithms for constructing force fields of polyatomic molecules on the base of experimental data -- 6.1. …”
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Multivariate statistics : proceedings of the 6th Tartu Conference, Tartu, Estonia, 19-22 August 1999

Table of Contents: “…s F-statistic: A simulation studyZero-boundary Voronoi partitions -- On expected values of fourth-degree matrix products of a multinormal matrix variate -- A multivariate Buckley-James estimator -- A new algorithm of the linear discriminant function using integer programming -- Robustification of “approximating approachâ€? …”
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Systemic Risk from Global Financial Derivatives. by Markose, Sheri M.

Table of Contents: “…Empirical (Small World) Core-Periphery Network Algorithm; 4. …”
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Computational materials discovery

Table of Contents: “…3.1.2 Empirical Interatomic Potentials3.1.3 Machine Learning Interatomic Potentials; 3.2 Simple Problem: Fitting of Potential Energy Surfaces; 3.2.1 Representation of Atomic Systems; 3.2.2 An Overview of Machine Learning Methods; 3.3 Machine Learning Interatomic Potentials; 3.3.1 Representation of Atomic Environments; 3.3.2 Existing MLIPs; 3.4 Fitting and Testing of Interatomic Potentials; 3.4.1 Optimization Algorithms; 3.4.2 Validation and Cross-validation; 3.4.3 Learning on the Fly; 3.5 Discussion; 3.5.1 Which Potential Is Better?…”
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