Search Results - empirical (((algorithm OR algorithm) OR algorithm) OR (algorithmic OR algorithms))

The digital coloniality of power : epistemic disobedience in the social sciences and the legitimacy of the digital age by Stingl, Alexander I.

Table of Contents: “…: a geopolitical question -- Danse macabre : the sacred, the rational, and the algorithm -- Implifications : biomedical relevance, digital cultural health capital, and governance 3.0 -- Cyborg gazing patient vitreous : the body as technology, technological object, and techno-normativity -- The digital classroom -- In plena vita¿before and beyond the curtain call -- Notes -- Works cited -- Index.…”
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Biological systematics : principles and applications by Brower, Andrew V. Z. (Andrew Van Zandt), 1962-, Schuh, Randall T.

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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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Quantifying human resources : uses and analyses by Coron, Clotilde

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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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Handbook of Mathematical Induction. by Gunderson, David S.

Table of Contents: “…Chapter 14: Logic and languageChapter 15: Graphs; Chapter 16: Recursion and algorithms; Chapter 17: Games and recreations; Chapter 18: Relations and functions; Chapter 19: Linear and abstract algebra; Chapter 20: Geometry; Chapter 21: Ramsey theory; Chapter 22: Probability and statistics; Part III: Solutions and hints to exercises; Chapter 23: Solutions: Foundations; Chapter 24: Solutions: Inductive techniques applied to the infinite; Chapter 25: Solutions: Paradoxes and sophisms; Chapter 26: Solutions: Empirical induction; Chapter 27: Solutions: Identities.…”
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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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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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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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Statistical analysis techniques in particle physics : fits, density estimation and supervised learning by Narsky, Ilya, Porter, Frank Clifford

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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…Using parametric functions to solve systems of linear fuzzy equations-an improved algorithm / A. Vroman, G. Deschrijver, and E.E. Kerre. …”
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Linkage Analysis and Gene Mapping. by WANG, Jiankang

Table of Contents: “…Theoretical Frequencies of 4 Homozygotes in Permanent Populations -- Genotypic Frequencies of Two Co-Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Dominant Locus in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Recessive Loci in Temporary Populations -- Estimation of Two-Point Recombination Frequency -- Maximum Likelihood Estimation of Recombination Frequency in DH Populations -- General Procedure on the Maximum Likelihood Estimation of Recombination Frequency -- Estimation of Recombination Frequency Between One Co-Dominant and One Dominant Marker in F2 Population -- Initial Values in Newton Algorithm -- EM Algorithm in Estimating Recombination Frequency in F2 Populations -- Effects on the Estimation of Recombination Frequency from Segregation Distortion -- Exercises -- Three-Point Analysis and Linkage Map Construction -- Three-Point Analysis and Mapping Function -- Genetic Interference and Coefficient of Interference -- Mapping Function -- Construction of Genetic Linkage Maps -- Marker Grouping Algorithm -- Marker Ordering Algorithm -- Use of the k-Optimal Algorithm in Linkage Map Construction -- Rippling of the Ordered Markers -- Integration of Multiple Maps -- Comparison of the Recombination Frequency Estimation in Different Populations -- LOD Score in Testing the Linkage Relationship in Different Populations -- Accuracy of the Estimated Recombination Frequency -- Least Population Size to Declare the Significant Linkage Relationship and Close Linkage -- Linkage Analysis in Random Mating Populations.…”
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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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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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EEG Signal Processing and Machine Learning. by Sanei, Saeid

Table of Contents: “…3.6 Dynamic Modelling of Neuron Action Potential Threshold -- 3.7 Summary -- References -- Chapter 4 Fundamentals of EEG Signal Processing -- 4.1 Introduction -- 4.2 Nonlinearity of the Medium -- 4.3 Nonstationarity -- 4.4 Signal Segmentation -- 4.5 Signal Transforms and Joint Time-Frequency Analysis -- 4.5.1 Wavelet Transform -- 4.5.1.1 Continuous Wavelet Transform -- 4.5.1.2 Examples of Continuous Wavelets -- 4.5.1.3 Discrete-Time Wavelet Transform -- 4.5.1.4 Multiresolution Analysis -- 4.5.1.5 Wavelet Transform Using Fourier Transform -- 4.5.1.6 Reconstruction -- 4.5.2 Synchro-Squeezed Wavelet Transform -- 4.5.3 Ambiguity Function and the Wigner-Ville Distribution -- 4.6 Empirical Mode Decomposition -- 4.7 Coherency, Multivariate Autoregressive Modelling, and Directed Transfer Function -- 4.8 Filtering and Denoising -- 4.9 Principal Component Analysis -- 4.9.1 Singular Value Decomposition -- 4.10 Summary -- References -- Chapter 5 EEG Signal Decomposition -- 5.1 Introduction -- 5.2 Singular Spectrum Analysis -- 5.2.1 Decomposition -- 5.2.2 Reconstruction -- 5.3 Multichannel EEG Decomposition -- 5.3.1 Independent Component Analysis -- 5.3.2 Instantaneous BSS -- 5.3.3 Convolutive BSS -- 5.3.3.1 General Applications -- 5.3.3.2 Application of Convolutive BSS to EEG -- 5.4 Sparse Component Analysis -- 5.4.1 Standard Algorithms for Sparse Source Recovery -- 5.4.1.1 Greedy-Based Solution -- 5.4.1.2 Relaxation-Based Solution -- 5.4.2 k-Sparse Mixtures -- 5.5 Nonlinear BSS -- 5.6 Constrained BSS -- 5.7 Application of Constrained BSS -- Example -- 5.8 Multiway EEG Decompositions -- 5.8.1 Tensor Factorization for BSS -- 5.8.2 Solving BSS of Nonstationary Sources Using Tensor Factorization -- 5.9 Tensor Factorization for Underdetermined Source Separation -- 5.10 Tensor Factorization for Separation of Convolutive Mixtures in the Time Domain.…”
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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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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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Computer-Aided Learning and Analysis for COVID-19 Disease. by Dhiman, Gaurav

Table of Contents: “…Cover -- Special issue (part 1) on computer-aided learning and analysis for COVID-19 disease -- COVID-19: risk prediction through nature inspired algorithm -- E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology -- Pattern analysis: predicting COVID-19 pandemic in India using AutoML -- Predicting future diseases based on existing health status using link prediction -- Detection of COVID-19 cases through X-ray images using hybrid deep neural network -- Time series analysis of COVID-19 cases…”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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