Search Results - empirical ((((algorithmses OR algorithmen) OR algorithmicn) OR algorithms) OR algorithms)

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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Discourse, of course : an overview of research in discourse studies

Table of Contents: “…Bateman -- Schemes and tropes in visual communication : the case of object grouping in advertisements / Alfons Maes and Joost Schilperoord -- Text types and dynamism of genres / Sungsoon Wang -- Academic and professional written genres in disciplinary communication : theoretical and empirical challenges / Giovanni Parodi -- Why investigate textual information hierarchy? …”
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Retail futures : the good, the bad and the ugly of the digital transformation

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Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models. by Holdaway, Keith R.

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Missing Data Methods : Cross-Sectional Methods and Applications. by Drukker, David M.

Table of Contents: “…SimulationsEmpirical application to portfolio allocation; Conclusion; Notes; Acknowledgment; References; Efficient estimators of Bx and Bw; Variances of Bx and Bw; The case of observed Y; Nonlinear difference-in-difference treatment effect estimation: A distributional analysis; Introduction; Methodology; Monte Carlo simulation; Empirical application; Conclusion; Notes; Acknowledgment; References; Bayesian analysis of multivariate sample selection models using gaussian copulas; Introduction; Copulas; Model; Estimation; Applications; Concluding remarks; Acknowledgments; References.…”
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Big data and differential privacy : analysis strategies for railway track engineering by Attoh-Okine, Nii O.

Table of Contents: “…4.3 Taxonomy of Big Data Analytics in Railway Track Engineering4.4 Data Engineering; 4.5 Remarks; References; Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis; 5.1 Hilbert-Huang Transform; 5.1.1 Traditional Empirical Mode Decomposition; 5.1.1.1 Side Effect (Boundary Effect); 5.1.1.2 Example; 5.1.1.3 Stopping Criterion; 5.1.2 Ensemble Empirical Mode Decomposition (EEMD); 5.1.2.1 Post-Processing EEMD; 5.1.3 Complex Empirical Mode Decomposition (CEMD); 5.1.4 Spectral Analysis; 5.1.5 Bidimensional Empirical Mode Decomposition (BEMD); 5.1.5.1 Example.…”
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A Case for the Existence of God. by Overman, Dean L.

Table of Contents: “…Chapter 11: Recorded Experiences of Encounters with the Divine Bear Witness to a Way of Knowing that Includes Kierkegaard's KENDSKAB, BUBER'S I-Thou, OTTO'S Wholly Other, AND MARCEL'S MysteryChapter 12: THESE NINE WITNESSES TESTIFY TO ANOTHER WAY OF KNOWING THAT IS COMPATIBLE WITH THE EMPIRICAL AND THE METAPHYSICAL RATIONAL WAYS OF KNOWING, BUT IS BEYOND THE DESCRIBABLE AND REQUIRES PERSO; Chapter 13: CONCLUDING REFLECTIONS AND SUMMARY; AFTERWORD; Appendix A: THE NEW MATHEMATICS OF ALGORITHMIC INFORMATION THEORY IS RELEVANT TO THEORIES CONCERNING THE FORMATION OF THE FIRST LIVING MATTER.…”
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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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Data Mining and Statistics for Decision Making by Tufféry, Stéphane, Tufféry, Stéphane

Table of Contents: “…Learning algorithms -- 8.7. The main neural networks -- Automatic clustering methods -- 9.1. …”
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Social-behavioral modeling for complex systems

Table of Contents: “…Cover; Title Page; Copyright; Contents; Foreword; List of Contributors; About the Editors; About the Companion Website; Part I Introduction and Agenda; Chapter 1 Understanding and Improving the Human Condition: A Vision of the Future for Social-Behavioral Modeling; Challenges; Challenge One: The Complexity of Human Issues; Challenge Two: Fragmentation; Empirical Observation; Empirical Experiments; Generative Simulation; Unification; Challenge Three: Representations; Challenge Four: Applications of Social-Behavioral Modeling; About This Book; Roadmap for the Book; References…”
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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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Intelligent computational systems : a multi-disciplinary perspective

Table of Contents: “…3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. …”
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Stochastic filtering with applications in finance by Bhar, Ramaprasad

Table of Contents: “…Non-linear filter for futures price system. 6.4. Data used in empirical study. 6.5. Empirical results. 6.6. Concluding remarks -- 7. …”
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Reviews in computational chemistry.

Table of Contents: “…Small Molecule Docking and Scoring; Introduction; Algorithms for Molecular Docking; The Docking Problem; Placing Fragments and Rigid Molecules; Flexible Ligand Docking; Handling Protein Flexibility; Docking of Combinatorial Libraries; Scoring; Shape and Chemical Complementary Scores; Force Field Scoring; Empirical Scoring Functions; Knowledge-Based Scoring Functions; Comparing Scoring Functions in Docking Experiments: Consensus Scoring.…”
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Social media analytics for user behavior modeling : a task heterogeneity perspective by Nelakurthi, Arun Reddy, He, Jingrui

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

Table of Contents: “…An extended branch-and-bound algorithm for fuzzy linear bilevel programming / G. …”
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Computational phraseology

Table of Contents: “…Monocollocable words : a type of language combinatory periphery / František Čermák -- Translation asymmetries of multiword expressions in machine translation : an analysis of the TED-MWE corpus / Johanna Monti, Mihael Arcan and Federico Sangati -- German constructional phrasemes and their Russian counterparts : a corpus-based study / Dmitrij Dobrovol'skij -- Computational phraseology and translation studies : from theoretical hypotheses to practical tools / Jean-Pierre Colson -- Computational extraction of formulaic sequences from corpora : two case studies of a new extraction algorithm / Alexander Wahl and Stefan Th. Gries -- Computational phraseology discovery in corpora with the MWETOOLKIT / Carlos Ramisch -- Multiword expressions in comparable corpora / Peter Ďurčo -- Collecting collocations from general and specialised corpora : a comparative analysis / Marie-Claude L'Homme and Daphnée Azoulay -- What matters more : the size of the corpora or their quality? …”
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Computational materials engineering : achieving high accuracy and efficiency in metals processing simulations by Pietrzyk, Maciej, 1947-, Madej, Łukasz, Rauch, Lukasz, Szeliga, Danuta

Table of Contents: “…Toward Increase of the Efficiency of Modeling -- 2.1 Improvement of Numerical Algorithms -- 2.1.1 Metamodeling -- 2.1.2 Inverse analysis -- 2.1.2.1 General formulation of the inverse problem -- 2.1.2.2 Regularization -- 2.1.2.3 Methods of regularizations -- 2.1.2.4 Numerical computations and regularization in the finite-dimension setting -- 2.1.3 Sensitivity analysis -- 2.1.3.1 Local SA -- 2.1.3.2 Global SA -- 2.1.3.3 The implementation of SA algorithms -- 2.1.3.4 A strategy for the identification of the model parameters -- 2.2 Improvement of Hardware -- 2.2.1 General idea of high-performance computing -- 2.2.2 Development of clusters -- 2.2.3 Development of heterogeneous architectures -- 2.2.4 Development of grid environments -- 3. …”
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Indoor Geolocation Science and Technology : At the Emergence of Smart World and IoT. by Pahlavan, Kaveh, 1951-

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