Search Results - (((((((want OR pants) OR wanting) OR markant) OR cantor) OR anne) OR shape) OR hints) algorithms.

Agent-based computing

Table of Contents: “…Analysis of Algorithm -- 2.1.1. Chain-Like Agent Structure -- 2.1.2. …”
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Advances in Knowledge-Based and Intelligent Engineering and Information Systems by Frydman, Claudia

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Fast sequential Monte Carlo methods for counting and optimization by Rubinstein, Reuven Y.

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Applied modelling, numerical simulation, computation and optimization. by De Souza Neto, Eduardo

Table of Contents: “…Modeling and control of an unbalanced magnetic rotor-bearing system asa bearingless motorNumerical simulation of the influence of particle shape on the mechanical properties of rockfill materials; Heat dissipation analysis of continuously-moving plate undergoing thermal processing using Laplace Adomian decomposition method; A shortcut algorithm for solving long-term conjugate heat transfer problems based on quasi-steady flow; Identification of plastic anisotropy using spherical indentation on different anisotropic yield criterions.…”
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Firewall design and analysis by Liu, Alex X.

Table of Contents: “…Diverse firewall design. 3.1. Construction algorithm. 3.2. Shaping algorithm. 3.3. Comparison algorithm. 3.4. …”
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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…Algorithms --…”
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Future Farming. by Shukla, Praveen Kumar

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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Rediscovering mathematics : you do the math by Simonson, Shai

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Rough fuzzy image analysis : foundations and methodologies

Table of Contents: “…1. Cantor, fuzzy, near, and rough sets in image analysis / James F. …”
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Digital holography by Picart, Pascal

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Uniqueness questions in reconstruction of multidimensional objects from tomography-type projection data by Golubyatnikov, V. P. (Vladimir P.)

Table of Contents: “…Stability estimates for recovering the shapes of convex bodies from the shapes of their projections -- Chapter 3. …”
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Stochastic Models in Reliability Engineering. by Cui, Lirong

Table of Contents: “…5.3 Time-Varying Reliability Evaluation Based on MDD -- 5.3.1 The Construction of System MDD -- 5.3.2 System Reliability Evaluation Based on MDD -- 5.4 Numerical Studies -- 5.4.1 Illustrative Example -- 5.4.2 System MDD for the Illustrative Example -- 5.4.3 System Reliability Assessment for the Illustrative Example -- 5.5 Conclusions -- References -- Chapter 6 An Upside-Down Bathtub-Shaped Failure Rate Model Using a DUS Transformation of Lomax Distribution -- 6.1 Introduction -- 6.2 DUS-Lomax Distribution -- 6.3 Shapes -- 6.3.1 Shape of Probability Density Function…”
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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications by Lantz, Brett

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Differential algebra and related topics - proceedings of the international workshop. by SIT, WILLIAM Y.

Table of Contents: “…6 Reduction Algorithms 7 Rosenfeld Properties of an Autoreduced Set ; 8 Coherence and Rosenfeld's Lemma ; 9 Ritt-Raudenbush Basis Theorem ; 10 Decomposition Problems ; 11 Component Theorems ; 12 The Low Power Theorem ; Appendix: Solutions and hints to selected exercises ; References…”
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Artificial intelligence with Python : build real-world artificial intelligence applications with Python to intelligently interact with the world around you by Joshi, Prateek

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Multi-agent machine learning : a reinforcement approach

Table of Contents: “…2.12 Eligibility TracesReferences; Chapter 3: Learning in Two-Player Matrix Games; 3.1 Matrix Games; 3.2 Nash Equilibria in Two-Player Matrix Games; 3.3 Linear Programming in Two-Player Zero-Sum Matrix Games; 3.4 The Learning Algorithms; 3.5 Gradient Ascent Algorithm; 3.6 WoLF-IGA Algorithm; 3.7 Policy Hill Climbing (PHC); 3.8 WoLF-PHC Algorithm; 3.9 Decentralized Learning in Matrix Games; 3.10 Learning Automata; 3.11 Linear Reward-Inaction Algorithm; 3.12 Linear Reward-Penalty Algorithm; 3.13 The Lagging Anchor Algorithm; 3.14 L R-I Lagging Anchor Algorithm; References.…”
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Compressed sensing : theory and applications

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Radon Transforms and Tomography. by Quinto, Eric Todd

Table of Contents: “…Expository Papers -- Local tomography and related problems -- Tomography problems arising in synthetic aperture radar -- Introduction to local tomography -- Algorithms in ultrasound tomography -- Radon transforms, differential equations, and microlocal analysis -- Supplementary bibliography to A bibliographic survey of the Pompeiu problem -- II. …”
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Pervasive Computing in Cognitive Communications Applications. by Ghinea, George

Table of Contents: “…Cover -- Guest editorial -- Single-loop sigma delta modulator design and verification for cognitive IoT applications -- Efficient task scheduling on virtual machine in cloud computing environment -- High gain switched beam Yagi-Uda antenna for millimeter wave communications -- PAPR reduction using OQAM for spectrum sensing -- Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm -- Soft computing based audio signal analysis for accident prediction -- Kuznets curve with parabola shaped ultra-wideband antenna with defected ground plane for communications…”
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MATLAB for Machine Learning. by Ciaburro, Giuseppe

Table of Contents: “…Multiple linear regression with categorical predictorPolynomial regression; Regression Learner App; Summary; Chapter 5: Pattern Recognition through Classification Algorithms; Predicting a response by decision trees; Probabilistic classification algorithms -- Naive Bayes; Basic concepts of probability; Classifying with Naive Bayes; Bayesian methodologies in MATLAB; Describing differences by discriminant analysis; Find similarities using nearest neighbor classifiers; Classification Learner app; Summary; Chapter 6: Identifying Groups of Data Using Clustering Methods; Introduction to clustering.…”
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