Search Results - (((((((anti OR wyant) OR semantic) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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Essential US for trauma : E-FAST

Table of Contents: “…Foreword -- Preface -- 1 Basic physics, instrumentation and knobology -- 2 E-FAST protocol: Introduction and focused questions -- 3 Abdominal views: Anatomy, Techniques, Scanning tips and tricks, abnormal images -- 4Thoracic views: Anatomy, Techniques, Scanning Tips and Tricks, Abnormal Images -- 5Including EFAST in Trauma Algorithm: When? What to do next? -- 6 The role of EFAST in Comprehensive US Trauma Management (ABCDE-US) Facing Clinical Scenarios -- 7 Pre-Hospital US in Trauma: Role and Tips -- 8CEUS-FAST: What is it?…”
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Epistemological Foundations of Mathematical Experience

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Evolutionary optimization

Table of Contents: “…Cover -- Contents -- Preface -- Contributing Authors -- Part I Introduction -- 1 Conventional Optimization Techniques -- 1 Classifying Optimization Models -- 2 Linear Programming -- 3 Goal Programming -- 4 Integer Programming -- 5 Nonlinear Programming -- 6 Simulation -- 7 Further Reading -- 2 Evolutionary Computation -- 1 What Is Evolutionary Computation -- 2 A Brief Overview of Evolutionary Computation -- 3 Evolutionary Algorithm and Generate-and-Test Search Algorithm -- 4 Search Operators -- 5 Summary -- Part II Single Objective Optimization -- 3 Evolutionary Algorithms and Constrained Optimization -- 1 Introduction -- 2 General considerations -- 3 Numerical optimization -- 4 Final Remarks -- 4 Constrained Evolutionary Optimization -- 1 Introduction -- 2 The Penalty Function Method -- 3 Stochastic Ranking -- 4 Global Competitive Ranking -- 5 How Penalty Methods Work -- 6 Experimental Study -- 7 Conclusion -- Appendix: Test Function Suite -- Part III Multi-Objective Optimization -- 5 Evolutionary Multiobjective Optimization -- 1 Introduction -- 2 Definitions -- 3 Historical Roots -- 4 A Quick Survey of EMOO Approaches -- 5 Current Research -- 6 Future Research Paths -- 7 Summary -- 6 MEA for Engineering Shape Design -- 1 Introduction -- 2 Multi-Objective Optimization and Pareto-Optimality -- 3 Elitist Non-dominated Sorting GA (NSGA-II) -- 4 Hybrid Approach -- 5 Optimal Shape Design -- 6 Simulation Results -- 7 Conclusion -- 7 Assessment Methodologies for MEAs -- 1 Introduction -- 2 Assessment Methodologies -- 3 Discussion -- 4 Comparing Two Algorithms: An Example -- 5 Conclusions and Future Research Paths -- Part IV Hybrid Algorithms -- 8 Hybrid Genetic Algorithms -- 1 Introduction -- 2 Hybridizing GAs with Local Improvement Procedures -- 3 Adaptive Memory GA's -- 4 Summary -- 9 Combining choices of heuristics -- 1 Introduction -- 2 GAs and parameterised algorithms -- 3 Job Shop Scheduling -- 4 Scheduling chicken catching -- 5 Timetabling -- 6 Discussion and future directions -- 10 Nonlinear Constrained Optimization -- 1 Introduction -- 2 Previous Work -- 3 A General Framework to look for SPdn -- 4 Experimental Results -- 5 Conclusions -- Part V Parameter Selection in EAs -- 11 Parameter Selection -- 1 Introduction -- 2 Parameter tuning vs. parameter control -- 3 An example -- 4 Classification of Control Techniques -- 5 Various forms of control -- 6 Discussion -- Part VI Application of EAs to Practical Problems -- 12 Design of Production Facilities -- 1 Introduction -- 2 Design for Material Flow When the Number of I/O Points is Unconstrained -- 3 Design for Material Flow for a Single I/O Point -- 4 Considering Intradepartmental Flow -- 5 Material Handling System Design -- 6 Concluding Remarks -- 13 Virtual Population and Acceleration Techniques -- 1 Introduction -- 2 Concept of Virtual Population -- 3 Solution Acceleration Techniques -- 4 Accelerated GA and Acceleration Sche.…”
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Big Data, IoT, and Machine Learning : Tools and Applications. by Agrawal, Rashmi

Table of Contents: “…Chapter 3 Reviews Analysis of Apple Store Applications Using Supervised Machine Learning -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Machine Learning Algorithms -- 3.2.2 Feature Extraction Algorithms -- 3.3 Proposed Methodology -- 3.3.1 Data Collection -- 3.3.2 Feature Extraction -- 3.3.3 Data Analysis and Sentiment Analysis -- Text Processing -- 3.3.4 Text Normalisation -- 3.4 Feature Extraction Algorithm -- 3.4.1 CountVectorizer -- 3.4.2 TfidfVectorizer (TF-IDF) -- 3.5 Supervised ML Classification -- 3.6 Experiment Design -- 3.7 Experimental Results and Analysis…”
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Function Estimates. by Marron, J. S.

Table of Contents: “…Contents -- Preface -- Logspline density estimation -- Statistical encounters with B-splines -- Estimation of a transfer function in a nongaussian context -- Evaluating the performance of an inversion algorithm -- Harmonic splines in geomagnetism -- Problems in estimating the anomalous gravity potential of the earth from discrete data -- What regression model should be chosen when the statistician misspecifies the error distribution? …”
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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Knowledge discovery for business information systems

Table of Contents: “…Problem Description -- 3. The FUP Algorithm for the Insertion Only Case -- 4. The FUP Algorithm for the Deletions Only Case -- 5. …”
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Introduction to graph theory by Voloshin, Vitaly I. (Vitaly Ivanovich), 1954-

Table of Contents: “…What Is Mathematical Induction; 7.2. Graph Theory Algorithms and Their Complexity; 7.3. Answers and Hints to Selected Exercises; 7.4. …”
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High Performance Computing Systems and Applications & OSCAR Symposium. by International Symposium on High Performance Computing Systems and Applications (17th : 2003 : Sherbrooke, Quebec)

Table of Contents: “…""The Impact of Hyper-Threading on Communication Performance in HPC Clusters""""A Preliminary Feasibility Study of the LARPBS Optical Bus Parallel Model""; ""Efficient Broadcast on Computational Grids""; ""The Canadian Internetworked Scientific Supercomputer""; ""Distributed Beagle: An Environment for Parallel and Distributed Evolutionary Computations""; ""A Brief Introduction to UPC""; ""SCOJO -- Share-Based Job Coscheduling with Integrated Dynamic Resource Directory in Support Or Grid Scheduling""…”
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Conversations on violence : an anthology

Table of Contents: “…What Does an Anti-Fascist Life Feel Like? -- Natasha Lennard -- 16. …”
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Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances. by Nagy, Zsolt

Table of Contents: “…Optimizing the Minmax Algorithm with Alpha-Beta PruningDRYing up the Minmax Algorithm -- The NegaMax Algorithm; Using the EasyAI Library; Activity 4: Connect Four; Summary; Regression; Introduction; Linear Regression with One Variable; What Is Regression?…”
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Web-based learning : men and machines - proceedings of the first international conference on web-based learning in china (icwl 2002). by REGGIE, KWAN

Table of Contents: “…Patterns of Web Based Learning in the Semantic Web Era ; PART THREE Tools.…”
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Internet+ and electronic business in China : innovation and applications

Table of Contents: “…Emotional analysis of online reviews on e-business platforms -- Chapter 14. Semantic search of online reviews on e-business platforms -- Part IV. …”
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Applied data analytics : principles and applications by Agbinya, Johnson I.

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Comorbidity in migraine

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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Technologies for Engineering Manufacturing Systems Control in Closed Loop. by Preuße, Sebastian

Table of Contents: “…Intro; 1 Introduction; 2 Basic Principles; 2.1 Technologies for studying Behavior; 2.2 Plants; 2.3 Controllers; 2.3.1 IEC 61131-3; 2.3.2 IEC 61499-1; 2.4 System Models; 2.4.1 Syntax; 2.4.2 Semantics; 2.5 Basics of Specifications; 2.5.1 Computation Tree Logic; 2.5.2 Extended Computation Tree Logic; 2.5.3 Timed Computation Tree Logic; 2.5.4 Symbolic Timing Diagrams; 2.6 Closed-Loop Composition; 2.7 Model Checking; 2.7.1 General Remarks; 2.7.2 Model Checking Algorithm; 2.8 Summary; 3 Formal Modeling of Plant, Controller, and the Closed Loop; 3.1 Demonstration Example; 3.2 Formal Plant Modeling.…”
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Twelfth Scandinavian Conference on Artificial Intelligence : SCAI 2013

Table of Contents: “…Trap Escape for Local Search by Backtracking and Conflict ReverseIncorporating Prior Knowledge when Learning Mixtures of Truncated Basis Functions from Data; Expansion of the Variational Garrote to a Multiple Measurement Vectors Model; Classification of Movement Patterns in Skiing; Combining Constraint Types from Public Data in Aerial Image Segmentation; Formalizing Theatrical Performances Using Multi-Agent Organizations; A Hierarchical Model for Continuous Gesture Recognition Using Kinect; Expressive Planning Through Constraints…”
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Quantitative pharmacology and individualized therapy strategies in development of therapeutic proteins for immune-mediated inflammatory diseases

Table of Contents: “…Disease Interception in Autoimmune Diseases: From a Conceptual Framework to Practical Implementation / Anish Suri -- The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (UC) / Reena Khanna, Brian G Feagan -- Mechanism and Physiologically Based PK/PD Model in Assisting Translation from Preclinical to Clinical: Understanding PK/PD of Therapeutic Proteins at Site-of-Action / Xi Chen, Weirong Wang -- Application of Minimal Anticipated Biological Effect Level (MABEL) in Human Starting Dose Selection for Immunomodulatory Protein Therapeutics -- Principles and Case Studies / Haiqing Wang, Zheng Yang, Rong Shi -- Model-Based Meta-Analysis Use in the Development of Therapeutic Proteins / Timothy J Taylor, Bill Frame, Angela D Taylor -- Utility of Joint Population Exposure-Response Modeling Approach to Assess Multiple Continuous and Categorical Endpoints in Immunology Drug Development / Chuanpu Hu, Honghui Zhou -- Modeling Approaches to Characterize Target-Mediated Pharmacokinetics and Pharmacodynamics for Therapeutic Proteins / Leonid Gibiansky, Ekaterina Gibiansky -- Tutorial: Numerical (NONMEM) Implementation of the Target-Mediated Drug Disposition Model / Leonid Gibiansky, Ekaterina Gibiansky -- Translational Considerations in Developing Bispecific Antibodies: What Can We Learn from Quantitative Pharmacology? …”
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