Search Results - (((((((ant OR wante) OR semantic) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Hands-On Deep Learning with TensorFlow. by Boxel, Dan Van

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Designing and Researching of Machines and Technologies for Modern Manufacture.

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Danforth's obstetrics and gynecology.

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Atlas of minimally invasive surgery for lung and esophageal cancer

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Artificial intelligence and data mining approaches in security frameworks

Table of Contents: “…87 -- 5.1.2 Purpose of Spamming 88 -- 5.1.3 Spam Filters Inputs and Outputs 88 -- 5.2 Content-Based Spam Filtering Techniques 89 -- 5.2.1 Previous Likeness–Based Filters 89 -- 5.2.2 Case-Based Reasoning Filters 89 -- 5.2.3 Ontology-Based E-Mail Filters 90 -- 5.2.4 Machine-Learning Models 90 -- 5.2.4.1 Supervised Learning 90 -- 5.2.4.2 Unsupervised Learning 90 -- 5.2.4.3 Reinforcement Learning 91 -- 5.3 Machine Learning–Based Filtering 91 -- 5.3.1 Linear Classifiers 91 -- 5.3.2 Naïve Bayes Filtering 92 -- 5.3.3 Support Vector Machines 94 -- 5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94 -- 5.4 Performance Analysis 97 -- 5.5 Conclusion 97 -- References 98 -- 6 Artificial Intelligence in the Cyber Security Environment 101 Jaya Jain -- 6.1 Introduction 102 -- 6.2 Digital Protection and Security Correspondences Arrangements 104 -- 6.2.1 Operation Safety and Event Response 105 -- 6.2.2 AI2 105 -- 6.2.2.1 CylanceProtect 105 -- 6.3 Black Tracking 106 -- 6.3.1 Web Security 107 -- 6.3.1.1 Amazon Macie 108 -- 6.4 Spark Cognition Deep Military 110 -- 6.5 The Process of Detecting Threats 111 -- 6.6 Vectra Cognito Networks 112 -- 6.7 Conclusion 115 -- References 115 -- 7 Privacy in Multi-Tenancy Frameworks Using AI 119 Shweta Solanki -- 7.1 Introduction 119 -- 7.2 Framework of Multi-Tenancy 120 -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI 122 -- 7.4 Related Work 125 -- 7.5 Conclusion 125 -- References 126 -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129 Shubhi Srivastava, Ankit Kumar and Shiv Prakash -- 8.1 Introduction 129 -- 8.1.1 Biometric 131 -- 8.1.2 Categories of Biometric 131 -- 8.1.2.1 Advantages of Biometric 132 -- 8.1.3 Significance and Scope 132 -- 8.1.4 Biometric Face Recognition 132 -- 8.1.5 Related Work 136 -- 8.1.6 Main Contribution 136 -- 8.1.7 Novelty Discussion 137 -- 8.2 The Proposed Methodolgy 139 -- 8.2.1 Face Detection Using Haar Algorithm 139 -- 8.2.2 Feature Extraction Using ILBP 141 -- 8.2.3 Dataset 143 -- 8.2.4 Classification Using SVM 143 -- 8.3 Experimental Results 145 -- 8.3.1 Face Detection 146 -- 8.3.2 Feature Extraction 146 -- 8.3.3 Recognize Face Image 147 -- 8.4 Conclusion 151 -- References 152 -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155 S K Rajesh Kanna, O. …”
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Nonlinear science and complexity

Table of Contents: “…Bruzón -- Applying a new algorithm to derive nonclassical symmetries / M.S. …”
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Lippincott's primary care orthopaedics

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Evolutionary Robust Control. by Feyel, Philippe

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Android Sensor Programming By Example. by Nagpal, Varun

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Stirling Cycle Engines : Inner Workings and Design. by Organ, Allan J.

Table of Contents: “…-- 3.6 The way forward -- 4 Equivalence conditions for volume variations -- 4.1 Kinematic configuration -- 4.2 'Additional' dead space -- 4.3 Net swept volume -- 5 The optimum versus optimization -- 5.1 An engine from Turkey rocks the boat -- 5.2 ... and an engine from Duxford -- 5.3 Schmidt on Schmidt -- 5.3.1 Volumetric compression ratio rv -- 5.3.2 Indicator diagram shape -- 5.3.3 More from the re-worked Schmidt analysis -- 5.4 Crank-slider mechanism again -- 5.5 Implications for engine design in general -- 6 Steady-flow heat transfer correlations -- 6.1 Turbulent -- or turbulent? …”
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Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

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Introduction to matrix-analytic methods in queues 2 : analytical and simulation approach - queues and simulation by Chakravarthy, Srinivas R.

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Techniques and key points for endoscopic cranial base reconstruction by Pinheiro-Neto, Carlos, Peris-Celda, Maria

Table of Contents: “…Techniques and Key Points for Endoscopic Cranial Base Reconstruction -- MedOne Access Information -- Tittle Page -- Copyright -- Dedication -- Contents -- Videos -- Foreword -- Preface -- Acknowledgments -- Contributors -- Section I Introduction -- 1 Principles of Endoscopic Cranial Base Reconstruction -- 1.1 Introduction -- 1.2 Fundamentals of Endonasal Reconstruction -- 1.3 Repair Options -- 1.4 Vascularized Reconstruction -- 1.5 Reconstruction Decalogue -- 2 Operative Planning and Treatment Algorithm -- 2.1 Introduction -- 2.2 Preoperative Planning -- 2.3 Perioperative and Intraoperative Considerations -- 2.4 Treatment Algorithm -- 2.5 Postoperative Considerations -- 2.5.1 Inpatient Care -- 2.5.2 Outpatient Care -- Section II Nasoseptal Flap and Variations -- 3 Standard Nasoseptal Flap -- 3.1 Anatomy -- 3.2 Fundamentals -- 3.3 Indications -- 3.4 Limitations -- 3.5 Surgical Technique -- 3.5.1 Harvest -- 3.5.2 Reconstruction -- 3.6 Postoperative Care -- 3.7 Managing Complications -- 4 Rescue Nasoseptal Flap -- 4.1 Fundamentals -- 4.2 Indications -- 4.3 Limitations -- 4.4 Surgical Technique -- 4.5 Postoperative Care -- 4.6 Complications -- 5 Extended Nasoseptal Flap -- 5.1 Fundamentals -- 5.2 Indications -- 5.3 Limitations -- 5.4 Surgical Technique -- 5.4.1 Harvest -- 5.4.2 Reconstruction -- 5.5 Postoperative Care -- 5.5.1 One-Week Postoperative Visit -- 5.5.2 One-Month Postoperative Visit -- 5.5.3 Four-Month Postoperative Visit -- 5.6 Complications -- 6 Nasoseptal Flap Pedicle Release -- 6.1 Anatomy -- 6.2 Fundamentals -- 6.3 Indications -- 6.4 Limitations -- 6.5 Surgical Technique -- 6.5.1 360-Degree Bone Removal around the SPA Foramen (Osseous Release) -- 6.5.2 Ipsilateral Transpterygoid Approach -- 6.5.3 360-Degree Circumferential Incision in the Periosteum of the PPF around the SPA (Periosteal Release) -- 6.6 Postoperative Care.…”
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Mechanical and Electronics Engineering : proceedings of the International Conference on ICMEE 2009, Chennai, India, 24-26 July 2009

Table of Contents: “…Shafie -- The investigation of input shaping with different polarities for anti-sway control of a gantry crane system / Mohd. …”
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RSSDI Diabetes update 2018

Table of Contents: “…-- Chapter 42: Etiopathogenesis and Management of Diabetic Heart Failure -- Chapter 43: Diabetes and Stroke -- Chapter 44: Cellular Mechanism of Atherosclerosis in Diabetes Mellitus -- Chapter 45: Nondiabetic Ocular Complications in Diabetes -- Chapter 46: Nondiabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus: When to Suspect?.…”
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C# 7 and .NET Core 2.0 High Performance : Build highly performant, multi-threaded, and concurrent applications using C# 7 and .NET Core 2.0. by Mehboob Ahmed Khan, Ovais

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Nuclear Medicine Imaging: An Encyclopedic Dictionary by Thie, Joseph A.

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Understanding smart sensors by Frank, Randy

Table of Contents: “…ZigBee-Like Wireless -- 8.3.3. ANT+ -- 8.3.4.6LoWPAN -- 8.3.5. Near Field Communication (NFC) -- 8.3.6.Z-Wave -- 8.3.7. …”
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Fundamentals of Fluid Power Control. by Watton, John

Table of Contents: “…Control-Volume Flow Continuity -- PRV Flow -- Force Balance at the Spindle -- 5.13.3 Frequency Response from a Linearized Transfer Function Analysis -- 5.14 Servovalve Dynamics -- First-Stage, Armature, and Flapper-Nozzle -- Flapper-Nozzle and Resistance Bridge Flow Characteristic -- Force Balance at the Spool -- 5.15 An Open-Loop Servovalve-Motor Drive with Line Dynamics Modeled by Lumped Approximations -- Servovalve, Dynamics Included, Underlapped Spool -- Lines, Laminar Mean Flow, Two Lump Approximations per Line, Negligible Motor Internal Volume -- Motor Flow and Torque Equations -- 5.16 Transmission Line Dynamics -- 5.16.1 Introduction -- Servovalve-Cylinder with Short Lines and Significant Actuator Volumes -- Servovalve-Motor with Long Lines and Negligible Actuator Volumes -- 5.16.2 Lossless Line Model for Z and Y -- 5.16.3 Average and Distributed Line Friction Models for Z and Y -- 5.16.4 Frequency-Domain Analysis -- 5.16.5 Servovalve-Reflected Linearized Coefficients -- 5.16.6 Modeling Systems with Nonlossless Transmission Lines, the Modal Analysis Method -- 5.16.7 Modal Analysis Applied to a Servovalve-Motor Open-Loop Drive -- 5.17 The State-Space Method for Linear Systems Modeling -- 5.17.1 Modeling Principles -- 5.17.2 Some Further Aspects of the Time-Domain Solution -- 5.17.3 The Transfer Function Concept in State Space -- 5.18 Data-Based Dynamic Modeling -- 5.18.1 Introduction -- 5.18.2 Time-Series Modeling -- 5.18.3 The Group Method of Data Handling (GMDH) Algorithm -- 5.18.4 Artificial Neural Networks -- 5.18.5 A Comparison of Time-Series, GMDH, and ANN Modeling of a Second-Order Dynamic System -- 5.18.6 Time-Series Modeling of a Position Control System -- 5.18.7 Time-Series Modeling for Fault Diagnosis -- 5.18.8 Time-Series Modeling of a Proportional PRV -- 5.18.9 GMDH Modeling of a Nitrogen-Filled Accumulator.…”
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Flood hazard identification and mitigation in semi- and arid environments

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