Search Results - (((((((ant OR win) OR semantic) OR wind) OR cantor) OR anne) OR shape) OR hints) algorithms.

Designing and Researching of Machines and Technologies for Modern Manufacture.

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Danforth's obstetrics and gynecology.

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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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Evolutionary Robust Control. by Feyel, Philippe

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Ecological modelling for sustainable development

Table of Contents: “…Ismail -- Modelling of Climatological Wind-Driven Circulation and Thermohaline Structures of Peninsular Malaysia's Eastern Continental Shelf using Princeton Ocean Model-Halimatun Muhamad , Fredolin T. …”
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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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Manipulative voting dynamics by Gohar, Neelam

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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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Aerospace Sensors. by Nebylov, Alexander

Table of Contents: “…Principles and examples of sensor integration -- 9.1 Sensor systems -- 9.1.1 The sensor system concept -- 9.1.2 Joint processing of readings from identical sensors -- 9.1.3 Joint processing of readings from cognate sensors with different measurement ranges -- 9.1.4 Joint processing of diverse sensors readings -- 9.1.5 Linear and nonlinear sensor integration algorithms -- 9.2 Fundamentals of integrated measuring system synthesis -- 9.2.1 Synthesis problem statement -- 9.2.2 Classes of dynamic system realization -- 9.2.3 Measurement accuracy indices -- 9.2.4 Excitation properties -- 9.2.5 Objective functions for robust system optimisation -- 9.2.6 Methods of dynamic system accuracy index analysis under excitation with given numerical characteristics of derivatives -- 9.2.6.1 Estimation of error variance -- 9.2.6.2 Example of error variance analysis -- 9.2.6.3 Use of equivalent harmonic excitation -- 9.2.6.4 Estimation of error maximal value -- 9.2.7 System optimization under maximum accuracy criteria -- 9.2.8 Procedures for the dimensional reduction of a measuring system -- 9.2.8.1 Determination of an optimal set of sensors -- 9.2.8.2 Analysis of the advantages of invariant system construction -- 9.2.8.3 Advantages of the zeroing of several system parameters -- 9.2.9 Realization and simulation of integration algorithms -- 9.3 Examples of two-component integrated navigation systems -- 9.3.1 Noninvariant robust integrated speed meter -- 9.3.2 Integrated radio-inertial measurement -- 9.3.3 Airborne gravimeter integration -- 9.3.4 The orbital verticant -- References…”
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Uncommon sense : investment wisdom since the stock market's dawn by Kemp, Michael E.

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Mobile magic : the saatchi and saatchi guide to mobile marketing and design by Eslinger, Tom

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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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Power system monitoring and control by Bevrani, Hassan

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XML for DB2 information integration

Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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Flood hazard identification and mitigation in semi- and arid environments

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Insight into fuzzy modeling by Novák, Vilém, 1951-, Perfilieva, Irina, 1953-, Dvořák, Antonín (Mathematician)

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Sigma-Delta Converters. by De la Rosa, José M.

Table of Contents: “…4.5.2 Effect of Finite Slew Rate on CT-Ms 133 -- 4.6 Sources of Distortion in CT-Ms 134 -- 4.6.1 Nonlinearities in the Front-end Integrator 134 -- 4.6.2 Intersymbol Interference in the Feedback DAC 136 -- 4.7 Circuit Noise in CT-Ms 137 -- 4.7.1 Noise Analysis Considering NRZ Feedback DACs 137 -- 4.7.2 Noise Analysis Considering SC Feedback DACs 139 -- 4.8 Clock Jitter in CT-Ms 140 -- 4.8.1 Jitter in Return-to-zero DACs 141 -- 4.8.2 Jitter in Non-return-to-zero DACs 142 -- 4.8.3 Jitter in Switched-capacitor DACs 144 -- 4.8.4 Lingering Effect of Clock Jitter Error 145 -- 4.8.5 Reducing the Effect of Clock Jitter with FIR and Sine-shaped DACs 147 -- 4.9 Excess Loop Delay in CT-Ms 149 -- 4.9.1 Intuitive Analysis of ELD 149 -- 4.9.2 Analysis of ELD based on Impulse-invariant DT-CT Transformation 151 -- 4.9.3 Alternative ELD Compensation Techniques 154 -- 4.10 Quantizer Metastability in CT-Ms 155 -- 4.11 Summary 159 -- References 160 -- 5 Behavioral Modeling and High-level Simulation 165 -- 5.1 Systematic Design Methodology of Modulators 165 -- 5.1.1 System Partitioning and Abstraction Levels 167 -- 5.1.2 Sizing Process 167 -- 5.2 Simulation Approaches for the High-level Evaluation of Ms 169 -- 5.2.1 Alternatives to Transistor-level Simulation 169 -- 5.2.2 Event-driven Behavioral Simulation Technique 171 -- 5.2.3 Programming Languages and Behavioral Modeling Platforms 172 -- 5.3 Implementing M Behavioral Models 173 -- 5.3.1 From Circuit Analysis to Computational Algorithms 173 -- 5.3.2 Time-domain versus Frequency-domain Behavioral Models 175 -- 5.3.3 Implementing Time-domain Behavioral Models in MATLAB 178 -- 5.3.4 Building Time-domain Behavioral Models as SIMULINK C-MEX S-functions 182 -- 5.4 Efficient Behavioral Modeling of M Building Blocks using C-MEX S-functions 188 -- 5.4.1 Modeling of SC Integrators using S-functions 188 -- 5.4.1.1 Capacitor Mismatch and Nonlinearity 190.…”
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Computational fluid-structure interaction : methods and applications by Bazilevs, Yuri

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Polymer extrusion

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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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