{"product_id":"machine-learning-algorithms-and-applications-hardback-9781119768852","title":"Machine Learning Algorithms and Applications (Hardback) 9781119768852","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning Algorithms and Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eMettu Srinivas (Edited by), G Sucharitha (Author), G. Sucharitha (Edited by), Anjanna Matta (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119768852, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 September 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eMachine Learning Algorithms \u003c\/b\u003eis for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.\u003c\/p\u003e \u003cp\u003eThe book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Machine Learning for Industrial Applications 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyank Jain and Gagandeep Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.1.1 Open Government Data Initiative 4\u003c\/p\u003e \u003cp\u003e1.1.2 Air Quality 4\u003c\/p\u003e \u003cp\u003e1.1.3 Impact of Lockdown on Air Quality 5\u003c\/p\u003e \u003cp\u003e1.2 Literature Survey 5\u003c\/p\u003e \u003cp\u003e1.3 Implementation Details 6\u003c\/p\u003e \u003cp\u003e1.3.1 Proposed Methodology 7\u003c\/p\u003e \u003cp\u003e1.3.2 System Specifications 8\u003c\/p\u003e \u003cp\u003e1.3.3 Algorithms 8\u003c\/p\u003e \u003cp\u003e1.3.4 Control Flow 10\u003c\/p\u003e \u003cp\u003e1.4 Results and Discussions 11\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 21\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning 23\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShreedhar Rangappa, Ajay A. and G. S. Rajanna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 23\u003c\/p\u003e \u003cp\u003e2.2 Conventional Silkworm Egg Detection Approaches 24\u003c\/p\u003e \u003cp\u003e2.3 Proposed Method 25\u003c\/p\u003e \u003cp\u003e2.3.1 Model Architecture 26\u003c\/p\u003e \u003cp\u003e.3.2 Foreground-Background Segmentation 28\u003c\/p\u003e \u003cp\u003e2.3.3 Egg Location Predictor 30\u003c\/p\u003e \u003cp\u003e2.3.4 Predicting Egg Class 31\u003c\/p\u003e \u003cp\u003e2.4 Dataset Generation 35\u003c\/p\u003e \u003cp\u003e2.5 Results 35\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 37\u003c\/p\u003e \u003cp\u003eAcknowledgment 38\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Wind Speed Prediction System Using Deep Neural Networks 41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJaseena K. U. and Binsu C. Kovoor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 42\u003c\/p\u003e \u003cp\u003e3.2 Methodology 45\u003c\/p\u003e \u003cp\u003e3.2.1 Deep Neural Networks 45\u003c\/p\u003e \u003cp\u003e3.2.2 The Proposed Method 47\u003c\/p\u003e \u003cp\u003e3.2.2.1 Data Acquisition 47\u003c\/p\u003e \u003cp\u003e3.2.2.2 Data Pre-Processing 48\u003c\/p\u003e \u003cp\u003e3.2.2.3 Model Selection and Training 50\u003c\/p\u003e \u003cp\u003e3.2.2.4 Performance Evaluation 51\u003c\/p\u003e \u003cp\u003e3.2.2.5 Visualization 51\u003c\/p\u003e \u003cp\u003e3.3 Results and Discussions 52\u003c\/p\u003e \u003cp\u003e3.3.1 Selection of Parameters 52\u003c\/p\u003e \u003cp\u003e3.3.2 Comparison of Models 53\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 57\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections 61\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVarshaneya V., S. Balasubramanian and Darshan Gera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 61\u003c\/p\u003e \u003cp\u003e4.2 Related Work 62\u003c\/p\u003e \u003cp\u003e4.3 Preliminaries 63\u003c\/p\u003e \u003cp\u003e4.3.1 ResNet 63\u003c\/p\u003e \u003cp\u003e4.3.2 Squeeze-and-Excitation Block 64\u003c\/p\u003e \u003cp\u003e4.4 Proposed Model 66\u003c\/p\u003e \u003cp\u003e4.4.1 Effect of Bridge Connections in ResNet 66\u003c\/p\u003e \u003cp\u003e4.4.2 Res-SE-Net: Proposed Architecture 67\u003c\/p\u003e \u003cp\u003e4.5 Experiments 68\u003c\/p\u003e \u003cp\u003e4.5.1 Datasets 68\u003c\/p\u003e \u003cp\u003e4.5.2 Experimental Setup 68\u003c\/p\u003e \u003cp\u003e4.6 Results 69\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 73\u003c\/p\u003e \u003cp\u003eReferences 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hitting the Success Notes of Deep Learning 77\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSakshi Aggarwal, Navjot Singh and K.K. Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Genesis 78\u003c\/p\u003e \u003cp\u003e5.2 The Big Picture: Artificial Neural Network 79\u003c\/p\u003e \u003cp\u003e5.3 Delineating the Cornerstones 80\u003c\/p\u003e \u003cp\u003e5.3.1 Artificial Neural Network vs. Machine Learning 80\u003c\/p\u003e \u003cp\u003e5.3.2 Machine Learning vs. Deep Learning 81\u003c\/p\u003e \u003cp\u003e5.3.3 Artificial Neural Network vs. Deep Learning 81\u003c\/p\u003e \u003cp\u003e5.4 Deep Learning Architectures 82\u003c\/p\u003e \u003cp\u003e5.4.1 Unsupervised Pre-Trained Networks 82\u003c\/p\u003e \u003cp\u003e5.4.2 Convolutional Neural Networks 83\u003c\/p\u003e \u003cp\u003e5.4.3 Recurrent Neural Networks 84\u003c\/p\u003e \u003cp\u003e5.4.4 Recursive Neural Network 85\u003c\/p\u003e \u003cp\u003e5.5 Why is CNN Preferred for Computer Vision Applications? 85\u003c\/p\u003e \u003cp\u003e5.5.1 Convolutional Layer 86\u003c\/p\u003e \u003cp\u003e5.5.2 Nonlinear Layer 86\u003c\/p\u003e \u003cp\u003e5.5.3 Pooling Layer 87\u003c\/p\u003e \u003cp\u003e5.5.4 Fully Connected Layer 87\u003c\/p\u003e \u003cp\u003e5.6 Unravel Deep Learning in Medical Diagnostic Systems 89\u003c\/p\u003e \u003cp\u003e5.7 Challenges and Future Expectations 94\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 94\u003c\/p\u003e \u003cp\u003eReferences 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks 99\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDiwakar Tripathi, Damodar Reddy Edla, Annushree Bablani and Venkatanareshbabu Kuppili\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 100\u003c\/p\u003e \u003cp\u003e6.1.1 Motivation 100\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 101\u003c\/p\u003e \u003cp\u003e6.3 Proposed Model for Credit Scoring 103\u003c\/p\u003e \u003cp\u003e6.3.1 Stage-1: Feature Selection 104\u003c\/p\u003e \u003cp\u003e6.3.2 Proposed Criteria Function 105\u003c\/p\u003e \u003cp\u003e6.3.3 Stage-2: Ensemble Classifier 106\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 107\u003c\/p\u003e \u003cp\u003e6.4.1 Experimental Datasets and Performance Measures 107\u003c\/p\u003e \u003cp\u003e6.4.2 Classification Results With Feature Selection 108\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 112\u003c\/p\u003e \u003cp\u003eReferences 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization 117\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSreeja M. U. and Binsu C. Kovoor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 118\u003c\/p\u003e \u003cp\u003e7.2 Related Works 119\u003c\/p\u003e \u003cp\u003e7.3 Feature Agglomeration Clustering 122\u003c\/p\u003e \u003cp\u003e7.4 Proposed Methodology 122\u003c\/p\u003e \u003cp\u003e7.4.1 Pre-Processing 123\u003c\/p\u003e \u003cp\u003e7.4.2 Modified Block Clustering Using Feature Agglomeration Technique 125\u003c\/p\u003e \u003cp\u003e7.4.3 Post-Processing and Summary Generation 127\u003c\/p\u003e \u003cp\u003e7.5 Results and Analysis 129\u003c\/p\u003e \u003cp\u003e7.5.1 Experimental Setup and Data Sets Used 129\u003c\/p\u003e \u003cp\u003e7.5.2 Evaluation Metrics 130\u003c\/p\u003e \u003cp\u003e7.5.3 Evaluation 131\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 138\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Machine Learning for Healthcare Systems 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier 143\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSaroj Kumar Pandeyz, Rekh Ram Janghel and Vaibhav Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 143\u003c\/p\u003e \u003cp\u003e8.2 Materials and Methods 145\u003c\/p\u003e \u003cp\u003e8.2.1 MIT-BIH Arrhythmia Database 146\u003c\/p\u003e \u003cp\u003e8.2.2 Signal Pre-Processing 147\u003c\/p\u003e \u003cp\u003e8.2.3 Feature Extraction 147\u003c\/p\u003e \u003cp\u003e8.2.4 Classification 148\u003c\/p\u003e \u003cp\u003e8.2.4.1 XGBoost Classifier 148\u003c\/p\u003e \u003cp\u003e8.2.4.2 AdaBoost Classifier 149\u003c\/p\u003e \u003cp\u003e8.3 Results and Discussion 149\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 155\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePintu Kumar Ram and Pratyay Kuila\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 159\u003c\/p\u003e \u003cp\u003e9.2 Related Works 161\u003c\/p\u003e \u003cp\u003e9.3 An Overview of Gravitational Search Algorithm 162\u003c\/p\u003e \u003cp\u003e9.4 Proposed Model 163\u003c\/p\u003e \u003cp\u003e9.4.1 Pre-Processing 163\u003c\/p\u003e \u003cp\u003e9.4.2 Proposed GSA-Based Feature Selection 164\u003c\/p\u003e \u003cp\u003e9.5 Simulation Results 166\u003c\/p\u003e \u003cp\u003e9.5.1 Biological Analysis 168\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 172\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Machine Learning for Security Systems 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching 177\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRitesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 177\u003c\/p\u003e \u003cp\u003e10.1.1 Related Works 178\u003c\/p\u003e \u003cp\u003e10.2 Preliminary Details 179\u003c\/p\u003e \u003cp\u003e10.2.1 Fusion 181\u003c\/p\u003e \u003cp\u003e10.3 Experiments and Results 182\u003c\/p\u003e \u003cp\u003e10.3.1 Databases 182\u003c\/p\u003e \u003cp\u003e10.3.2 Experimental Results 182\u003c\/p\u003e \u003cp\u003e10.3.2.1 Same Spectral Matchings 183\u003c\/p\u003e \u003cp\u003e10.3.2.2 Cross Spectral Matchings 184\u003c\/p\u003e \u003cp\u003e10.3.3 Feature-Level Fusion 186\u003c\/p\u003e \u003cp\u003e10.3.4 Score-Level Fusion 189\u003c\/p\u003e \u003cp\u003e10.4 Conclusions 190\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Fake Social Media Profile Detection 193\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eUmita Deepak Joshi, Vanshika, Ajay Pratap Singh, Tushar Rajesh Pahuja, Smita Naval and Gaurav Singal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 194\u003c\/p\u003e \u003cp\u003e11.2 Related Work 195\u003c\/p\u003e \u003cp\u003e11.3 Methodology 197\u003c\/p\u003e \u003cp\u003e11.3.1 Dataset 197\u003c\/p\u003e \u003cp\u003e11.3.2 Pre-Processing 198\u003c\/p\u003e \u003cp\u003e11.3.3 Artificial Neural Network 199\u003c\/p\u003e \u003cp\u003e11.3.4 Random Forest 202\u003c\/p\u003e \u003cp\u003e11.3.5 Extreme Gradient Boost 202\u003c\/p\u003e \u003cp\u003e11.3.6 Long Short-Term Memory 204\u003c\/p\u003e \u003cp\u003e11.4 Experimental Results 204\u003c\/p\u003e \u003cp\u003e11.5 Conclusion and Future Work 207\u003c\/p\u003e \u003cp\u003eAcknowledgment 207\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks 211\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eE. M. V. Naga Karthik and Madan Gopal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 212\u003c\/p\u003e \u003cp\u003e12.2 Related Work 213\u003c\/p\u003e \u003cp\u003e12.3 Methods and Materials 215\u003c\/p\u003e \u003cp\u003e12.3.1 Feature Extraction Using SURF 215\u003c\/p\u003e \u003cp\u003e12.3.2 Feature Extraction Using Conventional Methods 216\u003c\/p\u003e \u003cp\u003e12.3.2.1 Local Orientation Estimation 216\u003c\/p\u003e \u003cp\u003e12.3.2.2 Singular Region Detection 218\u003c\/p\u003e \u003cp\u003e12.3.3 Proposed CNN Architecture 219\u003c\/p\u003e \u003cp\u003e12.3.4 Dataset 221\u003c\/p\u003e \u003cp\u003e12.3.5 Computational Environment 221\u003c\/p\u003e \u003cp\u003e12.4 Results 222\u003c\/p\u003e \u003cp\u003e12.4.1 Feature Extraction and Visualization 223\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 226\u003c\/p\u003e \u003cp\u003eAcknowledgements 226\u003c\/p\u003e \u003cp\u003eReferences 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features 229\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eM. Srinivas, Sanjeev Saurav, Akshay Nayak and Murukessan A. P.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 230\u003c\/p\u003e \u003cp\u003e13.2 Related Work 232\u003c\/p\u003e \u003cp\u003e13.3 Proposed Method 235\u003c\/p\u003e \u003cp\u003e13.3.1 Convolutional Neural Network 236\u003c\/p\u003e \u003cp\u003e13.3.1.1 Convolution Layer 236\u003c\/p\u003e \u003cp\u003e13.3.1.2 Pooling Layer 237\u003c\/p\u003e \u003cp\u003e13.3.1.3 ReLU Layer 238\u003c\/p\u003e \u003cp\u003e13.3.1.4 Fully Connected Layer 238\u003c\/p\u003e \u003cp\u003e13.3.2 Histogram of Gradient 239\u003c\/p\u003e \u003cp\u003e13.3.3 Facial Landmark Detection 240\u003c\/p\u003e \u003cp\u003e13.3.4 Support Vector Machine 241\u003c\/p\u003e \u003cp\u003e13.3.5 Model Merging and Learning 242\u003c\/p\u003e \u003cp\u003e13.4 Experimental Results 242\u003c\/p\u003e \u003cp\u003e13.4.1 Datasets 242\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 245\u003c\/p\u003e \u003cp\u003eAcknowledgement 245\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Machine Learning for Classification and Information Retrieval Systems 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 AnimNet: An Animal Classification Network using Deep Learning 249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKanak Manjari, Kriti Singhal, Madhushi Verma and Gaurav Singal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 249\u003c\/p\u003e \u003cp\u003e14.1.1 Feature Extraction 250\u003c\/p\u003e \u003cp\u003e14.1.2 Artificial Neural Network 250\u003c\/p\u003e \u003cp\u003e14.1.3 Transfer Learning 251\u003c\/p\u003e \u003cp\u003e14.2 Related Work 252\u003c\/p\u003e \u003cp\u003e14.3 Proposed Methodology 254\u003c\/p\u003e \u003cp\u003e14.3.1 Dataset Preparation 254\u003c\/p\u003e \u003cp\u003e14.3.2 Training the Model 254\u003c\/p\u003e \u003cp\u003e14.4 Results 258\u003c\/p\u003e \u003cp\u003e14.4.1 Using Pre-Trained Networks 259\u003c\/p\u003e \u003cp\u003e14.4.2 Using AnimNet 259\u003c\/p\u003e \u003cp\u003e14.4.3 Test Analysis 260\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 263\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis 267\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlok Kumar and Renu Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 268\u003c\/p\u003e \u003cp\u003e15.2 Related Work 269\u003c\/p\u003e \u003cp\u003e15.3 The Proposed System 271\u003c\/p\u003e \u003cp\u003e15.3.1 Feedback Collector 272\u003c\/p\u003e \u003cp\u003e15.3.2 Feedback Pre-Processor 272\u003c\/p\u003e \u003cp\u003e15.3.3 Feature Selector 272\u003c\/p\u003e \u003cp\u003e15.3.4 Feature Validator 274\u003c\/p\u003e \u003cp\u003e15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge 274\u003c\/p\u003e \u003cp\u003e15.3.4.2 Removal of Least Significant Terms on the Basis of Contextual Knowledge 276\u003c\/p\u003e \u003cp\u003e15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words 277\u003c\/p\u003e \u003cp\u003e15.3.4.4 Removal of Terms Having Similar Sense 278\u003c\/p\u003e \u003cp\u003e15.3.4.5 Removal of Terms Having Same Root 279\u003c\/p\u003e \u003cp\u003e15.3.4.6 Identification of Multi-Term Features 279\u003c\/p\u003e \u003cp\u003e15.3.4.7 Identification of Less Frequent Feature 279\u003c\/p\u003e \u003cp\u003e15.3.5 Feature Concluder 281\u003c\/p\u003e \u003cp\u003e15.4 Result Analysis 282\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 286\u003c\/p\u003e \u003cp\u003eReferences 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding 289\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAmol P. Bhopale and Ashish Tiwari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 290\u003c\/p\u003e \u003cp\u003e16.2 Related Work 291\u003c\/p\u003e \u003cp\u003e16.3 Proposed Approach 292\u003c\/p\u003e \u003cp\u003e16.3.1 Phrase Extraction 292\u003c\/p\u003e \u003cp\u003e16.3.2 Corpus Annotation 294\u003c\/p\u003e \u003cp\u003e16.3.3 Phrase Embedding 294\u003c\/p\u003e \u003cp\u003e16.4 Experimental Setup 297\u003c\/p\u003e \u003cp\u003e16.4.1 Dataset Preparation 297\u003c\/p\u003e \u003cp\u003e16.4.2 Parameter Setting 297\u003c\/p\u003e \u003cp\u003e16.5 Results 298\u003c\/p\u003e \u003cp\u003e16.5.1 Phrase Extraction 298\u003c\/p\u003e \u003cp\u003e16.5.2 Phrase Embedding 298\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 303\u003c\/p\u003e \u003cp\u003eReferences 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Image Anonymization Using Deep Convolutional Generative Adversarial Network 305\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAshish Undirwade and Sujit Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 306\u003c\/p\u003e \u003cp\u003e17.2 Background Information 310\u003c\/p\u003e \u003cp\u003e17.2.1 Black Box and White Box Attacks 310\u003c\/p\u003e \u003cp\u003e17.2.2 Model Inversion Attack 311\u003c\/p\u003e \u003cp\u003e17.2.3 Differential Privacy 312\u003c\/p\u003e \u003cp\u003e17.2.3.1 Definition 312\u003c\/p\u003e \u003cp\u003e17.2.4 Generative Adversarial Network 313\u003c\/p\u003e \u003cp\u003e17.2.5 Earth-Mover (EM) Distance\/Wasserstein Metric 316\u003c\/p\u003e \u003cp\u003e17.2.6 Wasserstein GAN 317\u003c\/p\u003e \u003cp\u003e17.2.7 Improved Wasserstein GAN (WGAN-GP) 317\u003c\/p\u003e \u003cp\u003e17.2.8 KL Divergence and JS Divergence 318\u003c\/p\u003e \u003cp\u003e17.2.9 DCGAN 319\u003c\/p\u003e \u003cp\u003e17.3 Image Anonymization to Prevent Model Inversion Attack 319\u003c\/p\u003e \u003cp\u003e17.3.1 Algorithm 321\u003c\/p\u003e \u003cp\u003e17.3.2 Training 322\u003c\/p\u003e \u003cp\u003e17.3.3 Noise Amplifier 323\u003c\/p\u003e \u003cp\u003e17.3.4 Dataset 324\u003c\/p\u003e \u003cp\u003e17.3.5 Model Architecture 324\u003c\/p\u003e \u003cp\u003e17.3.6 Working 325\u003c\/p\u003e \u003cp\u003e17.3.7 Privacy Gain 325\u003c\/p\u003e \u003cp\u003e17.4 Results and Analysis 326\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 328\u003c\/p\u003e \u003cp\u003eReferences 329\u003c\/p\u003e \u003cp\u003eIndex 331\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430937325848,"sku":"9781119768852","price":140.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119768852.jpg?v=1784765781","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-algorithms-and-applications-hardback-9781119768852","provider":"Freshly Printed Books","version":"1.0","type":"link"}