Machine learning algorithms and applications:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken ; Beverly
Scrivener Publishing
2021
|
| Schriftenreihe: | Sustainable computing and optimization
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 332 Seiten Illustrationen, Diagramme |
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| adam_text | Contents Acknowledgments Preface Part 1: Machine Learning for Industrial Applications 1 2 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services Priyank Jain and Gagandeep Kaur 1.1 Introduction 1.1.1 Open Government Data Initiative 1.1.2 Air Quality 1.1.3 Impact of Lockdown on Air Quality 1.2 Literature Survey 1.3 Implementation Details 1.3.1 Proposed Methodology 1.3.2 System Specifications 1.3.3 Algorithms 1.3.4 Control Flow 1.4 Results and Discussions 1.5 Conclusion References Automatic Counting and Classification of Silkworm Eggs Using Deep Learning Shreedhar Rangappa, Ajay A. and G. S. Rajanna 2.1 Introduction 2.2 Conventional Silkworm Egg Detection Approaches 2.3 Proposed Method 2.3.1 Model Architecture xv xvii 1 3 4 4 4 5 5 6 7 8 8 10 11 21 21 23 23 24 25 26
vi 3 4 Contents 2.3.2 Foreground-Background Segmentation 2.3.3 Egg Location Predictor 2.3.4 Predicting Egg Class 2.4 Dataset Generation 2.5 Results 2.6 Conclusion Acknowledgment References 28 30 31 35 35 37 38 38 A Wind Speed Prediction System Using Deep Neural Networks Jaseena K. U. and Binsu C. Kovoor 3.1 Introduction 3.2 Methodology 3.2.1 Deep Neural Networks 3.2.2 The Proposed Method 3.2.2.1 Data Acquisition 3.2.2.2 Data Pre-Processing 3.2.2.3 Model Selection and Training 3.2.2.4 Performance Evaluation 3.2.2.5 Visualization 3.3 Results and Discussions 3.3.1 Selection of Parameters 3.3.2 Comparison of Models 3.4 Conclusion References 41 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections Varshaneya V, S. Balasubramanian and Darshan Gera 4.1 Introduction 4.2 Related Work 4.3 Preliminaries 4.3.1 ResNet 4.3.2 Squeeze-and-Excitation Block 4.4 Proposed Model 4.4.1 Effect of Bridge Connections in ResNet 4.4.2 Res-SE-Net: Proposed Architecture 4.5 Experiments 4.5.1 Datasets 4.5.2 Experimental Setup 4.6 Results 42 45 45 47 47 48 50 51 51 52 52 53 57 57 61 61 62 63 63 64 66 66 67 68 68 68 69
Contents 4.7 5 6 Conclusion References Hitting the Success Notes of Deep Learning Sakshi Aggarwal, Navjot Singh and K.K. Mishra 5.1 Genesis 5.2 The Big Picture: Artificial Neural Network 5.3 Delineating the Cornerstones 5.3.1 Artificial Neural Network vs. Machine Learning 5.3.2 Machine Learning vs. Deep Learning 5.3.3 Artificial Neural Network vs. Deep Learning 5.4 Deep Learning Architectures 5.4.1 Unsupervised Pre-Trained Networks 5.4.2 Convolutional Neural Networks 5.4.3 Recurrent Neural Networks 5.4.4 Recursive Neural Network 5.5 Why is CNN Preferred for Computer Vision Applications? 5.5.1 Convolutional Layer 5.5.2 Nonlinear Layer 5.5.3 Pooling Layer 5.5.4 Fully Connected Layer 5.6 Unravel Deep Learning in Medical Diagnostic Systems 5.7 Challenges and Future Expectations 5.8 Conclusion References Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks Diwakar Tripathi, Damodar Reddy Edla, Annushree Bablani and Venkatanareshbabu Kuppili 6.1 Introduction 6.1.1 Motivation 6.2 Literature Survey 6.3 Proposed Model for Credit Scoring 6.3.1 Stage-1 : Feature Selection 6.3.2 Proposed Criteria Function 6.3.3 Stage-2: Ensemble Classifier 6.4 Results and Discussion 6.4.1 Experimental Datasets and Performance Measures 6.4.2 Classification Results With Feature Selection vii 73 74 77 78 79 80 80 81 81 82 82 83 84 85 85 86 86 87 87 89 94 94 95 99 100 100 101 103 104 105 106 107 107 108
viii Contents 6.5 7 Conclusion References Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization Sreeja M. U. and Binsu C. Kovoor 7.1 Introduction 7.2 Related Works 7.3 Feature Agglomeration Clustering 7.4 Proposed Methodology 7.4.1 Pre-Processing 7.4.2 Modified Block Clustering Using Feature Agglomeration Technique 7.4.3 Post-Processing and Summary Generation 7.5 Results and Analysis 7.5.1 Experimental Setup and Data Sets Used 7.5.2 Evaluation Metrics 7.5.3 Evaluation 7.6 Conclusion References Part 2: Machine Learning for Healthcare Systems 8 9 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier Saroj Kumar Pandeyz, Rekh Ram Janghel and Vaibhav Gupta 8.1 Introduction 8.2 Materials and Methods 8.2.1 MIT-ВІН Arrhythmia Database 8.2.2 Signal Pre-Processing 8.2.3 Feature Extraction 8.2.4 Classification 8.2.4.1 XGBoost Classifier 8.2.4.2 AdaBoost Classifier 8.3 Results and Discussion 8.4 Conclusion References GSA-Based Approach for Gene Selection from Microarray Gene Expression Data Pintu Kumar Ram and Pratyay Kuila 9.1 Introduction 112 113 117 118 119 122 122 123 125 127 129 129 130 131 138 138 141 143 143 145 146 147 147 148 148 149 149 155 156 159 159
Contents 9.2 9.3 9.4 ix Related Works An Overview of Gravitational Search Algorithm Proposed Model 9.4.1 Pre-Processing 9.4.2 Proposed GSA-Based Feature Selection 9.5 Simulation Results 9.5.1 Biological Analysis 9.6 Conclusion References 161 162 163 163 164 166 168 172 172 Part 3: Machine Learning forSecurity Systems 175 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey 10.1 Introduction 10.1.1 Related Works 10.2 Preliminary Details 10.2.1 Fusion 10.3 Experiments and Results 10.3.1 Databases 10.3.2 Experimental Results 10.3.2.1 Same Spectral Matchings 10.3.2.2 Cross Spectral Matchings 10.3.3 Feature-Level Fusion 10.3.4 Score-Level Fusion 10.4 Conclusions References 11 Fake Social Media Profile Detection Umita Deepak Joshi, Vanshika, Ajay Pratap Singh, Tushar Rajesh Puhuja, Smita Naval and Gaurav Singal 11.1 Introduction 11.2 Related Work 11.3 Methodology 11.3.1 Dataset 11.3.2 Pre-Processing 11.3.3 Artificial Neural Network 11.3.4 Random Forest 11.3.5 Extreme Gradient Boost 177 177 178 179 181 182 182 182 183 184 186 189 190 190 193 194 195 197 197 198 199 202 202
x Contents 11.4 11.5 11.3.6 Long Short-Term Memory Experimental Results Conclusion and Future Work Acknowledgment References 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks E. M. V. Naga Karthik and Madan Gopal 12.1 Introduction 12.2 Related Work 12.3 Methods and Materials 12.3.1 Feature Extraction Using SURF 12.3.2 Feature Extraction Using Conventional Methods 12.3.2.1 Local Orientation Estimation 12.3.2.2 Singular Region Detection 12.3.3 Proposed CNN Architecture 12.3.4 Dataset 12.3.5 Computational Environment 12.4 Results 12.4.1 Feature Extraction and Visualization 12.5 Conclusion Acknowledgements References 13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features M. Srinivas, Sanjeev Saurav, Akshay Nayak and Murukessan A. P. 13.1 Introduction 13.2 Related Work 13.3 Proposed Method 13.3.1 Convolutional Neural Network 13.3.1.1 Convolution Layer 13.3.1.2 Pooling Layer 13.3.1.3 ReLU Layer 13.3.1.4 Fully Connected Layer 13.3.2 Elistogram of Gradient 13.3.3 Facial Landmark Detection 13.3.4 Support Vector Machine 13.3.5 Model Merging and Learning 204 204 207 207 207 211 212 213 215 215 216 216 218 219 221 221 222 223 226 226 226 229 230 232 235 236 236 237 238 238 239 240 241 242
Contents 13.4 13.5 Experimental Results 13.4.1 Datasets Conclusion Acknowledgement References Part 4: Machine Learning for Classification and Information Retrieval Systems 14 AnimNet: An Animal Classification Network using Deep Learning Kanak Manjari, Kriti Singhal, Madhushi Verma and Gaurav Singal 14.1 Introduction 14.1.1 Feature Extraction 14.1.2 Artificial Neural Network 14.1.3 Transfer Learning 14.2 Related Work 14.3 Proposed Methodology 14.3.1 Dataset Preparation 14.3.2 Training the Model 14.4 Results 14.4.1 Using Pre-Trained Networks 14.4.2 Using AnimNet 14.4.3 Test Analysis 14.5 Conclusion References 15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis Alok Kumar and Renu Jain 15.1 Introduction 15.2 Related Work 15.3 The Proposed System 15.3.1 Feedback Collector 15.3.2 Feedback Pre-Processor 15.3.3 Feature Selector 15.3.4 Feature Validator 15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge xi 242 242 245 245 245 247 249 249 250 250 251 252 254 254 254 258 259 259 260 263 264 267 268 269 271 272 272 272 274 274
xii Contents 15.3.4.2 15.4 15.5 Removal of Least Significant Terms on the Basis of Contextual Knowledge 15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words 15.3.4.4 Removal of Terms Having Similar Sense 15.3.4.5 Removal of Terms Having Same Root 15.3.4.6 Identification of Multi-Term Features 15.3.4.7 Identification of Less Frequent Feature 15.3.5 Feature Concluder Result Analysis Conclusion References 16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding Amol P. Bhopale and Ashish Tiwari 16.1 Introduction 16.2 Related Work 16.3 Proposed Approach 16.3.1 Phrase Extraction 16.3.2 Corpus Annotation 16.3.3 Phrase Embedding 16.4 Experimental Setup 16.4.1 Dataset Preparation 16.4.2 Parameter Setting 16.5 Results 16.5.1 Phrase Extraction 16.5.2 Phrase Embedding 16.6 Conclusion References 17 Image Anonymization Using Deep Convolutional Generative Adversarial Network Ashish Undirwade and Sujit Das 17.1 Introduction 17.2 Background Information 276 277 278 279 279 279 281 282 286 286 289 290 291 292 292 294 294 297 297 297 298 298 298 303 303 305 306 310
Contents Black Box and White Box Attacks Model Inversion Attack Differential Privacy 17.2.3.1 Definition 17.2.4 Generative Adversarial Network 17.2.5 Earth-Mover (EM) Distance/Wasserstein Metric 17.2.6 Wasserstein GAN 17.2.7 Improved Wasserstein GAN (WGAN-GP) 17.2.8 KL Divergence and JS Divergence 17.2.9 DCGAN Image Anonymization to Prevent Model Inversion Attack 17.3.1 Algorithm 17.3.2 Training 17.3.3 Noise Amplifier 17.3.4 Dataset 17.3.5 Model Architecture 17.3.6 Working 17.3.7 Privacy Gain Results and Analysis Conclusion References 17.2.1 17.2.2 17.2.3 Index xiii 310 311 312 312 313 316 317 317 318 319 319 321 322 323 324 324 325 325 326 328 329 331
|
| adam_txt |
Contents Acknowledgments Preface Part 1: Machine Learning for Industrial Applications 1 2 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services Priyank Jain and Gagandeep Kaur 1.1 Introduction 1.1.1 Open Government Data Initiative 1.1.2 Air Quality 1.1.3 Impact of Lockdown on Air Quality 1.2 Literature Survey 1.3 Implementation Details 1.3.1 Proposed Methodology 1.3.2 System Specifications 1.3.3 Algorithms 1.3.4 Control Flow 1.4 Results and Discussions 1.5 Conclusion References Automatic Counting and Classification of Silkworm Eggs Using Deep Learning Shreedhar Rangappa, Ajay A. and G. S. Rajanna 2.1 Introduction 2.2 Conventional Silkworm Egg Detection Approaches 2.3 Proposed Method 2.3.1 Model Architecture xv xvii 1 3 4 4 4 5 5 6 7 8 8 10 11 21 21 23 23 24 25 26
vi 3 4 Contents 2.3.2 Foreground-Background Segmentation 2.3.3 Egg Location Predictor 2.3.4 Predicting Egg Class 2.4 Dataset Generation 2.5 Results 2.6 Conclusion Acknowledgment References 28 30 31 35 35 37 38 38 A Wind Speed Prediction System Using Deep Neural Networks Jaseena K. U. and Binsu C. Kovoor 3.1 Introduction 3.2 Methodology 3.2.1 Deep Neural Networks 3.2.2 The Proposed Method 3.2.2.1 Data Acquisition 3.2.2.2 Data Pre-Processing 3.2.2.3 Model Selection and Training 3.2.2.4 Performance Evaluation 3.2.2.5 Visualization 3.3 Results and Discussions 3.3.1 Selection of Parameters 3.3.2 Comparison of Models 3.4 Conclusion References 41 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections Varshaneya V, S. Balasubramanian and Darshan Gera 4.1 Introduction 4.2 Related Work 4.3 Preliminaries 4.3.1 ResNet 4.3.2 Squeeze-and-Excitation Block 4.4 Proposed Model 4.4.1 Effect of Bridge Connections in ResNet 4.4.2 Res-SE-Net: Proposed Architecture 4.5 Experiments 4.5.1 Datasets 4.5.2 Experimental Setup 4.6 Results 42 45 45 47 47 48 50 51 51 52 52 53 57 57 61 61 62 63 63 64 66 66 67 68 68 68 69
Contents 4.7 5 6 Conclusion References Hitting the Success Notes of Deep Learning Sakshi Aggarwal, Navjot Singh and K.K. Mishra 5.1 Genesis 5.2 The Big Picture: Artificial Neural Network 5.3 Delineating the Cornerstones 5.3.1 Artificial Neural Network vs. Machine Learning 5.3.2 Machine Learning vs. Deep Learning 5.3.3 Artificial Neural Network vs. Deep Learning 5.4 Deep Learning Architectures 5.4.1 Unsupervised Pre-Trained Networks 5.4.2 Convolutional Neural Networks 5.4.3 Recurrent Neural Networks 5.4.4 Recursive Neural Network 5.5 Why is CNN Preferred for Computer Vision Applications? 5.5.1 Convolutional Layer 5.5.2 Nonlinear Layer 5.5.3 Pooling Layer 5.5.4 Fully Connected Layer 5.6 Unravel Deep Learning in Medical Diagnostic Systems 5.7 Challenges and Future Expectations 5.8 Conclusion References Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks Diwakar Tripathi, Damodar Reddy Edla, Annushree Bablani and Venkatanareshbabu Kuppili 6.1 Introduction 6.1.1 Motivation 6.2 Literature Survey 6.3 Proposed Model for Credit Scoring 6.3.1 Stage-1 : Feature Selection 6.3.2 Proposed Criteria Function 6.3.3 Stage-2: Ensemble Classifier 6.4 Results and Discussion 6.4.1 Experimental Datasets and Performance Measures 6.4.2 Classification Results With Feature Selection vii 73 74 77 78 79 80 80 81 81 82 82 83 84 85 85 86 86 87 87 89 94 94 95 99 100 100 101 103 104 105 106 107 107 108
viii Contents 6.5 7 Conclusion References Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization Sreeja M. U. and Binsu C. Kovoor 7.1 Introduction 7.2 Related Works 7.3 Feature Agglomeration Clustering 7.4 Proposed Methodology 7.4.1 Pre-Processing 7.4.2 Modified Block Clustering Using Feature Agglomeration Technique 7.4.3 Post-Processing and Summary Generation 7.5 Results and Analysis 7.5.1 Experimental Setup and Data Sets Used 7.5.2 Evaluation Metrics 7.5.3 Evaluation 7.6 Conclusion References Part 2: Machine Learning for Healthcare Systems 8 9 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier Saroj Kumar Pandeyz, Rekh Ram Janghel and Vaibhav Gupta 8.1 Introduction 8.2 Materials and Methods 8.2.1 MIT-ВІН Arrhythmia Database 8.2.2 Signal Pre-Processing 8.2.3 Feature Extraction 8.2.4 Classification 8.2.4.1 XGBoost Classifier 8.2.4.2 AdaBoost Classifier 8.3 Results and Discussion 8.4 Conclusion References GSA-Based Approach for Gene Selection from Microarray Gene Expression Data Pintu Kumar Ram and Pratyay Kuila 9.1 Introduction 112 113 117 118 119 122 122 123 125 127 129 129 130 131 138 138 141 143 143 145 146 147 147 148 148 149 149 155 156 159 159
Contents 9.2 9.3 9.4 ix Related Works An Overview of Gravitational Search Algorithm Proposed Model 9.4.1 Pre-Processing 9.4.2 Proposed GSA-Based Feature Selection 9.5 Simulation Results 9.5.1 Biological Analysis 9.6 Conclusion References 161 162 163 163 164 166 168 172 172 Part 3: Machine Learning forSecurity Systems 175 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey 10.1 Introduction 10.1.1 Related Works 10.2 Preliminary Details 10.2.1 Fusion 10.3 Experiments and Results 10.3.1 Databases 10.3.2 Experimental Results 10.3.2.1 Same Spectral Matchings 10.3.2.2 Cross Spectral Matchings 10.3.3 Feature-Level Fusion 10.3.4 Score-Level Fusion 10.4 Conclusions References 11 Fake Social Media Profile Detection Umita Deepak Joshi, Vanshika, Ajay Pratap Singh, Tushar Rajesh Puhuja, Smita Naval and Gaurav Singal 11.1 Introduction 11.2 Related Work 11.3 Methodology 11.3.1 Dataset 11.3.2 Pre-Processing 11.3.3 Artificial Neural Network 11.3.4 Random Forest 11.3.5 Extreme Gradient Boost 177 177 178 179 181 182 182 182 183 184 186 189 190 190 193 194 195 197 197 198 199 202 202
x Contents 11.4 11.5 11.3.6 Long Short-Term Memory Experimental Results Conclusion and Future Work Acknowledgment References 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks E. M. V. Naga Karthik and Madan Gopal 12.1 Introduction 12.2 Related Work 12.3 Methods and Materials 12.3.1 Feature Extraction Using SURF 12.3.2 Feature Extraction Using Conventional Methods 12.3.2.1 Local Orientation Estimation 12.3.2.2 Singular Region Detection 12.3.3 Proposed CNN Architecture 12.3.4 Dataset 12.3.5 Computational Environment 12.4 Results 12.4.1 Feature Extraction and Visualization 12.5 Conclusion Acknowledgements References 13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features M. Srinivas, Sanjeev Saurav, Akshay Nayak and Murukessan A. P. 13.1 Introduction 13.2 Related Work 13.3 Proposed Method 13.3.1 Convolutional Neural Network 13.3.1.1 Convolution Layer 13.3.1.2 Pooling Layer 13.3.1.3 ReLU Layer 13.3.1.4 Fully Connected Layer 13.3.2 Elistogram of Gradient 13.3.3 Facial Landmark Detection 13.3.4 Support Vector Machine 13.3.5 Model Merging and Learning 204 204 207 207 207 211 212 213 215 215 216 216 218 219 221 221 222 223 226 226 226 229 230 232 235 236 236 237 238 238 239 240 241 242
Contents 13.4 13.5 Experimental Results 13.4.1 Datasets Conclusion Acknowledgement References Part 4: Machine Learning for Classification and Information Retrieval Systems 14 AnimNet: An Animal Classification Network using Deep Learning Kanak Manjari, Kriti Singhal, Madhushi Verma and Gaurav Singal 14.1 Introduction 14.1.1 Feature Extraction 14.1.2 Artificial Neural Network 14.1.3 Transfer Learning 14.2 Related Work 14.3 Proposed Methodology 14.3.1 Dataset Preparation 14.3.2 Training the Model 14.4 Results 14.4.1 Using Pre-Trained Networks 14.4.2 Using AnimNet 14.4.3 Test Analysis 14.5 Conclusion References 15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis Alok Kumar and Renu Jain 15.1 Introduction 15.2 Related Work 15.3 The Proposed System 15.3.1 Feedback Collector 15.3.2 Feedback Pre-Processor 15.3.3 Feature Selector 15.3.4 Feature Validator 15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge xi 242 242 245 245 245 247 249 249 250 250 251 252 254 254 254 258 259 259 260 263 264 267 268 269 271 272 272 272 274 274
xii Contents 15.3.4.2 15.4 15.5 Removal of Least Significant Terms on the Basis of Contextual Knowledge 15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words 15.3.4.4 Removal of Terms Having Similar Sense 15.3.4.5 Removal of Terms Having Same Root 15.3.4.6 Identification of Multi-Term Features 15.3.4.7 Identification of Less Frequent Feature 15.3.5 Feature Concluder Result Analysis Conclusion References 16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding Amol P. Bhopale and Ashish Tiwari 16.1 Introduction 16.2 Related Work 16.3 Proposed Approach 16.3.1 Phrase Extraction 16.3.2 Corpus Annotation 16.3.3 Phrase Embedding 16.4 Experimental Setup 16.4.1 Dataset Preparation 16.4.2 Parameter Setting 16.5 Results 16.5.1 Phrase Extraction 16.5.2 Phrase Embedding 16.6 Conclusion References 17 Image Anonymization Using Deep Convolutional Generative Adversarial Network Ashish Undirwade and Sujit Das 17.1 Introduction 17.2 Background Information 276 277 278 279 279 279 281 282 286 286 289 290 291 292 292 294 294 297 297 297 298 298 298 303 303 305 306 310
Contents Black Box and White Box Attacks Model Inversion Attack Differential Privacy 17.2.3.1 Definition 17.2.4 Generative Adversarial Network 17.2.5 Earth-Mover (EM) Distance/Wasserstein Metric 17.2.6 Wasserstein GAN 17.2.7 Improved Wasserstein GAN (WGAN-GP) 17.2.8 KL Divergence and JS Divergence 17.2.9 DCGAN Image Anonymization to Prevent Model Inversion Attack 17.3.1 Algorithm 17.3.2 Training 17.3.3 Noise Amplifier 17.3.4 Dataset 17.3.5 Model Architecture 17.3.6 Working 17.3.7 Privacy Gain Results and Analysis Conclusion References 17.2.1 17.2.2 17.2.3 Index xiii 310 311 312 312 313 316 317 317 318 319 319 321 322 323 324 324 325 325 326 328 329 331 |
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| id | DE-604.BV047657842 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:51:39Z |
| indexdate | 2024-07-10T09:18:30Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033042732 |
| oclc_num | 1298743091 |
| open_access_boolean | |
| owner | DE-739 DE-703 DE-11 |
| owner_facet | DE-739 DE-703 DE-11 |
| physical | xvii, 332 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Scrivener Publishing |
| record_format | marc |
| series2 | Sustainable computing and optimization |
| spelling | Machine learning algorithms and applications edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta Hoboken ; Beverly Scrivener Publishing 2021 xvii, 332 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Sustainable computing and optimization Machine learning Computer algorithms Computer algorithms fast Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Algorithmus (DE-588)4001183-5 s Deep learning (DE-588)1135597375 s DE-604 Srinivas, Mettu ca. 20./21. Jh. Sonstige (DE-588)1249708893 oth Sucharitha, G. ca. 20./21. Jh. Sonstige (DE-588)1249710278 oth Matta, Anjanna ca. 20./21. Jh. Sonstige (DE-588)1249710618 oth Erscheint auch als Online-Ausgabe 978-1-119-76926-2 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033042732&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Machine learning algorithms and applications Machine learning Computer algorithms Computer algorithms fast Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd Deep learning (DE-588)1135597375 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)1135597375 (DE-588)4001183-5 |
| title | Machine learning algorithms and applications |
| title_auth | Machine learning algorithms and applications |
| title_exact_search | Machine learning algorithms and applications |
| title_exact_search_txtP | Machine learning algorithms and applications |
| title_full | Machine learning algorithms and applications edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta |
| title_fullStr | Machine learning algorithms and applications edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta |
| title_full_unstemmed | Machine learning algorithms and applications edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta |
| title_short | Machine learning algorithms and applications |
| title_sort | machine learning algorithms and applications |
| topic | Machine learning Computer algorithms Computer algorithms fast Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd Deep learning (DE-588)1135597375 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Machine learning Computer algorithms Maschinelles Lernen Deep learning Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033042732&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT srinivasmettu machinelearningalgorithmsandapplications AT sucharithag machinelearningalgorithmsandapplications AT mattaanjanna machinelearningalgorithmsandapplications |