Machine Learning Algorithms and Applications. Группа авторов

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СКАЧАТЬ 9.3 An Overview of Gravitational Search Algorithm 9.4 Proposed Model 9.5 Simulation Results 9.6 Conclusion References

      8  Part 3: Machine Learning for Security Systems 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching 10.1 Introduction 10.2 Preliminary Details 10.3 Experiments and Results 10.4 Conclusions References 11 Fake Social Media Profile Detection 11.1 Introduction 11.2 Related Work 11.3 Methodology 11.4 Experimental Results 11.5 Conclusion and Future Work Acknowledgment References 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks 12.1 Introduction 12.2 Related Work 12.3 Methods and Materials 12.4 Results 12.5 Conclusion Acknowledgements References 13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features 13.1 Introduction 13.2 Related Work 13.3 Proposed Method 13.4 Experimental Results 13.5 Conclusion Acknowledgement References

      9  Part 4: Machine Learning for Classification and Information Retrieval Systems 14 AnimNet: An Animal Classification Network using Deep Learning 14.1 Introduction 14.2 Related Work 14.3 Proposed Methodology 14.4 Results 14.5 Conclusion References 15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis 15.1 Introduction 15.2 Related Work 15.3 The Proposed System 15.4 Result Analysis 15.5 Conclusion References 16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding 16.1 Introduction 16.2 Related Work 16.3 Proposed Approach 16.4 Experimental Setup 16.5 Results 16.6 Conclusion References 17 Image Anonymization Using Deep Convolutional Generative Adversarial Network 17.1 Introduction 17.2 Background Information 17.3 Image Anonymization to Prevent Model Inversion Attack 17.4 Results and Analysis 17.5 Conclusion References

      10  Index

      11  End User License Agreement

      List of Illustrations

      1 Chapter 1Figure 1.1 Workflow of the application.Figure 1.2 Basic steps of recurrent neural network.Figure 1.3 Screenshot of fetched data.Figure 1.4 Predicted values in Bengaluru in December, 2017.Figure 1.5 Predicted values in Bengaluru in June, 2020.Figure 1.6 Predicted values in New Delhi in December, 2017.Figure СКАЧАТЬ